Kadale Consultants (UK)

23rd November 2019

Kadale Consultants (UK)

FY 16 Food for Progress Mid-term Evaluation

Malawi Strengthening Inclusive Markets for Agriculture

(MSIKA) Program

Implemented by Venture37

Funded by the United States Department of Agriculture

Final Report

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Program: Food for Progress

Agreement Number: C//πсмнπнлмсκллсπллFunding Year: Fiscal Year 201сProject Duration: 201с-20нмImplemented by: Land O’Lakes

Evaluation Authored by: YŀŘŀƭŜ Cƻƴǎǳƭǘŀƴǘǎ ό¦Yύ

DISCLAIMER: This publication was produced at the request of the United States Department of Agriculture. It was prepared by an independent third-party evaluation firm. The author’s views expressed in this publication do not necessarily reflect the views of the United States Department of Agriculture or the United States Government.

Accessibility Note: An accessible version of this document can be made available by contacting [email protected]

mailto:[email protected]

MSIKA Mid-Term Evaluation Page ii [email protected]

Table of Contents List of Tables ...................................................................................... iii Acknowledgements ............................................................................. v Acronyms ........................................................................................... vi Executive Summary .......................................................................... vii 1 Introduction ............................................................................. 1

1.1 Purpose of the Mid Term Evaluation ........................................................ 1 1.2 The MSIKA Program ................................................................................ 1 1.3 Cyclone Idai ............................................................................................. 3

2 Methodology and Implementation ......................................... 4 2.1 Methodology ............................................................................................ 4

2.1.1 Household Survey ............................................................................................. 4 2.1.2 Focus Groups.................................................................................................... 8 2.1.3 Key Informant Interviews ................................................................................... 9 2.1.4 Review of MSIKA Documents and Data .......................................................... 10

2.2 Limitations .............................................................................................. 10 3 Results and Findings ............................................................ 12

3.1 Farmer Based Organisations ................................................................. 12 3.1.1 Background and outline of work with FBOs ..................................................... 12 3.1.2 FBOs summary data ....................................................................................... 12 3.1.3 Training overview ............................................................................................ 13 3.1.4 Lead Farmers .................................................................................................. 18 3.1.5 Sales Agreements ........................................................................................... 19 3.1.6 Processing and Storage .................................................................................. 20 3.1.7 Access to Market Information .......................................................................... 20

3.2 Beneficiary Producers ............................................................................ 22 3.2.1 MSIKA Beneficiary Producer Sample Frame ................................................... 22 3.2.2 Beneficiary Totals ............................................................................................ 22 3.2.3 Sample Profile ................................................................................................. 23 3.2.4 Growing Practices ........................................................................................... 25 3.2.5 Crop Inputs ..................................................................................................... 36 3.2.6 Land Area Under Improved Practices .............................................................. 39 3.2.7 Effects of Cyclone Idai on Production .............................................................. 40 3.2.8 Production ....................................................................................................... 41 3.2.9 Post-Harvest Handling .................................................................................... 45 3.2.10 Post-Harvest Losses ................................................................................... 51 3.2.11 Gender ........................................................................................................ 55 3.2.12 Farm Management Practices ....................................................................... 56 3.2.13 Sales Volume .............................................................................................. 58 3.2.14 Sales Value ................................................................................................. 60 3.2.15 Access to Finance ....................................................................................... 62 3.2.16 Investment and Processing ......................................................................... 63 3.2.17 Employment ................................................................................................ 63

3.3 Banks and MFIs ..................................................................................... 64 3.3.1 Finance for SMEs ............................................................................................ 64 3.3.2 Finance for Producers ..................................................................................... 65

3.4 Processors ............................................................................................. 66 3.4.1 Training in Good Manufacturing Practices ....................................................... 67 3.4.2 MBS certification ............................................................................................. 67 3.4.3 Other Support to Processors ........................................................................... 68


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3.4.4 Performance data ............................................................................................ 69 3.5 Government ........................................................................................... 71

3.5.1 Policy Level ..................................................................................................... 72 3.5.2 Training District Extension Staff ...................................................................... 72 3.5.3 Malawi Bureau of Standards ........................................................................... 74 3.5.4 MSU/Malawi University ................................................................................... 75

4 Conclusions and Recommendations ................................... 76 4.1 Key Conclusions .................................................................................... 76 4.2 Key Recommendations .......................................................................... 79

Annex 1: Indicator Table ................................................................... 82 Annex 2: Terms of Reference/Scope of Work ................................. 87 Annex 3: Inception Report ................................................................ 95 Annex 4: Research Instruments ..................................................... 106 Annex 5: FGD and KII details ......................................................... 168 Annex 6: Additional Tables ............................................................ 170

List of Tables Table 1: Planned and actual responses by district ................................................................ 6 Table 2: Usable responses by crop and by district ................................................................ 6 Table 3: FGDs planned and conducted by crop, district and location .................................... 9 Table 4: KIIs planned and conducted by category and location .......................................... 10 Table 5: Performance rating of FBOs ................................................................................. 13 Table 6: MSIKA activities reported by FBO members in the producer survey ..................... 13 Table 7: FBOs trained in agricultural production, by type of training ................................... 14 Table 8: FBOs trained in marketing, by type of training ...................................................... 15 Table 9: FBOs trained in PHH and storage, by type of training ........................................... 16 Table 10: FBOs trained in financial records, literacy and VSL ............................................ 17 Table 11: FBOs trained in governance, leadership and cooperatives ................................. 18 Table 12: Proportion of Lead Farmers in the Survey ........................................................... 19 Table 13: Lead farmer activities .......................................................................................... 19 Table 14: Sources of market information ............................................................................ 21 Table 15: Information received from MSIKA, mid-term ....................................................... 21 Table 16: Beneficiary producers, by district, sex & crop, mid-term survey .......................... 22 Table 17: Comparison of demographics, baseline and mid-term ........................................ 23 Table 18: Sources of income and land area, baseline and mid-term ................................... 24 Table 19: Summary of MSIKA activities engaged in, at mid-term ........................................ 25 Table 20: Knowledge of five or more practices, baseline and mid-term .............................. 26 Table 21: Unprompted Knowledge of Five or More Practices – Field Crops, at mid-term.... 27 Table 22: Unprompted knowledge of five or more practices for tree crops, at mid-term ...... 28 Table 23: Source of first learning of growing practices by crop, at mid-term ....................... 29 Table 24: Application of new agricultural practices per category, at mid-term ..................... 30 Table 25: Application of new practices by crop, at mid-term ............................................... 31 Table 26: Use of practices for the first time, field crops, mid-term ....................................... 33 Table 27: Use of practices for the first time, tree crops, mid-term ....................................... 34 Table 28: Why not used the other practices, by crop, July 2018 – June 2019 ..................... 35 Table 29: Inputs used, field crops, mid-term ....................................................................... 36 Table 30: Inputs used, tree crops, mid-term ....................................................................... 37 Table 31: Suppliers of inputs, baseline vs mid-term ............................................................ 37 Table 32: Ease of getting inputs, baseline vs mid-term ....................................................... 38 Table 33: Stock levels of inputs, baseline vs mid-term ........................................................ 39 Table 34: Land area in ha under improved practices, mid-term .......................................... 39


MSIKA Mid-Term Evaluation Page iv [email protected]

Table 35: Overall weather conditions, mid-term .................................................................. 40 Table 36: Production affected by heavy rains in March 2019 .............................................. 40 Table 37: Extent of impact of heavy rains in March 2019 .................................................... 41 Table 38: Mid-term producer yields (all respondents) in kg/ha, mid-term ............................ 42 Table 39: Revised baseline yields in kg/ha ......................................................................... 43 Table 40: Yield comparison, mid-term vs baseline by crop, kg/ha & percentage change .... 43 Table 41: Producers not affected by cyclone - yield comparison by crop ............................ 44 Table 42: Yield comparison of male and female yields by crop, mid-term and baseline ...... 44 Table 43: Knowledge of PHH practices, field crops, mid-term survey ................................. 46 Table 44: Knowledge of PHH practices, tree crops, mid-term ............................................. 46 Table 45: Source of learning for PHH practices by crop, mid-term ...................................... 47 Table 46: Source of learning for PHH practices by crop, baseline ...................................... 47 Table 47: Use of PHH practices field crops, mid-term ......................................................... 48 Table 48: Use of PHH practices field crops, baseline ......................................................... 49 Table 49: Use of PHH practices tree crops, mid-term ......................................................... 49 Table 50: Use of PHH practices tree crops, baseline .......................................................... 50 Table 51: First used new PHH practice, field crops, mid-term ............................................. 51 Table 52: First used new PHH practice, tree crops, mid-term ............................................. 51 Table 53: Post-harvest losses by crop and by sex, mid-term .............................................. 52 Table 54: Post-harvest losses by crop and by sex, baseline ............................................... 53 Table 55: Point at which harvest was spoiled, mid-term ..................................................... 54 Table 56: Place of storing crops, mid-term ......................................................................... 54 Table 57: Building or refurbishing storage calculation for mid-term population .................... 55 Table 58: Overall work on crop by sex, mid-term ................................................................ 55 Table 59: Work on the tasks, by sex, mid-term ................................................................... 56 Table 60: Knowledge of improved farm management practices, mid-term .......................... 56 Table 61: Use of improved farm management practices, mid-term ..................................... 57 Table 62: Indicator 8: Application of improved farm management practices, mid-term ........ 57 Table 63: Farm records kept by respondents (unprompted) ............................................... 58 Table 64: Volume sold in kg/ha, revised baseline, mid-term all and not affected by Idai ..... 60 Table 65: Volume sold in kgs/ha, comparison of baseline to mid-term not affected ............ 60 Table 66: Value of crop sold in $/ha, comparison of mid-term vs baseline .......................... 61 Table 67: Prices in Malawi Kwacha, comparison of mid-term vs baseline ........................... 61 Table 68: Growers that sold nothing, mid-term ................................................................... 62 Table 69: Indicator 22, number of people hired for four weeks or more, mid-term .............. 64 Table 70: Focus Group Discussions – Detailed Breakdown .............................................. 168 Table 71: KIIs – Detailed Breakdown ................................................................................ 169 Table 72: Practices Used by Crop, Baseline v Mid-term ................................................... 170


MSIKA Mid-Term Evaluation Page v [email protected]

Acknowledgements The Kadale mid-term evaluation team acknowledges the valuable assistance received from the Land O’Lakes Venture37 team. This study would not have been possible without the active support and commitment of both the headquarter staff and the Malawi Strengthening Inclusive Markets for Agriculture (MSIKA) team based in Lilongwe, Malawi. Our considerable thanks to the Venture37 Monitoring, Evaluation and Learning (MEL) team, who assisted us on all aspects of the work, notably the approaches for monitoring the MSIKA program and the data that has already been collected. The team was always ready to respond to our many questions and to provide both data and logistical help for our evaluation activities. Beyond the Monitoring team, we also wish to thank other MSIKA staff for giving their valuable time to explain the many technical facets of the MSIKA program and facilitating the availability of the technical team members who provided much of the detail about the program and its progress. We are grateful for the technical inputs of Venture37 staff that guided us in the understanding of the technical aspects of the work. The team was also ably assisted by the Field Based Co-ordinators for the five target districts. Kadale also benefitted from a dedicated field team ably led by our supervisors. The core team worked long hours under great pressure to deliver this evaluation. The research team benefitted from the time and knowledge of nearly 50 key informants, from Government of Malawi officers, to owners of processing businesses, to the committees of farmer-based organisations, too numerous to name here. Finally, and most importantly, we wish to thank over 700 Malawians who agreed to be interviewed and participate in focus groups to tell us about their experiences of working with the MSIKA program. They are the intended beneficiaries and we were humbled by their openness and commitment to improve their difficult circumstances with the assistance of MSIKA and all those who are part of it, and who support it. Kadale Consultants, Lilongwe, Malawi.


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Acronyms AEDC Agricultural Extension Development Co-ordinator AEDO Agricultural Extension Development Officer DADO District Agricultural Development Office/Officer DAES Department of Agricultural Extension Services DARTS Department of Agricultural Research and Technical Service DC District Commissioner EPA Extension Planning Area FBO Farmer Based Organisation FGD Focus Group Discussion GAP Good Agricultural Practices GoM Government of Malawi GMP Good Manufacturing Practices Ha Hectare HO Horticultural Officer HH Household HHH Head of Household I3 (Fund) Innovation, investment and incentive (Fund) Kg(s) Kilogramme(s) KII Key Informant Interview MBS Malawi Bureau of Standards MEL Monitoring, Evaluation and Learning MFI Micro-finance Institution MGF Matching Grant Fund MK Malawi Kwacha MoAIWD Ministry of Agriculture, Irrigation and Water Development MoIT Ministry of Industry and Trade MSIKA Malawi Strengthening Inclusive Markets for Agriculture (Program) MSU Michigan State University mT Metric Tonne (1,000 kgs) MTE Mid-Term Evaluation NGO Non-governmental Organisation PHH Post-Harvest Handling PMM Performance Measurement and Management PMP Performance Management Plan PPI Progress out of Poverty Index SACCO Savings and Credit Co-operative Organisation SPV Special Purpose Vehicle TNS/PFS TechnoServe/Partners in Food Solutions T&T Techniques and Technologies – also known collectively as ‘practices’ ToR Terms of Reference ToT Training of Trainers USD United States Dollar ($) USDA United States Department of Agriculture VSL Village Savings and Loan


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Executive Summary The mid-term evaluation (MTE) of the Malawi Strengthening Inclusive Markets for Agriculture (MSIKA) was conducted from June to November 2019 by Kadale Consultants Ltd. Overview of the Project The MSIKA program is a five-year value-chain development program targeting 36,000 producers, 210 Farmer Based Organisations (FBOs) and 24 processors. It operates in five districts in Central and Southern Malawi (Dedza, Lilongwe, Mangochi, Mchinji, Ntcheu) and across seven crop value-chains (tomato, onion, potato, mango, citrus, guava and chili). MSIKA is implemented by Land O’Lakes Venture37 (‘Venture37’). MSIKA is a multi-faceted program with integrated components notably: training beneficiary producers in agricultural, post-harvest handling (PHH) and farm management practices, along with finance and marketing; strengthening FBOs; working with MFIs and banks to increase credit for producers and small and medium enterprises (SMEs); working with processors to enhance processing and obtain certification; working with Ministry of Agriculture on a new Horticulture Policy and training of district extension staff, establishing new product standards Malawi Bureau of Standards (MBS) and researching improved growing practices. Evaluation Approach The evaluation involved a survey of 646 beneficiary producers across the five districts, 46 key informant interviews (KIIs) and 16 focus group discussions (FGDs). The sample frame for the survey was 8,468 beneficiary producers trained in agricultural production as at December 31st, 2018. Data is reported against the Life of Project (LoP) targets and the impact baseline. Due to the low number of respondents for guava, chili and citrus, the results for these crops have higher margins of error. Revisions to the Baseline, LoP Targets and Indicators The baseline needs updating, as the yield, post-harvest loss, sales and price outlier analysis was found to be incomplete, and also did not accurately reflect all baseline calculations. The consultants found that the implemented baseline questionnaire prompted respondents for knowledge of agricultural, PHH and farm practices, whereas the indicators are set as unprompted. LoP targets that are knowledge-related will be challenging to achieve, as they were set based on responses to prompted, rather than unprompted, questions. Other indicators need revising to better reflect MSIKA’s activities by removing overlaps and redundant indicators. Cyclone Idai Cyclone Idai had negative impacts on yields, with 59.6% of producers reporting they were negatively affected on at least one crop. Due to this, the consultants removed the data for those affected when calculating mid-term yields, losses and sales. Other mid-term calculations, such as use of practices, are not affected by the Cyclone. Key Findings MSIKA works with different participants, so key findings are grouped by participant types. Overall, the MSIKA program is making progress towards many of its LoP targets. Farmer Based Organisations

As at July 31st, 2019, MSIKA is working with 219 FBOs, which is higher than the LoP target of 200. According to MSIKA’s internal assessment, 85 of these are low performing, with the remainder being high or medium performing. The basic agri-production training has been widely delivered, covering 97.2% of FBOs. However, there are gaps in other categories of agri-production training, such as climate smart agriculture training covering 23.5% of FBOs, and in marketing training, such as introduction


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to markets covering 53.1% of FBOs. There are gaps in PHH training (24.4% of FBOs), finance (e.g. financial literacy at 47.4% of FBOs) governance (56.8% of FBOs) and leadership (39.2% of FBOs). MSIKA reports that its FBCs and specialists are very stretched to deliver this training and their other work. The KIIs with FBOs revealed that very few FBOs have storage facilities or are processing crops. There are also few sales agreements and market linkages that have been made. In conclusion, FBOs provide a useful means to organize engagement with producers for delivering MSIKA activities, but are unlikely to become sustainable entities that can maintain activities and store, process or collective sell crops.

Beneficiary producers

The findings for producers are based on the mid-term sample frame of 8,468 producers, as of December 31st, 2018. Since that date, MSIKA has made substantial progress by training 28,829 producers, as at September 30th, 2019. A total of 56.7% of the producers at mid-term knew at least five improved agricultural practices which is lower than the LoP target of 95.0%, but higher than the most recent semi-annual at 39.0%. The most commonly known practices were sowing seeds in a nursery, applying fertiliser and irrigation. These were followed by a secondary group of: composting, manuring, ridging, plant and ridge spacing, earthing up, spraying and planting in grooves in nursery. The LoP target of 95.0% was based on a baseline result of 80.5%; however, the baseline used prompted knowledge, so the LoP target appears to have been set too high. At mid-term, 99.7% of producers applied at least one new agricultural practice with a mean of 9.8 new practices per producer. There were much higher adoption rates for field compared to tree crops. The nine practices most commonly adopted are composting, manuring, mulching, irrigation, soil and water conservation, selecting varieties, spraying, water capture and pruning. Uptake of more practices will lead to increased yields and ultimately sales. The consultant applied the baseline yield calculation method which resulted in different figures to those reported in the baseline report, which is probably an updating error. The consultants also found that full account of outliers had not been taken. There is also a methodological issue in using an aggregate indicator, as the mix of higher/lower yielding crops for baseline and mid-term producers will differ. A better method is to compare progress crop by crop, not in aggregate. Comparing the revised baseline yields to those for mid-term producers not affected by Idai, there has been progress by MSIKA, notably for the three most commonly grown crops of tomato (+17%), onion (+20%) and potato (+20%). MSIKA will need to accelerate that progress if it is to hit the LoP yield target of 75%. The consultants found relatively low unprompted knowledge of PHH practices. There were higher application rates for improved PHH practices for field crops than for tree crops. For both the agricultural and PHH training, the mid-term is a good point to review the curriculum to assess why some practices are not taken up, e.g. due to cost, complexity and relevance, and to revise the curriculum accordingly. In terms of number of beneficiaries, production, land area and sales, tomato and potato represent around 80% of MSIKA’s results, with onion representing a further 10%. Tree crops and chili contribute limited results (around 10%) and the performance of these for most indicators is generally poorer than the three main field crops. This appears to be due to producers not seeing trees as crops to actively manage and invest in; rather they see them as yielding produce and income without investing money or effort. MSIKA has promoted chili, but progress has been slow, as this is a relatively new crop for many producers and there was poor access to seed.


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For producers not affected by Idai, there were increases in sales volume for tomato (+22.0%), potato (+4.8%), guava (+4.0%) and onion (+3.8%), with declines for mango (-44.1%), chili (-40.8%) and citrus (-32.5%). The mid-term producers had lower unit prices compared to those at baseline. After removing yield and price outliers and adjusting for the lower prices, there were higher sales per hectare (ha) for tomato (32.5%), onion (70.9%), potato (23.6%) and guava (152.0%), but lower for chili (-51.7%) and citrus (-41.8%), with no change for mango. MFIs/Banks/VSLs

MSIKA worked with banks and MFIs to increase access to finance. After a long assessment and initiation, MSIKA selected a bank with a wide branch network in Malawi to work with to establish a functioning facility for SME lending. Lending commenced in March 2019. It has been limited in number (seven) and value (US $93,000) of loans at mid-point, however, the selected bank will give larger second loans to the initial group and make new loans to other small and medium enterprises (SMEs). MSIKA works with a microfinance institution (MFI) targeting rural borrowers, to increase agricultural lending. MSIKA introduced producers from 81 groups in two FBOs to the MFI, of which 996 received loans. The repayment rate was 98% and both the MFI and MSIKA are working to expand the initiative. This approach is low cost for MSIKA as the loan capital is all from the MFI, with MSIKA identifying and training producers through its FBOs. As the MFI is a well-established MFI, it will likely continue to offer these loans after the end of the MSIKA program, which means the lending mechanism will be sustained. MSIKA reports that as of 30th September 2019, it had trained 666 VSL groups, typically with 15-20 members, equating to an estimated 10,000-13,000 members. VSLs are self-sustaining, so many of these are likely to continue operating beyond MSIKA, Processors

MSIKA has established relationships with 18 processors, involving improving the application of good manufacturing practices (GMP)/hygiene standards, financial management, storage, processing, access to finance and access to markets. There is evidence from the KIIs of the uptake of GMPs and hygiene improvements, increases in storage, and improvements in processing, with indications that further progress will be made. However, there has not been much progress on the number of sales agreements or increases in employment by the processors. While development of processing in these value-chains is important, there is not yet a strong connection between the processors and the producers/FBOs. Eleven of the 18 processors are micro-enterprises with very limited financial and human resources and with weak management capacity suggesting they have low potential to increase investment, purchases, storage/warehousing and employment. Three of the processors are relatively large (100+ permanent and temporary staff), and two are medium-sized (20+ employees), all with potential to implement changes. Government

MSIKA’s engagement with GoM covers development of a new Horticulture Policy, training of government district-based extension staff, developing product standards with the Malawi Bureau of Standards and research on crop practices/farmer field schools. For the new Horticulture Policy, the process moved quickly in the early stages due to MSIKA’s support, but has slowed due to lack of a budget allocation for this work within the Ministry of Agriculture, and disruption due to the election period and its aftermath. It is uncertain as to when the work on the Horticulture Policy will continue and be completed, though the consultant found that Horticulture officials are strongly committed to pursuing the process. On the training of GoM’s district-based extension officers, the ToTs have relevant content. However, MSIKA’s expectations for the onward training of producers by GoM extension staff were not fulfilled. There has been more implementation by Lead Farmers.


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The work with MBS to develop 10 new standards for products made out of the seven target crops, such as tomato sauce and mango achar, has moved quickly, but also held up by the election and its aftermath. This is likely to be resolved and the process will be completed. MSIKA’s engagement with MSU on research is linked to the farmer field schools at the demonstration sites. Getting the most out of this research investment requires maximising the use of demonstrations sites for the farmer field schools. Opportunity for refocusing

Across the MSIKA program, there are activities and categories of beneficiaries where there has been more progress than in others. Training in agriculture, PHH and farm management has led to improved knowledge and use of practices, which in turn has led to improvements in the yields, losses and sales particularly for tomato, potato and onions. Some FBOs perform better than others and have more potential to develop. The work with financial institutions has increased access to finance, as has the promotion of VSLs. Finally, the work with the better resourced and more capable processors has led to improvements in their processing. Key Recommendations A mid-term evaluation is a good point for a program to re-focus its activities. While there can be merit in addressing program areas where there is under-performance, more is likely to be achieved by focusing on aspects of MSIKA where progress has been made, as this suggests that the program has a successful approach. As resources are constrained, then focusing on fewer, better performing activities, will be more efficient and effective. The following recommendations are made: 1. The baseline and LoP targets need revising to address several corrections in the baseline data, and based on which the LoP targets were set. The baseline did not ask for unprompted knowledge, so targets relating to this need reducing. The baseline values for yields, losses and sales (volume and value) have outliers that need removing. 2. Venture37 and USDA should revise the indicators to reflect the current and revised activities, and reduce their overall number. Several indicators are redundant as the activities are not being pursued (#16 # of research initiatives), incorrectly worded such that they cannot be measured (#19 practices applied by organisations), overlap to a great degree (#5 and #6 on soil fertility), have too low thresholds to be useful measures (#7 and #8 apply new agricultural and farm management practices), report crop data in aggregate across crops which is not a sound approach (#1 yields and #26 PHH), have incorrect calculations (#3 indirect beneficiaries), are very difficult to measure accurately (#26 PHH), target activities that are unrealistic to pursue (#31, percent of FBOs aware of international production and handling standards) and would be better as numerical rather than percentage targets (#25, percent of processors getting certification). Overall, there are too many indicators and disaggregates (124 in total), which can distract the team from focusing on the more important ones. A reduction would improve MSIKA’s focus 3. MSIKA should focus on the three most commonly grown field crops among its beneficiary producers and cease or limit its activities on tree crops and chili. The three most common crops account for around 80% of MSIKA’s beneficiaries, land area, volumes produced and sales. MSIKA’s producers have more knowledge about and have applied more practices for these three crops. Tree crops are not seen as crops to invest time or resources on, but to harvest without investing. Chili has encountered difficulties and many chili producers have not seen gains in yields due to a range of challenges. 4. MSIKA should focus on fewer FBOs, specifically the high and medium performing FBOs and reduce the range of modules they are trained in to be the most relevant ones. MSIKA’s own assessment is that there are 85 low performing FBO. It is unlikely that these can readily become medium or high performing, as there are deeper challenges, usually around the quality of leadership and governance that are not solved by training. While FBOs


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can be a useful conduit for training producers, many FBOs will struggle to become well-functioning sustainable entities. MSIKA should reduce the number of modules, as there are considerable gaps in training of FBOs. Less relevant modules like international standards and cooperative development should be dropped, and focus moved to those most relevant to the members’ livelihoods, such as agriculture, PHH, marketing and finance. 5. MSIKA should review its training curricula to determine which agricultural, PHH and farm management practices could be taken up by field crop producers and it should prioritise refresher training over training new producers. MSIKA’s curricula for training covers a very large number of practices, some of which are now widely implemented (composting, ridging, etc.), and some that very few apply (fish soup for pests and testing acidity). MSIKA should focus its training on those practices that fall between these two extremes, to increase adoption of those that are capable of being applied but that are not applied by most producers. In addition, MSIKA should focus on refresher training than on new groups of producers, to consolidate and deepen learning and promote uptake among those that already know the basic practices. 6. MSIKA should undertake analysis and research to determine why women have lower yields than men. Lower knowledge and uptake of practices by women is part of the problem, but does not sufficiently explain the differences. This is part of a wider pattern of under-performance of female producers in Malawi. 7. MSIKA should consolidate its work with processors by focusing on those that are most responsive to its inputs and that have potential to enable MSIKA to meet its processor-related targets of certification, buying from producers, investment and employment. 13 of MSIKA’s 18 processors are micro or small enterprises with very limited resources to invest and capacity to implement change. MSIKA will get more return from the five medium and large enterprises that have been willing to implement changes to date. 8. MSIKA should continue to promote VSL as an efficient, effective and sustainable way to deliver improved access to savings and loans. The uptake of VSLs established by MSIKA has been good (38.7% of producers) with almost a third of producers (32.4%) taking a loan through a MSIKA VSL. Having more producers in VSL groups will increase access to savings and credit. 9. MSIKA should continue to work with the MFI to increase the number of groups/ producers that are organised and trained in order to access agricultural loans. The initial results of 996 loans with 98% repayment strongly suggests there is scope for increased uptake. As a well-established MFI, the MFI is likely to continue post-program end. 10. MSIKA should continue to work with the selected bank to enable more SMEs to access loans. The initial uptake of seven loans for $93,000 has been modest, which is a function of caution exercised by the Bank. The Bank says it will give larger loans to most of the initial borrowers, and there is a pipeline of around 30 SMEs that could be offered loans. 11. MSIKA should focus its training of trainers on lead farmers rather than on GoM extension staff. GoM extension staff have struggled to conduct onward training in agricultural practices, due to their requirement for resources in order to conduct training along with other motivational challenges. In contrast, producers more commonly cite Lead Farmers as a source of knowledge. Lead Farmers are more likely to deliver the training and they have the advantage of being an in-community resource available to producers and can demonstrate practices on their own land.


MSIKA Mid-Term Evaluation Page 1 [email protected]

1 Introduction This report presents the independent Mid-term Evaluation (MTE) of the Malawi Strengthening Inclusive Markets for Agriculture (MSIKA), undertaken by Kadale Consultants Ltd (‘Kadale’), from June through to November 2019. Land O’Lakes Venture37 (Venture37) is implementing MSIKA. This MTE has been conducted for the United States Department of Agriculture (USDA) with the full of co-operation Venture37.

1.1 Purpose of the Mid Term Evaluation The purpose of midterm evaluation is to analyze and document the extent to which the MSIKA program has achieved its goals and objectives and to explain any deviations from its plans. In addition, there are several specific objectives for the evaluation:

• Assess the relevance of the project strategy and approach, as well as the validity of assumptions made during project design;

• Measure progress made toward key results, including effectiveness and efficiency of interventions in achieving targets;

• Assess the operational aspects of the project, such as project management; • Document lessons learned, challenges and unanticipated effects; • Identify enablers and constraints to progress (both internal and external factors)

that have supported or limited success of the project; • Assess sustainability efforts to date; • Provide recommendations to strengthen project performance, efficiency and

sustainability; • Provide recommendations for areas of focus for the final evaluation, including

reviewing and strengthening data collection systems and metrics in preparation for the final evaluation; and

• Provide recommendations on the activities that could be discontinued without greatly affecting the impact of the project to account for a reduction in monetization proceeds.

Conducting an independent MTE is a requirement from USDA for Venture37. This report provides USDA with a picture of the progress to date, and prospects for reaching the life of project targets/results. This report provides the MSIKA team with insights and recommendations to assist the team to deliver the project more relevantly and effectively, with greater impact and higher quality, in an efficient and sustainable manner.

1.2 The MSIKA Program MSIKA is a five-year value chain development project that aims to reach 36,000 beneficiary producers, 210 farmer-based organizations (FBOs), and 24 processors in the fruit and vegetable value chains across five target districts in the Southern and Central Regions of Malawi. MSIKA aims to catalyze increased value addition and income for value chain actors by facilitating improved processing, increased crop yields, reduced post-harvest losses, expanded market linkages between producers and processors resulting in more efficient domestic trade and potential for exports of processed products in the longer term. MSIKA’s objectives can be divided into two main strands of work:

• Increasing agricultural productivity through training producers in improved agricultural techniques and technologies (‘practices’), farm management,



marketing and financial management. In addition, MSIKA will increase productivity through increasing availability of improved inputs, improving infrastructure to support on-farm production and facilitating access to finance.

• Expanding trade of agricultural products training producers on improved post-harvest processes, facilitating aggregation and collective marketing, facilitating linkages between buyers and sellers, improving market and trade infrastructure, and improving processed product quality.

MSIKA is implementing multiple activities that form seven program components: 1. Agricultural productivity MSIKA has trained producers in a range of agricultural practices organised under Farmer Based Organisations (FBOs), trained GoM staff and lead farmers to deliver training and extension, and provided access to cutting edge horticulture research through demonstration Yankho PlotsTM in collaboration with Michigan State University (MSU) working with a Malawi University 2. Post-Harvest Training and Infrastructure: MSIKA has trained producers in PHH and storage to reduce on-farm and off-farm losses and improve the quality of farm produce, facilitated market linkages for quality farm produce and supported access to finance for improving on-farm and off-farm post-harvest structures. MSIKA has facilitated FBOs to develop as potential storage and aggregation centres. 3. Processing: MSIKA has worked with processors to improve the efficiency and profitability of value-added manufacturing through targeted assistance to processors. This includes providing technical expertise to processors on all aspects of processing, handling of raw material and finished products, and certification. 4. Capacity building of FBOs: MSIKA has worked to organise producers into clubs and the clubs into community based FBOs. It has organized agricultural, post-harvest, marketing and financial training of producers through the FBOs as well as provided training to FBO leadership and members in governance/leadership, international quality standards and financial management. It is conducting assessment of FBOs for potential transformation into cooperatives. 5. Market access: MSIKA has worked to facilitate buyer-seller relationships through linking FBOs to processors and other buyers, by providing access to market information through FBOs, by facilitating selected FBOs to participate in trade fairs and by facilitating buyer-seller events. There has also been training for producers and FBOs in marketing, such as market research, costing/pricing and collective selling. 6. Financial Services: MSIKA has facilitated SME lending through working with a Malawian bank and agricultural lending through working with a Malawian MFI. MSIKA has also trained FBOs and members in village savings and loan (VSL) methodologies, and worked with community level VSL agents to establish VSLs for MSIKA producers. Finally, there has been financial literacy training for producers and financial management training for FBOs and processors. 7. Enabling environment: MSIKA has supported the development of a draft Horticulture Policy and been building the capacity of government extension staff. It has worked with the Malawi Bureau of Standards (MBS) to establish and disseminate product quality standards. It has worked with academics at a Malawi university, to research the effects of different composting/fertiliser combinations on soil fertility and to disseminate this and other learning. The MSIKA program targets seven crop value chains: tomatoes, onions, potatoes, mango, citrus (oranges, tangerines and lemons), guava and chili (birds eye chili, paprika and red cayenne). MSIKA works across five districts: Dedza, Lilongwe, Mangochi, Mchinji and Ntcheu.



1.3 Cyclone Idai USDA and Venture37/MSIKA requested that the evaluation ask about the effects of Cyclone Idai that hit South East Africa in March 2019. Idai was the second most deadly cyclone/storm ever in the Southern Hemisphere, with over 1,300 attributable deaths, across Mozambique, Malawi and Zimbabwe. Of these countries, Malawi was the least affected, but heavy rains caused flooding and crop damage, as well as several deaths. March is the time when most rain fed field crops are maturing for harvest in the April to June period, so the expectation was of more damage/losses to field crops. Kadale added questions to the research instruments to determine who had been affected, in what ways and to what extent. The report provides an analysis of the effect of Cyclone Idai in section 3.2.7. on production, which is the major area of impact.



2 Methodology and Implementation This section sets out the methodology and how the evaluation was conducted.

2.1 Methodology The evaluation adopted a mixed method, non-experimental approach, combining quantitative data from a household survey of beneficiary households (HHs), with qualitative data through focus groups discussions (FGDs) with producers and key informant interviews (KIIs) with a wide range of stakeholders. The stakeholder groups were: MSIKA technical staff; high, medium and low performing FBOs; processors and SMEs; GoM staff at headquarters and in the districts, and in research institutions; and financial institutions. In addition, the consultants reviewed MSIKA documents, and the independent impact assessment baseline report and database. For the quantitative work, the MTE adopted a non-experimental pre-post intervention approach to measure the key performance indicators. The pre-intervention data came from the impact baseline study that used a comparative group approach of registered MSIKA beneficiaries and a control group. The consultant therefore compared the baseline MSIKA beneficiaries with the MSIKA producers in the mid-term. It should be noted that the survey conducted at mid-term did not track the same participants that were contacted at impact baseline. The quantitative design should therefore be technically understood to compare estimates of two independent samples of the MSIKA participants at baseline and mid-term. USDA and Venture37 also asked the consultants to look at the effects of Cyclone Idai, which brought heavy rains, in a production season characterised by heavy rains and flooding. In total, 59.6% of respondents stated that they had been affected by Cyclone Idai on at least one of their crops. Because of the high proportion of the survey respondents affected, for certain questions, such as yields, data is presented for the respondents not affected by the Cyclone on at least one of their crops.

2.1.1 Household Survey The key activities were: Tool Development

The consultants conducted KIIs with MSIKA staff to discuss activities implemented by MSIKA and how these should be captured through the mid-term survey. The consultants revised the baseline instrument to reflect changes in the practices that MSIKA was promoting. Not all practices in the baseline were in the MSIKA training package, and there are practices in the mid-term that are not in the baseline. It is not surprising that there were changes, as a baseline is anticipating what will be done in the future. The consultants sought to maintain as much comparability as possible with the baseline and with MSIKA’s own semi-annual surveys. For the four field crops (tomato, onion, Irish potato and chili), there are up to three growing/harvesting cycles per year depending on the producer. For tree crops (mango, citrus and guava) there is only one growing/harvesting cycle. The mid-term instrument was expanded to capture land area, yields, losses and sales for all growing cycles for each crop. The team translated the instrument into Chichewa and coded it into Open Data Kit. The instrument was piloted and was further refined based on feedback. MSIKA staff were fully consulted on the technical aspects of the instrument and provided inputs at each stage. Overall, there is good alignment between the mid-term and baseline instruments and data. The final instrument in Word is included in Annex 4. A final Excel version for ODK is provided to Venture 37 separately.



Training

Enumerators were trained for three days on 24-26th July 2019, with a field practice day and review of the field practice on 27th July. The training consisted of the background to MSIKA, running through the survey section by section to clarify questions and responses, and practicing questions and responses. MSIKA provided technical explanations for certain topics so that enumerators would understand the agronomy of the crops. There were sessions on use of ODK and tablet collection, conduct in the field, sampling, substitution protocol, quality control/checking procedures and data uploading. The field day was to test that the enumerators could administer the instrument and record the data on the tablets. At the end of the training Kadale selected 20 out of 23 trainees for four teams of one supervisor and four enumerators. Sampling

The Venture37/MSIKA team and the consultants discussed the sample frame for the producer household survey and decided that it should be all producers trained in agricultural production as at December 31st, 2018. This consists of 8,468 beneficiary producers, split 4,315 females and 4,153 males. Initially, the intention was to include those trained up to March 31st, 2019, which would have increased the sample frame considerably to 23,102 producers. However, recently trained producers would not have had sufficient time to implement the practices, so it was decided to focus on those that had had sufficient time to implement the practices and to see the results. Venture37 requested that the sample of producers represent the sample frame with 95% confidence and be representative at the value chain level with 90% confidence. The requirement to reach the 90% confidence level per value chain was re-prioritised, as it would have required purposive sampling of some Extension Planning Areas (EPAs) where there were concentrations of growers (‘hotspots’) of the less commonly grown crops of citrus, guava and chili. As beneficiaries grow multiple crops, the expectation was that there would be enough responses to reach the 90% threshold for all crops, while acknowledging that this might not be the outcome in practice. It was agreed that the sampling approach should be proportional to population size for the five districts. The sample was not designed to be able to report results by district, as significant differences between districts were not expected; rather it was to ensure that the sample was broadly representative of the locations that MSIKA works in. The study had a two-tier selection approach for respondents. Within each district, there was random selection of villages that had a minimum of 10 beneficiaries, and then random selection of beneficiaries within each selected village. In addition to selected beneficiaries, substitutes were also selected, along with a replacement protocol. The sample size calculation formula was: n = N*X / (X + N – 1), where X = Zα/2

2 *p*(1-p) / MOE2 Through this sample size determination formula, the minimum sample was calculated at 619 beneficiaries. This was adjusted to a minimum of 630 beneficiary interviews to allow for some responses that might be unusable/incomplete. As the sample was split between four teams, the planned sample was increased to a 638 to take advantage of the teams being in locations where they could get interviews and to allow for possible shortfalls on some days if there were unexpected factors. The interviews were split by district based on the PPS explained above and are set out in Table 1 below. Survey Implementation

The survey was implemented over a 12-day period (30th July to 10th August), which is similar timing to the impact baseline. MSIKA staff made introductions to village leaders to get local approvals but were not involved in the survey.



Overall, the calculated minimum sample level of 619 was exceeded, with 649 interviews and 646 usable questionnaires. As set out in Table 1 below, there was a shortfall of 10 (90 instead of 100) interviews in Lilongwe District due to difficulties finding beneficiaries in Chileka and Chitekwere EPAs. This was due to different reasons ranging from FBO leaders saying there had not been any training and a risk of violence due to the ongoing election dispute to a high level of ‘no-shows’ in several places. There was an issue with Chentchelere FBO, Kanyama EPA, Dedza District, where all those selected said they were newly registered and not trained, contrary to the MSIKA database.1 These were replaced using the sampling protocol to select the next person on the substitute list until these were exhausted. Due to these issues, the field time was extended with several teams used to complete the work in Lilongwe. As the difference in numbers of respondents is not substantially different and followed the randomised method, Kadale’s view is that the sample is valid. Table 1: Planned and actual responses by district

Date Dedza Lilongwe Mangochi Mchinji Ntcheu Total Difference Plan Actual Plan Actual Plan Actual Plan Actual Plan Actual Plan Actual

30-Jul - - 40 24 - - - - - - 40 24 (16) 31-Jul 20 20 - - 20 20 20 19 20 20 80 79 (1) 01-Aug 20 20 - - 20 24 20 21 20 16 80 81 1 02-Aug 25 25 - - 21 21 20 23 25 24 91 93 2 03-Aug 25 25 - - 21 22 20 20 25 25 91 92 1 04-Aug - - - - - - - - - - - - - 05-Aug 25 18 - - 21 22 20 25 25 26 91 87 (4) 06-Aug 10 20 - - - - 20 25 25 25 55 66 11 07-Aug - - - - - - 20 10 10 17 30 41 11 08-Aug - - 30 21 - - 20 - - - 50 41 (9) 09-Aug - - 30 20 - - - - - - 30 20 (10) 10-Aug - - - 25 - - - - - - - 25 25 Total 125 128 100 90 103 109 169 150 153 153 638 649 11

The actual sample by crop is set out in Table 2. There were sufficient responses for a 90% confidence level with a margin of error of 5% for tomato and potato. Onions, mango and chilies were sufficient for a confidence level of 90% with a 10% margin of error, with guava just falling short of this mark. For citrus, only seven growers were interviewed, so the citrus results have to be taken with considerable caution, as they are likely not to be representative of the full population. Table 2: Usable responses by crop and by district

District Tomato Onion Potato Mango Citrus Guava Chili Total

all Crops

Respondents Mean # crops/ respondent

Dedza 41 7 115 15 4 17 17 216 128 1.69 Lilongwe 80 34 24 4 - 5 1 148 89 1.66 Mangochi 94 4 13 9 - - 14 134 108 1.24 Mchinji 124 69 109 47 2 27 7 385 168 2.29 Ntcheu 125 23 38 34 1 10 28 259 153 1.69 Male Producers

195 71 120 43 4 27 28 488 257 1.90

Female Producers

269 66 179 66 3 32 39 654 389 1.68

Total 464 137 299 109 7 59 67 1,142 646 1.77 Min. Sample for 90% margin of error 5%

259 247 254 245 136 206 211

Min Sample for 90% mrgin of error 10%

67 67 67 66 55 63 64

1 MSIKA is confident that the selected and named producers were trained. The denial of having been trained by these individuals is hard to understand.



One likely reason that the sample for fruit trees was smaller than anticipated was that the consultants agreed with the MSIKA team to apply the minimum number of trees that a producer must have for the producer to be asked about this crop2. Respondents needed to have at least seven mango trees or at least four guava or four citrus trees to be interviewed about those crops. The threshold was set so as to include only those producers that grow or could grow these trees as a livelihood. In the baseline, there was no threshold on number of trees for inclusion. In addition, chili, citrus and guava are not widely grown, making it difficult to reach the threshold without being purposive in selection. Data Cleaning, Analysis and Comparison with the Baseline

The data was cleaned and tabulated to provide a first-cut analysis. The impact baseline sample was drawn from registered farmers who were expected to become MSIKA beneficiaries, while the mid-term survey was drawn from actual beneficiaries, so some differences were expected. Kadale tested the comparability through a regression analysis based on key characteristics, such as land size, education level, and household size. The analysis showed that there was an acceptable level of comparability between the two samples. The profiles of the baseline and mid-term samples are compared in the findings (section 3). The initial analysis found unexpected differences with the baseline. After rechecking the analysis, the consultants reviewed the baseline, but were unable to recreate the baseline data without the Stata files. After some delay, the consultants were able to source these files from the baseline consultants and commenced an in-depth review of the baseline data. This involved reviewing baseline calculations and methodology, such as the treatment of outliers. This review was beyond the original scope. When reviewing the analysis files, Kadale identified that the baseline analysis had removed high end outliers for tomato, but not for the other six crops. This led to a large difference between the baseline and the mid-term yield values, even accounting for the impact by Cyclone Idai. Kadale decided that setting a maximum yield threshold at baseline and mid-term for all crops would reduce the impact of outliers across all the crops. The consultants decided to use the Food and Agriculture Organisation’s (FAO) annual maximum crop yields based on the application of Good Agricultural Practices (GAP) in kilograms/hectare (kgs/ha)3 for vegetables and kilograms/tree for fruits. These maxima were applied, involving the removal of outliers above the specified level and resulting in re-calculating the baseline and mid-term data. The result of this is that Kadale recommends changes to the baseline values for the yield, post-harvest handling (PHH) loss and sales indicators to update the baseline values so as to be calculated consistently and based on an appropriate method to remove outliers. The mid-term findings are reported against the updated baseline figures so that the comparison is accurate. The consultants also noted that the impact baseline asked only about the “most recent harvest” cycle, not all cycles for the previous 12 months. Although the Word version of the baseline instrument refers to the previous 12 months, the final Excel/ODK version refers to the most recent harvest. It is the latter that was implemented. As a result, the consultants make comparisons between the baseline and mid-term based only on the most recent growing/harvesting cycle. The consultants found that several questions that inform indicators about knowledge of agricultural, PHH and farm management practices were asked as prompted questions, not unprompted as required by the indicators. As with the issue of the most

2 Note that they could be asked about other crops. 3 These are set per crop in the context of Malawi as a maximum yield in kgs/ha or kgs/tree.



recent harvest, the Word/pdf version stated unprompted and this is what the training was based on, but the final implemented version was changed to prompted. This explains why the unprompted scored for knowledge of agricultural practices is much lower in the mid-term than in the baseline. These issues are highlighted in the appropriate section of the findings and it means it is not possible to directly compare the baseline knowledge and mid-term knowledge. The consultants also noted that there were price outliers in the baseline that had not been removed. These had resulted in some very high sales values, beyond what is realistic for these types of households. Kadale has adjusted the baseline values in the findings section to take account of these, and provided an explanation of what was changed and why. A cleaned dataset and the Stata files are provided to MSIKA as separate electronic files alongside this report. There is a considerable amount of data in the dataset that will enable MSIKA to review the findings and explore issues that it thinks are important. Kadale has sought to set out in the findings (section 3) what it thinks is most useful, particular focusing on progress towards the Life of Project (LoP) targets.

2.1.2 Focus Groups Kadale, with inputs from the MSIKA team, developed an FGD topic guide to lead the FGD discussions. The guide is included in Annex 4. The Kadale team held 16 focus group discussions (FGDs) with beneficiary producers as participants to collect qualitative information. The Kadale team conducted FGDs across the five MSIKA districts with men and women who were growing the MSIKA target crops. The FGDs were conducted by either the field research supervisor for that district or one of the central research team, as determined by logistical ease. Overall, the focus groups consisted of 144 respondents (57 male and 87 female) with respondent ages ranging from 20 to 68, covering growers of all seven target crops, across all five districts. The consultants conducted the FGDs as planned, other than that it was difficult to find sufficient producers of chili, guava and citrus, particularly where it was planned to have male only, or female only groups. In the case of chili, producers of other field crops were added to expand the group. For guava and citrus, mango producers were added. The result was that there were more mixed sex groups and more multiple crop groups than anticipated. Overall, the consultants do not think mixing sexes affected the contribution of women. While it would have been ideal to focus on one crop, MSIKA trains producers in a set of generic good agricultural practices, focusing more on managing field crops and tree crops generally. There were three FGDs that fell below the minimum target of eight participants, with one having six participants and two having seven participants. This was due to logistical difficulties in mobilizing enough beneficiary producers of the required types, as producers were dispersed across several villages rather than concentrated in one location. Despite this, the groups were of a sufficient size for a reasonable discussion. Overall, the consultants were satisfied with the quality of discussion in the FGDs and were able to gather the diversity of views and qualitative inputs that were necessary.



Table 3: FGDs planned and conducted by crop, district and location District Crop (s) District Total

1 Dedza Irish Potatoes

4 2 Dedza Tomato 3 Dedza Onion (and other field crops) 4 Dedza Mixed (mango, onion, tomato, leaves, guava) 5 Lilongwe Tomato

3 6 Lilongwe Onion 7 Lilongwe Chillies 8 Mangochi Tomato 2 9 Mangochi Fruits (mixed) 10 Mchinji Chillies

4 11 Mchinji Irish Potatoes 12 Mchinji Citrus 13 Mchinji Mango 14 Ntcheu Irish Potatoes

3 15 Ntcheu Guava 16 Ntcheu Mango Total 16

A more detailed version of the FGDs conducted, including number of male and female participants, and reasons for changes in composition, is provided in Annex 5: FGD and KII details. The findings of the FGDs are summarised in an Excel matrix so that responses for each question can be reviewed together. This matrix has been shared separately with MSIKA and Venture37.

2.1.3 Key Informant Interviews The consultants conducted key informant interviews (KIIs) with stakeholders to understand their engagement with MSIKA, obtain feedback on the implementation, and determine the outcomes that they have seen or achieved resulting from the program. KIIs were conducted with the following key stakeholder groups:

1. MSIKA monitoring and evaluation staff, technical staff and management; 2. Farmer Based Organisations (FBOs) - one high, medium and low performing

FBO per district; 3. Processors and business partners that have been supported; 4. District Agricultural Development Officers (DADO), Horticultural Crops Officers

(HCOs), Agricultural Extension Development Coordinators (AEDCs) and Agricultural Extension Development Officers (AEDOs) in the target districts;

5. Financial institutions that have partnered with MSIKA; 6. Govt. Ministry Headquarters and Research Scientists; and 7. USDA.

The consultants developed different KII guides for the discussion with each type of key stakeholder. These were tailored to the specific details of the stakeholders as required. See Annex 4 for the KII instruments. The KIIs were conducted by the team leader (MSIKA staff, processors and businesses, GoM HQ and Scientists, Financial Institutions) and the field team leaders (FBOs and GoM District staff). Due to logistical and language reasons, the district-level KIIs were more appropriate to be conducted by the field team leaders. The consultants were able



to conduct the intended KIIs, other than for one processor was unavailable in Blantyre, despite making a specific trip to meet them. Initially, Venture37 wanted the evaluation to conduct a statistically relevant sample of the FBOs and processors. However, due to time and budget constraints, Venture37 and the consultants decided that the evaluation would conduct KIIs with a sample of FBOs across the districts and selected processors to enable validation of the monitoring data collected by Venture37, as well as gathering qualitative insights. Table 4: KIIs planned and conducted by category and location

Organization Planned Actual Location FBO Committees 15 16 All 5 districts Ministry of Agriculture District based staff 10 11 All 5 districts Ministry of Agriculture, HQ 1 1 Lilongwe University Research scientists 1 1 Lilongwe District Financial institutions 2 2 Lilongwe Processors 5 4 Lilongwe, Blantyre & Dedza Other SMEs 1 1 Lilongwe MSIKA Management 2 2 Lilongwe MSIKA M&E Team 1 1 Lilongwe MSIKA Technical Team 5 5 Lilongwe USDA 1 1 USA via Skype Total 44 45

A more detailed version of the KIIs conducted, including details of respondents is provided in Annex 5: FGD and KII details.

2.1.4 Review of MSIKA Documents and Data Venture37 provided the consultants with key documents and data at the start of the evaluation. These included the program description, evaluation plan, performance monitoring plan (PMP), internal MEL Plan, lists of beneficiaries and key stakeholders for the sample frames, semi-annual reports to USDA, the baseline report, database and instruments, and a listing of FBOs and training conducted at FBOs. The evaluation team reviewed these documents and data to inform the evaluation. Insights from this information that support the evaluation’s primary data collection are reported in the findings section. The role of this documentation and data was to inform the development of the instruments and the sampling frame, as well as to provide information on other results not directly measured by the survey, FGDs and KIIs.

2.2 Limitations 1. Verification, rather than collection of primary data for some participants.

As noted in comments on the KIIs, it was not possible to gather new data on all aspects of this multi-faceted programme through representative surveys of all the stakeholder groups within the time and resources available. Therefore, the resulting design combined a representative HH survey of the MSIKA population of 8,468 beneficiary producers as of 31st December 2018, with FGDs, KIIs and reviews of data/reports both for verification and to generate insights. The implication and potential limitation for USDA and MSIKA is that Kadale has generated primary research findings, such as on training knowledge and use, yields, losses and sales, but that other reported data in the Logmon has been selectively verified rather than newly gathered, such as the number of loans (indicator 12). In the consultants’ view, this is the appropriate way to conduct an exercise of this nature, as it is costly and inefficient to gather full primary data for all aspects of the program.



2. Integrating MSIKA semi-annual and mid-term data

The decision to define the mid-term sample frame for the HH survey as those that had been trained as at 31st December 2018, makes it difficult to integrate the mid-term results with MSIKA’s own results that are reported to the end of March and the end of September each year. MSIKA trained a large number of beneficiaries in the first calendar quarter of 2019, and some of these will have implemented the training(s), but not all, so it is not possible to extrapolate the mid-term findings to the latest MSIKA population. A further complication is that the mid-term HH survey covered the growing and harvesting period July 2018 to June 2019, so the measurement period is also not synchronized with the MISKA reporting periods. 3. Cyclone Idai and other dynamics

As stated earlier, and as will be seen from the results, Idai affected 41.5% of MSIKA producer beneficiaries to different levels. Removing all of these from the sample adversely affects its representativeness and increases the margins of error. The consequence is that on yields, crop losses and sales, the analysis looks at the whole mid-term sample and separately a sub-sample of those not affected by Idai. For all other analysis, the whole sample is used. Kadale notes that the prices of most crops, other than chili (which was the crop most heavily affected by Idai) were lower in the mid-term than in the baseline. This results in a lower value of sales for the same volume for the other crops. The consultants have provided an analysis of the effect in the relevant section (see 3.2.13). 4. Estimation and calculations by beneficiary producers

Yield, losses and sales data are based on reporting by producers, which involves recall of information for up to three growing cycles per year. Recall errors are possible. In addition, one of the recommendations made at baseline was for MSIKA to develop standard measurement units due to the range of bags, pails, baskets and other containers used for harvesting and selling, and uncertainty on their weights. MSIKA did develop standard measurements by weighing commonly available units and these have been adopted in its semi-annual studies and in this mid-term. However, when producers are asked to estimate volumes (production, losses and sales) by numbers of specified containers, there is a risk of error due to reliance on memory, that they may use non-standard containers and that bags/containers can be filled to different levels. Therefore, caution is advised on the precision of production, losses and sales measurement.



3 Results and Findings This section sets out the results and findings of the mid-term evaluation. As noted in the methodology section, the findings are drawn from the beneficiary producer household survey, FGDs, KIIs with stakeholders and Venture37 staff, and the review and analysis of documents and data provided by MSIKA. A summary of the indictors based on the mid-term evaluation is included in Annex 1.

3.1 Farmer Based Organisations A key part of MSIKA’s approach is to work with and through Farmer Based Organisations (FBO)4 to strengthen the capacity of the FBOs and their members. As noted in the methodology section, the consultants conducted KIIs with the committees of 16 FBOs, across the five districts. These KIIs were also seeking to verify information provided by MSIKA about the FBO performance. This section focuses on the progress and performance of MSIKA’s work around FBOs.

3.1.1 Background and outline of work with FBOs MSIKA undertook a mapping exercise at the start of the project for potential beneficiaries and existing FBOs that the team could work with. This process was undertaken in collaboration with the Ministry of Agriculture’s district structures, such as the District Agricultural Development Office (DADO), and with other NGOs operating in the target districts. This was done in order to identify Extension Planning Areas (EPAs) where the target crops were grown and where there were potential beneficiary producers that were not yet supported by other organizations. Once the target EPAs were agreed, MSIKA called meetings at Group Village Head (GVH) level, typically covering two to three villages, for producers of the target crops. The aim was to introduce MSIKA and for producers to form clubs of 15-30 members. Between 5-10 clubs were then formed into an FBO within a small geographic area of about a 5km radius, within which it would be practical for members to get together. The expectation was to have membership from 75 up to about 300. Where FBOs already existed, then their structures were used rather than change them. The MSIKA team report that 15-20% of their FBOs existed prior to MSIKA’s arrival. Each club has five committee members (Chairperson, Vice-Chairperson, Secretary, Vice-Secretary and Treasurer) and the leaders of the clubs (Chairpersons) form the committee for their FBO. Out of this group of leaders, the FBOs elect officials in the same roles as for the clubs. MSIKA encourages FBOs to form at least the following four sub-committees: Markets, Finance, Production (chaired by a Lead Farmer), and Disciplinary (usually chaired by an elder in the community) There may be other committees, particularly in long established groups. Sub-committees include FBO and club leaders and members.

3.1.2 FBOs summary data MSIKA has several indicators and related targets for its work with FBOs. In summary, MSIKA reports that as at 30th September 2019, it was working with 219 FBOs, consisting of 55 in Dedza (25.1% of FBOs), 35 in Lilongwe (16.0%), 39 in

4 FBOs can include registered co-operatives, registered associations and non-registered groups. There can be confusion with names as FBOs use the terms co-operative and association when they are not formally registered as these. For simplicity, the term FBO encompasses any grouping of producers whether formally registered or not.



Mangochi (17.8%), 41 in Mchinji (18.7%) and 49 in Ntcheu (22.4%). This is a relatively even spread, with Dedza and Ntcheu having the most FBOS. As at the mid-term survey cut-off date of December 31st, 2019. MSIKA was working with 119 FBOs, with the other 100 trained from January to September 30th, 2019. MSIKA categorizes these FBOs, based on the MSIKA team’s own assessment, into high performing - 72 (32.4%); medium performing - 62 (27.9%) and low performing - 85 (38.3%).

Table 5: Performance rating of FBOs

District

Performance Rating

High Medium Low Total

# % # % # % #

Dedza 15 20.8 15 24.2 25 29.4 55

Lilongwe 10 13.9 6 9.7 19 22.4 35

Mangochi 10 13.9 14 22.6 15 17.6 39

Mchinji 19 26.4 14 22.6 8 9.4 41

Ntcheu 18 25.0 13 21.0 18 21.2 49

Total 72 100.0 62 100.0 85 100.0 219

The consultants were not able to make a capacity assessment of the FBOs in this evaluation process, because the sample of 16 FBOs was not representative. MSIKA is planning to undertake a formal assessment of FBO capacity in the final quarter of 2019 using Venture37’s PM2 tool. Beneficiary producers were asked the activities that MSIKA had included them in. The most common were training in basic agri-production (93.2%) and PHH (91.8%) and training in VSL. These were followed by training in marketing (82.8%), in financial literacy (73.8%) and in gender equality (73.4%). Table 6: MSIKA activities reported by FBO members in the producer survey

Activities Male % Female % Total %

a. Training improved horticulture practices 241 93.8 361 92.8 602 93.2 b. Training in horticulture post-harvest handling 234 91.1 359 92.3 593 91.8 c. Training in marketing 210 81.7 325 83.5 535 82.8 d. Training in financial literacy 189 73.5 288 74.0 477 73.8 e. Training in gender equality 199 77.4 275 70.7 474 73.4 f. Training/membership of a VSL group 220 85.6 248 89.5 568 87.9 g. Linkage to an MFI 160 62.3 257 66.1 417 64.6 h. Linkage to markets to sell horticulture products 153 59.5 247 63.5 700 61.9 i. Participated in international/district trade fair 73 28.4 89 22.9 162 25.1 j. Other 1 0.4 - - 1 0.2 Total 257 100 389 100 646 100

3.1.3 Training overview MSIKA places a high priority on training FBO committees and members. There are two types of training:



1. Training the FBO leadership on how to be an effective organization; and 2. Training using the FBOs as a grouping mechanism for beneficiary producers in

agricultural production, PHH and storage, financial management and marketing, that is described in the section above.

This FBO section focuses on FBO leadership training with beneficiary producer training covered in the section on producer beneficiaries that follows (see section 3.2 below). Training is delivered by MSIKA’s technical specialists and by its Field Based Co-ordinators (FBCs), of which there are two per district. It is reported by MSIKA that the FBCs are very stretched in undertaking the training and other roles. MSIKA provided a training tracker, as of July 2019, from which the consultants have analysed the training that MSIKA reports providing to 213 FBOs.5 The training falls into five categories:

1. Agricultural production; 2. Marketing; 3. PHH and storage; 4. Financial Management; and 5. FBO Development.

Agricultural Production Training

Agricultural production training covers: basic agri-production training, soil fertility management, integrated pest management, climate smart agriculture and irrigation. MSIKA intends to train all beneficiary producers in basic agricultural production. It is a foundation course to introduce producers to good agricultural practices that can be applied widely across field and tree crops. It is followed by refresher and additional training in other more in-depth agricultural production topics. An analysis of the training tracker data finds that 97.2% of FBOs (207/213) have had training for members in basic agri-production practices. The next most common types of training are integrated pest management (49.3%) and integrated soil fertility management (41.3%). Climate smart agriculture (23.5%) is lagging and irrigation (0%) is still to be run. Training is intended to be an ongoing process, so it is not be expected that all FBOs and members would have been trained in subjects at mid-term. Some training is time critical to fit with the timing for planting and crop management. The 2019 training plan focuses on integrated soil fertility management, integrated pest management and climate smart agriculture.

Table 7: FBOs trained in agricultural production, by type of training

Agricultural Production Training # of FBOs Trained % Trained Basic agri-production 207 97.2 Integrated soil fertility mgt 88 41.3 Integrated pest management 105 49.3 Climate smart agriculture 50 23.5 Irrigation 0 0 Total FBOs 213 100

5 The training tracker has data for 213 FBOs out of the reported total of 219. The difference is attributed to different dates of preparation of the two documents. This does not substantively affect the analysis.



Based on the FBO committee KIIs, the agricultural production training is highly valued. They appreciated the content, and the way the training was delivered:

“Before each session they would even ask us what we want to revise, and we would tell them, and they would help us.” FBO, Dedza

Improving links to markets and increasing sales are important dimensions of MSIKA, which are intended to ensure that increased volume of crop production can be sold. Marketing Training

The marketing training modules are: 1. Introduction to marketing; 2. Marketing principles; 3. Market searching; 4. Costing and pricing; and 5. Customer care management.

MSIKA reports training between 49.8% to 53.1% of FBOs across these modules. The marketing training is mostly delivered as a block of five modules, so that if FBOs are trained, they generally receive training in all five modules. The list of trained FBOs reports that 99 FBOs have not received any marketing training. The KIIs with the FBO committees confirmed that not all FBOs had received marketing training. Those that had received some training talked about being told about selling to local markets (both physical marketplaces and also institutions), being given lists of markets, discussing about market days, pricing of produce, growing products to appeal to particular markets and having a marketing committee that does market research.

'We want them to inform us of the markets. If we fail, it should be our fault not theirs” FBO, Dedza

A theme that came through in the KIIs was that there is a high expectation that MSIKA should make actual links with markets, beyond informing FBOs where there are markets. MSIKA staff reports that it has facilitated 52 sales agreements between FBOs and supermarkets, lodges/hotels, aggregators and processors. MSIKA staff also report that there is a challenge with the sales agreements, in that FBOs are not able to satisfy the customers’ requirements in terms of quantity, quality and consistency of supply. From the KIIs and the survey, there is very limited collective selling going on at the FBOs as producers prefer to sell individually, and the FBOs lack capacity to organise producers for collective selling and to supply sales agreements. At this mid-term point in the program, there is a gap in marketing training coverage, though MSIKA has plans to train more FBOs across its five marketing training modules.

Table 8: FBOs trained in marketing, by type of training

Marketing Training # of FBOs Trained % Trained Introduction to Marketing 113 53.1 Principles of Marketing 112 52.6 Market Searching 108 50.7 Costing and Pricing 106 49.8 Customer care Management 110 51.6 Total FBOs 213 100



PHH Training

MSIKA aims to reduce losses through improved post-harvest handling (PHH) and storage. The PHH and storage training modules are:

1. Post-harvest handling; 2. Storage requirements; and 3. International handling standards.

At the mid-term point, 24.4% of FBOs had received training in PHH, 23.5% in storage requirements and 18.8% in international standards. This suggests that around three quarters of FBOs have not yet received PHH and storage training. There is training planned on all three PHH and storage modules in each month from April-December 2019, with the second highest budget allocation after agri-production training. This suggests that there will be an increase in PHH and storage training coverage by calendar year end. From the KIIs, none of the 16 FBOs had a storage/warehousing facility. As found in the beneficiary producer survey (see section 3.2.9), around 20% of producers have increased their storage space. However, there are still considerable gaps in the knowledge and uptake of practices, particularly for fruits. This supports the case for more PHH training, particularly as many of the FBOs in the mid-term sample frame as at 31st December 2018 had reportedly undergone PHH training of some type, and yet there were still gaps in knowledge and use. Based on a MSIKA phone survey of the 119 FBOs trained as of 31st December 2018, MSIKA reports achievement of 71.8% for indicator #31, which is the “Percentage of FBOs who are aware of international production and handling standards as a result of USDA assistance”. The LoP target is 75.0%, so MSIKA reports that it is making good progress. The KIIs with FBO committees obtained very little feedback on PHH and storage training. Specifically, on their awareness of international standards, the responses were either not aware, or that it had been mentioned by MSIKA briefly. From the consultant’s perspective, indicator 31 is an inappropriate one for FBOs, as none of the 16 visited had, or appeared likely to be able to develop, the capability to develop export opportunities for fruits and vegetables. In addition, none of them were processing fruits or vegetables into products. The issue of international standards only appears to be relevant for FBOs with members that grow chilis, paprika and red cayenne, which are primarily for the export market. Table 9: FBOs trained in PHH and storage, by type of training

Post Harvest Handling & Storage Training # of FBOs Trained % Trained Post-Harvest Handling 52 24.4 Storage Requirements 50 23.5 International Handling Standards 40 18.8 Total FBOs 213 100

Financial and VSL Training

MSIKA recognises the importance of financial literacy skills and knowledge for producers. It provides training across three modules:

1. Keeping financial records; 2. Financial literacy; and 3. Village savings and loans (VSL).

The coverage of training across the three modules is very similar with 49.3% of FBOs trained in ‘keeping financial records’, 47.4% trained in ‘financial literacy’ and 49.3% trained in ‘VSL’. As with marketing, those FBOs that are trained in this area tend to



have had all three types of training. There are some exceptions, which MSIKA indicates are due to the particular needs of FBOs, particularly the well-established ones, so that if one area has been covered before by another organization or is not relevant, then it is not included in the training package for that FBO. MSIKA reports that delivery of financial training was delayed in Lilongwe due to challenges with the particular staff. A key part of the VSL training model is to work through village agents who operate as mobilisers/facilitators for establishing VSLs. These agents are often individuals who have already been trained in VSL methodologies by other players, such as from government projects. Training in VSL establishment and operation is done by the agents and by MSIKA’s FBCs. FBO members are invited to form new VSLs, where these do not exist, but it is not compulsory to be a member. MSIKA VSLs do not allow non-FBO members to join, although FBO members are not restricted from joining non-FBO VSLs, as VSLs are now much more common in rural areas. The subject of VSLs is covered more widely in section 3.2.15 below. Table 10: FBOs trained in financial records, literacy and VSL

Financial & VSL Training # of FBOs Trained % Trained Keeping Financial Records 105 49.3 Financial Literacy 101 47.4 Village Savings and Loans 105 49.3 Total FBOs 213 100

Governance, Leadership and Cooperative Development

The final area of training is governance, leadership and cooperative development. This type of training focused initially on the leadership, but MSIKA realised that the leaders change, so there is also awareness raising for the members on governance issues, so that they have some knowledge of the issues. MSIKA’s focus has been on FBO governance, with 56.8% of FBOs trained in this, followed by leadership skills with 35.2% of FBOs trained. At the mid-term point, MSIKA reported that no FBOs were trained in cooperative education. Not all FBOs require training in all aspects of governance. As noted earlier, about 25% of FBOs were operational before MSIKA with some trained by other parties, so 100% training coverage is not expected. The training is conducted mainly by the FBO specialist/team lead, and is continuing, but has the lowest budget allocation of the five training areas. There is no training planned in cooperative education, as this has to be done by the Ministry of Industry and Trade which is responsible for training and registering agricultural cooperatives. This training is a month long and has to be paid for. In practice, the groups cannot afford this. MSIKA staff stated that around 10% of FBOs have potential to become cooperatives. The consultant agrees that converting into cooperatives is unrealistic for most of the FBOs, both because of the relatively small size of FBOs (up to 300 members) and the weak capacities of many FBOs. Based on the 16 FBOs interviewed, most will not be able to meet the more demanding requirements for becoming cooperatives and maintaining that status. An additional challenge would be whether these cooperatives can raise sufficient capital from members to invest in facilities and working capital and be able to hire full time management and staff to operate them. There is a risk that FBOs become cooperatives in name only and are unable to function independently. The MSIKA team report that implementation of the practices promoted in the governance training is mixed. Practices include regular meetings and application of by-laws. FBO members have clearly participated in, and benefitted from, a range of training courses, so it is possible to conclude that the FBOs provide a useful co-



ordination point for delivery of training. However, MSIKA says there is a challenge for FBO members to see the benefits of implementing these practices to create a well-functioning FBO. From the KIIs with FBO committees, it was clear that a key benefit would be for sales agreements to be made and supplied, but if the FBO is not well functioning, then it will be unlikely to obtain orders or manage sales. This appears to be a Catch 22 situation. Table 11: FBOs trained in governance, leadership and cooperatives

Governance, leadership & cooperative training # of FBOs Trained % Trained FBO Governance Structures 121 56.8 Leadership Skills 75 35.2 Cooperative Education 0 0 Total FBOs 213 100

According to the MSIKA team, FBOs are required to keep records. This includes membership records, market requirements such as buyer specifications, sales records for member sales, production records, visitor records and financial records. In general, MSIKA reports that these records are being kept, though financial records are not kept in all cases due to lack of transactions. The main committee of an FBO must hold monthly leadership and keep a record of proceedings, which MSIKA says is reasonably well adhered to. There should be annual FBO membership meetings, but according to MSIKA, this is not happening at all FBOs. FBO members are required to pay a fee to be a member. MSIKA reports that about 30% collect membership fees, ranging from MK 500-3,000 (US $0.66-4.00), but mostly less than MK 1,000 (US $1.34). At this level of income, it will be difficult for FBOs to invest in facilities and provide services to producers. Indicator 336 is the number of FBOs trained in improved financial and organizational management, with MSIKA reported progress at 135 against an LoP target of 190 (71.1% achievement), as at 30th September 2019. From the FBO KIIs, 10 of the 16 committees said they had received finance and organisational management training. There was a slight variance between the FBOs and MSIKA on which had been trained, but this is likely explained by committee changes over time. As one FBO commented:

“The people who first had the positions in this FBO were the ones that were trained, but they quit the group….” FBO Ntcheu

This is a reminder of the need for ongoing and repeat trainings for leaders, since there is going to be committee membership turnover, because these are elected positions. Indicator 30 is the number of FBOs using improved financial management practices and systems as a result of USDA assistance. MSIKA reports a cumulative 40 FBOs implementing practices as at September 30th, 2019 against an LoP target of 126. From the KIIs, the responses covered a range of topics, with record keeping mentioned by all. The FBOs also mentioned duties of officials and clear roles for them, VSL development and operations, transparency and accountability, having separate committees, budgeting and marketing as practices they were implementing. Overall, this supports the data from MSIKA on implementation.

3.1.4 Lead Farmers Lead Farmers play an important role in the MSIKA model, typically with 2-3 Lead Farmers located within each FBO. These Lead Farmers have already been selected by the communities, as part of a wider government initiative, so it was appropriate for

6 Indicators 30 and 33 were not measured by the consultants, as the mid-term did not interview a representative sample. Rather, the consultants sought to verify the data provided by MSIKA.



MSIKA to work with these individuals than to select different individuals to be trained as Lead Farmers. MSIKA provides training to the Lead Farmers in agricultural, mainly horticultural, and PHH practices. The Lead Farmers are expected to train FBO members in these practices and to give the members extension help as required. The Lead Farmers are also required to establish demonstration plots to show other FBO members the benefits of using the improved practices promoted by MSIKA. Respondents to the survey of beneficiary producers were asked if they were a Lead Farmer, with the responses set out in Table 12 below. Out of 646 respondents, 88 identified themselves as Lead Farmers (13.6%). Of these, 48 (54.5%) were female and 40 (45.5%) were male. Although female Lead Farmers were in the majority, there was a slight over-representation of men, which reflects the broader choices of the community, than of any influence MSIKA has brought to bear. Table 12: Proportion of Lead Farmers in the Survey

A25. Are you a Lead Farmer trained by Land O Lakes/MSIKA? Sex No % Yes % Total % Male 217 38.9 40 45.5 257 39.8 Female 341 61.1 48 54.5 389 60.2 Total 558 100 88 100 646 100

Respondents who were Lead Farmers were asked about what activities they had undertaken (unprompted) for MSIKA, as set out in Table 13 below. 94.0% stated they had trained others, though only 79.8% had submitted training forms. The majority (83.3%) said they had established a demonstration plot. The training and the demonstration work are key for MSIKA, so this high level of application is a positive result. The difference between the training and the submitting of training forms echoes a point made by the MSIKA team that there is more training going on than is reported. The effectiveness of the training is covered in the section on agricultural production (see section 3.2.8). Table 13: Lead farmer activities

A25a. As a lead farmer, have you done any of the following? (Prompted) Activities Male % Female % Total %

Trained others 38 97.4 41 91.1 79 94 Submitted training forms 30 76.9 37 82.2 67 79.8 Demonstration plot 33 84.6 37 82.2 70 83.3 Other 2 5.1 2 4.4 4 4.8 Total 39 100 45 100 84 100

3.1.5 Sales Agreements MSIKA works with FBOs to link them to potential buyers and lead to sales agreements for the FBO membership to fulfil, as measured by Indicator 29, which is the number of sales agreements with FBOs. MSIKA reports cumulative achievement of 52 as at September 30th, 2019, against an LoP target of 45 representing 115.6% achievement. MSIKA provides FBOs with sales books and the FBOs ask beneficiary producer members to provide sales data to their club, which is then aggregated at FBO level. The MSIKA team gather this sales data and aggregate it for reporting under indicators 21 (sales volume) and 23 (sales value). MSIKA staff indicate that there is a challenge getting FBOs to keep sales records, with about 60% keeping them. The difficulty is around collective selling, as the usual practice for beneficiary producers is to manage their own sales individually. From the KIIs, most (13/16) of the FBOs had not organised members to sell collectively. For the others, there was an attempt at getting members to sell collectively,



though this had yet come to fruition. None of the 16 FBOs reported that they had sales agreements with buyers. A typical comment was:

“We have not secured any agreement with anybody, we expected Land O’Lakes to link us to buyers, but they have not”, FBO Lilongwe

3.1.6 Processing and Storage FBOs are seen as by MSIKA as being potential processors of crop and for providing storage. From the FBO KIIs, none reported that they were processing crop, or that they had storage facilities. Linked to this, none were seeking MBS certification. In most cases, the FBOs are newly formed by MSIKA based on bringing together several clubs. With a membership of up to 300, and a membership fee of up to MK 1,000 per member, even if all members paid their fees, the FBO would have a maximum of MK 300,000/year (US $411) to cover its expenses, leaving very little to invest in buildings for storage or in processing. Processing requires equipment and expertise to operate it, and is likely also to require power. Processing requires a flow or raw material, which will require working capital to cover the time between getting paid and the producers wanting to be paid. The consultant’s view is that storage and processing are unlikely to happen at the present state of development of FBOs, unless a third party, such as government or a project/NGO pays for these. Given the limited capacity and resources of FBOs, it is unlikely that they will engage in processing in the near future.

3.1.7 Access to Market Information MSIKA aims to increase access to market information for beneficiary producers through their FBOs. Indicator 18 is “the percentage of producers who have access to current market information through their cooperatives or producer associations.” MSIKA reports a cumulative result as at September 30th, 2019 of 17.2% against an LoP target of 65% and a baseline of 12.9%. In the survey, producers were asked where they got market information (unprompted) on a range of topics, such as location of buyers, names of possible buyers, where to buy inputs, where to access finance, buyers requirements, weather, and market prices. The findings are set out in Table 14 below. The most commonly stated sources of market information were ‘MSIKA extension workers’ (62.2%), ‘radio’ (59.3%), ‘vendors/traders’ (38.9%) and ‘MSIKA lead farmers’ (38.2%). In relation to FBOs, the relevant responses were ‘fellow FBO member’ (12.5%) and from ‘FBO leadership’ (4.6%). The overall proportion of respondents in the mid-term that obtained market information from their FBOs was 14.9%. MSIKA continues to work with FBOs and conduct training and capacity building, such that FBOs can be better providers of market information.



Table 14: Sources of market information I2. Where do you get information regarding the following? (unprompted) Information Source Male % Female % Total %

Neighbor/friend 108 42.0 156 40.1 264 40.9 Govt extn officer 40 15.6 50 12.9 90 13.9 LOL/MSIKA extension worker 163 63.4 239 61.4 402 62.2 LOL/MSIKA lead farmer 91 35.4 156 40.1 247 38.2 Private buyer extension officer 2 0.8 4 1.0 6 0.9 NGO/Project extension officer 19 7.4 25 6.4 44 6.8 Vendors/traders 112 43.6 139 35.7 251 38.9 Radio 163 63.4 220 56.6 383 59.3 SMS 12 4.7 17 4.4 29 4.5 FBO leadership 12 4.7 18 4.6 30 4.6 Fellow FBO Member 34 13.2 47 12.1 81 12.5 Posters 3 1.2 1 0.3 4 0.6 Agricultural shows 4 1.6 1 0.3 5 0.8 Trade Fairs 1 0.4 3 0.8 4 0.6 Other sources 9 3.5 15 3.9 24 3.7 Did not get this info 102 39.7 178 45.8 280 43.3 Total 257 100.0 389 100.0 646 100.0

In terms of the types of information producers had received, Table 15 sets out the proportion of respondents that had received information from MSIKA. The most common types were inputs (81.3%), buyer requirements (80.6%), access to finance (74.8%) and places to sell (73.6%). Overall, respondents obtained a wide range of information from MSIKA. Table 15: Information received from MSIKA, mid-term

I1a Have you received any of the following information from LOL/MSIKA in the past 12 months from July 2018-June 2019?

Information Male % Female % Total % Sell location 165 71.4 264 75.0 429 73.6 Possible buyers 135 58.4 202 57.4 337 57.8 Inputs 193 83.5 281 79.8 474 81.3 Access to finance 169 73.2 267 75.9 436 74.8 Buyers’ requirements 190 82.3 280 79.5 470 80.6 Weather 157 68.0 216 61.4 373 64.0 Prices 147 63.6 214 60.8 361 61.9 Base 231 100.0 352 100.0 583 100.0

Multiple response possible

Of interest, beneficiary producers were asked for the best source for each type of information. MSIKA had the highest rating of any source, being rated between 43-54%, with MSIKA Extension Staff/Lead Farmers rated between 14-23%. Radio was rated higher than MSIKA Extension Staff/Lead Farmers on weather and prices, but lower on all other types of information. This suggests that members are heavily reliant on MSIKA for information and not relying on their FBOs. It is unclear that FBOs could develop the capacity to find much of the information that is currently being supplied by MSIKA. MSIKA seeks to build this capacity through establishing marketing committees, but these are yet to supplant MSIKA. In conclusion, there has been progress from the baseline on FBOs as a source of information, but this is still well short of the target and it looks unlikely that MSIKA will reach this in the remaining project period without some change. The LoP target of 65% accessing information via their FBOs looks unattainable at the current rate of progress



and in the consultant’s view is too ambitious for FBOs to be able to be a much more important source of market information. It is worth bearing in mind that MSIKA Lead Farmers will continue to be an ongoing source of information, and it could be appropriate to extend the coverage of this indicator to include information from MSIKA Lead Farmers.

3.2 Beneficiary Producers Producer households (HH) that grow the targeted fruit and vegetables are the main beneficiaries of the MSIKA program. To be included as a beneficiary producer for the survey, an individual must have been trained in basic agricultural production by MSIKA as at 31st December 2018. Based on this criterion, Venture37 provided a list of 8,468 beneficiary producers trained as at 31st December 2018. This is the beneficiary producer sample frame for the mid-term HH survey.

3.2.1 MSIKA Beneficiary Producer Sample Frame This section provides a profile of the beneficiary producers at mid-term to provide an overall picture of the population that was sampled. Comparisons are made with the baseline when appropriate. Table 16 summarizes the profile of the beneficiary producers by district, sex and crop for the mid-term sample. Beneficiary producers are spread across five districts, with the proportional split per district ranging from the lowest at 16% in Lilongwe to the highest at 25% in Mchinji. At 31st December 2018, the beneficiary producers were members of 212 FBOs. FBOs have an average of 40 members, with membership ranging from 30 members per FBO in Dedza to 52 members per FBO in Mchinji. MSIKA has trained slightly more female producers (51%) than male producers (49%). Beneficiary HHs produce a mean average 2.01 target crops. Tomato (33.2% of all responses) is the most commonly grown crop, followed by Irish potato (23.1%), onion (16.6%) and mango (14.9%). Citrus, guava and chili accounted for 12.2% combined. Table 16: Beneficiary producers, by district, sex & crop, mid-term survey

District Individuals trained in basic agri-production # of

FBOs Total Male Female Tomato Onion Irish Potato Mango Citrus Guava Chili

Dedza 54 1,609 640 969 703 453 1,354 230 44 123 306 Lilongwe 34 1,340 387 503 889 585 500 74 24 25 175 Mangochi 40 1,386 511 875 1,095 190 189 320 17 39 167 Mchinji 41 2,125 1,277 898 1,610 1,096 1,254 1,042 147 472 127 Ntcheu 43 2,008 938 1,070 1,351 495 624 869 39 185 179 Total 212 8,468 4,153 4,315 5,648 2,819 3,921 2,535 271 844 954

3.2.2 Beneficiary Totals According to MSIKA data, the program reached 36,920 individuals directly as of September 30, 2019, which is 102.6% of the LoP target (Indicator 2). Of these, 16,568 were male (82.2% of LoP target) and 20,352 were female (128.5% of LoP target). The proportion of female beneficiaries is 55.12%. Therefore MSIKA reports that the LoP target for indicator 2 has been met. The vast majority (35,488) are producers who received short term agricultural productivity training (Indicator 4). This suggests that MSIKA has overcome a slow start in reaching direct beneficiaries in years one and two and is on track to meet its target for beneficiary numbers. As noted earlier, 8,468 producers were trained at the mid-term cut-off point. This points to a very large increase in producers trained in the first nine months of 2019. The



implication is that many beneficiaries have had limited time to implement the training. The effects are expected to show through in subsequent measurement periods. Indirect beneficiaries (indicator 3) are a function of the direct beneficiaries, so the achievement on indicator 3 is a multiple of indicator 2. The consultants note that the target and the results for indirect beneficiaries are based on multiplying the direct beneficiaries by 5.6, rather than by taking out the direct beneficiary from the HH total to reduce the multiplier of indirect beneficiaries to 4.6. This would reduce the target and the results pro-rata, so it does not affect the percentage performance, but it does overstate the number of indirect beneficiaries.

3.2.3 Sample Profile Below is a summary of information about the beneficiaries in the sample, which is compared to the baseline where that information is available and appropriate. These are discussed in brief, and summarised in Table 17 and Table 18 below. The majority of the midterm sample was female (60.2%). This was a higher proportion of females than in the sample frame (54.2%) or in the baseline (49.7%). The consultant is not concerned about this sample profile, as the study and FGDs found that many households do farming as a family, so both men and women are involved in the farming tasks discussed in the report. As a result, the different split of sexes is not relevant. Except for the sex of the respondent, the demographics of the baseline and midterm samples are very similar. Both baseline and midterm respondents were on average a little over 40, with some education (89.1% midterm versus 88.0% at baseline), and were married (81.9%). Households in the midterm sample were nearly all headed by males (99.1%) and had 5.6 members, the same as the 5.6 members at baseline. Slightly fewer respondents (62.1%) identified as the head of household (HofHH), compared to the baseline (70.3%). The gap between mid-term and baseline samples is reduced if the 4.0% that said they were joint head are added to the mid-term total. For those respondents that were not the HofHH, the relationship was mostly that of spouse. The age and education level of household heads and non-household heads are very similar. Table 17: Comparison of demographics, baseline and mid-term

Demographic Baseline Mid-term Male Female All N Male Female All N

Sex of Respondent 50.3% 49.7% 100% 590 60.2% 39.8% 100% 646 Age (years) 43.1 40.0 42.2 590 41 39.6 40.5 646 Household Size 5.7 5.5 5.6 590 6.9 4.7 5.6 646 Marital Status Married 93.3% 70.3% 81.9% 590 94.9% 73.3% 81.9% 646 Divorced, separated, widowed, never married 6.7% 29.7% 18.1% 590 5.1% 26.7% 18.1% 646 Education Level of Respondent Standard 1-8 63.0% 62.8% 62.9% 590 61.5% 68.4% 65.6% 424 Form 1-4 27.3% 15.0% 21.2% 590 30.4% 14.9% 21.1% 136 Further Education 1.3% 0.3% 0.8% 590 1.2% 0.3% 0.6% 4 None 6.7% 17.4% 12.0% 590 5.8% 12.9% 10.1% 65 Adult Literacy 1.7% 4.4% 3.1% 590 1.2% 3..6% 2.6% 17 Head of household Head of household is respondent 95.3% 45.1% 70.3% 415 95.3% 40.1% 62.1% 401 Head of household is other 4.7% 54.9% 29.7% 175 2.3% 1.3% 1.7% 11 Relationship to HofH – spouse 7.1% 91.3% 84.6% 148 0.4% 53.2% 32.2% 208 Relationship to HofH – other 92.9% 8.7% 15.4% 27 - - - - Age of HofH – mean 40 38 40 590 44 63 44 646



Table 18 below sets out information on the income sources of the sampled households. All households at the mid-term were farming crops (100%), while over a quarter (26.3%) also had a business. This breakdown is similar to the baseline. Nearly all respondents (95.5%) at the mid-term said that crop farming was their most important income source. This is higher than at the baseline, where 89.7% indicated crop farming, but 5.9% indicated their business. The baseline and midterm samples differed substantially in the amount of land they farmed. Midterm households farmed an average of 0.66 hectares, only half of the 1.19 hectares farmed by the baseline sample. This indicates that the size of these plots of the actual participants are quite a bit smaller than expected at the baseline. Male respondents farmed more land at both baseline and midterm. Caution should be taken when interpreting the exact land size. Respondents were asked at mid-term, if they were estimating their land area or if they knew it. Over half (55.9%) were estimating. There was no equivalent data in the baseline. Table 18: Sources of income and land area, baseline and mid-term

Demographic Baseline Mid-term Male Female All N Male Female All N

Source of Income Crop Farming 99.0% 95.2% 97.1% 590 100% 100% 100% 646 Livestock 7.7% 3.4% 5.6% 590 6.6% 3.9% 5.0% 646 Wage Labor 18.5% 25.3% 21.9% 590 11.7% 22.1% 18.0% 646 Formal Work 2.0% 2.7% 2.4% 590 1.2% 0.8% 0.9% 646 Business 26.3% 30.7% 28.5% 590 26.5% 26.2% 26.3% 646 Other 3.4% 5.8% 4.6% 590 1.2% 0.3% 0.6% 646 Most important source of income Crop farming 92.2% 87.1% 89.7% 590 94.6% 96.1% 95.5% 646 Livestock 0.7% 0.0% 0.3% 590 0.8% 0.8% 0.8% 646 Wage labor 1.4% 5.6% 3.4% 590 1.2% 1.3% 1.2% 646 Formal work 0.7% 0.3% 0.5% 590 0.4% 0.3% 0.3% 646 Business 5.1% 7.0% 6.0% 590 3.1% 1.5% 2.2% 646 Land Land area mean (ha) 1.39 1.05 1.22 590 0.77 0.59 0.66 646

To provide a picture of their engagement with MSIKA, mid-term respondents were asked (prompted) to state which MSIKA activities they have been involved in. Summary responses set out in Table 19 below. Interestingly, not all respondents reported receiving training from MSIKA or a MSIKA lead farmers despite this being a requirement to be included in the sample. About nine out of ten (93.2%) reported receiving training by MSIKA/lead farmers in improved horticulture (93.2%), and PHH (91.8%). A very high percentage of respondents (87.9%) were trained and/or members of a VSL group. Many had received training in financial literacy (73.8%), gender equality (73.4%), and marketing (82.8%). A majority of respondents were linked to a micro-finance institution (MFI) (64.6%) and linked to markets (61.9). There is room to link more producers with finance and market in the future.



Table 19: Summary of MSIKA activities engaged in, at mid-term Have you had any of the following activities from Land O’Lakes? (prompted)

Activities Male % Female % Total % a. Training in improved horticulture practices 241 93.8 361 92.8 6.2 93.2 b. Training in horticulture post-harvest handling 234 91.1 359 92.3 593 91.8 c. Training in marketing 210 81.7 325 83.5 535 82.8 d. Training in financial literacy 189 73.5 288 74.0 477 73.8 e. Training in gender equality 199 77.4 275 70.7 474 73.4 f. Training/membership of a VSL group 220 85.6 348 89.5 568 87.9 g. Linkage to an MFI 160 62.3 257 66.1 417 64.6 h. Linkages to markets to sell horticulture products 153 59.5 247 63.5 4 61.9 i. Participated in international/District fair 73 28.4 89 22.9 162 25.1 j. Other 1 0.4 - - 1 0.2 Total 257 100 389 100 646 100


3.2.4 Growing Practices The knowledge and application of improved growing techniques and technologies (termed ‘practices’7) is a necessary step for increasing yields and quality, as well as for diversifying crops and making production more climate smart and resilient.

3.2.4.1 Knowledge of Practices Knowledge of growing practices is a stepping-stone to application. Indicator 14 measures the percentage of producers who can recite five or more improved agricultural techniques and technologies. Survey respondents were asked to state what practices they knew for each target crops that they grow (unprompted). The list of practices was developed in close collaboration with the MSIKA team based on the training provided.8 It is useful to note that unprompted scores give an indication of what people are able to recall. A high score would indicate widespread knowledge, but a low score may not necessarily mean lack of or low knowledge; rather it might be that this practice was not associated with the question or was not at the front of a respondent’s mind when asked. Table 20 below sets out the results on unprompted knowledge of practices that have been promoted by MSIKA. Across all beneficiary producers, 56.7% were able to state at least five improved practices, with similar scores for male and female respondents. The mid-term score is higher than the most recent semi-annual score at 39.0%, (42% male; 37% female) suggesting good progress. Tomato, onion and chili beneficiary producers were the most likely to know five practices unprompted (all above 40%), with much lower scores for tree crop beneficiary producers (all less than 20%). The overall impact baseline score was 80.5%, which is higher than the mid-term results. Following a review of the baseline instruments, the consultants identified that the final ODK questionnaire for use in the tablets was changed from the final training version to prompt the respondents by reading out a practice and asking the

7 The term ‘practices’ is more commonly used by MSIKA staff and not techniques and technologies. This will be used to encompass both techniques and technologies, except in indicator statements which will use the original wording of techniques and technologies 8 This list had to be shortened to 20-30 of the most common ones, as the list of practices was very long. The evaluators were concerned that very long lists for multiple crops would affect respondent willingness to be interviewed. The implication is that scores might have been higher, but probably not substantially so.



respondents to say if they knew them. This change to a prompted question explains why the baseline score is much higher, as respondents are more likely to remember a practice when reminded of it by a data collector. This renders the baseline difficult to use for measuring progress on the indicator, as the indicator is based on unprompted knowledge. Therefore, the best comparison to make for assessing progress is between the semi-annual and mid-term scores. This leaves a challenge for Venture37, as the target is set at a high 95.0%, which the consultants presume is based on the high (prompted) baseline score of 80.5%. With an achievement of 56.7% at mid-term, there is still considerable work to be done to reach the target. There is a case for the target to be lowered, as it was likely based on a prompted level of knowledge. FGDs found that the beneficiaries have been trained in the production of all seven crops and could give many good examples of specific practices that were new and useful. This covered a wide range of the promoted practices, suggesting that the training had been done and was comprehensive in coverage. "They are encouraging us….they gave us some booklets that explain how mango grows and how grafting is done" FGD participant, Mchinji Table 20: Knowledge of five or more practices, baseline and mid-term

Crop

Know five practices

Baseline (prompted) Mid-term (unprompted)

% %

Total 80.5 56.7

Males 85.0 55.6

Females 76.0 57.3

Tomato 90.4 57.8

Onion 89.8 43.1

Potato 78.2 34.4

Mango 32.3 11.9

Citrus 36.5 14.3

Guava 47.9 18.6

Chili 83.9 41.8

Table 21 below sets out the percentage of respondents that could name a particular practice for the target field crops. The mean average score of knowledge across the practice for each crop: tomato (28 practices) at 22.2%, onion (27 practices) at 15.7%, potato (19 practices) is 20.5% and chili (26 practices) is 15.6%. The most commonly known practices were seeds in a nursery, applying fertiliser and irrigation. This was followed by a secondary group in terms of being commonly known of composting, manuring, ridging, plant and ridge spacing, earthing up, spraying and planting in grooves in nursery. The survey findings are supported by feedback in the FGDs: "I was taught to manage my tomato garden from the nursery stage by sowing tomato seeds on nursery, tilling nursery before sowing, Applying manure on nursery, drawing shallow lines in the soil to sow seeds, mulching on the sown seeds, sprinkling water in the morning and evening." FGD participant (female), Mangochi



There were 14 practices where that less than 10% of respondents knew, such as succession planting, soil and water conservation and choosing variety. It is interesting that the application rates of specific practices (discussed in the next section) are much higher than the unprompted knowledge. This suggests that the practice is either not at the forefront of the respondent’s mind, or they did not think of it as a ‘practice’ when they were being asked. Table 21: Unprompted Knowledge of Five or More Practices – Field Crops, at mid-term

Field crop practices known % of Respondents Tomato Onion Potato Chili

Composting 40.3 46.0 35.1 20.9 Manuring 32.3 29.9 30.1 35.8 Ridging 28.0 29.2 62.2 46.3 Mulching 37.1 13.9 2.0 19.4 Earthing up n/a 35.8 39.8 n/a Crop rotation 5.4 1.5 5.0 1.5 Minimum tillage 5.2 1.5 n/a 3.0 Planting seeds in a nursery before planting out 51.5 51.8 n/a 55.2 Succession planting 3.2 n/a 2.3 6.0 Staking 36.0 n/a n/a n/a Using irrigation 59.5 36.5 35.8 41.8 Soil and water conservation 6.3 2.2 3.3 3.0 Choosing the variety 7.3 3.6 7.4 1.5 Draining excess water 2.8 2.2 n/a - Spraying for pest and disease 37.9 24.1 29.8 22.4 Testing soil acidity 0.6 - n/a - Scouting for pests and disease n/a n/a 12.0 n/a Adding lime or ash to soil to reduce acidity 3.0 1.5 n/a - Using recommended fertilizer & application rates 60.1 43.1 66.6 43.3 Using recommended plant and ridge spacing 28.7 29.2 26.4 28.4 Uprooting infected plants and burning n/a n/a 3.3 n/a De-suckering or removing unwanted side-shoots 26.3 n/a n/a n/a Land preparation n/a n/a n/a 6.0 Clipping plant ends to allow seedlings to sprout n/a 2.2 n/a n/a Sterilizing nursery beds before planting 26.9 16.1 n/a 17.9 Scouting for pests and diseases 8.2 2.9 n/a 1.5 Chitting n/a n/a 12.4 n/a Uprooting infected plants and burning 7.1 3.6 n/a 3.0 Sowing seed in row-/groove nursery 41.8 25.5 n/a 22.4 Using fish soup/sugar solution insect bait 0.6 - - - Planting in sunken beds 39.2 18.2 n/a 7.5 Hardening off before planting out 12.3 1.5 n/a 9.0 Selecting the best seedlings for planting out 12.1 1.5 14.0 10.4 Other 0.6 0.7 1.0 n/a Total respondents 464 137 299 67

Table 22 below sets out the percentage of respondents that could name a particular practice for the target tree crops. The mean average percentage of respondents that knew a practice for tree crops was mango (19 practices) at 13.1%, citrus (19 practices) at 21.1% and guava (19 practices) at 16.1%. The most commonly known practices were composting, pruning, irrigation, digging planting holes and water capture. Most other practices were known by less than 15% of respondents. This suggests there is potential to increase unprompted knowledge. It is also noted that, as with field crops, the application rates are generally much higher than the unprompted knowledge for many practices, suggesting that respondents did



not have these practices at the forefront of their minds or did not associate the question for practices with some of the practices they were applying. Table 22: Unprompted knowledge of five or more practices for tree crops, at mid-term

Tree crop practices known % of respondents Mango Citrus Guava

Composting 38.5 57.1 55.9 Manuring 28.4 42.9 37.3 Pruning 28.4 42.9 45.8 Mulching 4.6 14.3 13.6 Spraying for pests and disease 10.1 14.3 15.3 Water capture 30.3 42.9 27.1 Using irrigation 25.7 42.9 30.5 Soil and water conservation 1.8 14.3 6.8 Draining excess water 0.9 - - Testing soil acidity 0.9 - 1.7 Adding lime or ash to soil to reduce acidity - - - Scouting for pests and diseases 2.8 14.3 6.8 Uprooting infected plants and burning 2.8 14.3 3.4 Using fish soup/sugar solution as bait 0.9 - 1.7 Grafting 17.4 14.3 10.2 Budding 16.5 14.3 6.8 Staking young trees 9.2 - 11.9 Digging of planting holes prior to planting 24.8 42.9 30.5 Deflowering of first flowers 5.5 28.6 1.7 Other 4.6 - - Total respondents 91 7 48

Respondents were asked the source from whom they first learnt the practices they had stated. The question structure meant it was possible to have more than 100% in total, since they were responding on each practice they had mentioned. So, a score of 73.3% on tomato means that 73.3% of tomato growers stated that at least one practice was first learnt from that source. Table 23 shows the result of the sources of information on the improved practices. The highest score across all crops was ‘MSIKA extension worker/lead farmer’, often by a wide margin and wider on field crops than tree crops. The second most common source was ‘Always known’, interpreted to be passed down knowledge, followed by ‘relatives/other farmers’. Although the score for ‘GoM Extension workers’ was high on citrus, the sample was very small, with all other scores for GoM in the range of 10-15% of respondents. ‘Other NGOs’ and ‘FBOs’ both scored below 10% and often below 5% of respondents. Other sources were even lower.



Table 23: Source of first learning of growing practices by crop, at mid-term

Source Tomato Onion Potato Chili Mango Citrus Guava Always known 38.6 19.0 26.8 23.9 21.7 28.6 30.5 GoM extension worker 14.2 10.2 14.1 10.4 11..3 42.9 11.9 LOL extension worker LOL/MSIKA lead farmer 73.3 73.0 60.4 76.1 49.1 57.1 47.5 NGO extension worker 9.1 2.2 5.7 6.0 1.9 - 1.7 Radio 0.2 - - 1.5 1.9 - 1.7 My Farmer organization 3.4 2.9 2.0 3.0 2.8 14.3 3.4 Relative/other farmers 20.9 23.4 25.2 11.9 19.8 - 20.3 Saw drama 0.4 2.2 0.3 - 1.9 - 1.7 Saw in newspaper 0.4 - - - - - - Saw on television 0.6 - - - - - - MSIKA training 6.5 2.9 3.7 1.5 5.7 - 3.4 Training by other organization 2.6 0.7 0.7 1.5 0.9 - 1.7 School 0.9 1.5 0.7 - 0.9 - 1.7 Agro-dealer - - - - - - - Vendor 0.2 0.7 - - - - - Do not know/cannot remember 0.6 - 0.3 - - - - Total respondents 464 137 299 67 91 7 48

The conclusion is that MSIKA’s beneficiary producers have gained much of their knowledge on improved agricultural practices from MSIKA, scoring well ahead of knowledge passed down or shared within the community. 3.2.4.2 Application of New Practices Indicator 7 measures the number of individuals who have applied new techniques or technologies, as a result of USDA assistance. To measure this indicator required a review of the baseline instrument, as MSIKA adopted a different approach to train producers than anticipated at the baseline. The change was to train producers in a wide range of practices applicable across many crops, plus some specific practices for particular crops. There were additional practices at mid-term than measured at baseline, so direct comparisons are more difficult. The question in the baseline and the mid-term was prompted, with respondents asked about specific named practices for all the target crops that they grew. The practices that MSIKA focused on are grouped as follows:

1. Crop Genetics 2. Pest Management 3. Disease Management 4. Soil Fertility and Conservation 5. Irrigation 6. Water Management 7. Climate Mitigation or Adaptation 8. Other

The findings on application of practices are that 99.7% (all but 2 out of 646) of respondents had applied one or more new practice in the period July 2018-June 2019. This is a very high achievement level equating to 8,442 of the mid-term population, which is all except 26 beneficiary producers! Comparing men and women, 100.0% of men applied one or more new agricultural practice, while women applied 99.5%.



The LoP target for applying one or more new practice (indicator 7) is 31,680 out of 36,000 individuals trained in agricultural practices (Indicator 4), which is a rate of 88.0% using at least one new agricultural practice. As the mid-term population was relatively small compared to the population by September 30, 2019, then the total number of people applying new practices (Indicator 7) is also relatively small, at 8,442 (26.6% of LoP target). However, if the 99.7% uptake rate for one or more new practices in the mid-term population is maintained, and applied to the cumulative number trained in agricultural production as of September 30th of 35,488, equating to 35,382, which is 112% of the LoP target. The target for indicator 7 looks like it will be over-achieved. Table 24 below shows the application of practices within the eight categories defined above. The achievement ranged between 71.8 % to 99.7% of respondents. The application highest rates were for other (99.1%), disease management (98.6%), climate mitigation and adaptation (97.7%), crop genetics (97.5%), soil fertility (97.5%), and pest management (96.9%). The lowest scores were for irrigation (71.8%) and water management (non-irrigation) (88.4%), which are still good scores for application. The results for men and women were very similar. The baseline and mid-term surveys had several practices that were common to both surveys, but there were some practices in the baseline that were not asked in the mid-term, such as green manuring, and some practices asked in the mid-term that were not in the baseline, such as staking young trees. This means it is not valid to directly compare the disaggregated mid-term categories to the baseline categories. Table 24: Application of new agricultural practices per category, at mid-term

Category/Disaggregate % applying Mid-term Population applying

Total applying 99.7 8,442 Male 100 3,369 Female 99.5 5,073 New 79.3 6,711 Continuing 20.4 1,730 Crop Genetics 97.5 8,258 Pest Management 96.9 8,206 Disease Management 98.6 8,350 Soil-related fertility and conservation 97.5 8,258 Irrigation 71.8 6,082 Water Management (non-irrigation based) 88.4 7,485 Climate mitigation or adaption 97.7 8,271 Other 99.1 8,389

An overall achievement rate of 99.7% of respondents applying one or more new practices is good, but it potentially hides other aspects of the performance on this indicator. The consultants calculated the mean number of new improved practices per beneficiary producer was 9.8.9 Table 25 indicates the number of new practices applied by crop. This ranged from implementing no practices to a highest score of 26 new practices applied, both on tomato and onion. The mean number of new practices applied per producer was highest for chili (8.63), tomato (7.22) and onion (6.81). Tree crops had the lowest number of new practices applied per producer (mango 2.28, citrus 2.57, guava 1.78). The modes for all the crops were zero, meaning the most common response was that beneficiaries were applying no new practices. The four field crops have 22-34% of producers not applying any new practices, while the tree crops have 43-53% of

9 Beneficiaries had a mean of 2.1 target crops, so 9.8 is based on growing more than one crop.



producers not applying any new techniques. This suggests there is potential to increase uptake by the lagging beneficiaries. Table 25: Application of new practices by crop, at mid-term

Crop Total producers

New Practices applied

Mean % applying none

Tomato 464 3,352 7.22 22.4% Onion 137 933 6.81 26.3% Potato 299 1,022 3.42 34.4% Mango 109 248 2.28 46.8% Citrus 7 18 2.57 42.9% Guava 59 105 1.78 52.5% Chili 67 578 8.63 25.4% Total 646 6,256 9.68 30.2%

Table 72 in Annex 6 provides the detail for each practice, including the percentage that are using it and the comparison with the baseline usage rates. This table covers 44 practices, across the seven crops with baseline, mid-term and the differences. Across the field crops, land preparation, ridge and plant spacing, and use of nursery generally scored the highest. For tree crops, pruning scored the highest, followed by composting and manuring. Use of fish soup, testing soil acidity, and use of ash/lime generally scored poorly across most crops. There were somewhat better scores, but still low, for grafting (applicable only to tree crops) and removing excess water. While these might be considered as practices for MSIKA to promote more, they also need reviewing as to why uptake is so low and potentially de-prioritized. There were nine practices where there has been an increase in use of 20% or greater between baseline and midterm across at least three crops. These practices were: composting, manuring, mulching, irrigation, soil and water conservation, selecting varieties, spraying, water capture and pruning. There were a further eight where there had been an increase of 20% or greater across one or two crops. These can be seen as the most commonly applied new practices. There were nine practices for chili for which the increase in use was 20% or greater, eight practices for mango and guava, and seven for citrus (small sample), suggesting considerable new use for chili and tree crops. Though the uptake of new practices was greater for these crops, it is noted that these are the less commonly grown crops among the sample and population. There were only three instances where the mid-term uptake of practices was lower than the baseline – manuring for onion, budding for guava and digging planting holes for citrus (small sample). With so many practices across seven crops, it would have been surprising that all of them showed an increase; so the consultants see this as a positive overall result for the uptake of new agricultural practices. Beneficiary producers may have been using practices before MSIKA or leant them from other sources, so that the impact of these is not exclusively attributable to MSIKA. Asking about practices used for the first time in the last year, combined with the information on source, enables clearer attribution to MSIKA for the subsequent results. Respondents were asked in the beneficiary survey what practices they had used for the first time in the last year (July 2018-June 2019). In general, the scores for first use were high, across a range of practices and particularly so for field crops. Due to the amount of data and that there are different practices for field and for tree crops, the analysis considers field crops and tree crops separately. Starting with field, crops, Table 26 below sets out the first use of practices for the four field crops. Across



the field crops, planting in a nursery, sowing in rows (nursery), ridging and use of fertiliser scored consistently well, with fish soup, testing soil acidity, use of lime/ash and minimum tillage scoring generally lower. For tomato, 13 practices have been applied for the first time by over 40% of respondents. Plant and ridge spacing, and sowing in groove/rows in nursery were applied for the first time by over 50% of respondents. There was a high uptake of practices relating to nursery establishment and operation, as well as for pest management and use of recommended fertiliser. The linked practices of testing for acidity and use of lime/ash scored poorly, along with the use of fish soup. For onion, 11 practices were applied for the first time by over 40% of respondents. Over 50% of respondents applied recommended ridge/plant spacing and recommended usage of fertiliser for the first time. As with tomato, practices relating to nursery, use of recommended fertiliser and pest management scored highly. The linked practices of testing for acidity and use of lime/ash scored poorly, as did use of fish soup. For potato, 3 practices were applied for the first time by over 40% of respondents, with the highest scoring being ridge spacing (49.0%) and use of recommended fertiliser (45.4%). No practices were used by over 50% of respondents. The scores for potato were generally lower than for tomato or onion. There were relatively good scores for land preparation, seed potato and variety selection relative, and for pest/disease management. As with other crops, the use of fish soup scored poorly. For chili, 14 practices were used for the first time by over 40% of respondents. Chili had the highest overall scores of all the crops, with seven practices scoring over 60.0%. This might be because chili was a relatively new crop for many beneficiary producers. The lowest uptake by respondents was for fish soup, soil acidity, use of lime/ash and draining excess water, all at less than 20%.



Table 26: Use of practices for the first time, field crops, mid-term

Practice Field Crop Tomato Onion Potato Chili

Composting 31.7 30.7 31.6 38.0 Manuring 33.9 27.7 25.0 48.0 Ridging 32.8 30.7 35.7 64.0 Earthing up n/a 33.7 32.7 n/a Mulching 40.3 34.7 11.2 48.0 Crop rotation 22.2 21.8 29.1 30.0 Minimum tillage 13.1 6.9 n/a 12.0 Planting seeds in a nursery before planting out 38.8 36.6 n/a 74.0 Staking 44.7 n/a n/a n/a Succession planting 23.6 n/a 19.4 38.0 Using irrigation 31.4 29.7 17.9 32.0 Soil and water conservation 24.4 24.8 23.0 28.0 Draining excess water 16.7 19.8 n/a 16.0 Testing soil acidity 5.8 5.0 n/a 8.0 Adding lime or ash to soil to reduce acidity 6.4 15.8 n/a 16.0 Choosing the variety 45.6 46.5 35.7 54.0 De-sucking or removing unwanted side shoot 46.9 n/a n/a n/a Spraying for pests and disease 39.7 44.6 29.6 38.0 Using recommended plant and ridge spacing 56.7 59.4 49.0 74.0 Using recommended fertiliser and application 48.1 51.5 45.4 44.0 Sterilizing nursery beds before planting 48.1 45.5 n/a 60.0 Scouting for pests and diseases 40.3 42.6 27.6 44.0 Uprooting infected plants and burning 43.6 46.5 37.8 56.0 Sowing seed in row/groove nursery 52.5 48.8 n/a 72.0 Using fish soup/sugar solution as bait 7.2 5.9 4.1 - Planting in sunken beds 48.9 45.5 n/a 38.0 Hardening off before planting out 43.9 49.5 n/a 50.0 Selecting the best seedlings for planting out 44.4 45.5 n/a n/a Clipping plant ends so seedlings make fresh sprouts n/a 35.6 n/a n/a Chitting n/a n/a 22.4 n/a Using raised beds n/a 38.6 n/a 50.0 Seed selection n/a n/a 44.4 60.0 Land preparation n/a n/a n/a 64.0 Total 360 101 196 50.0

The results for fruits are set out in Table 27 below. Overall for tree crops pruning, composting, manuring, staking and water capture had higher first-time application rates compared to fish soup, testing soil acidity, use of lime/ash grafting, budding and draining excess water which had the lowest first-time application rates. For mango, two practices were applied for the first time by over 40% of respondents, being pruning (50.0%) and composting (46.6%). There were relatively good scores for the water-related practices of water management, soil and water conservation and irrigation. Compared to the field crops, mango generally had lower first-time use rates. As noted earlier, the citrus sample was very small, so the results are not reliable. Composting, manuring, soil and water capture, irrigation and staking young trees were applied for the first time by 50% or more of respondents. Other water related practices (soil and water management, draining excess water) and several pest/disease related practices (spraying, uprooting, fish soup) scored zero or poorly.



For guava, only one practice (pruning) scored over 40%, but with a very high 57.1%. There was low/no first use of practices for grafting and budding, soil acidity and liming, irrigation and fish soup. Table 27: Use of practices for the first time, tree crops, mid-term

Practice Tree Crop Mango Citrus Guava

Composting 46.6 50.0 39.3 Manuring 29.3 75.0 32.1 Pruning 50.0 25.0 57.1 Mulching 17.2 25.0 21.4 Spraying for pests and disease 17.2 - 21.4 Water Capture 37.9 75.0 25.0 Using Irrigation 22.4 50.0 7.1 Soil and Water conservation 27.6 - 25..0 Draining excess Water 17.2 - 10.7 Testing soil acidity 3.4 25.0 - Adding Lime or ash to soil to reduce acidity 8.6 - 3.6 Scouting for pest and disease 19.0 25.0 17.9 Uprooting infected plants and burning 25.9 - 21.4 Using fish soup/sugar as insect bait 10.3 - 10.7 Grafting 15.5 - 7.1 Budding 15.5 25.0 7.1 Staking young trees 25.9 50.0 28.6 Digging of planting holes prior to planting 17.2 25.0 21.4 Deflowering of first flowers 20.7 - 17.9 Total 58 4 28

Respondents were asked why they had not used the ‘other’10 practices between July 2018 to June 2019. Respondents had been read a list of practices for the crop they were responding about and had already stated the ones they had used and had not used. ‘Other’ therefore refers to the practices not used. This was a multiple response question for which the enumerator classified the single or multiple responses given rather than reading out the list of possible responses. Respondents were asked to give more than one response (“any other reasons?”). The question was asked for the specific practices relating to a specific crop and are reported in the table below by crop. Male and female responses were very similar. Table 28 sets out the reasons given for not using practices by crop. The most common reason across all crops was that the respondent ‘did not know it’, with all crops having more than 86% of respondents saying they did not know it. This was followed by the ‘need did not arise’ (all crops 65% or above), followed by ‘expensive to use’ and ‘inadequate resources’, which are both related to affordability. The fifth most common reason was that it was ‘time consuming’. The ‘did not know’ response is interesting, as all these producers were trained in agricultural production, yet there were practices they did not know. This might indicate that some practices were not covered in the training, or that these may have been forgotten by the respondent. It would be surprising if respondents knew all the practices, as there were more than 40 practices for some crops, and on average respondents were growing two crops, so there was a lot of information to ‘know’. The large number of practices makes it difficult for all the practices to be communicated effectively. Respondents may not have fully heard parts of the training, or lost

10 Described as ‘other’ as the respondents had been read a list of practices and stated the ones they used, so this refers to the ones they did not use.



concentration or just forgotten the practice, especially as these are adult learners often with limited education. The second most common response was that ‘The need did not arise’, is not surprising in that from a wide range of possible practices to apply, so at least some are not going to be required, such as planting new trees, and irrigation. The third most common response concerning costs/resources suggests that some practices are too expensive for some respondents to apply. This mirrored responses from the FGDs, which specifically highlighted buying D-compound fertiliser and agro-chemicals for pest and disease management as being too expensive. The cost issue relates to use of all purchased inputs, such as certified seed/seedlings, fertiliser, agro-chemicals for pest and disease, and equipment (pumps, and irrigation equipment etc.). This is perhaps inevitable, given the poverty of many respondents, and something that MSIKA is seeking to address through improving yields and sales to generate profits for investing in production. The ‘time consuming response’ is a reminder that although there can be seasonal under-employment, labour may be constrained in the peak growing season, such that additional tasks are not realistic to undertake. The least commonly given reasons were: ‘inefficient’, ‘too difficult for farmers’, ‘did not understand’, ‘not confident to use’ and ‘not confident it will work’. To clarify, this means that these practices are not seen as inefficient, too difficult, understood or confident that they will work or to use them. In other words, the practices are mainly appropriate. “There are some who have adopted the spacing, but I am not so sure if they are doing it correctly. We measure using our feet and sticks, so the spacing may not be exactly what LoL trained us to do." FGD participant, Makwangawala. Table 28: Why not used the other practices, by crop, July 2018 – June 2019

Why did you not use the other practices? (prompted)

Reason Tomato Onion Potato Mango Citrus Guava Chili % % % % % % %

Did not know it 92.9 97.1 90.6 86.2 100.0 89.8 95.5 Expensive to use it 38.0 33.6 30.6 38.5 14.3 44.1 35.8 Time consuming 32.0 24.1 25.3 21.1 42.9 33.9 22.4 Inefficient 16.0 5.8 9.1 7.3 14.3 1.7 9.0 Not confident it will work 22.9 22.6 23.6 15.6 28.6 16.9. 25.4 Not confident I can use it properly 16.2 10.9 15.2 8.3 42.9 18.6 17.9 Too difficult for farmer to do it 13.4 8.8 15.2 11.0 14.3 11.9 16.4 Did not understand it 25.7 21.9 22.2 21.1 14.3 13.6 28.4 Inadequate resources 40.0 45.3 44.8 27.5 14.3 25.4 34.3 Need did not arise 73.4 73.7 65.0 84.4 100 81.4 73.1 Other 1.1 - - 0.9 - - 7.5 All respondents for that crop 100 100 100 100 100 100 100 Respondents - n 463 137 297 109 7 59 67

Indicator 5 is the percentage of producers utilising improved soil fertility management practices. The calculation for Indicator 5 includes a few additional practices compared to Indicator 7, such as ridging, fertiliser/input use and making vegetable beds. Indicator 5 has a high score of 99.2% (baseline 81.2%, target 90%). Men achieved 100.0% (baseline 86.0%) and women 98.7% (baseline 76.0%).



3.2.5 Crop Inputs Through the survey, producers were asked about their access to inputs, and the inputs they use on different crops. While the use of crop inputs does not inform any program indicator(s), it is a stepping-stone to higher productivity. There was some change in the list of inputs between the baseline and the mid-term. The lists for field and tree crops differ, so these are reviewed separately. Table 29 below sets out the inputs for field crops. For field crops, a high proportion of respondents (mostly in the 60-80% range) were using fertilizers, insecticides, watering cans, sprayers, certified seed, compost/manure and fungicide, particularly for tomato, onion and potato, with lower scores in general for chili. There were relatively few respondents using motorized pumps, herbicides and lime/soil improvers ranging from 0-10.4% of respondents. The low use of lime/soil improvers mirrors the low knowledge and use described in previous sections. Motor pumps are likely to be too expensive for most producers, while herbicides are not broadly known by producers. Table 29: Inputs used, field crops, mid-term

Inputs used in the last 12 months

Tomato Onion Potato Chili Base Mid-

term Base Mid-term Base Mid-

term Base Mid-term

% % % % % % % % a. Recycled seed own crop 35.4 34.1 23.7 12.4 n/a 62.5 36.7 35.8 b. Recycled seed bought/other source

27.0 20.0 15.5 17.5 n/a 46.8 40.0 17.9

c. Certified hybrid or OPV seed

49.2 67.5 75.3 81.8 n/a 25.8 36.7 58.2

d. Seedlings from another 7.5 20.0 11.3 21.9 6.6 14.4 6.7 17.9 e. Insecticide 77.8 85.1 71.1 73.7 58.5 73.9 36.7 56.7 f. Fungicide 64.9 73.9 57.7 54.0 47.7 68.6 40.0 49.3 g. Foliar feed/fertilizer 77.2 90.7 85.6 93.4 92.7 96.3 46.7 50.7 h. Sprayer 66.7 70.3 70.1 65.0 58.9 71.2 40.0 43.3 i. Compost/manure (organic) 52.3 67.5 68.0 85.4 32.1 57.9 23.3 46.3 j. Watering can 76.6 84.9 73.2 93.4 37.6 69.6 40.0 53.7 k. Gravity or a manual pump 17.1 28.4 20.6 29.9 25.8 36.5 3.3 34.3 l. Motorized pump 4.8 5.8 1.0 5.1 10.5 10.4 - 3.0 m. Herbicide (to kill weeds) 1.5 4.7 3.1 2.9 2.1 3.7 - - n. Lime or soil improvers 0.6 4.3 - 5.8 0.3 4.0 - 7.5 Total respondents 333 464 97 137 287 299 30 67

Multiple responses possible

The data for tree crops is set out in Table 30 below. For tree crops, around two thirds of respondents stated that they used compost, which was by far the mostly commonly used input. Over 30% of respondents used watering cans and/or gravity/manual pumps, along with seedlings grown by themselves. MSIKA promotes using certified/ improved seedlings, but seedlings grown by producers is more common. The lowest usage rates were for motorized pumps and lime/soil improvers, for the same reasons as given for field crops. Overall, the use of inputs was lower for tree crops than for field crops. This appears to reflect the views, expressed by MSIKA staff, GoM staff and in FGDs that tree crops are mostly seen as something to harvest than something to invest in, because they do grow and produce fruit without active cultivation/investment.



Table 30: Inputs used, tree crops, mid-term

Inputs used in the last 12 months

Mango Citrus Guava Base Midterm Base Midterm Base Midterm

% % % % % % a. Seedlings grown by self 84.7 59.1 56.3 33.3 57.4 43.2 b. Seedlings bought/recycled 20.2 27.3 40.6 - 30.9 21.6 c. Certified/improved seedlings 7.4 16.7 15.6 - 16.0 10.8 d. Grafting Material 4.9 21.2 10.9 16.7 2.1 8.1 e. Insecticide 4.9 19.7 25.0 16.7 12.8 16.2 f. Fungicide 4.9 16.7 12.5 33.3 8.5 13.5 g. Foliar feed/fertilizer 4.9 15.2 9.4 33.3 7.4 18.9 h. Sprayer 7.4 18.2 14.1 - 11.7 16.2 i. Compost/manure (organic) 30.7 65.5 43.8 66.7 38.3 70.3 j. Watering can 21.5 36.4 26.6 50.0 29.8 37.8 k. Gravity or a manual pump 6.1 31.8 3.1 50.0 3.2 35.1 l. Motorized pump - 3.0 - - 1.1 2.7 m. Lime or soil improvers - 7.6 - - 1.1 5.4 Total respondents 163 66 64 6 94 37


The source of inputs is set out in Table 31. The most frequent stated source of inputs is ‘own/personal’, at 67-100% of respondents across the different crops. For field crops, ‘agro-dealers’ are the second most frequent source at 74-80% of respondents for tomato, onion and potato, though at a lower rate of 51% for chili. In addition, 51% of chili producers said they get inputs from neighbours. Agro-dealers are less commonly a source, as stated by 30-40% of tree crop respondents. This is the same range that said they get inputs from ‘neighbours/friends’. MSIKA is stated as a source by between 3-11% of respondents, other than by citrus growers (0%) and by chili growers (30%). In comparison with the baseline, there is no clear change across all crops towards or from one source to another. Table 31: Suppliers of inputs, baseline vs mid-term 11

Source of inputs

Tomato Onion Potato Mango Citrus Guava Chili Base MTE Base MTE Base MTE Base MTE Base MTE Base MTE Base MTE

Agro-dealer 72.1 75.4 88.7 80.3 69.3 74.2 8.0 30.0 28.1 40.0 13.8 30.3 60.0 50.7 Vendor 42.6 47.8 39.2 46.7 46.7 59.4 7.4 16.7 18.8 - 12.8 18.2 23.3 29.9 LOL/MSIKA n/a 11.2 n/a 11.7 n/a 6.0 n/a 8.3 n/a - n/a 3.0 n/a 29.9 An NGO /project

5.1 5.8 3.1 2.2 2.1 3.0 5.5 5.0 12.5 - 3.2 - 10.0 3.0

Government 2.7 3.7 - 1.5 0.7 5.0 1.2 - 4.7 20.0 1.1 - 3.3 3.0 FBO n/a 2.8 n/a 1.5 n/a 0.7 n/a 3.3 n/a - n/a - n/a 1.5 Neighbor /friend

46.5 58.4 27.8 50.4 54.7 64.4 17.2 36.7 25.0 40.0 29.8 30.3 60.0 50.7

Own/Personal 77.5 84.7 79.4 92.7 74.9 77.9 85.9 75.0 65.6 100 66.0 87.9 60.0 67.2 Communal 22.5 20.7 37.1 21.9 30.0 26.5 17.2 35.0 18.8 - 17.0 24.2 10.0 22.4 Other 5.1 n/a 7.2 n/a 4.2 n/a 5.5 n/a 3.1 n/a 8.5 n/a 3.3 n/a Total 333 464 97 137 287 298 163 60 64 5 94 33 30 67


11 Note that there was no option for MSIKA as the source in the baseline. Also neighbors/ friends and FBO were not split in the baseline, so the score for neighbors/friends includes the FBO for the baseline. As MSIKA FBOs did not exist at baseline (though others did), and the scores at mid-term are low, it is most appropriately compared to the neighbour/friend score.



The data for ease of buying inputs is summarised in Table 32. For tomato, onion and potato, between 67-70% of respondents found accessing inputs ‘somewhat hard’ or ‘very hard’. Growers of tree crops reported generally easier access to inputs, but this was partly because around a third did not buy any inputs at all. Compared to the baseline, the mid-term survey found a reduction in ‘very hard to get’, but also a reduction in ‘very easy to get’, with more middle scores of either ‘somewhat easy’ or ‘somewhat hard’. There is not a clear change towards harder or easier access. Table 32: Ease of getting inputs, baseline vs mid-term

Ease of access for

inputs

Tomato Onion Potato Mango Citrus Guava Chili

Base MTE Base MTE Base MTE Base MTE Base MTE Base MTE Base MTE

Very easy 18.0 8.4 16.3 12.4 12.1 10.4 34.0 21.1 26.8 42.9 28.5 23.7 12.9 9.0

Somewhat easy 22.8 24.1 26.5 17.5 26.0 20.4 10.6 9.2 12.7 14.3 10.9 11.9 12.9 31.3

Somewhat hard 33.0 50.2 29.6 49.6 34.9 48.8 6.8 22.9 25.4 14.3 11.7 20.3 32.3 47.8

Very hard 24.3 17.2 26.5 20.4 24.9 20.4 4.3 4.6 7.0 14.3 6.6 1.7 29.0 11.9

Did not purchase inputs 1.2 - - - 0.3 - 42.1 31.2 26.8 14.3 40.1 33.9 12.9 -

Does not know 0.6 - 1.0 - 1.7 - 2.1 11.0 1.4 - 2.2 8.5 - -

Total respondents 333 464 464 137 289 299 235 109 71 7 137 59 31 67

Table 33 sets out the responses on stock levels. Respondents were asked if the inputs they wanted were in stock when they tried to buy them. For the field crops, between 65-75% of respondents said they were ‘not always in stock’, with 25-34% saying they were ‘always in stock’. Compared to the baseline, there has been a shift from more frequent responses of always in stock for field crops (52-64%) to lower levels of always in stock at the mid-term (22-34%). The results for tree crops are less clear, noting that citrus has a very small sample, and the results are influenced by the lower use of inputs for tree crops discussed earlier. From the FGDs, most producers said it was difficult to access inputs, especially agro-chemicals, because:

1. The agro-dealers that stock the desired inputs are far from the producers; 2. The agro-dealers are closed or have insufficient stock when producers go for

the inputs; 3. Producers are familiar with the names, but not the appearance of the

recommended inputs, especially agro-chemicals, which means they feel unsure about what to buy; and

4. Unscrupulous traders/vendors deliberately sell wrong, expired or counterfeit inputs, taking advantage of producers’ ignorance about the inputs.

FGDs found that seeds are the easiest input to access, because they know them well enough to easily identify and most agrodealers stock sufficient.



Table 33: Stock levels of inputs, baseline vs mid-term

How often in stock?

Tomato Onion Potato Mango Citrus Guava Chili Base MTE Base MTE Base MTE Base MTE Base MTE Base MTE Base MTE

Always 53.4 32.3 52.7 34.4 60.2 29.8 65.8 44.0 40.0 83.3 58.8 43.6 63.6 22.4

Not always 44.4 65.7 46.2 65. 38.0 69.6 23.7 53.3 53.3 16.7 29.4 46.2 31.8 74.6

Never 1.3 1.7 1.1 - 1.5 0.7 - - - - 2.9 - 4.5 3.0

Does not know 1.0 0.2 - 0.7 0.4 - 10.5 6.7 6.7 - 8.8 10.3 - -

Respondents 311 464 93 137 274 299 75 30 30 6 34 39 22 67

MSIKA has been working with 75 agro-dealers. The effect of this is beneficial to improving access, but many producers still rely on their own inputs, neighbors/friends and other sources and there is not a clear improvement in access to inputs.

3.2.6 Land Area Under Improved Practices Indicator 6 measures the land area under improved growing techniques and technologies (‘growing practices’). The HH survey asked for the improved practices that were applied and then the amount of land on which those practices were applied. The HH survey measured the land area in hectares (ha) for the 12-month period from July 2018-June 2019, covering up to three separate planting and harvest cycles for tomato, onion and potato, up to two cycles for chili and one cycle for tree crops. All cycles were totalled to give the total land area. Table 34 sets out the land area under improve practices. The total land area for the sample group on which one or more improved practices was applied, across all growing/harvest cycles12, was 246.31 ha. The mean land area on which improved growing practices were applied was 0.38 ha from July 2018 – June 2019. If this mean is applied to the mid-term population, improved growing practices were applied on an estimated 3,229 ha, disaggregated as set out in the table below. MSIKA reported 12.77 ha of demonstration plots on which practices were applied. Table 34: Land area in ha under improved practices, mid-term

Indicator % of

individuals utilizing T&T

Ha of land under

improved T&T

Ha under improved T&T – total 99.7 3,229 Ha under improved T&T – new 79.5 2,567 Ha under improved T&T – continuing 20.5 662 Ha under improved T&T – crop genetics 97.5 3,149 Ha under improved T&T – pest management 96.9 3,129 Ha under improved T&T – disease management 98.6 3,184 Ha under improved T&T – soil related fertility & conservation 97.5 3,149 Ha under improved T&T – irrigation 71.8 2,319 Ha under improved T&T – water management (non irrigation) 88.4 2,854 Ha under improved T&T – climate mitigation or adaption 97.7 3,154 Ha under improved T&T – other 99.1 3,199

Of note, 81.6% of the land in the mid-term sample under improved practices was contributed by tomato (42.3%) and potato (39.3%), as these are the two most commonly grown crops and application rates for new practices are high. Fruits accounted for just 5.2% of the land area brought under improve practices, as these are

12 Land under each cycle was counted separately and summed to give the total.



the least commonly grow crops in terms of land area, as trees are generally not grown in dedicated orchards, but scattered where they happen to grow. In the FGDs, producers stated how these trees were just inherited with their land or spread by natural forces e.g. humans/animals eating fruit and scattering the seeds. Most of the producers saw fruit trees as something to harvest, but not to invest in improving the trees or planting new trees to convert land to a fruit crop. In the survey, respondents were asked if these were continuing or newly applied practices to their land. As might be expected from the large increase in those trained in the third year of MSIKA, the ratio of new to continuing was very close to 80:20.13 It is possible that producers may have additional land that they have not applied the practices to, as they were testing them out, but based on the low land holdings in much of Central Malawi, at >1 ha per household, and the importance of maize growing, there may be limited scope to increase the uptake from 0.38 ha under improved practices per household. Rather, an increase in land area is more likely to come from additional producers being trained. As MSIKA is ‘on target’ with this indicator, there is no need to increase producer numbers to increase land area under improved practices.

3.2.7 Effects of Cyclone Idai on Production Respondents were asked if the weather in the period July 2018 – June 2019 was ‘favourable’, ‘moderate’ or ‘bad’ for each crop grown, as set out in Table 35. For the four field crops, the most common response was that the weather was ‘bad’, ranging from 36 – 52%. Chili and potato producers were most likely to report ‘bad’ weather. Around one third of respondents for field crops said the weather was ‘moderate’, with one fifth reporting ‘favourable’ growing conditions. For fruit crops, for which the maturing/harvest season does not fall in the period when Idai struck, the responses were more spread, but with a bias towards ‘moderate’ and ‘favourable’ responses overall. Table 35: Overall weather conditions, mid-term

Weather Tomato Onion Potato Mango Citrus Guava Chili % % % % % % %

Favorable 20.7 32.1 16.7 29.8 57.1 38.2 19.4 Moderate 36.6 31.4 32.4 37.5 28.6 23.6 28.4 Bad 42.7 36.5 50.8 31.7 14.3 36.4 52.2 Does not know - - - 1.0 - 1.8 - Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Respondents 464 137 299 104 7 55 67

Table 36 sets out the impact of heavy rains in March 2019. Asked whether the heavy rains14 had reduced production, the majority of tomato (51%), potato (56%) and chili (55%) producers said yes. For onion, 34% said yes, while for tree crops the responses ranged from zero to 25%. Overall, field crops were more affected than tree crops. Table 36: Production affected by heavy rains in March 2019

Production affected

Tomato Onion Potato Mango Citrus Guava Chili % % % % % % %

Yes 49.1 66.4 44.1 82.0 100.0 81.4 44.8 No 50.9 33.6 55.9 27.0 - 18.6 55.2 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Respondents 464 137 299 109 7 59 67

13 Note that two respondents did not apply any improved practices in the mid-term year or before, so the total does not add to 100%. 14 The consultants asked about the heavy rains in March, some of which precede Cyclone Idai, as producers would not necessarily know that the rains related to Idai or not.



Of those that said their production was affected, they were asked about their degree of loss, with the results set out in Table 37. Chili producers reported that they were the most affected by the rains, with 35% saying that they lost ‘all’ or ‘nearly all’ their crop and a further 24% losing ‘around half’ the crop. Next most affected were potato growers, with 21% losing ‘all’ or ‘nearly all’ and a further 26% losing ‘nearly half’ their crop. Third most affected was tomato, with 10% losing ‘all’ or ‘nearly all’, with 36% losing ‘around half’ their crop. No onion growers reported losing ‘all’ or ‘nearly all’ their crop, however, 37% reported losing half their crop. For the tree crops, 30% of mango growers report losing half their crop, as did 55% of guava growers. Citrus growers reported only minor losses. No fruit growers reported losing all or most of their crop. Table 37: Extent of impact of heavy rains in March 2019

Degree of loss Tomato Onion Potato Mango Citrus Guava Chili % % % % % % %

A small amount of my crop was lost 53.4 63.0 53.3 70.4 - 45.5 40.5 About half my crop was lost 36.4 37.0 25.7 29.6 - 54.5 24.3 Nearly all my crop was lost 5.5 - 15.6 - - - 10.8 All my crop was lost 4.7 - 5.4 - - - 24.3 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Respondents 236 46 167 27 - 11 37

Field crops have multiple cycles/year, so this loss relates primarily to the rain fed cycle (January to March/April) rather than all cycles15, albeit the most important growing season in terms of volumes, so there is a likely effect on yield (see next section). For fruits, there is only one cycle in the season, so although the impacts were less, this is the only growing/harvest cycle in the July 2019 – June 2018 season.

3.2.8 Production A key purpose of the training and other activities is to increase yields for producers. This is measured through Indicator 1, “Percentage change in yield of beneficiary producers as a result of USDA assistance”. The baseline value is zero and the target is 75%. The baseline only had data for one growing cycle, so the mid-term data is reported for the comparable cycle only. The baseline result and LoP targets are stated as an aggregate yield across all crops. In the consultant’s assessment, this is not a valid approach as the mean has to be weighted and it is difficult to do a fair comparison with later periods, as the contributions of each crop may differ. For example, yields on chili are much lower than potato or mango, so if there is a higher proportion of chili producers, then the average yield will be lower because it produces a much lower yield per hectare. It is more appropriate to report yields per crop and compare these over time, than report a single mean yield change across all crops. As noted in the methodology section, the baseline also needed to be revised, as the calculation method was inconsistent between crops, such that the yields for six of the crops (all but tomato) contained outliers that considerably skewed the yields. The consultants also note that a 75% increase is a substantial change in performance, because it is subject to factors that are beyond the program’s control such as weather,

15 Chili has two cycles, so it was more affected.



pest and disease conditions. This will be overcome through the use of a difference in difference method, as planned for the end line. The baseline mean yield values have been adjusted (see methodology section) to take account of the inconsistency in the calculations of the means, effectively removing high values beyond the maximum possible upper range.16 This has lowered most of the baseline means considerably, which the consultants believe is valid. In addition, the baseline did not have any yield data for men and women and for fruit crops, so the consultants have calculated the missing data and added median values for comparison. The mid-term findings on yield are standardised into kgs/ha to allow comparisons between crops and with the baseline. Median yields17 have been calculated as well as means, as these tend to moderate more extreme values. Table 38 sets out the mean and median yields by crop standardized to kgs/ha. The crop with the highest yield in kgs/ha was citrus with a mean of 7,181 kgs/ha followed by guava at 4,963. Onion, potato and mango were in a similar range of 3,700-4,000 kgs/ha. Tomato, which is the most commonly grown crop, yielded 2,790 kgs/ha, with chili much lower at 410 kgs/ha. All median values were lower than their equivalent means, notably citrus and guava. The comparison of median values shows the range for all crops is between 1,900 kgs/ha and 3,000 kgs/ha, apart from chilli which is much lower at 247 kgs/ha. In terms of the relative performance of male and female mean yields, males generally produced more than female producers, with the highest proportional gaps in tomato, onion and chili yields. The exception was citrus for which women had a much higher yield on citrus18. If median values are used, the gap is closer in proportional terms and similar on crops like tomato, mango and guava, but still substantial for onion, potato and chili, with women still scoring higher yields on citrus. Table 38: Mid-term producer yields (all respondents) in kg/ha, mid-term

Yield in kgs/ha

Mid Term Evaluation Male Female Overall

Mean Median N Mean Median Sample Mean Median N Tomato 3,796 2,371 195 2,061 1,482 269 2,790 1,897 464 Onion 5,231 3,804 70 2,591 1,304 65 3,960 2,173 135 Potato 4,387 3,372 116 3,246 2,075 178 3,696 2,421 294 Mango 3,788 2,716 43 3,706 2,794 65 3,739 2,716 108 Guava 5,129 2,959 27 4,822 2,959 32 4,963 2,959 59 Citrus 3,106 2,325 4 12,614 3,333 3 7,181 2,431 7 Chili 541 346 27 319 173 39 410 247 66

As noted in the methodology, the baseline results have been updated to remove outliers above the FAO maximum yields per ha. Table 39 sets out the revised baseline yields and Table 40 sets out the comparison between the mid-term and baseline with the outliers removed from both using the same methodology. Comparing the mid-term to the revised baseline, the mid-term had higher mean yields for tomato (+7%), onion (+11%), potato (+11%), guava (+31%) and citrus (+30%) with lower mean yields for mango (-14%) and chili (-12%). For median values, tomato, mango, guava and chili medians are higher than the mean difference, while citrus, potato and onion are lower, presenting a mixed picture.

16 Based on FAO maximum values if farmers use GAP. 17 Medians can be useful measures of the average in a dataset that has a zero lowest value, but an unlimited ceiling, such that higher values can distort the mean average. 18 Small sample for citrus, with wide margins of error.



Table 39: Revised baseline yields in kg/ha

Crop Impact Baseline

Male Female Overall Mean Median N Mean Median N Mean Median N

Tomato 3,305 1,537 173 1,858 880 157 2,617 1,217 330 Onion 4,358 2,748 58 2,061 797 30 3,575 2,005 88 Potato 3,946 2,983 136 2,684 1,637 130 3,329 2,341 266 Mango 4,017 2,580 112 4,660 2,886 118 4,347 2,737 230 Guava 3,365 1,463 76 4,388 2,204 55 3,796 2,065 131 Citrus 4,947 3,056 35 6,086 4,894 35 5,517 4,350 70 Chili 426 247 18 523 198 13 467 247 31

Table 40: Yield comparison, mid-term vs baseline by crop, kg/ha & percentage change Crop

(kg/ha) Baseline MTE – Overall MTE vs Baseline

Mean Median Mean Median Mean Median Tomato 2,617 1,217 2,790 1,897 7% 56% Onion 3,575 2,005 3,960 2,173 11% 8% Potato 3,329 2,341 3,696 2,421 11% 3% Mango 4,347 2,737 3,739 2,716 -14% -1% Guava 3,796 2,065 4,963 2,959 31% 43% Citrus 5,517 4,350 7,181 2,431 30% -44% Chili 467 247 410 247 -12% 0%

As noted, Cyclone Idai had an effect on producer yields, so comparison with the baseline is also made based on those producers that said they were not affected by Idai. Table 41 below sets out the revised comparison of the baseline yields with the mid-term yields for those not affected by Idai. The mid-term means for those not affected by the Cyclone compared to the baseline show increases in the mean yields for tomato (+17%), onion (+20%), potato (+20%), guava (+46%), citrus (+30%), with declines for mango (-19%) and chili (-22%). The change in the field crop yields (tomato, onion and potato) when comparing the whole sample, with those not affected by Idai, is consistent with the expected impact from Idai, which struck in the period when most field crops were maturing. For chili, the mean worsened compared to the mean for the whole sample (falling to -22% from -12%), but it should be noted that the sample of those not affected by Idai had a wider margin of error. Also, from the FGDs, chili production appears to have been affected by a range of issues, including lack of availability of seed and the limited experience of many producers in growing this crop. Chili is also very susceptible to pest and disease damage, if not treated, so any inability to afford agro-chemical treatments would impact on production. Median values for those not affected by Idai have a similar pattern to the whole mid-term sample. Overall, the comparison of the mid-term sample not affected by Idai, with the revised baseline (outliers excluded consistently), points to steady progress in yield increases for field crops, other than chili, for which there were other production challenges highlighted above. For tree crops, while there were bigger increases for guava and citrus, the samples for these were relatively small so the margin of error is greater. The reversal in yields for mango may be a function of variations between years and that there is much less application of improved practices in fruit crops.



Table 41: Producers not affected by cyclone - yield comparison by crop Crop

(kg/ha) Baseline MTE Not affected by

Cyclone MTE vs Baseline

Mean Median Mean Median Mean Median Tomato 2,617 1,217 3,054 1,897 17% 56% Onion 3,575 2,005 4,289 2,377 20% 19% Potato 3,329 2,341 3,980 2,766 20% 18% Mango 4,347 2,737 3,511 2,611 -19% -5% Guava 3,796 2,065 5,553 3,204 46% 55% Citrus 5,517 4,350 7,181 2,431 30% -44% Chili 467 247 362 247 -22% 0%

Table 42 below compares male and female yields. The consultant found no clear pattern, with yield changes higher for men for onion, mango, citrus, guava and chili, and higher for women on tomato and potato. It is important to note that tomato and potato are much more commonly grown crops and on much larger land areas than other crops, so while women may report lower changes in yields compared to men across more crops, they may benefit from greater yield changes on the two biggest crops in MSIKA’s portfolio. Table 42: Yield comparison of male and female yields by crop, mid-term and baseline

Crop Mid term vs Baseline – Not affected by Cyclone

Male Female Mean Median Mean Median

Tomato 21% 54% 33% 80% Onion 31% 48% 27% 64% Potato 27% 5% 48% 58% Mango -4% 7% -29% -10% Guava 66% 102% 26% 83% Citrus -37% -24% 107% -32% Chili 23% 28% -48% -30%

The MSIKA team conduct an initial training, then provide refresher training along with support from extension staff/lead farmers and learning from other producers, such that knowledge, use and yields should increase as the engagement deepens. In addition, MSIKA’s view is that it takes time for the full benefit of the training to be felt, as producers may implement a few new improved practices. The consultants agree that it is difficult to assess the full progress for all producers based on one season only, especially when that season was impacted by Cyclone Idai and heavy. In conclusion, interpreting the results is complex. The consultant’s view is that adjusting the baseline values is both appropriate and necessary to ensure a proper comparison of baseline and mid-term performance. It is also appropriate to adjust the mid-term results to be those not affected by Cyclone Idai, even if those results have wider margins of error. Therefore, the consultant reports the means for those not affected by the Idai in the summary indicator table (see annex 1), along with the original baseline per the report19 and the baseline with the outliers removed. It is worth noting overall that tomato and potato, along with onion account for 80% of the production by land area where new practices are applied, and so are the most important crops for MSIKA. With positive increases in mean yields between 17-20%, these three crops are showing progress towards the LoP overall target of 75%, even if there is considerable progress still required.

19 The reader is reminded that the figures in the report, were not the same as the calculated figures from the baseline database using the baseline consultant’s own methodology.



The consultant notes the use of a single mean average kgs/ha for the baseline, for the LoP target and for MSIKA reporting. This ‘mean of mean’ hides the differing performance for each crop, which as can be seen from above is very mixed. A better method is to set targets for each crop and for MSIKA to report changes by crop, rather than aggregate these in a single ‘mean of means’. This has the additional benefit of making the disaggregated changes for each crop more visible. This applies to indicator 26 on losses as well. Although sales are not reported as a mean of means, it would also be beneficial to separate results by crop so that differential performance between crops is clearer.

3.2.9 Post-Harvest Handling As with yield, knowledge of harvest and post-harvest handling (PHH) practices, and the application of those improved practices, are stepping-stones to reducing losses. The question on knowledge of PHH practices was unprompted. Unprompted scores will be lower than prompted, but give a better picture of what people know well, as these are the things they can recall when asked. A good score indicates widespread knowledge; however, a low score may not necessarily mean the person lacks knowledge. Rather, it might be that this practice was either not associated with the question or was not to the front of a person’s mind. PHH practices differ between field and tree crops, so these are reported separately. The findings for field crops are set out in Table 43 and those for tree crops in Table 44. Across field and tree crops, ‘timely harvesting’ and the different ‘means of harvesting’ are well known, with some limited scope to improve. ‘Storing in cool places’, ‘handling carefully’/’soft litter’, are among the more commonly known practices, but scores are generally lower than 50%, so there is scope to improve. Knowledge is low about stacking, over-filling, sorting (by variety, and by maturity), grading, washing, and some of the means of harvesting. The reason for stacking and overfilling might be producers’ concern over the high cost of transport, as recommended stacking and not overfilling would increase transport costs, even if it reduced losses. For the beneficiaries, the certainty of incurring transport cost is probably more a driver in deciding to over-fill bags/containers. Washing/cleaning may be impractical if there is not a ready source of water. Grading and sorting require labour, which may be a problem in some households. There may be insufficient understanding of the way part-rotten produce will stimulate rotting of produce around it.20 There may also be an attempt to hide less good produce in baskets to ‘fool’ bulk buyers. The PHH practices scores were very low for mango, citrus and guava, and generally high for chili. For tomato, onion and potato, the scores were around 20%, suggesting that there is scope to improve knowledge for the high-volume crops. The level of knowledge of practices for field crops was lower in the mid-term than the baseline on many of the practices. The consultants re-checked the data in the baseline, but still found high scores for the baseline. It was then determined that the final baseline questionnaire changed in the ODK version to prompting for this question. As a result, the baseline scores are higher than they would be unprompted and so no comparison is made with the mid-term.

20 Mixing over-ripe produce with under-ripe accelerates ripening, as in “one bad apple spoils the barrel”! Separation by maturity and removing over-ripe/spoiled produce is very important.



Table 43: Knowledge of PHH practices, field crops, mid-term survey

Knowledge of PHH Practices Tomato %

Onion %

Potato %

Chili %

Timely Harvesting 86.3 25.5 n/a 68.7 Lifting when tops have been bent over and dried n/a 61.3 n/a n/a Harvesting when plant tops wilt and start to wither; slashing tops close to soil to cure in soil a day before harvesting n/a n/a 62.9 n/a Lifting potatoes with a fork/prong not a hoe n/a n/a 11.4 n/a Drying (curing) onions in the sun or under a shade before storing to improve in keeping quality n/a 59.9 n/a n/a Trimming dried stems and roots before packing n/a 21.9 n/a n/a Rubbing off surface dirt after harvest; or drying & curing n/a n/a 13.0 n/a Handling carefully to avoid damage 51.1 n/a 21.7 41.8 Storing in cool dry places to avoid damage 67.7 n/a 56.9 49.3 Grading by color, size and shape 29.1 22.6 37.1 38.8 Sort/grade by variety, size/maturity, damaged, ripeness 21.6 24.1 30.4 25.4 Packing – loading into appropriate containers for storage or transportation 26.9 32.8 32.8 44.8 Not over-filling baskets & bags or using very big bags, as this damages product at the bottom 22.0 21.2 n/a n/a Not stacking more than three containers high to avoid crushing tomatoes in lower containers 19.6 n/a n/a n/a Adding soft dry grass as soft litter 45.9 n/a n/a n/a Store in cool dry well ventilated places n/a n/a 23.4 49.3 Total respondents 464 137 299 67


Table 44: Knowledge of PHH practices, tree crops, mid-term

Knowledge of PHH Practices Mango %

Citrus %

Guava %

Harvesting when ripening started & color changes 75.2 85.7 66.1 Harvesting by climbing tree 78.9 42.9 81.4 Harvesting by using a special harvesting pole 29.4 28.6 27.1 Catching fruit before it falls using bags or spreading sheets 13.8 - 18.6 Handling carefully to avoid damage 23.9 28.6 42.4 Storing in cool places and out of the sun prior to sale 17.4 42.9 27.1 Grading by color, size and shape 14.7 14.3 27.1 Sorting by variety, size/maturity, ripeness, removing damaged 5.5 14.3 8.5 Packing – loading commodities into appropriate containers for storage or transportation 22. 28.6 10.2 Storing in crates to prevent damage in transporting 0.9 - 3.4 Washing to improve appearance 11.0 - 23.7 Not over-filling or using big baskets & bags as this damages fruit 4.6 - 11.9 Total respondents 109 7 59


In terms of the ‘first’ source of that knowledge, the responses are specific to each crop. Respondents were asked the source where they first learnt the practices they had stated (unprompted), with one category being MSIKA Extension Worker/Lead Farmer. The question allowed for multiple responses, so it was possible to have more than 100% in total. A score of 70.3% on tomato means that 70.3% of growers stated that at least one practice was first learnt from MSIKA. The source of learning is set out below in Table 45 for the mid-term and Table 46 for the baseline. ‘MSIKA Extension worker/lead farmer’ was consistently the highest source for field crops with a wide margin over the next highest source, which was either ‘always known’ or a ‘relative/other farmer’.



For the tree crops, ‘always known’ was the most common source for respondents. ‘Always known’ is interpreted to be passed down knowledge. MSIKA was the source for 33.9% to 45.9% of respondents. ‘GoM extension staff’ as a source ranged from 0-14%, with virtually none learning from other NGOs and only 0-3% learning from FBOs. Other sources were negligible. As noted earlier, the baseline question was asked as a prompted question, so it is not valid to compare the mid-term to it. MSIKA is making progress on knowledge, but the reliance on ‘always known’ for tree crops suggests there is scope to improve. Table 45: Source of learning for PHH practices by crop, mid-term

Source of first learning Tomato %

Onion %

Potato %

Mango %

Citrus %

Guava %

Chili %

Always known 26.1 9.5 19.1 63.3 71.4 71.2 16.4 GoM extension worker 9.1 4.4 11.4 7.3 14.3 8.5 6.0 MSIKA Extension Worker/Lead Farmer 70.3 67.9 59.2 45.9 42.9 33.9 68.7 NGO Extension Worker 1.9 2.9 4.0 0.9 - 1.7 6.0 Radio 0.4 - - - - - - My Farmer organization 1.7 0.7 3.0 0.9 - - - Relative/ other farmers 12.9 19.7 20.4 12.8 - 13.6 11.9 Saw drama - - 0.3 - - - - Saw in newspaper - - - - - - - Saw on television 0.2 - 2.7 - - - - MSIKA Training 6.7 2.2 0.7 2.8 - 1.7 4.5 Training by other organizations 1.9 1.5 - 0.9 - - - School 0.4 1.5 - - - - - Agro-dealer - - - - - - - Vendor 0.9 0.7 - 0.9 - - 1.5 DNK/cannot remember 0.2 - 0.3 - - - - Respondents 464 137 299 109 7 59 67

Table 46: Source of learning for PHH practices by crop, baseline

Baseline Source of first learning Tomato

% Onion

% Potato

% Mango

% Citrus

% Guava

% Chili

% Always known 57.6 39.6 36.9 70.0 74.6 56.9 45.2 GoM extension worker 40.0 41.7 48.4 28.6 30.6 29.3 51.6 MSIKA Extension Worker/Lead Farmer n/a n/a n/a n/a n/a n/a n/a NGO Extension Worker 12.7 8.3 10.8 7.5 1.7 7.8 19.4 Radio 12.7 10.4 15.8 3.8 8.5 8.6 3.2 My Farmer organization 2.7 3.1 4.3 1.9 1.7 1.7 - Relative/ other farmers 9.7 12.5 14.0 8.5 8.5 4.3 16.1 Saw drama 47.0 47.9 45.2 43.7 50.8 42.2 38.7 Saw in newspaper 0.9 3.1 1.4 0.5 1.7 0.9 - Saw on television 0.3 - - - - 0.9 3.2 MSIKA Training - - 0.7 0.5 1.7 - - Training by other organizations n/a n/a n/a n/a n/a n/a n/a School 2.7 6.3 4.3 0.9 1.7 1.7 9.7 Agro-dealer 0.9 2.1 1.8 2.8 3.4 0.9 - Vendor 0.6 1.0 2.2 0.5 - 0.9 - DNK/cannot remember 0.9 3.1 0.4 1.9 3.4 5.2 16.1 Other 6.7 5.2 .6 6.1 18.6 9.5 3.2 Respondents 330 96 279 213 59 116 31



Respondents were asked which practices they used from a prompted list. The findings for field crops are set out in Table 47 and for tree crop findings in Table 48. The highest scores on PHH use were for ‘timely harvest’, ‘harvesting methods’, ‘cool storing’, ‘appropriate containers’, ‘not overfilling’ and ‘handling carefully’. The lowest scores were still relatively high, especially for chili, tomato and guava. Exceptions were ‘lifting potato with a fork’, ‘washing and storing in crates for fruits’. This suggests that there is less scope to increase use. It could be that respondents did not associate the practices with the question on unprompted knowledge, or they are overstating their actual use. It is also possible that they may not be applying the practice in the optimal manner or all the time. There is likely to be some scope to improve application and further reduce losses. The use of PHH practices for field crops has increased across most practices over the baseline, though baseline scores were quite high to start with. It should be noted that there were several changes to the mid-term questions to reflect the practices being taught, which did not appear in the baseline or were worded differently, making direct comparisons more difficult. Table 47: Use of PHH practices field crops, mid-term

PHH Practices Used Tomato %

Onion %

Potato %

Chili %

Timely Harvesting 93.7 n/a n/a 80.0 Lifting when tops have been bent over and dried n/a 85.3 75.3 n/a Lifting potatoes with a fork/prong, not a hoe n/a n/a 13.9 n/a Harvesting when plant tops wilt and start to wither; slashing tops close to soil to cure in soil a day before harvesting n/a 59.6 n/a n/a Drying (curing) onions in the sun or under a shade before storing to improve in keeping quality n/a 81.6 n/a n/a Rubbing off surface dirt after harvest; or drying & curing n/a n/a 66.1 n/a Trimming dried stems and roots before packing n/a 75.0 n/a n/a Handling carefully to avoid damage 94.6 n/a 93.6 95.0 Storing in cool dry places to avoid damage 95.0 n/a 86.8 96.7 Grading by color, size and shape 81.6 78.7 93.6 81.7 Sort/grade by variety, size/maturity, damaged, ripeness 85.3 79.4 93.2 86.7 Packing – loading into appropriate containers for storage or transportation 78.2 90.4 92.5 90.0 Not over-filling baskets & bags or using very big bags, as this damages product at the bottom 90.5 85.3 n/a n/a Not stacking more than three containers high to avoid crushing tomatoes in lower containers 91.1 n/a n/a n/a Adding soft dry grass as soft litter 89.2 n/a n/a n/a Store in cool dry well-ventilated places n/a n/a 59.7 95.0 Total respondents 463 136 295 60




Table 48: Use of PHH practices field crops, baseline Baseline

PHH Practices Used Tomato %

Onion %

Potato %

Chili %

Timely Harvesting 94.1 n/a n/a 96.6 Lifting when tops have been bent over and dried n/a 60.6 66.5 n/a Lifting potatoes with a fork/prong, not a hoe n/a n/a 6.8 n/a Harvesting when plant tops wilt and start to wither; slashing tops close to soil to cure in soil a day before harvesting n/a 60.6 n/a n/a Drying (curing) onions in the sun or under a shade before storing to improve in keeping quality n/a 27.7 n/a n/a Rubbing off surface dirt after harvest; or drying & curing n/a n/a 36.7 n/a Trimming dried stems and roots before packing n/a 51.1 n/a n/a Handling carefully to avoid damage 76.1 n/a 70.1 75.9 Storing in cool dry places to avoid damage 82.0 n/a 68.3 62.1 Grading by color, size and shape 71.2 58.5 82.7 79.3 Sort/grade by variety, size/maturity, damaged, ripeness n/a n/a n/a n/a Packing – loading into appropriate containers for storage or transportation 63.4 55.3 53.2 6.9 Not over-filling baskets & bags or using very big bags, as this damages product at the bottom 73.2 55.3 64.0 58.6 Not stacking more than three containers high to avoid crushing tomatoes in lower containers 69.9 n/a n/a 55.2 Adding soft dry grass as soft litter 70.6 n/a n/a 31.0 Store in cool dry well-ventilated places n/a n/a - n/a Total Respondents 306 94 278 29


The mid-term findings for PHH practices for tree crops are set out in Table 49 and the baseline in Table 50. In fruits, the picture is more mixed than field crops. There is progress in handling, sorting/grading and use of containers. Table 49: Use of PHH practices tree crops, mid-term

Use of PHH Practice Mango % Citrus % Guava % Harvesting when ripening started & color changes 88.1 100.0 93.2 Harvesting by climbing tree 90.8 57.1 84.7 Harvesting by using a special harvesting pole 47 71.4 42.4 Catching fruit before it falls using bags or spreading sheets 56.9 100.0 64.4 Handling carefully to avoid damage 89.0 85.7 91.5 Storing in cool places and out of the sun prior to sale 79.8 85.7 84.7 Grading by color, size and shape 83.5 85.7 76.3 Sorting by variety, size/maturity, ripeness, removing damaged 72.5 71.4 67.8 Packing – loading commodities into appropriate containers for storage or transportation 86.2 85.7 84.7 Storing in crates to prevent damage in transporting 32.1 42.9 39.0 Washing to improve appearance 73.4 28.6 78.0 Not over-filling or using big baskets & bags as this damages fruit 84.4 100.0 78.0 Total respondents 92 7 59




Table 50: Use of PHH practices tree crops, baseline All fruits - baseline

Practice used Mango % Citrus % Guava % a. Harvesting when ripening started & color changes 86.8 84.0 84.4 b. Harvesting by climbing tree 88.7 42.0 78.0 c. Harvesting by using a special harvesting pole 70.1 74.0 48.6 d. Catching fruit before it falls using bags or spreading sheets 35.3 44.0 42.2 e. Handling carefully to avoid damage 52.0 52.0 51.4 f. Storing in cool places and out of the sun prior to sale 44.6 42.0 42.2 g. Sorting/Grading by color, size and shape 48.0 48.0 46.8 h. Packaging 34.3 24.0 35.8 i. Storing in crates to prevent damage in transporting 9.3 8.0 9.2 j. Washing to improve appearance 43.1 8.0 13.8 k. Not over-filling or using big baskets & bags as this damages fruit 43.1 42.0 49.5 Total respondents 204 50.0 109.0


Respondents were asked what PHH practices they had used for the first time in the last year. Table 51 contains the findings on first use of PHH for field crops and Table 52 has the findings for tree crops. The findings on first used in the mid-term survey are very encouraging with the uptake of practice such as ‘timely harvesting’, ‘grading’, ‘sorting’, ‘cool storage’, and ‘careful handling’ first used by over 60% of respondents. Even for practices with relatively low scores, the uptake was often in the 30-60% range. Storing in crates and appropriate containers, lifting by fork and climbing to harvest were the lowest scoring. For storage, this could be for cost reasons. For lifting by fork, this might be down to the long-standing use of hoes and/or unavailability/cost of forks. MSIKA has not put as much emphasis on PHH training, as on growing practices, prior to 2019, and this may be influencing the scores on fruits and the generally lower scores on PHH than on improved growing practices. Further consolidation and intensifying of PHH training should yield improvements and as the yield losses at mid-term are close to the LoP target, there is potential for MSIKA to exceed the target.



Table 51: First used new PHH practice, field crops, mid-term

Use of PHH practices for the first time Tomato %

Onion %

Potato %

Chili %

Timely Harvesting 60.8 n/a n/a 77.1 Lifting when tops have been bent over and dried n/a 61.8 59.3 n/a Lifting potatoes with a fork/prong, not a hoe n/a n/a 8.7 n/a Harvesting & selling green/fresh onions with stalks on n/a 18.4 n/a n/a Drying (curing) onions in the sun or under a shade before storing to improve in keeping quality n/a 61.8 n/a n/a Rubbing off surface dirt after harvest; or drying & curing n/a n/a 42.7 n/a Trimming dried stems and roots before packing n/a 21.1 n/a n/a Handling carefully to avoid damage 54.9 n/a 52.0 71.4 Storing in cool dry places to avoid damage 64.1 n/a 50.0 88.6 Grading by color, size and shape 66.3 21.1 69.3 65.7 Sort/grade by variety, size/maturity, damaged, ripeness 63.7 30.3 63.3 74.3 Packing – loading into appropriate containers for storage or transportation 47.6 34.2 48.7 85.7 Not over-filling baskets & bags or using very big bags, as this damages product at the bottom 60.8 25.0 n/a n/a Not stacking more than three containers high to avoid crushing tomatoes in lower containers 57.5 n/a n/a n/a Adding soft dry grass as soft litter 55.7 n/a n/a n/a Store in cool dry well-ventilated places n/a n/a 35.3 85.7 Total Respondents 273 76 150 35

Table 52: First used new PHH practice, tree crops, mid-term

Use of PHH Practice for the first time Mango % Citrus % Guava % Harvesting when ripening started & color changes 28.3 66.7 50.0 Harvesting by climbing tree 10.9 33.3 25.0 Harvesting by using a special harvesting pole 37.0 66.7 25.0 Catching fruit before it falls using bags or spreading sheets 32.6 66.7 35.0 Handling carefully to avoid damage 43.5 100.0 45.0 Storing in cool places and out of the sun prior to sale 47.8 66.7 55.0 Grading by color, size and shape 54.3 66.7 80.0 Sorting by variety, size/maturity, ripeness, removing damaged 50.0 66.7 70.0 Packing – loading commodities into appropriate containers for storage or transportation 47.8 66.7 55.0 Storing in crates to prevent damage in transporting 13.0 100.0 15.0 Washing to improve appearance 32.6 33.0 55.0 Not over-filling or using big baskets & bags as this damages fruit 47.8 100.0 656.0 Total respondents 46 3 20

3.2.10 Post-Harvest Losses Indicator 26 measures change in post-harvest losses. The LoP target for post-harvest losses is 14.0%. This is calculated as the total weight of the losses across all growing/ harvesting cycles divided by the total production for those cycles for each crop and then aggregated to an overall loss. Table 54 sets out the revised baseline and the mid-term results are set out in Table 53 below. The consultants removed the results of producers who gave a production value above the FAO maximum yield, as the losses are a proportion of the yield and so problematic to include when working out the losses. The removal of the yield outliers



has resulted in a much lower loss at baseline of 12.5% across all crops, compared to the original baseline of 22.0%. Based on its semi-annual data, MSIKA reports that losses have been reduced to 14.0% and therefore it has achieved the LoP target. The mid-term evaluation found that across the seven crops, the losses were 15.9% of production, which is 90.3% of the LoP target. Measuring crop losses is a very difficult task, as losses occur at several points from field/harvest through to market. Measuring production is also difficult, but at least the producer can record whole units harvested, such as bags or pails. When it comes to losses, these can be part of a whole unit (a bag of produce all going rotten), but more likely losses are part units where the producer sees that some of the vegetables or fruits in a bag have rotted, so removes only those affected. This might be repeated several times. A further difficulty in measuring losses is defining the loss at harvest time. It is difficult to assess the loss for a potato plant that is dug up at harvest with a mix of usable and not usable potatoes. Ditto with fruits that have fallen before collection, as to whether they count as harvested. Protocols for enumerators can help, but there are genuine difficulties for producers to estimate losses. Therefore, data for losses needs to be treated with caution. In terms of the different crops, as noted earlier, the largest volume field crops in the mid-term sample are tomato with losses of 14.5%, potato at 17.1% and onion at 12.4%, so their weighting in the results overall is greater, even though the percentage losses for citrus (28.8%), guava (20.3%) and chili (19.5%) are higher. FGD participants reported reductions in losses. Most talked about tomato, as it is widely regarded as the most perishable of the crops, hence prone to high losses: "Talking of losses in tomato, we do not have that here. We have all the tomato up to selling, except when it overstays too long at the market before buyers come." FGD participant, Lilongwe "In the past when I harvested 10 pails, 3 or 4 would be damaged beyond sale potential but now it has reduced to one or two pails." FGD participant, Dedza The losses for men were 14.7% and for women were 17.3%. The differences vary by crop, notably in tomato (12.5% vs 17.2% in favour of men)21, though the losses are similar for onion and potato. It is worth noting that training in PHH has not yet have been applied to all cycles of harvesting, due to the relatively recent nature of the PHH training. MSIKA indicated that a lot of the PHH training had been conducted in the first calendar quarter of 2019, so has not been applied by all the MTE sample/population for a full 12 months. This suggests that there should be improvements over the coming year. Table 53: Post-harvest losses by crop and by sex, mid-term

Crop Male Female Total Yield Loss % Yield Loss % Yield Loss %

Tomato 96,126 12,062 12.5 68,112 11,70 17.2 164,238 23,763 14.5 Onion 41,080 4,752 11.6 16,808 2,404 14.3 57,888 7,156 12.4 Potato 78,610 13,460 17.1 98,550 16,844 17.1 177,160 30,304 17.1 Mango 22,826 4,337 19.0 37,349 6,880 18.4 60,176 11,217 18.6 Citrus 315 49 15.6 1,218 392 32.2 1,533 441 28.8 Guava 4,611 758 16.4 4,908 1,175 23.9 9,519 1,932 20.3 Chili 2,144 617 28.8 1,936 178 9.2 4,080 795 19.5 Total 245,712 36,035 14.7 228,882 39,572 17.3 474,594 75,608 15.9

21 Noting that for fruits and chilli there are differences between men and women, but the samples are relatively small, so the margin of error is greater.



Table 54: Post-harvest losses by crop and by sex, baseline

Crop Male Female Total Yield Loss % Yield Loss % Yield Loss %

Tomato 94,495 12,734 13.5 58,762 7,961 13.5 153,22 20,695 13.5 Onion 48,227 3,707 7.7 7,373 1,031 14.0 55,601 4,738 8.5 Potato 196,234 14,827 7.36 81,387 7,202 8.8 277,620 22,029 7.9 Mango 72,995 17,723 24.3 54,055 10,380 19.2 127,050 28,103 22.1 Citrus 4,545 78 1.7 4,107 52 1.3 8,652 130 1.5 Guava 8,613 2,041 23.7 6,366 2,014 31.6 14,979 4,054 27.1 Chili 1,771 97 5.5 971 236 24.3 2,742 333 12.1 Total 426,880 51,206 12.0 213,021 28,874s 13.6 639,865 80,081 12.5

Respondents were asked the point at which the crop was spoiled, covering pre-harvest, harvest and post-harvest periods. The findings are set out in Table 55. The most common point at which losses occur was ‘on-farm storage’22, with 60-67% of citrus, tomato, onion and potato reporting this. The second most common point was ‘prior to harvest’, which includes damage from pest, disease, theft, etc. reported by 29-57% of respondents.23 Guava, mango and tomato loses prior to harvest were particularly common, as reported by 47-57% of respondents. FGDs reported that theft of ripe fruits, including by children, was an issue. Pest and disease losses were common, as were losses of fruit to birds/bats. The third most common point was ‘at harvest’ reported by 30-42% of respondents. This includes losses in the field while harvesting and transporting back to the farm. Produce may get spoiled where it is physically difficult to harvest, notably for tree crops, or where the produce is delicate (tomato) or easily damaged at harvesting, such as digging up potatoes. There were some lower scores on harvesting and storage in the early responses on use of PHH practices, which is where improvements could be made in uptake of practices. Also, even if respondents say they use the practices, they may not be applying them carefully or consistently enough. Damage at the market was reported for tomato, guava and mango by 10-20% of respondents, but none for chili. ‘Could not be sold prior to spoil’ was stated by 3-6% of respondents for most crops, other than for chili which had a relatively high 17.9%. The reason for the latter is not clear, but it might be due to slow sales and the practice of wetting the crop to improve its appearance and weight, which leads to development of moulds in storage. Wetting was mentioned by processors in KIIs as a problem. It could also be that chili respondents classified their responses differently, as they also had high scores for ‘does not know’.

22 On farm storage refers to the point after harvest where the crop is held on the farm prior to selling to a trader or at a market. 23 Damage prior to harvest was not included in the calculation of postharvest losses.



Table 55: Point at which harvest was spoiled, mid-term Point at which crop was lost

Tomato %

Onion %

Potato %

Mango %

Citrus %

Guava %

Chili %

Damage prior to harvest (pest/birds) 47.2 28.5 33.8 49.5 33.3 56.5 31.3 Damage at harvest 30.6 11.7 32.8 42.3 33.3 30.4 13.4 Damaged at farm (storage) 63.4 64.2 59.9 49.5 66.7 52.2 37.3 Damaged during transport 9.9 2.9 2.0 4.1 - 8.7 3.0 Damaged at market 10.1 4.4 1.3 16.5 - 19.6 - Could not be sold prior to spoil 3.4 5.8 6.4 4.1 - 4.3 17.9 Does not know 7.3 16.1 8.4 1.0 - 2.2 25.4 Respondents 464 137 299 97 7 46 67


Respondents were asked how they store their crops (non-crop specific), with multiple responses possible. The findings are set out in Table 56 below. Unsurprisingly, the most common place is inside the house (81.1%), followed by a separate building/store at 26.8%. Only 0.3% stored at their FBO, which matches the KII responses for FBOs that they do not have any storage facilities. Women were more likely than men to store in the house, while men were more likely to have a separate store. Compared to the baseline, more mid-term respondents said they were storing in their houses (81.1% vs 55.1%) and around the same if the total for separate building and nkhokwe/outside store were added together. There is a small shift from outside to inside storage. Table 56: Place of storing crops, mid-term

Location of storage Baseline Mid-term All% Male % Female % Total %

Inside my house 55.1 72.4 86.9 81.1 In a separate building/storage 20.8 34.6 21.6 26.8 In a Nkokwe/outside store 10.2 3.5 3.3 3.4 In baskets/bags outside or in a pile 8.0 1.9 1.3 1.5 FBO Warehouse 0.2 0.8 - 0.3 Do not store (direct to market) 8.0 3.1 1.8 2.3 Other 1.5 n/a n/a n/a None 9.2 n/a n/a n/a Total 590 257 389 646


Respondents were asked if they had built or restored their place of storage. A total of 21.7% said they had built or restored a storage building, room or shed. As a lot of storage is in the house, it is possible that refurbishment or building relates to the house, as a multi-purpose building rather than specifically for storage, but it is not possible to say from the data. The survey did not ask whether they had built or restored. The consultant asked the field supervisors for their estimates, which were given as approximately 25% new and 75% restored. The building and/or restoring of storage contributes to Indicator 27. Table 57 below sets out the calculation for this indicator.



Respondents gave estimates of the area floor space and height. This is prone to error, as measurements were estimated by respondents and calculated by the enumerators, not precisely measured, so upper outliers were removed. The estimated areas were totalled and divided by the number of respondents for this question, giving a mean average of 26.78 cubic meters (cu/m). This equates to 5.22 cu/m. per beneficiary, which is an estimate 44,228 cu/m for the mid-term population, of which an estimated 33,171 cu/m is restored (75.0%) and 11,057 cu/m is new (25.0%). Table 57: Building or refurbishing storage calculation for mid-term population

Respondents reporting built or restored 126 Sum of all responses – cumulative 3,374 Mean volume built or restored storage 26.78 Mid=term sample 646 Mean volume built/restored across whole sample 5.22 Mid-term population 8,468 Estimated volume across mid-term population 44,228

The LoP target is 75,750, so with this addition in 2018-19 to a prior total of 53 cu/m, MSIKA has reached 58.5% of the LoP target.

3.2.11 Gender Respondents were asked about who (men/women) does the work on the crop(s) that were grown by their household. The question aimed to determine if the work on the crop across all tasks was done by men, by women or in some combination. The mid-term findings are set out in Table 58 below. The options for respondents (prompted) were: ‘only by men’, ‘more by men than women’, ‘men and women equally’, ‘more by women than men’, and ‘only by women’. The tasks were split into: ‘land preparation’, ‘planting’, ‘managing while growing’, ‘harvesting’, ‘handling’ and ‘selling’. The data is reported for all the tasks in aggregate below. So, for a response of ‘only by men’, 23.9% of 464 tomato growers reported one or more tasks was done ‘only by men’. As the question covered a range of tasks, it is possible for the results to add to more than 100% It was most commonly stated that men and women do the work equally, ranging from 71-86% of respondents. This was followed by similar scores for ‘only by men’ ‘only by women’ and ‘more by men than by women’. The least common was ‘more by women than by men’. There is no discernible pattern between crops. Table 58: Overall work on crop by sex, mid-term

Who does the work on the crop?

Tomato %

Onion %

Potato %

Mango %

Citrus %

Guava %

Chili %

Only by men 23.9 19.0 22.1 18.3 - 10.2 22.4 More by men than women 23.9 27.0 21.7 21.1 28.6 15.3 17.9 By men and women Equally 76.7 74.5 76.6 80.7 71.4 86.4 82.1 More by women than by men 12.7 8.0 10.0 7.3 14.3 3.4 7.5 Only by women 25.6 19.0 24.4 23.9 - 15.3 23.9 Does not know - - 1.0 10.1 - 13.6 6.0 Total 464 137 299 109 7 59 67

Multiple response possible.



Respondents were asked to provide the split of key growing tasks by sex across all crops, including selling and managing the money. The findings are set out in Table 59 below. The mid-term found that there are relatively even balances, but with men more involved in land preparation and managing the proceeds, and marginally more involved in planting and managing the crop. Table 59: Work on the tasks, by sex, mid-term

All crops work done

by:

Land Prep Planting Manage Harvesting Handling Post Selling Managing # % # % # % # % # % # % # %

Only by men 85 7.4 46 4.0 51 4.5 25 2.2 43 3.8 95 8.3 140 12.3 More by men than by women 186 16.3 170 14.9 167 14.6 154 13.5 132 11.6 138 12.1 134 11.7 By men and women equally 688 60.2 737 64.5 743 65.1 781 68.4 743 65.1 653 57.2 645 56.5 More by women than by men 44 3.9 45 3.9 45 3.9 49 4.3 57 5.0 48 4.2 30 2.6 Only by women 125 10.9 128 11.2 129 11.3 126 11.0 159 13.9 191 16.7 176 15.4 DNK 14 1.2 16 1.4 7 0.6 7 0.6 8 0.7 17 1.5 17 1.5 Responses 1,142 100. 1,142 100 1,142 100 1,142 100 1,142 100 1,142 100 1,142 100


3.2.12 Farm Management Practices MSIKA has trained beneficiary producers in farm management. Respondents were asked for their knowledge of improved farm management practices, which was unprompted. The findings are set out in Table 60 below. ‘Timely buying of inputs’ (57.1%) and ‘plan production for the upcoming season’ (50.6%) were stated by over 50% of respondents. Those known by less than 20% of the respondents were: ‘sell together with other farmers’ (12.4%), ‘understand the specifications of buyers’ (14.2%) and ‘keep good farm records’ (17.0%). As with improved growing practices and PHH, those that are stated are those that are well known to the respondents and at the forefront of their minds. Those that are not stated may not necessarily be unknown, but were not at the forefront of their minds, or they did not associate the knowledge they have with the question. It is also possible that they did not need these farm management practices, or simply forget to state them. Table 60: Knowledge of improved farm management practices, mid-term

H1. What are the farm management practices you know? (unprompted) Farm Management Practice Male % Female % Total %

a. Keep good farm records 61 23.7 49 12.6 110 17.0 b. Sell together with other farmers 39 15.2 41 10.5 80 12.4 c. Do costings for growing crops 108 42.0 120 30.8 228 35.3 d. Calculate profits after selling 112 43.6 143 36.8 255 39.5 e. Plan production for upcoming season 126 49.0 201 51.7 327 50.6 f. Plan production for specific market 56 21.8 75 19.3 131 20.3 g. Understand the specifications for specific buyers 38 14.8 54 13.9 92 14.2 h. Separating commodities into the different grades 63 24.5 82 21.1 145 22.4 i. Timely buying of inputs 152 59.1 217 55.8 369 57.1 Total 257 100 389 100 646 100


Indicator 15 is the “percent of agricultural producers in target region who can identify key characteristics of a well-managed farm.” The LoP target was 90.0% and the baseline found that 65.0% could identify at least one characteristic. The mid-term survey found that all (100%) respondents could identify at least one characteristic. This



result is the highest possible achievement. It may suggest that the threshold was a bit low, such that naming two or more would be more challenging. Moving from knowledge to application is a key step for increasing performance. Respondents were asked which management practices they used in the last year. The findings are set out in Table 61 below. The mid-term survey found that there was much higher application (prompted), than knowledge of (unprompted), farm management practices. This suggests the training was effective in stimulating action. The two lagging practices are ‘sell together with other farmers’ and ‘keep good farm records’. From the consultant’s wider experience in Malawi, both of these are practices that producers have difficulty adopting. Table 61: Use of improved farm management practices, mid-term

H2. Have you used these management practices in the last 12 months? Management Practice Male % Female % Total %

Keep good farm records 106 42.2 94 24.5 200 31.5 Sell together with other farmers 46 18.3 79 20.6 125 19.7 Do costings for growing crops 192 76.5 271 70.6 463 72.9 Calculate profits after selling 210 83.7 314 81.8 524 82.5 Plan production for upcoming season 233 92.8 338 88.0 571 89.9 Plan production for specific market 164 65.3 225 58.6 389 61.3 Understand the specifications for specific buyers 163 64.9 218 56.8 381 60.0 Separating commodities into the different grades 214 85.3 309 80.5 523 82.4 Timely buying of inputs 227 90.4 326 84.9 553 87.1 Total 251 100 384 100 635 100


The effect of MSIKA’s training of producers in farm management practices is measured by Indicator 8: “number of individuals who have applied improved farm management practices (i.e. governance, administration, or financial management), as a result of USDA assistance.” The mid-term findings are set out in Table 62 The mid-term finding is that 98.3% of producers applied one or more improved farm management practices. This equates to 8,324 out of 8,468 producers, a very high rate of achievement for this indicator. However, as with the application of improved growing practices, having such a high level of achievement masks insights into the particular practices that are adopted and the reasons behind that. Table 62: Indicator 8: Application of improved farm management practices, mid-term

Indicator 8 and disaggregates # who applied

% of sub-sample

Total # of producers applying

# who applied improved farm mgt practices – Producers 635 98.3 8,324 # who applied improved farm mgt practices – Male 251 97.7 3,290 # who applied improved farm mgt practices – Female 384 98.7 5,034

MSIKA wanted to know which specific records respondents kept (unprompted). The findings are set out in Table 63 below. ‘Costs’ (90.9%), ‘sales’ (85.5%) and ‘production’ (70.9%) were the most commonly kept records by far. Others scored below 21%, though not all are necessarily relevant, for example loan records if no loan is taken. As noted, these were unprompted, so the respondent may not recall the records they do keep or associate it as a record to be listed in response to the question. This might include a seasonal calendar or input instructions as they might be more associated with production, than farm management.



Table 63: Farm records kept by respondents (unprompted)

H1.b What records did you keep? (unprompted) Farm Records Male % Female % Total %

Input instructions (type, usage – mostly pesticides for dilution) 15 24.6 8 16.3 23 20.9 Costs (inputs bought, hired labor, rent, etc.) 58 95.1 42 85.7 100 90.9 Production (area planted, date planted, chemicals used, volume harvested) 46 75.4 32 65.3 78 70.9 Sales (each sales transaction, volume and value) 52 85.2 42 85.7 94 85.5 Seasonal Calendar for each crop 14 23.0 6 12.2 20 18.2 Loan records (repayment tracking) 6 9.8 3 6.1 9 8.2 History of crops grown in previous season (to inform the type of crops grown) 2 3.3 1 2.0 3 2.7 Total 61 100 49 100 110 100


As with the production and post-harvest practices, the survey sought to find out where they first learnt of the practices they use. The most common sources of where the respondent first learned about the records24 they stated they knew were an MSIKA trainer (48.0%) and a MSIKA extension worker (30.3%), with the next most common source being a parent/neighbour (18.9%). All other sources were below 8.0%. This suggests that MSIKA has been the main source of knowledge on improved farm management practices for these producers, providing good attribution for the change.

3.2.13 Sales Volume The outcome of all the training, and organising of farmers with market linkages and information (see FBO section 3.1) is that sales are expected to increase in both volume (Indicator 21) and value (Indicator 23). Indictor 21 is the volume of commodities sold by producers in metric Tonnes (mT), with the volumes reported by respondents added for all planting and harvest cycles for the seven crops. The total volume sold by the mid-term sample producers was 579 mT, which is an average of 896 kgs/producer. Extrapolating this to the mid-term population of 8,468, the total volume of sales in mT is therefore estimated to be 7,565 mT for the year July 2018 to June 2019. This compares to the LoP target of 54,063 mT and MSIKA’s reported 61,702 mT sold as at September 2019. MSIKA’s data is based on data collected by FBOs and adding data from processors. That data is also cumulative, whereas the mid-term referred to a one-year period. Finally, there are now many more beneficiary producers that have been trained than were covered in the mid-term survey. Tomato and potato accounted for 39.4% and 39.3% of the mid-term total sales volumes, making a combined 78.7% of sales volume reported. Onion contributed a further 11.4% of sales volume and mango 8.2%. Only 1.8% came from citrus, guava and chili. This reflects the difficulties in finding growers of these crops in the sampling. This concentration of sales reinforces other information on number of growers, and land area, that MSIKA should focus efforts on these three field crops, which accounted for around 90% of sales volumes. It is worth noting that the mid-term figure would have been higher but for the impact of Cyclone Idai, which reduced production for many producers (see 3.2.7). It is difficult to estimate what production would have been, but it was not insignificant given the severity of the impact reported in section 3.2.7. The total volume will also be greater

24 As with the similar question on improved growing practices, it is possible for the percentages to total more than 100%.



once the production for the large number of new beneficiary producers in 2019 is taken into account, over four times the number in the MTE. Finally, there will be additional production in the final period of MSIKA. All of these factors suggest that even though there is a divergence between the mid-term and the MSIKA semi-annual data on production volume, MSIKA is likely to reach the LoP target for Indicator 21. Standardising data to be sales per ha facilitates an appropriate comparison between baseline and mid-term than sales per producer. The baseline had larger land areas, so sales per producer would be expected to be higher. The baseline value was 7.3 mT/ha, however, as noted in the yield section, there were outliers in the baseline sample that had not been consistently removed, resulting in the baseline value of 7.3 mT/ha being a considerable overstatement of the true value. There were also sales outliers, in that the volume of sales exceeded the volume of production minus losses. The consultants allowed for a margin of error, and removed those that still had sales exceeding production minus losses. The mid-term consultant also noted that this baseline was based on the mean sales per ha for vegetables (and chili) added to the mean sales per ha for fruits. This ignores weighting, which is necessary as some crops produce a more volume per ha than others. If the profile of crops in later surveys differs, then a direct comparison cannot be made. Therefore, there is need for weighting of the respective samples. Therefore, the consultant removed the yield and sales outliers and re-weighted the samples for comparison. The impact baseline and findings for the mid-term, both for the whole sample and for those affected by Idai are set out in Table 64. A comparison of the adjusted baseline and the mid-term results for those not affected by Idai is set out in Table 65. The data for sales volume is reported by crop in kgs25/ha so as to enable comparisons between the crops and the baseline. The highest mean volume of sales in the mid-term for those not affected by Idai is for citrus (5,093 kg/ha), followed by onion (3,657 kg/ha), guava (3,355 kg/ha) and potato (3,076 kg/ha). The lowest by a long way is chili at 277 kg/ha. In comparison with the adjusted baseline, the highest increase in sales volume is for tomato at +22.0%. There are smaller increases for potato (+4.8%), guava (+4.0%) and onion (+3.8%). On the other hand, there are large declines for mango (-44.1%), chili (-40.8%) and citrus (-32.5%). The increased sales volume for tomato is important for producers, as it is the most commonly grown crop, so increases in sales volume suggest more widespread benefits. Potato is the second most widely grown and onion the third, so increase for them also have wider benefits than changes in less commonly grown crops. The sample for citrus was very small, so all results require considerable caution. Some producers in the FGDs reported difficulties in selling chilies even though buyers also highlighted difficulties in buying chilies. This is not simply a matter of making connections as the buyers indicated that they needed minimum volumes, so it may be that the small amount of chili produce is not sufficient to make it attractive to buyers. As noted in earlier sections, the relatively short period that the beneficiary producers have had to implement new practices and benefit from market linkages, is likely to be a factor in the lower than target performance, as seen on sales volumes. It is possible that higher losses for some crops has resulted in lower sales (see Table 53). Although declines in the other crops are not welcome, increases in the three biggest crops, accounting for around 80% of the MSIKA beneficiary producers’ production and sales is positive.

25 Metric tonnes are split into 1,000 kgs so that the differences are clearer.



Table 64: Volume sold in kg/ha, revised baseline, mid-term all and not affected by Idai Crop Kg/ha

Revised Baseline Mid-term all Mid-term – not affected Mean Median Mean Median Mean Median

Tomato 2,126 886 2,327 1,449 2,584 1,581 Onion 3,524 2,198 3,254 1,630 3,657 2,174 Potato 2,934 1,989 2,714 1,729 3,076 2,075 Mango 4,966 3,568 2,997 2,038 2,775 1,926 Guava 3,226 2,204 3,006 1,691 3,355 1,628 Citrus 7,542 6,528 5,093 2,269 5,093 2,269 Chili 468 242 318 178 277 163

Table 65: Volume sold in kgs/ha, comparison of baseline to mid-term not affected

Crop Mean Volume Sold Adjusted baseline kg/ha MTE – not affected kg/ha Difference %

Tomato 2,126 2,594 22.0 Onion 3,524 3,657 3.8 Potato 2,934 3,076 4.8 Mango 4,966 2,775 -44.1 Citrus 7,542 5,093 -32.5 Guava 3,226 3,355 4.0 Chili 468 277 -40.8

3.2.14 Sales Value The calculation for Indicator 23, sales value in US $, included the seven crops across all growing/harvesting cycles from July 2018 to June 2019. Sales volumes were multiplied by the reported average prices (for each cycle) and converted to US $s at the rate of MK 726.82. The total annual sales in US $ for the sample group was $111,399, which equates to US $172.44/beneficiary. Across the whole population of 8,468 beneficiaries the mean value of sales of US $172.44/beneficiary equates to total sales for the mid-term beneficiary producers of US $1,460,257 for the period July 2018-June 2019. Of note, sales of tomato accounted for 54.8% of all sales, with potato accounting for 25.6%, making a combined total of 80.4% of sales value. Onion accounted for 9.3%, chili for 4.9% and mango for 4.4%. Guava and citrus only accounted for 0.9%. This highlights again that some crops are more important for MSIKA in achieving its results. The LoP target is $18,681,943 and MSIKA reports cumulative sales of $17,072,599 which is 91.4% of the LoP target. The same points apply on comparing the mid-term sales value finding with the MSIKA sales volume reporting. To allow comparisons, the sales are standardised into US $ of sales/ha. As noted with the sales volumes, the baseline had yield and sales outliers. In addition, the consultants also noted some price outliers. These were removed to enable a comparison between the baseline and mid-term to be made. Finally, the mid-term data for those producers not affected by Cyclone Idai is reported. For the overall mid-term sample, the sales of citrus at $1,163/ha and potato at $1,086/ha were considerably higher than sales of other crops, noting the very small sample for citrus. These were followed by Onion at $751/ha, chili at $722/ha and tomato at $680/ha. For the mid-term sample for those not affected by Idai, the picture changes with onion having the highest sales at $1,191/ha, followed by citrus at $1,104/ha and potato at $1,097/ha. Chili fell to $637/ha, while tomato increased to $740/ha and guava to $471/ha, with mango almost unchanged. The three field crops of onion, potato and tomato produce high sales values per ha.



Comparing these to the baseline, for the mid-term producers not affected by Idai, there were big increases for potato (+76.9%), onion (+58.6%) and chili (+31.8%), with guava also increasing (+10.0%). There were declines for mango (-48.8%), citrus (-26.1%) and tomato (-13.2%). Table 66: Value of crop sold in $/ha, comparison of mid-term vs baseline

Crop sales/ha

(USD)

Impact baseline – outliers removed

MTE (overall) outliers removed

MTE (Not affected by Idai) – outliers

removed MTE overall vs baseline

MTE not affected vs

baseline Mean $ N Mean $ N Mean $ N % %

Tomato 853 268 680 464 740 228 -20.2 -13.2 Onion 751 68 751 135 1,191 89 0.0 58.6 Potato 620 200 1,086 294 1,097 128 75.2 76.9 Mango 483 100 256 108 248 82 -47.0 -48.8 Citrus 1,494 34 1,163 7 1,104 7 -22.2 -26.1 Guava 428 37 301 59 471 48 -29.8 10.0 Chili 483 26 722 66 637 30 49.3 31.8

Kadale noted that the mean prices in Malawi Kwacha in the mid-term were generally lower than in the baseline, so that the value of the sales in the mid-term is suppressed. The detail of this is set out in Table 67. Tomato prices were 24.7% lower, onion 6.0% lower and mango 4.0% lower than the baseline. Adjustment for these would offset the fall in sales value for tomato and further increase the sales value for potato. However, higher prices for citrus (+64.6%), chili (+34.9%), potato (+5.0%) and guava (+1.4%). Table 67: Prices in Malawi Kwacha, comparison of mid-term vs baseline

Crop Impact Baseline Mid-term Difference Price (MK) Price (USD) Price (MK) Price (USD) Tomato 268 0.40 202 0.31 -24.7 Onion 175 0.36 164 0.20 -6.0 Potato 157 0.48 165 0.17 5.0 Mango 76 0.15 73 0.10 -4.0 Citrus 143 0.24 235 0.32 64.6 Guava 90 0.16 92 0.12 1.4 Chili 674 1.69 909 1.90 34.9

On the basis that sales were suppressed this season due to Cyclone Idai, that mid-term beneficiaries have not had a full year to implement the various training, that some prices in the mid-term evaluation period were lower in US $ than in the baseline period and that there are now many more beneficiary producers trained by MSIKA, then the consultants conclude that it is likely that MSIKA will exceed its LoP sales value target. It was noted in the mid-term survey that a small number of respondents did not sell any of their crop. Details are set out in Table 68 below. The mid-term found that 24.2% of chili growers, 15.3% of guava growers and 11.6% of potato growers did not sell anything. The most common reason they gave was that they ‘did not have a surplus to sell’, especially for chili and potato growers, which means they lost the whole crop. The second most common reason was that they ‘could not get a buyer’ which most affected guava and chili growers. From the KIIs, two of the chili buyers said that they could not get enough chilies. One reported that he can go to an FBO/location where he was told there is sufficient chilies, but these are often in very small quantities and insufficient to make the trip economic. So, it might be that although there is demand for chili, if the quantities are too small, the grower may not find a willing processor/exporter buyer, though producers may find a small trader willing to buy.



‘Poor quality’ was the third most common reason and was an issue for guava and potato growers. This may be related to pest and disease damage, and damage at harvest, on the farm and at the market. Table 68: Growers that sold nothing, mid-term

Crop Growers # that sold nothing % of that crop Tomato 464 6 1.3 Onion 135 - 0.0 Potato 294 34 11.6 Mango 108 6 5.6 Citrus 7 - 0.0 Guava 59 9 15.3 Chili 66 16 24.2 Total Growers 1,133 71 6.3

Respondents were asked about the primary way they sold, including selling individually, selling with other producers in their club/group or selling with other producers outside their club/group. The overwhelming method was selling as an individual by 92-100% of respondents depending on the crop. Selling with other members of a club/group ranged from 0-6%, with the highest responses for chili and tomato producers. Respondents were asked about the locations where they sold their crop, with multiple responses possible. The most common response was ‘traders who came to me’ for all crops, other than guava. The proportion of respondents who gave this response ranged from 64% to 86%. For guava, the most common response was ‘local market’ (59%), followed by ‘traders who came to me’ (46%). ‘Local market’ was the second most common response for the other six crops, ranging from 28.6% to 44.8% of respondents. The only other substantial response was for ‘neighbours’ at 17-38%, other where this was not common. Less than 1% sold to an NGO/farmer group all crops, other than chili which was mentioned by 6% of respondents. In terms of preferences as to the most important market, this mirrored the response frequency on where they sold their crops.

3.2.15 Access to Finance This section covers access to finance (A2F) issues from the perspective of the beneficiary producers. A2F enables producers to invest in their crop, with the expectation of a higher return, such as from improved seed, treatments for pests/disease and fertiliser. A total of 12.1% of respondents reported that someone in the household had a bank account, with 94.9% of these saying it was for savings. A further 4.6% had an account with a Savings and Credit Co-operative Organisations (SACCO). More commonly, respondents were members of village savings and loan (VSL) groups, with 38.7% being members of a MSIKA-organised group and a further 31.7% being members of a non-MSIKA organised group. The remaining 34.4% were not members of any VSL group. A total of 32.4% of respondents or members of their households had taken a loan from a MSIKA VSL in the past year, which equates to 83.7% of the 38.7% of the respondents who said they are members of MSIKA VSLs had been able to access a loan. The importance of VSLs is further highlighted by the responses that only five producers had taken formal loans from a bank, MFI or SACCO, three from a processor and four from an agro-dealer. Collectively, these represent only 1.9% of the MSIKA beneficiary producers, compared to VSLs at 32.4% of respondents accessing loans.



MSIKA reports that as at 30th September 2019, it had trained 666 VSL groups, typically with 15-20 members. This would equate to 10,000-13,000 members.26 It is encouraging that two thirds of MSIKA producers are in a MSIKA VSL or another VSL group, as these have proven to be effective at enabling rural households to save, and also as a source of small, short term loans. The big advantage for the members is that although interest is charged on the loans, the accumulated interest earned by the group is returned to the members, typically for the main period for buying inputs, in October and November. From the consultant’s perspective, the formation of VSLs and training of members should be encouraged, as it increases access to savings and loans that beneficiary producers need to invest in their production and increase their returns. In addition to improved access, producers also need to understand financial issues. 59.9% of beneficiary producers reported receiving financial training from MSIKA. From discussion with the MSIKA team, the financial training is being rolled out to all FBOs, so in the coming year most MSIKA beneficiaries should have been trained. Overall, there is good progress on access to finance. This topic is also covered in the sections on agricultural lending through MSIKA’s relationship with the MFI in section 3.3.2 below and in section 3.1.3 on FBOs.

3.2.16 Investment and Processing This section covers investment and processing issues from the perspective of the beneficiary producers. Respondents were asked if they undertook any processing. Out of the sample, 10 producers (1.5%) said that they did process. Eight of the 10 ‘processed’ potatoes, and of these seven cooked chips for sale as a snack food. One other made tomato sauce. One person processed mango, but the product was not disclosed. Processing by producers is not common and appears unlikely to be of significance in the near future. Reference has been made in 3.2.9 to the building and restoring of storage. This is a form of investment. Respondents were asked to state how much they had spent on refurbishment/building. The total spent on equipment and land in July 2018 to June 2019 was a mean US $13.40 equating to US $113,447 across the 8,468 mid-term population.

3.2.17 Employment This section covers employment issues from the beneficiary producer survey. Indicator 22 is the “number of jobs attributable to USDA”. Producers are one of the groups that contributes to the total, along with processors, FBOs and other MSIKA partners. Respondents were asked if they hired anyone for four weeks or more (full time) in the period July 2018 – June 2019, with 18.4% saying they did. For comparison, the baseline reported 0.09 jobs created per beneficiary household, against 0.25 calculated for the mid-term. The number of people hired by the 18.4% that said they hired someone for four weeks or more, was split by male and female, and converted to jobs per respondent across

26 MSIKA says there may be duplicates in this total, so could not give a precise figure at the time of reporting.



the sample (646). This was then multiplied by the mid-term population (8,468) to give the total of people hired for four weeks or more. It is estimated that beneficiary producers contributed 2,150 people hired for four weeks or more27 between July 2018 – June 2019. Table 69: Indicator 22, number of people hired for four weeks or more, mid-term

Category # of jobs in sample

Jobs per respondent

Jobs for Mid-term population %

Sample/Population 646 - 8,468 - Total females employed 57 0.09 747 37.8 Total males employed 107 0.17 1,403 65.2 Total people employed 164 0.25 2,150 100.0

3.3 Banks and MFIs MSIKA aims to stimulate agricultural and small and medium enterprise (SME) lending.

This section reviews MSIKA’s work with the Bank on SME finance and with the MFI for agricultural loans. The relevant indicators are 11 (# of individuals receiving financial services), 12 (# of loans disbursed) and 13 (value of loans disbursed). Results are those reported by MSIKA.

The information in this section comes from KIIs with MSIKA staff, the MFI and the bank, and four processors who received loans.

3.3.1 Finance for SMEs MSIKA intended to have a Special Purpose Vehicle (SPV) either as a fund managed by MSIKA or through a bank. The advice MSIKA received was to work through a bank or a Malawian entity to manage it. MSIKA went through a lengthy process of assessment before it finalised a relationship with the bank. MSIKA provided an initial loan of $250,00028 in March 2019 as a fund for on-lending by the bank. In return, the bank agreed to ease its collateral requirements29, and to charge a lower interest rate than normal30, on the basis that MSIKA would assist SMEs to prepare screened proposals that are then put to the bank, which is described as the ‘pipeline’. MSIKA reports that the bank has used $93,000 on-lent to seven processors and agro-dealers at the time of the mid-term evaluation.31

MSIKA identifies small and medium agri-processors and agro-dealers with potential to buy from, or supply, its producers. The MSIKA financial specialist undertakes due diligence on these SMEs and assists them to improve their proposals, primarily through

27 Although the definition of a job is a person hired for at least four weeks, the term job may be misleading, as these are most likely causal and short-term hiring, such as at planting, weeding and harvesting stages, rather than permanent full-time jobs. However, it is correct to call these jobs under the USDA definition. 28 At 2% of the forex value. 29 This is based on cash collateral of 20-25%, rather than land/vehicles or other ‘hard’ collateral. 30 The bank rate is 21% plus $30 processing fee versus other banks at 25% plus 2% processing fee. 31 This has included one default, which the bank thinks was a person who intended to default from the start; the remainder of the portfolio is operating at an acceptable level.



group sessions. After receiving the proposals, the bank does its own due diligence and proposal assessment, involving visits and gathering evidence of sales and cashflow.

Prior to agreeing to make a loan, the bank requires the SMEs to open a bank current account and to be depositing funds from the business, so as to build a track record of cashflow. The bank staff report that the main issue for the bank is the low turnover of money through any bank32 account, compared to the business revenues claimed by the SMEs in their proposals and relative to the desired loan sizes. The bank said that it takes a deliberate stance to offer relatively small first loans compared to what is being asked, and making this first loan to be primarily for working capital, not for fixed asset purchase33. The bank does this so that it can see how the SME manages the loan over six months. If the SME shows good cash turnover through the account and the ability to service the loan without problems, then the bank expects to disburse larger second loans that can be for fixed asset purchase. This should result in an increase in the amounts disbursed over the coming 12 months, as the initial loans were only disbursed from March 2019.

The reduced loan size is reported to be a problem for some SMEs who perceive this to be too much effort to get a small loan, but the feedback from two of the SME owners who were interviewed was that this was something they understood and so they were focused on servicing the loan properly in order to get the bigger loan they were originally seeking. MSIKA continues to support SMEs to prepare and submit applications, with a further four being proposed in August, and a pipeline of 30 being prepared for submission. The MSIKA finance specialist was confident that the uptake would increase substantially over the coming six months. It has taken MSIKA up to the project mid-point to get a functioning mechanism for SME lending up and running, and so far, the loans have been quite limited in number and size. This was longer than intended, which was partly out of MSIKA’s control, as they were close to agreeing with a different Bank, only for the arrangement to fall through. From the discussion with the selected bank, it appears that the process is making headway and that the bank is satisfied with the model. The addition of a new staff member to the bank’s team suggests that they are seeing this as worth additional investment, which is encouraging. MSIKA requires that the processors or agro-dealers have some link to the FBOs and beneficiary producers. From the interviews with four processors, their links with the FBOs and beneficiary producers was tenuous, with little evidence of specific links, and only relatively small quantities of crop being purchased so far. So, while the SME finance model appears to be operating reasonably, the limited number and value of loans so far, and the tenuous links between the processors and the FBOs suggests that the impact on beneficiary producers may fall well short of what MSIKA is aiming for.

3.3.2 Finance for Producers MSIKA has worked with an MFI to finance loans for producers with access to irrigation. MSIKA has provided technical assistance to the MFI, made links with FBOs, organised producers, and trained the producers in financial literacy34. The MFI provides its own funds, with an initial amount of MK 66 million (US $90,900) for 996 rainfed seasonal

32 I.e. An account at any bank, not necessarily through the bank. 33 Most of the applications have been mainly for fixed asset investment. 34 Budgeting, saving and record keeping.



agricultural loans35 of MK 20,000 – 100,000 ($US $27-135) across two FBOs in 2019. Producers have to have been members of a VSL with a track record of saving and repaying loans. They also have to have sales records to show that they are getting sufficient income to repay the loans. Repayment for these loans was due in June 2019 and is reported to be 98%. The MFI reports that it is very satisfied with the performance of the loans and the programme so far: “Results have been great.” Chief Executive, the MFI. The MFI has disbursed a further MK 13 million to producers with irrigation in Dedza for the winter crop (June-September 2019) and has a pipeline of 1,008 producers from MSIKA FBOs and recently trained in irrigation, which is across all the target Districts. MSIKA is aiming for about 5,000 rainy season loans, though the MFI said that MSIKA had talked about reaching 15,000 loans which would be beyond the MFI’s capital.

The MFI’s Chief Executive commented that the members of MSIKA VSL groups all had the same crop, so there is a lending risk, as all might fail if the growing, pest or disease conditions are poor. However, he also recognised that the risk was reduced because these producers have received training in these crops, which reduces the risk of crop losses and gives greater assurance of sufficient/higher yields and therefore revenue from sales. He also recognised that there is a reduced risk for groups where there was irrigation: “Irrigation makes it safer from drought, which is the most critical problem.” Chief Executive, MFI. Two clubs in Ntcheu were reported to be defaulting. The MFI’s view was that these groups thought that the funds were from MSIKA, and so were not expecting to repay. The MFI would like MSIKA staff to join them for meetings with groups, to clear such issues as these, but both the MFI and MSIKA said that co-ordination between the MFI and FBCs is a challenge because FBCs and the MFI’s staff have a great deal of work. The MFI has almost 80% female borrowers in their portfolio, but most of MSIKA’s producers that have obtained loans so far are men. Overall, the partnership with the MFI has operated well, to the satisfaction of both parties. There is potential to increase the number of loans through this partnership, which will improve access to finance for more MSIKA producer beneficiaries. In addition, as well as increased access, this is a sustainable approach that will likely continue beyond the life of MSIKA, as the MFI is a well-established and viable MFI, which now has links with these MSIKA producers that it can continue.

3.4 Processors MSIKA has worked with processors on linkages with FBOs, access to finance and support for these processors, including promoting good manufacturing practices (GMP) and certification by Malawi Bureau of Standards (MBS). The information in this section comes from KIIs with the MSIKA team and interviews with four processors. MSIKA provided short profiles of 12 processors that it has intensely worked with, along with a database of 18 processors with details of their contacts, products, MSIKA implemented activities, employment and sales. The selection of processors to interview was undertaken in conjunction with MSIKA to ensure that there was a sufficient spread of topics covered, across a range of products, and that there were some substantive activities to be discussed.

35 Fumba loans, payable after harvest. 6.5% per month interest, which has reduced to 6.0%.



The consultants met four processors specifically to discuss the support they had received from MSIKA and to verify the information provided by MSIKA for reporting. The four processors were located in Lilongwe and Blantyre and a cooperative in Dedza. This was not a representative sample and the intention was to verify the data that MSIKA has gathered to test its reliability, rather than to generate representative data. As each business is very different, it is would not be possible to extrapolate findings, hence the focus on a verification approach.

3.4.1 Training in Good Manufacturing Practices Indicator 32 is the number of processor staff trained in quality standards. The LoP target is 60, with MSIKA reporting a cumulative achievement of 152, which is 253.3% of achievement. Technoserve (TNS) was commissioned to undertake training in implementing and complying with good manufacturing practices (GMP) and standards, such as the Malawi Bureau of Standards (MBS) standard ‘MBS21’, which is for hygiene practices. The training also included aspects of innovation, as many processed products are similar, such as achars and chili sauces. Alongside training, MSIKA engaged with the processors to assess their needs and work out a project plan to address hygiene improvements and certification process, upgrading plant/equipment/buildings, undertaking product development, and improving marketing and financial planning. Some of these relationships are managed by TNS and the rest by the MSIKA team. TNS undertook its work by grouping enterprises, for a series of group sessions over six months, then ending its involvement. Although this is efficient, this process may not fully respond to the needs of the processors, and it limited the inputs to a relatively short period, when this degree and depth of change likely requires more time. MSIKA report that many processors needed to address relatively basic hygiene issues in their processing. A cooperative stated that on several occasions, it has had its products removed from shops due to the absence of MBS certification. MSIKA, through TNS, organised training in hygiene and GMP to which processors were able to send one or two representatives for training. Three processors and the cooperative, all confirmed that they had been trained in hygiene and GMP. In each case, the individuals who attended the training went on to train the rest of the processor’s employees. All the processors talked about GMP and practices that they had put in place. For example, a processor located in Lilongwe stated that they now documented the process and trained staff to follow it. A cooperative stated that they had adopted the need for the right clothes, and for using clean utensils. A Blantyre processor reported that it had all the floor cemented, painted, physically separated raw material from processed product, and made separate dressing room for women/men. Other changes mentioned were establishing cleaning stations, safety for staff and developing standard operating procedures. The respondents were very positive about the way the training was done, and that there had been individual follow ups. The adoption of several changes by each processor supports that the training was effective.

3.4.2 MBS certification Indicator 25 is the percentage of registered processing firms in target sectors that obtain certification with MBS related to product quality. The LoP target is 40%, with MSIKA reporting achievement of 39.0%.



MSIKA commissioned Michigan State University (MSU) to undertake a needs assessment and gap analysis on standards and certification. The work with MBS led to a process for defining standards that is ongoing (see section 3.5.3). Processors handling food products are required to operate to MBS standards, notably on food hygiene. They are also required to meet the standards set for particular food products, where those standards exist. Many of the processors were operating without certification or only with pre-certification status, which potentially creates hazardous situations. The absence of MBS certification for food products means that processors cannot sell their products through formal outlets, thereby reducing their sales. “For example if we found a shop to carry our products for us they would be thrown out by the MBS. It has happened before. We found markets yes, but we couldn’t display because of the certification.” The cooperative. The KIIs with processors brought out how MSIKA has helped them towards addressing MBS certification. For example, one processor had moved from Blantyre to Lilongwe. Their previous premises had been certified by MBS, but the new premises in Lilongwe had not. A Lilongwe processor was struggling to understand why this was a problem in Lilongwe. One Lilongwe processor now has the certification for the premises and for its main product which is a baobab juice. There was progress reported on certification for another Lilongwe processor. For the cooperative, progress has stalled because the premises are owned by the Ministry, so it is difficult to make changes to improve it which are necessary to attain certification. In summary, it is clear from the KIIs that MSIKA has worked with processors to progress MBS certification. Several processors have achieved pre-certification with MBS, which allows them to operate as if certified, but with a plan to work to resolve remaining issues and achieve full certification. At mid-term, it looks like MSIKA will be able to convert more of the processors to full certification by end of project. This is a challenging indicator for two reasons. Firstly, certification is out of the control of processors, and well beyond the control of MSIKA, as MSIKA depends on the processors taking action and then on MBS agreeing to the certification. There can be individual factors that mean certification is not achieved. Secondly, the indicator is a percentage, so it can be affected by MSIKA struggling to find capable and serious processors to work with. If MSIKA has to work with many nascent or micro-processors, they may not be able to reach certification in the lifetime of the project. As noted in section 3.4.4 below, a high proportion (13/18 – 72.2%) of its processors are small or micro-enterprises and may either not be willing to invest in reaching MBS certification, or they are unable to reach the required standards, which inevitably require investment.

3.4.3 Other Support to Processors As noted, MSIKA’s/TNS’s support to processors was tailored to each business, following a needs assessment. The training on GMP/hygiene and support towards reaching MBS standards is discussed above. The KIIs with the four processors revealed that there were a range of issues that were addressed or are being addressed with each processor. These are summarised in MSIKA’s database of processors, and the consultants found that these activities were verified with the four processors that were interviewed. The activities involved:

A Lilongwe Processor- training in GMP/hygiene, advice on the layout of their existing processing and planned processing factory, identification of necessary processing equipment, revision of their business plan for raising finance, and more structured record management.



The Cooperative - training in GMP/hygiene, training in basic marketing, basic accounting and bookkeeping, advice on reaching certification standards and participation in sales fairs. Another Lilongwe Processor – training in GMP/hygiene, training in bookkeeping and financial management, facilitation of MBS certification for premises/main product, link to the bank for loan (loan was disbursed – see 3.3.1) and product development. A Blantyre Processor - training in GMP/hygiene, development of hygiene manual, changes to key products (chili and tomato sauces), identification of processing equipment, assistance with development of a business plan for raising a loan, and support for a marketing strategy and branding of their products.

There were some planned activities that did not come to fruition. There have been offers and attempts to link the processors to FBOs who could supply product, but these have not resulted in any purchases by the four processors. There were also efforts to link the processors with end-buyers. While the link was made, it did not result in new business. Finally, there were links made between the processors and banks that were successful for a Lilongwe processor, but not for the Blantyre processor. From the KIIs, the consultants noted that the processors had high expectations particularly for new customers and finance. The highest expectation was on finance, where the processors thought at first they would get grants towards equipment and premises, and then that they would get loans. In the case of a Lilongwe processor, they did get the loan and have a good prospect of getting a larger second loan to fund a bottling plant. Another Lilongwe processor has been able to receive a grant towards their new factory, but are still short of the finance they need. A processor in Blantyre have tried to get a loan, but the conditions are too demanding. The processor appears disappointed not to get finance through a grant or loan. There is inevitably a challenge over raised expectations and there can be misunderstandings of what to expect from a project like MSIKA. MSIKA needs to continue to communicate clearly with processors and adjust their expectations throughout the process.

3.4.4 Performance data MSIKA provided information on 18 processors. Three of the 18 are relatively large enterprises. Two are classed as medium-sized enterprises, with the rest being small (5-20 employees) or micro (0-4 employees). Some processors are at a nascent stage, or non-operational due to moving cities, and the cooperative due to machine breakdown. It is a challenge in Malawi to find processors beyond a relatively small number of medium sized businesses. To exemplify this, 13 of the 18 processors have 11 or fewer full-time/permanent employees, and 11 processors have five or less full-time/ permanent employees. Work with processors contribute to a series of indicators:

• Indicator 19, number of private enterprises (et al) that applied improved techniques and technologies as a result of USDA assistance;

• Also include indicator # 21 and 23 as processors also contribute. • Indicator 22 number of jobs attributed to USDA assistance as a result of

USDA assistance; • Indicator 24, value of new public or private investment leveraged as a result

of USDA assistance;



• Indicator 27, total increase in installed storage capacity (dry and cold storage), as a result of USDA assistance; and

• Indicator 29, number of sales agreements with FBOs.

As noted earlier, indicator 19 is incorrectly worded,36 as it is not possible or even appropriate for private enterprises to apply improved techniques and technologies which are presumed to be for agriculture, PHH and farm management. The same is true for other organisations and the content of this indicator is already addressed under indicators for producers to whom these apply. For indicator 22, number of jobs created, the LoP target is 10,000, with MSIKA reporting a cumulative 5,798 jobs created. The contribution of producers to job creation was given in section 3.2.17 at 2,150 jobs for last year. The contribution of processors was not measured in the mid-term. For the four processors interviewed, it was not possible for respondents to say what jobs were attributable to MSIKA’s assistance. A Lilongwe processor reports that it employs up to 17 people. With MSIKA’s assistance, it has been able to secure a loan and will likely get the larger loan to invest in its bottling plant. This will create a number of jobs, but not more than an estimated five jobs. The certification from MBS was important to conserve the existing jobs, as without it, the Lilongwe processor would have faced problems selling its products. The processor in Blantyre reports that it employs 15 people including the management, with a further five called in as needed. There were not increases in jobs that were attributable to MSIKA, though if the Blantyre processor is able to secure funding for expanding its factory, then it will create additional jobs. The cooperative is currently not processing due to machinery breakdown since September 2018. It is a co-operative, but with limited resources and only employs one employee, a watchman. If it can resolve its machinery problem, and get MBS certification, it could increase employment, but these problems do not appear to be near to resolution. Another Lilongwe processor employs three people full time, including the owner and his son, with 6-10 seasonal workers. At present there is no additional employment attributable to MSIKA, though if the factory opens, then it would create additional employment. The biggest employers that MSIKA works with is a food and beverage manufacturer (over 200 employees), a hot sauce processor (70 full time and 32 seasonal) and a mango processor (33 full time and 100+seasonal). These are well established businesses, so these employee totals are not additional employment. A 20% increase in their employees would equate to an additional 86 employees. Overall, the 18 processors that MSIKA is working with report that there are 391 full time/permanent jobs, and around 170 seasonal jobs, totalling 561 jobs. MSIKA will not make a substantial contribution to its employment target from processors, even if it doubled employment in the remaining period. The most likely source of increased employment is from producers than processors. For indicator 24 - investment leveraged, there was evidence of investment by 3 of the 4 interviewed processors. The amounts are modest at present. The Blantyre processor reported a spend of around MK 5m (c. US $6,900), though with more investment pending for the new buildings. One Lilongwe processor wants to make an investment of around MK 50m (US $69,000). The other Lilongwe processor indicated that it was investing around MK 15m (US $20,700) to match the grant of the same amount that it

36 Number of private enterprises, producers organizations, water users associations, women’s groups, trade and business associations, and community-based organizations (CBOs) that applied improved techniques and technologies as result of USDA assistance



had received. There will likely be further investment beyond these amounts. The Cooperative, was struggling to make even very small investments, such that it can be discounted as a source of investment. MSIKA reports cumulative public and private investment of $550,597 against the overall target of £1,150,000, all of which has been private investment, including the loans taken. The private investment target is $150,000 and so has been exceeded. Indicator 27 is an increase in installed storage capacity. The LoP target is 75,750 cubic meters, of which 53,025 cubic meters is the target for dry storage and 22,725 cubic meters is the target for cold storage. MSIKA reports a cumulative increase of dry/total storage of 1,541 cubic meters. One Lilongwe processor reported an increase its storage capacity by approximately 300 cubic meters. The Blantyre processor had separated out its space, but not necessarily increased storage. The other Lilongwe processor had more clearly separated its space to provide storage. There are increases in storage, but limited relative to the target. In contrast, the producer survey found 44,228 cubic meters of storage added by producers (see 3.2.9). Indicator 29 relates to numbers of sales agreements with FBOs. From the four KIIs, there were no sales agreements reportedly made with MSIKA FBOs. One Lilongwe processor does not yet buy MSIKA target crops, but is interested in buying lemons for a new product. The Blantyre processor sources its chilies via traders, rather than direct with FBOs. The other Lilongwe processor travels to production areas to buy, but only purchased 5 mT in the last season, so volumes are very small. The cooperative is not purchasing at present, but would purchase from members if it did so. While not necessarily representative of all processors, there was little evidence of sales agreements with FBOs. The consultants also interviewed a spice buyer and processor, including about sales agreements. The processor had around 20 sales agreements with FBOs, for around 140 mT of paprika and 70 mT of chili in the most recent season. In support of these agreements, the processor supplied seed to some of the producers. The processor says it was able to source only 10 mT of paprika and 1 mT of chili from these agreements, which created a challenge as it has a regular supply contract for South Africa and so struggled to fulfil its own orders. Although production may have been reduced by the weather this season, the processor attributes the shortfall to a lot of side selling. Some of this may have been partly due to the processor’s lateness in buying due to cashflow challenges, but side-selling is a very common issue in paprika/chili, as well as other crops. The feedback from processors, from FBOs and from producers is that the market links facilitated by MSIKA are weak. The challenge for MSIKA is that even if it facilitates agreements, these may not be fulfilled, as is the case with the spice, so that the buyers are discouraged from making future agreements. The lack of volumes of the right quality, and shortfalls by buyers in terms of buying on time and being willing to match market prices, especially if they also provided inputs, is a well-known challenge. Sales agreements between buyers and FBOs have not led to consistent sales between these parties.

3.5 Government MSIKA has worked with Government of Malawi (GoM) in several ways, including on policy, training of extension staff, research in horticultural crop production and PHH, on research into improved practices with a Malawian University and MSU, and engaging with MBS on the development of product standards.



3.5.1 Policy Level MSIKA is working with the Ministry of Agriculture, Irrigation and Water Development (the ‘Ministry’) to develop a new Horticulture Policy. The policy process was originally initiated by the Ministry and a small group of professionals who wanted to see horticulture become more developed through a new strategy for horticulture. MSIKA noted that the National Agriculture Policy only had a half page on horticulture, which meant that its potential for transforming producer livelihoods was limited. Therefore, MSIKA engaged with this Ministry group with the aim of developing a standalone Horticulture Policy. MSIKA’s interest in a Horticulture Policy provoked debate in the Ministry group, over whether to update the Policy or the Strategy. The decision was to look at both concurrently. MSIKA who funded a consultant to support the process and supported a series of planning meetings. MSIKA supported this with an online survey of 60 stakeholders to gather insights for the revised policy. It also supported wider stakeholder meetings that were co-funded by MSIKA and the Ministry. With MSIKA’s support, the process has moved quickly, and the outcome has been a draft Horticulture Policy that the Ministry’s Department of Planning is aligning with the GoM rules on drafting of policies. At this mid-term point, neither MSIKA nor the Ministry have funds for this alignment stage, so the process has stalled since the first quarter of 2019. This is also partly related to the election in May 2019 and uncertain political situation that has been created. The Ministry is working to find funds to move to the final stages, which would be a ‘Management Meeting’ where all Departments of the Ministry are consulted, a wider consultation on the final draft, and a final Management Meeting for the draft policy to go to the Office of the President and Cabinet and Ministry of Justice who then take the final Policy to Cabinet for approval. MSIKA will support the development of the strategy technically but not financially. MSIKA indicate that another project is interested in the Strategy, so intend to work with them, though this has not yet progressed. In summary, there has been progress, but there are a number of stages to go before the Policy is put to Cabinet for approval.37 The process is uncertain, but there seems to be a strong drive from the Ministry to complete the process and support from the Principal Secretary behind it.

3.5.2 Training District Extension Staff MSIKA identified that the provision of extension services by GoM staff at district level was a major weakness. In response, MSIKA has worked on training for GoM extension staff through a master trainer approach to promote tested and proven practices. In a KII, the Director of Horticulture reported that only half to three quarters of Horticulture Officer posts are filled. She notes that many Horticulture Officers (HOs) do not know horticulture beyond some theory. The training of the HOs falls under the Director of Horticultures remit, and these HOs are meant to train the Agricultural Extension Development Coordinators (AEDC) and Agricultural Extension Development Officers (AEDO). Her key point was that training of HOs needs to include the practical aspects to ensure they are able to onwardly train AEDCs and AEDOs. MSIKA, through a sub-award to MSU, developed and adapted training materials based on a training needs assessment conducted in collaboration with the Department of

37 A Policy can be developed, but ultimately requires adoption by the Cabinet. Once the technical process is complete, a Policy should not face rejection or significant change at Cabinet level, but it is possible that it can be returned with instructions for changes, so the process of adoption is not certain.



Agricultural Research Services (DARS) and the Department of Agricultural Extension Services (DAES). This process consulted on good practices and sought existing training materials, so as not to reinvent materials. These materials were reviewed and used to develop a training of trainers (ToT) program, for those officers to be able to train producers with the MSIKA agricultural training. In addition, MSIKA has produced some extensive materials on growing these crops that are printed in English and Chichewa. The first ToT was held in November/December 2017 for GoM extension staff (87 from the target districts), MSIKA’s FBCs (10), a tobacco company’s extension staff (6) and lead farmers from FBOs (30). The program covered the content as the basic agriculture module for onward delivery to producers. There were two further ToTs in April 2018 for 100 GoM extension staff and lead farmers, and in quarter three 2018 for another 40 GoM extension staff. The ToT also covers training methods provided by MSU that trainers could use for training producers. MSIKA states that it learnt from the first two trainings that mixing extension staff with lead farmers does not work well, because the level of knowledge is different. This was changed for the third training. MSIKA’s overall assessment is that the ToTs were effective in passing on knowledge and skills, but that the challenge is getting GoM extension staff to apply these. This is because GoM staff demand additional resources to conduct the training, such as allowances for themselves and trainees, which MSIKA is neither willing, nor able, to support. It is noted that there are some extension officers who are willing to attend/deliver the training, particularly if MSIKA lead farmers are doing the training. However, as a result of the mixed response by GoM extension staff, MSIKA focuses on those extension staff and lead farmers that are committed. MSIKA has developed a ToT for PHH and storage, which is based on its producer training program, and started conducting training from February 2019 for field crops, other than chili. Chili and tree crops will be conducted closer to harvest time. This training covers alternative ways to handle and store the crops, including use of low cost, producer constructed facilities. The reach is relatively limited as at mid-term. This is seen in the response of producers on knowledge and uptake of PHH (section 3.2.9). The consultants conducted KIIs with DADOs, HOs, Agri-business Officers and AEDCs/AEDO. These reported that they were aware of the work of MSIKA and overall very positive about the focus on horticulture crops, which are often neglected, and the training content and approach. One government official stated that the training was tailor made for these crops and was what the producers needed. Another stated that the content was relevant, useful and simplified enough for producers. They gave examples of where they had seen a change in practices by producers: “In the past most farmers did not stake their tomatoes and their harvest was little and the fruits were small because the fruits did not have room to grow but with staking, using the same tomato variety farmers harvested more.” Government official, Ntcheu. The feedback on the effectiveness of the training is positive. Where the GoM staff have more critical comments is over the involvement of GoM in the delivery with a desire to control/specify the approaches: “For example I visited one FFS where the attendance was up to over 700. I don't think everyone can be able to learn in such a big group. We recommended that they have 30 farmers who they will train, and these farmers will then teach their colleagues in the practices, using the curriculum that we have here. They need to sit down with us and discuss.” Government Official, Ntcheu. "(I was)…only involved during trainings, but just side-lined on field visits and that can end with (MSIKA) saying things differently to the farmers.” Government Official, Lilongwe



“We are the policy holders, and we feel there is very little involvement of us in their plans, we are just being used in the implementation. We need to be part of the whole process.” Government Official, Ntcheu The above highlights a sense of GoM wanting to control the delivery and content. The consultants saw that there is as strong sense by GoM staff that GoM has to take the lead in all development activity, even if they lack the capacity and resources to fulfil that. The consultant’s view is that this issue of expecting/wanting to control things, is deep seated in GoM thinking and a legacy of the more command and control model from the Banda era, while not taking a more pluralistic and realistic approach that GoM does not have the capacity to control everything anymore. The issue of wanting greater involvement is interpreted by the consultant to also be about resources for GoM staff to join training. There is no restriction by MSIKA on GoM staff joining training, and the evidence points to them being invited, but the staff are not willing or able to join without being given allowances and transport. On a final note, MSIKA requires Lead Farmers and extension staff that deliver the training provide details of the training for MSIKA’s records. This provides the data for MSIKA reporting. It appears that not all trainings are being reported on, so the MSIKA database is likely to understate the number of producers trained. Overall, the ToTs appear to have relevant content based on a sound and participatory process of development. It is necessary to train GoM district, as there would be repercussions if the training ignored them, but MSIKA’s expectations were too high for what these GoM extension staff were going to be able and willing to do in terms of onward training. MSIKA should have focused more on lead farmers, and to have planned for separate training from the outset.

3.5.3 Malawi Bureau of Standards MSIKA worked closely with the Malawi Bureau of Standards (MBS) on the development of product standards. MBS has a set of standards concerning aspects of food processing, such as MBS21 concerning food hygiene. However, there are gaps for products made from the target crops, of which nine products were identified. This resulted in MBS putting forward a proposal to MSIKA. There is a well-defined process for introducing a new/adapted standard that involves initial research to see if there is a standard that can be adopted or adapted. If there is no international standard, then there is a defined process to develop a national standard. The process involves examining other standards that might provide some base information or that can be drawn upon for the new standard. This research/ formulation is followed by a formal process of consultation through a technical committee consisting of processors, research institutions, associations, the Ministry of Health, the City Councils (who may need to enact by-laws) and academic. After there is agreement on the draft standard, MBS circulates the draft via standard to the World Trade Organisation and on to the international community. MSIKA has supported MBS to work on the nine product standards, plus one on garlic38. The process is currently waiting for an opportunity to present to the Board of MBS; however, as the Board is appointed by GoM, this has been delayed in the aftermath of the elections. The hope is that approval will be possible in the near future followed by Gazetting, which is in the hands of the Ministry of Justice. In summary, the work with MBS has moved quickly, but is now held up due to the hiatus of the election period. It is likely that this will be resolved, and that the process will move to fruition.

38 This was to respond to a request from MBS for this standard to piggyback the other standards.



3.5.4 MSU/Malawi University MSIKA has been supporting work by MSU and a Malawi University on soil fertility through mounting soil health trials using different combinations of organic/inorganic fertilisers/composts for MSIKA’s target crops. The aim is to determine what physical, chemical and biological changes that these combinations result in, and the overall impact on plant health and yields. This information would then be incorporated into the training materials for the producers beneficiaries. The results are determined through field trials in the five districts for the seven crops. Alongside the research are demonstration plots for farmer field schools, at which extension staff and producers can see the results and learn about the research. This is a collaboration between MSU, as the contractor to MSIKA, and the Malawi University with two academics engaged in this work. The academics at the Malawi University set up the trials following a methodology agreed with MSU, and are monitoring the trials, collecting the necessary data for analysis, and sending data and samples to MSU for further analysis. The trials focus on testing different compost/fertilizer combinations, as well as trialling drip irrigation. The Malawi University academics report that the work is progressing, and the relationship with MSU is working well, but that they are restricted in the work by not having access to their own transport. Normally, they try to move with the MSIKA team where there is a visit from Lilongwe to the districts, but this is not ideal for monitoring. In terms of results, the academics’ preliminary conclusions are that the combination of organic with inorganic is positive for the biological measurement of the soil, with an increase nematode numbers and good crop yield. Getting the most out of this investment in research requires the effective use of the demonstrations sites for the farmers field schools. There is evidence of this, and MSIKA needs to leverage this to bring forward practices that can be readily adopted by many producers.



4 Conclusions and Recommendations This section draws conclusions and makes recommendations for the remainder of the MSIKA program with the aim to meet all the LoP targets.

4.1 Key Conclusions Across its many indicators, the MSIKA program is making progress towards many of its LoP targets. Farmer Based Organisations

MSIKA is working with 219 FBOs, at the mid-term point, which is above the LoP target of 210. Of these, MSIKA assesses that 85 are low performing, with the remainder being high or medium performing. There are likely to be considerable challenges to bring low performing FBOs to be medium or even high performing. MSIKA is likely to see a better return for its efforts if it were to focus on the better performing FBOs. The basic agri-production training is widely implemented through the FBOs, though with gaps in other aspects of agri-production, such as irrigation and climate smart agriculture, as well as in marketing (about half the FBOs trained), PHH and storage (about a quarter trained), finance (about half trained) and governance/leadership (a third to half trained). MSIKA reports that its FBCs and specialists are very stretched to deliver this training and their other work. Not all modules appear relevant, such as training in international standards, and not all have been implemented across many FBOs, such as irrigation. MSIKA should prioritise the most relevant modules and target FBOs that show most potential to develop and change, rather than provide all training modules to all FBOs. From the FGDs, very few FBOs have potential to establish storage facilities and processing, and capability to manage those successfully, so activities in this area should be de-prioritized. MSIKA should focus on increasing collective selling and aggregation, as that would give members a stronger incentive to engage in their FBOs and is more realistic to achieve. FBOs are a useful and efficient mechanism to organize producers to receive training, but they have limited potential due to their small size, limited resources and limited capacity to evolve into sustainable entities in the course of a program of this length. Beneficiary producers

The MSIKA team has accelerated key elements of the programme, notably the training of beneficiaries in improved agricultural ‘growing’ and PHH practices. This is seen in the large increase in the producer beneficiary population as at 30th September 2019 (36,920) compared to the mid-term population of 8,468 at 31st December 2018. A total of 56.7% of mid-term respondents knew five practices, which is progress over the most recent semi-annual result of 39.0%, but still leaves a long way to get to the 95% LoP target. The target was set based on a baseline of 80.5%, but the consultants have determined that the baseline result was based on prompted not unprompted knowledge. On that basis, the LoP target has been incorrectly set and needs revising. The application of improved growing practices is generally high. Importantly, the ‘first use’ of new practices was found to be high, suggesting that there will be further increases in results as these are applied across all growing cycles and in future years. MSIKA should aim for high adoption of multiple practices, as uptake of these practices is a stepping-stone to significant positive changes in yields, losses and sales. In relation to yields, this is a comparative result against a demanding aggregate LoP target of an increase over the baseline of 75%. As noted in the methodology, the consultants faced major challenges when the first comparison found a substantial fall



in yields compared to the baseline. Even after assessing and allowing for the impact of Cyclone Idai, the consultants found that the mid-term yields were still a lot lower. This led to a re-examination of the baseline data and the method for calculation, once the consultants accessed the baseline Stata files. Further analysis led to a recalculation of baseline values through removal of yield outliers. In the consultants’ view, the baseline needs to be re-set to the values indicated in the report. The comparison of those not affected by Cyclone Idai with the revised baseline shows reasonable progress by MSIKA on yields. There were yield gains for the three most important crops of tomato, onion and potato. MSIKA will need to accelerate that progress if it is to hit the LoP yield target of 75%. MSIKA should strengthen the initial training to focus on the most relevant practices and emphasize refresher training. MSIKA should also ensure the effectiveness of, and access to, extension support through well-trained lead farmers. The consultants found relatively low beneficiary unprompted knowledge, yet high application rates, for improved PHH practices for field crops. Uptake of PHH practices was lower for tree crops than field crops. It will be important for MSIKA to ensure that the application of more PHH practices occurs across all growing/harvest/cycles. For both the agricultural and PHH training, the mid-term is a good point to review the curriculum against an assessment of why some practices are not being taken up. Some are beyond producer resources, such as motorised pumps, while others may depend on inputs that are not readily available, such as seed for chili. This is an opportunity for the MSIKA team to review the curricula for all its training to see what needs more emphasis, what needs to be communicated more clearly, and what can/should be dropped. More content is not necessarily better; rather it can be better to focus on fewer things that beneficiary producers will get most benefit from and that they can apply within their resources. Disaggregating results by crop highlights some important points that can guide Venture37 in its direction/focus for MSIKA. In terms of number of beneficiaries, production, land area where practices were applied, etc.39 tomato and potato represent around 80% of MSIKA’s results, with onion representing around a further 10%. Tree crops and chili contribute limited results (collectively around 10%), and the performance of these is generally poorer than the three most common field crops on knowledge, use, yields and losses. Tree crops are not seen by most producers as crops to actively manage and invest in. Rather they are seen as yielding some produce and some income without investing money or effort. This view of tree crops is deeply rooted in producers. MSIKA has been promoting chili, but progress has been slow, with limited up-take and mixed results. The evidence points to ceasing work on the tree crops and chili to focus on the three main field crops, especially if resources are constrained. These three field crops are more prevalent in specific EPAs, which could become priority areas limited, while areas that are more dominated by tree crops and chili could be deprioritised. Sales volumes and value are important outcomes for MSIKA. The mid-term evaluation found that production was affected by Cyclone Idai and by heavy rainfall in the most recent growing cycle. There were also lower unit prices compared to the baseline that lowered crop sales values.40 Adjustments should be made to enable a fair comparison to be made. After adjustments, there is progress in sales volume and values.

39 And as seen in sales later on. 40 Although Idai and wet conditions would normally reduce prices, there are other factors that affect prices, so it is not a given that prices should be higher compared to the baseline. The consultants also noted that there are price outliers in the baseline that need to be removed.



MFIs/Banks/VSLs

MSIKA now has a functioning mechanism for SME lending, though the loans have been quite limited in number and size. The bank thinks that the process is making headway and it appears satisfied with the model. The bank has added a new staff member to the team, suggesting it sees this as worth additional investment. MSIKA’s work with the MFI to increase agricultural lending has been very positive, with both parties wanting to expand the initiative. The approach is low cost for MSIKA and proving to be very effective. The MFI has been encouraged to continue and expand by the high repayment rates from well-organised and trained MSIKA beneficiary producers. The MFI is a well-established MFI and will likely continue to offer these loans to these and other MSIKA beneficiary producers after the end of MSIKA, which is a positive result. MSIKA’s work to establish VSLs for FBO members provides improved access to finance for over 10,000 beneficiary producers. VSLs are self-sustaining community managed mechanisms with a high degree of continuity. MSIKA should continue to establish VSLs and train members in VSL. Processors

MSIKA, including through a contract with TNS, has established relationships with 18 processors. This has involved improving the application of GMPs/hygiene standards, financial management, storage, processing, access to finance and access to markets. There is uptake of GMPs and hygiene improvements, increases in storage, and improvements in processing with indications that further progress will be made. However, the outcomes of this work are relatively limited, particularly for sales agreements and additional employment. While development of processing in these value-chains is important, there is not yet a strong connection between the processors MSIKA is working with and the producers and FBOs. Eleven of the 18 processors are micro-enterprises with very limited potential to increase investment, purchases, storage/warehousing and employment. MSIKA’s efficiency and effectiveness will be limited if it invests much time and resource in these micro-enterprises. MSIKA and would be better focusing on the five processors that have shown willingness and capability to make progress. Government

MSIKA has engaged GoM on policy, training of extension staff, work with MBS and research on crop practices/farmer field schools. Updating the Ministry of Agriculture’s Horticulture Policy, moved quickly in the early stages due to the support from MSIKA, but has slowed because Ministry lacks the resources for meetings and stakeholder consultations, as well as due to the disruption from the election period. MSIKA needs to continue the engagement, but there is a risk that progress will be slow and not reach fruition as it requires resources. The ToTs for GoM’s district-based extension officers have relevant content based on a sound participatory process of development. It is necessary to train GoM district extension staff, as there could be difficulties operating if the training ignored them. However, MSIKA’s expectations were too high for the onward training of producers that these GoM extension staff were going to be able and willing to. MSIKA should focus more on training lead farmers. The work with MBS to develop 10 new product standards moved quickly, but is now held up due to the hiatus of the election period. It is likely that this will be resolved, and that the process will move to completion.



MSIKA’s engagement with MSU and the Malawi University on research is linked to the farmer field schools at the demonstration sites. Getting the most out of this investment in research requires maximising the use of the demonstrations sites for the farmers field schools. There is evidence of this happening. MSIKA needs to leverage this research to bring forward practices that can be readily adopted by many producers. Opportunity for refocusing

A mid-term is a good point to reflect on what activities have worked well, what has not worked well and what the future focus should be. It is possible to follow a strategy to focus on improving things that are not working so well; however, such a strategy assumes that more effort/resource will overcome the underlying reason(s) why these things are not working well. These reasons are likely to be complex and may not be amenable to change, such as producers taking a commercial attitude to tree crops. The alternative strategy is to re-focus resources and effort where there is progress or potential for progress, with the aim to accelerate this. As the past resource and efforts in these areas has already yielded results already, this is likely to be a better strategy to deliver increased results from MSIKA. Focusing does mean moving resources and effort away from areas that are not showing any or sufficient progress.

4.2 Key Recommendations The following recommendations are made: Methodological 1. It is recommended that the timing and period of coverage of future evaluations is aligned with project semi-annual periods, so that mid-term data can be part of the cycle of semi-annual reporting. The choice of the mid-term cut-off date inadvertently created a challenge with integrating data with MSIKA’s semi-annual reporting cycle, both in terms of the population trained (to 31st December 2018) and the cropping period covered, being July 2018 to June 2019. In many ways this was the best choice at the time, as there would have been different issues with using 31st March 2019, because many beneficiary producers would not have had time to implement training. Alignment with semi-annual data collection would enable better integration of results into the indicator reporting. 2. It is recommended that Stata/.do files are provided with databases to enable any party to be able to re-create results and clarify the methodology. This mid-term evaluation faced challenges as the first mid-term results on yields, losses and sales were very different to the baseline results. This put the focus on reviewing the baseline, for which the necessary information for reviewing was obtained from the baseline consultants. Even with the .do files, the mid-term consultants still had to review the data, set new parameters, such as defining yield maximum based on FAO levels, and re-run the data for baseline and mid-term. We note that Venture37 specifies that it should receive the Stata files for the MTE, which is the appropriate approach. 3. The baseline and LoP targets need revising to address several corrections in the baseline data, and based on which the LoP targets were set. The baseline did not ask for unprompted knowledge, so targets relating to this need reducing. The baseline values for yields, losses and sales (volume and value) have outliers that need removing. 4. Venture37 and USDA should revise the indicators to reflect the current and revised activities, and reduce their overall number. Several indicators are redundant as the activities are not being pursued (#16 # of research initiatives), incorrectly worded such that they cannot be measured (#19 practices applied by organisations), overlap to a great degree (#5 and #6 on soil fertility), have too low



thresholds to be useful measures (#7 and #8 apply new agricultural and farm management practices), report crop data in aggregate across crops which is not a sound approach (#1 yields and #26 PHH), have incorrect calculations (#3 indirect beneficiaries), are very difficult to measure accurately (#26 PHH), target activities that are unrealistic to pursue (#31, percent of FBOs aware of international production and handling standards) and would be better as numerical rather than percentage targets (#25, percent of processors getting certification). Overall, there are too many indicators and disaggregates (124 in total), which can distract the team from focusing on the more important ones. A reduction would improve MSIKA’s focus. 5. MSIKA should focus on the three most commonly grown field crops among its beneficiary producers and cease or limit its activities on tree crops and chili. The three most common crops account for around 80% of MSIKA’s beneficiaries, land area, volumes produced and sales. MSIKA’s producers have more knowledge about and have applied more practices for these three crops. Tree crops are not seen as crops to invest time or resources on, but to harvest without investing. Chili has encountered difficulties and many chili producers have not seen gains in yields due to a range of challenges. 6. MSIKA should focus on fewer FBOs, specifically the high and medium performing FBOs and reduce the range of modules they are trained in to be the most relevant ones. MSIKA’s own assessment is that there are 85 low performing FBO. It is unlikely that these can readily become medium or high performing, as there are deeper challenges, usually around the quality of leadership and governance that are not solved by training. While FBOs can be a useful conduit for training producers, many FBOs will struggle to become well-functioning sustainable entities. MSIKA should reduce the number of modules, as there are considerable gaps in training of FBOs. Less relevant modules like international standards and cooperative development should be dropped, and focus moved to those most relevant to the members’ livelihoods, such as agriculture, PHH, marketing and finance. 7. MSIKA should review its training curricula to determine which agricultural, PHH and farm management practices could be taken up by field crop producers and it should prioritise refresher training over training new producers. MSIKA’s curricula for training covers a very large number of practices, some of which are now widely implemented (composting, ridging, etc.), and some that very few apply (fish soup for pests and testing acidity). MSIKA should focus its training on those practices that fall between these two extremes, to increase adoption of those that are capable of being applied but that are not applied by most producers. In addition, MSIKA should focus on refresher training than on new groups of producers, to consolidate and deepen learning and promote uptake among those that already know the basic practices. 8. MSIKA should undertake analysis and research to determine why women have lower yields than men. Lower knowledge and uptake of practices by women is part of the problem, but does not sufficiently explain the differences. This is part of a wider pattern of under-performance of female producers in Malawi. 9. MSIKA should consolidate its work with processors by focusing on those that are most responsive to its inputs and that have potential to enable MSIKA to meet its processor-related targets of certification, buying from producers, investment and employment. 13 of MSIKA’s 18 processors are micro or small enterprises with very limited resources to invest and capacity to implement change. MSIKA will get more return from the five medium and large enterprises that have been willing to implement changes to date. 10. MSIKA should continue to promote VSL as an efficient, effective and sustainable way to deliver improved access to savings and loans. The uptake of



VSLs established by MSIKA has been good (38.7% of producers) with almost a third of producers (32.4%) taking a loan through a MSIKA VSL. Having more producers in VSL groups will increase access to savings and credit. 11. MSIKA should continue to work with the MFI to increase the number of groups/ producers that are organised and trained in order to access agricultural loans. The initial results of 996 loans with 98% repayment strongly suggests there is scope for increased uptake. As a well-established MFI, the MFIis likely to continue post-program end. 12. MSIKA should continue to work with the bank to enable more SMEs to access loans. The initial uptake of seven loans for $93,000 has been modest, which is a function of caution exercised by the bank. The bank says it will give larger loans to most of the initial borrowers, and there is a pipeline of around 30 SMEs that could be offered loans. 13. MSIKA should focus its training of trainers on lead farmers rather than on GoM extension staff. GoM extension staff have struggled to conduct onward training in agricultural practices, due to their requirement for resources in order to conduct training along with other motivational challenges. In contrast, producers more commonly cite Lead Farmers as a source of knowledge. Lead Farmers are more likely to deliver the training and they have the advantage of being an in-community resource available to producers and can demonstrate practices on their own land. Kadale Consultants Ltd., Plot 244, Bwaila Road, Area 15, Lilongwe, MALAWI. [email protected] Tel ++ 265 (0)1 770000



Land O’Lakes Mid-Term Evaluation Page 82 Household Survey Tool [Training Version] 24th July 2019

Annex 1: Indicator Table # Performance

Indicator Impact Baseline MTE value/% Target LoP LoP Actual

1

Percentage change in yield of beneficiary producers as a result of USDA assistance

All- original baseline per report (believed to be incorrect) Tomato: 2,817 kg/ha Onion: 7,091 kg/ha Potato: 13,667 kg/ha Mango: 15,920 kg/ha Citrus: 15,678 kg/ha Guava: 14,654 kg/ha Chili: 467 kg/ha

All – MTE not affected by Idai Overall: (-6.5%) Tomato 3,054 kg/ha +8.7% Onion 4,289 kg/ha (-39.1%) Potato 3,980 kg/ha (-70.9%) Mango 3,511 kg/ha (-45.5%) Guava 5,553 kg/ha (-6.3%) Citrus 7,181 kg/ha +12.9% Chili 362 kg/ha (-22.4%) 75.0%

All – MTE not affected by Idai Overall: (-6.5%) Tomato 3,054 kg/ha +8.7% Onion 4,289 (-39.1%) Potato 3,980 kg/ha (-70.9%) Mango 3,511 kg/ha (-45.5 %) Guava 5,553 kg/ha (-6.3%) Citrus 7,181 kg/ha +12.9% Chili 362 kg/ha (-22.4%)

All – original baseline per database Tomato: 2,810 kg/ha Onion: 7,045 kg/ha Potato: 13,667 kg/ha Mango: 6,442 kg/ha Citrus: 6,360 kg/ha Guava: 5,927 kg/ha Chili: 467 kg/ha

All – baseline ex outliers Tomato: 2,617 kg/ha Onion: 3,575 kg/ha Potato: 3,329 kg/ha Mango: 4,347 kg/ha Guava: 3,796 kg/ha Citrus: 5,517 kg/ha Chili: 467 kg/ha

All – MTE not affected by Idai Overall: +3.5% Tomato 3.054 kg/ha +16.7% Onion 4,289 kg/ha +20% Potato 3,980 kg/ha +19.6% Mango 3,511 kg/ha (-19.2%) Guava 5,553 kg/ha +46.3% Citrus 7,181 kg/ha +30.2% Chili 362 kg/ha (-22.5%)

All – MTE not affected by Idai Overall: +3.5% Tomato 3,054 kg/ha +16.7% Onion 4,289 kg/ha +20% Potato 3,980 kg/ha +19.6% Mango 3,511 kg/ha (-19.2%) Guava 5,553 kg/ha +46.3% Citrus 7,181 kg/ha +30.2% Chili 362 kg/ha (-22.5%)

Percentage change in yield of beneficiary producers as a result of USDA assistance - Male

Not Provided Overall: (-5.9%) Tomato 4,007 kg/ha +9,2% Onion 5,722 kg/ha (-31.0%) Potato 4,995 kg/ha (-72.7%) Mango 3,864 kg/ha (34.5%) Guava 5,578 kg/ha (-15.3%) Citrus 3,106 kg/ha (-37.2%) Chili 522 kg/ha +22.7%

80.0%

Overall: (-5.9%) Tomato 4,007 kg/ha +9,2% Onion 5,722 kg/ha (-31.0%) Potato 4,995 kg/ha (-72.7%) Mango 3,864 kg/ha (34.5%) Guava 5,578 kg/ha (-15.3%) Citrus 3,106 kg/ha (-37.2%) Chili 522 kg/ha +22.7%

Male – original Baseline (Consultant calculated) Tomato 3,668 kg/ha Onion 8,293 kg/ha Potato 18,271 kg/ha Mango 5,901 kg/ha Citrus 4,948 kg/ha Guava 6,589 kg/ha Chili 426 kg/ha

Male – baseline ex outliers Tomato: 3,305 kg/ha Onion 4,358 kg/ha Potato 3,946 kg/ha Mango 4,017 kg/ha Guava 3,363 kg/ha Citrus 4,947 kg/ha Chili 426 kg/ha

Overall +6.4% Tomato 4,007 kg/ha +21.2 % Onion 5,722 kg/ha +31.3% Potato 4,995 kg/ha +26.0% Mango 3,864 kg/ha (-3.8%) Guava 5,578 kg/ha +65.8% Citrus 3,106 kg/ha (-37.2%) Chili 522 kg/ha +22.5%

Overall +6.4% Tomato 4,007 kg/ha +21.2 % Onion 5,722 kg/ha +31.3% Potato 4,995 kg/ha +26.0% Mango 3,864 kg/ha (-3.8%) Guava 5,578 kg/ha +65.8% Citrus 3,106 kg/ha (-37.2%) Chili 522 kg/ha +22.5%

Percentage change in yield of beneficiary producers as a result of USDA assistance - Female

Not Provided Overall (-3.1%) Tomato 2,466 kg/ha +32.7% Onion 2,611 kg/ha (-43.1%) Potato 3,985 kg/ha (-54.2%) Mango 3,306 kg/ha (-52.6%) Guava 5,531 kg/ha +11.1% Citrus 12,614 kg/ha +63.1% Chili 270 (-48.4%)

70.0%

Overall (-3.1%) Tomato 2,466 kg/ha +32.7% Onion 2,611 kg/ha (-43.1%) Potato 3,985 kg/ha (-54.2%) Mango 3,306 kg/ha (-52.6%) Guava 5,531 kg/ha +11.1% Citrus 12,614 kg/ha +63.1% Chili 270 (-48.4%)

Female – original baseline Tomato 1,858 kg/ha Onion 4,587 kg/ha Potato 8,699 kg/ha Mango 6,969 kg/ha Guava 4,980 kg/ha Citrus 7,732 kg/ha Chili 523 kg/ha

Female – baseline ex outliers Tomato 1,858 kg/ha Onion 2,061 kg/ha Potato 2,684 kg/ha Mango 4,660 kg/ha Guava 4,338 kg/ha Citrus 6,086 kg/ha Chili 523 kg/ha

Overall +6.1% Tomato 2,466 kg/ha +32.7% Onion 2,611 kg/ha +26.7% Potato 3,985 kg/ha +48.5% Mango 3,306 kg/ha (-29.1%) Guava 5,531 kg/ha 26.0% Citrus 12,614 kg/ha +107.3% Chili 270 kg/ha (_48.4%)

Overall +6.1% Tomato 2,466 kg/ha +32.7% Onion 2,611 kg/ha +26.7% Potato 3,985 kg/ha +48.5% Mango 3,306 kg/ha (-29.1%) Guava 5,531 kg/ha 26.0% Citrus 12,614 kg/ha +107.3% Chili 270 kg/ha (48.4%)


# Performance Indicator Impact baseline MTE value/% Target LoP

LoP Actual

5

Percentage of producers utilizing improved soil fertility management practices 81.2% 99.2% 90% 99.2%

Percentage of producers utilizing improved soil fertility management practices – female 76.0% 98.7% 85% 98.7%

Percentage of producers utilizing improved soil fertility management practices – Male 86.0% 100.0% 95% 100.0%

6

Number of hectares of land under improved techniques or technologies as a result of USDA assistance

0

3,242 15,392 4,220

Number of hectares of land under improved techniques or technologies as a result of USDA assistance – New

2,559 15,392 3,537

Number of hectares of land under improved techniques or technologies as a result of USDA assistance - Continuing

660 0 660

Number of hectares of land under improved techniques or technologies as a result of USDA assistance – Crop Genetics

3,149 0 3,149

Number of hectares of land under improved techniques or technologies as a result of USDA assistance –

3,129 13,853 3,550

Number of hectares of land under improved techniques or technologies as a result of USDA assistance – Disease Management

3,184 13,853 3,631

Number of hectares of land under improved techniques or technologies as a result of USDA assistance – Soil-related fertility and conservation

3,149 11,544 4,099

Number of hectares of land under improved techniques or technologies as a result of USDA assistance – Irrigation

3,189 3,078 1,178

Number of hectares of land under improved techniques or technologies as a result of USDA assistance – Water management (non-irrigation based)

2,854 10,775 1,278

Number of hectares of land under improved techniques or technologies as a result of USDA assistance – Climate mitigation or adaption

3,154 10,775 868

Number of hectares of land under improved techniques or technologies as a result of USDA assistance – Other

3,199 0 587

Number of hectares of land under improved techniques or technologies as a result of USDA assistance – Total w/one or more improved techniques or technologies

3,242 15,392 15,577

7

Number of individuals who have applied new techniques or technologies as a result of USDA assistance

0

8,442 31,680 8,442

Number of individuals who have applied new techniques or technologies as a result of USDA assistance – Male

3,369 17,107 3,369

Number of individuals who have applied new techniques or technologies as a result of USDA assistance- Female

5,073 14,573 5,073

Number of individuals who have applied new techniques or technologies as a result of USDA assistance- New

6,711 31,680 6,7112

Number of individuals who have applied new techniques or technologies as a result of USDA assistance – Continuing

1,730 - 1,730

Number of individuals who have applied new techniques or technologies as a result of USDA assistance – Crop Genetics

8,258 - 8,258

Number of individuals who have applied new techniques or technologies as a result of USDA assistance – Pest Management

8,206 28,512 7,655

Number of individuals who have applied new techniques or technologies as a result of USDA assistance – Disease Management

8,350 8,744 8,350

Number of individuals who have applied new techniques or technologies as a result 8,258 23,760 8,442



LoP Actual

of USDA assistance – Soil-related fertility and conservation Number of individuals who have applied new techniques or technologies as a result of USDA assistance – Irrigation

6,082 6,336 8,363

Number of individuals who have applied new techniques or technologies as a result of USDA assistance- Water management (non-irrigation based)

7,485 22,176 8,363

Number of individuals who have applied new techniques or technologies as a result of USDA assistance – Climate mitigation or adaption

8,271 22,176 8,271

Number of individuals who have applied new techniques or technologies as a result of USDA assistance – Other

8,389 - 8,389

Number of individuals who have applied new techniques or technologies as a result of USDA assistance – Total w/one or more improved techniques or technologies

8,442 31,680 8,442

8

Number of individuals who have applied improved farm management practices (i.e. governance, administration, or financial management) as a result of USDA assistance

0

8,324 28,800 8,324

Number of individuals who have applied improved farm management practices (i.e. governance, administration, or financial management) as a result of USDA assistance – Male

3,290 15,550 3,290

Number of individuals who have applied improved farm management practices (i.e. governance, administration, or financial management) as a result of USDA assistance – Female

5,034 13,248 5,034

Number of individuals who have applied improved farm management practices (i.e. governance, administration, or financial management) as a result of USDA assistance – Producers

8,324 28,800 8,324

Number of individuals who have applied improved farm management practices (i.e. governance, administration, or financial management) as a result of USDA assistance – People in Firms

Not Measured

- -

Number of individuals who have applied improved farm management practices (i.e. governance, administration, or financial management) as a result of USDA assistance – People in Government

- -

Number of individuals who have applied improved farm management practices (i.e. governance, administration, or financial management) as a result of USDA assistance – People in Civil Society

- -

14

Percentage of producers who can recite five or more improved agricultural techniques and technologies

80.5% 56.7% 95.0% 56.7%

Percentage of producers who can recite five or more improved agricultural techniques and technologies – Female

76.0% 57.3% 92.0% 57.3%

Percentage of producers who can recite five or more improved agricultural techniques and technologies – Male

85.0% 55.6% 98.0% 55.6%

15

Percentage of agricultural producers in target region who can identify key characteristics of a well-managed farm

64.6% 100.0% 90.0% 100.0%

Percentage of agricultural producers in target region who can identify key characteristics of a well-managed farm – Female

63.0% 100.0% 88.0% 100.0%

Percentage of agricultural producers in target region who can identify key characteristics of a well-managed farm – Male

67.0% 100.0% 92.0% 100.0%



LoP Actual

18

Percentage of producers who have access to current market information through their cooperatives or producer associations

12.9% 14.9% 65.0% 15%

Percentage of producers who have access to current market information through their cooperatives or producer associations – Female

11.0% 14.1% 63.0% 14%

Percentage of producers who have access to current market information through their cooperatives or producer associations – Male

15.0% 16.0% 67.0% 16%

21 Volume of commodities (metric tons) sold by project beneficiaries

7,565 mT

54,063 61,702

Adjusted baseline Mean sales (mT/farmer) Overall: 0.98 Tomato 2.1 Onion 3.5 Potato 2.9 Mango 5.0 Citrus 7.5 Guava 3.2 Chili 0.5

Mean sales (mT/farmer) not affected by Idai Overall: 0.70 Tomato 2.6 Onion 3.7 Potato 3.1 Mango 2.8 Citrus 5.1 Guava 3.4 Chili 0.3

22

Number of jobs attributed to USDA assistance

0

2,150 10,740 2,544

Number of jobs attributed to USDA assistance – Female 747 4,940 881

Number of jobs attributed to USDA assistance – Male 1,403 5,800 1,663

23 Value of sales by project beneficiaries

Initial baseline value $2,122.36 per beneficiary Ex Outliers (weighted) Mean $200.99 per beneficiary

Total sales to date: $1,460,257 Mean $207.82 per beneficiary

16,681,943 $17,072,599

Baseline US $/ha Tomato 853 Onion 751 Potato 620 Mango 483 Citrus 1,494 Guava 428 Chili 483

MTE US $/ha Tomato 740 Onion 1,191 Potato 1,097 Mango 248 Citrus 1,104 Guava 471 Chili 637

n/a n/a

26

Percentage post-harvest losses for beneficiary producers

Mean across all crops 12.5% Tomato 13.5% Onion 8.5% Potato 7.9% Mango 22.1% Citrus 1.5% Guava 27.1% Chili 12.1%


14.0% 15.9%

Percentage post-harvest losses for beneficiary producers – Female



18.0% 17.3%

Percentage post-harvest losses for beneficiary producers – Male



10.0% 14.7%



LoP Actual

27

Total increase in installed storage capacity (dry or cold storage) as a result of USDA assistance

- 44,228 75,750 45,770

Total increase in installed storage capacity (dry or cold storage) as a result of USDA assistance – Dry

- 44,228 53,025 75,770

Total increase in installed storage capacity (dry or cold storage) as a result of USDA assistance – Cold

- - 22,725 -

Total increase in installed storage capacity (dry or cold storage) as a result of USDA assistance – Refurbished

- 33,171 45,450 33,947

Total increase in installed storage capacity (dry or cold storage) as a result of USDA assistance – New

- 11,057 30,300 11,829


Annex 2: Terms of Reference/Scope of Work Executive Summary This document contains the Terms of Reference (TOR) for conducting the midterm evaluation (MTE) of the five-year MSIKA Food for Progress Program, funded by the United States Department of Agriculture (USDA) and implemented by Land O’Lakes International Development (Land O’Lakes). The project is implemented in Mchinji, Dedza, Ntcheu, Lilongwe and Mangochi Districts of Malawi from October 1, 2018 – September 30, 2021. Land O’Lakes is updating this TOR for the midterm evaluation. This document includes background information on the MSIKA project, the desired methodology including objectives and illustrative questions, the timeframe for conducting these evaluations and a list of required deliverables. To maximize consistency in the evaluation approach and methodology, Land O’Lakes will hire the same evaluator to conduct the non-experimental initial baseline and midterm. The use of one team for the initial baseline and midterm will provide continuity in the evaluation process. The team will have a deeper understanding of the program and be able to build on each previous evaluation to strengthen subsequent evaluation designs, strategies and findings. A separate evaluation firm is contracted to carry out the quasi-experimental baseline and final evaluation. Background MSIKA is a five-year value chain development project that will reach 36,000 smallholder farmers, 210 farmer-based organizations (FBOs), and 24 processors in south central Malawi in the fruit and vegetable value chains. MSIKA will catalyze increased value addition and income for value chain actors by facilitating improved processing, increased crop productivity, improved post-harvest handling (PHH) and storage, expanded market linkages between farmers and processors, more efficient domestic trade, and increased potential exports of processed products in the long term. MSIKA interventions and market linkages will target generating a $18,681,943 million increase in value of sales by project participants and leveraging $1,150,000 in new public or private investment by 2021. MSIKA focuses on achieving the following objectives:

• Increase agricultural productivity in the fruit and vegetable sector by increasing the availability of improved inputs, improving infrastructure to support on-farm production, facilitating access to finance, and training farmers on improved agricultural techniques and technologies, as well as farm management.

• Expand trade of agricultural products in the fruit and vegetable sector by improving quality of postproduction agricultural products, training producers and processors on improved post-production processes, facilitating improved linkages between buyers and sellers, improving market and trade infrastructure, and facilitating improved management of buyer/seller groups.

Project Implementation MSIKA is implementing the following activities:

1. Facilitate improved agricultural productivity Land O’Lakes will provide tailored training, technical resources, and cutting-edge research to fruit and vegetable farmers to improve technical and business skills. Land O’Lakes will build capacity in tandem with, and transfer knowledge to, community leaders such as lead farmers and extension agents from the Ministry of Agriculture, Irrigation and Water Development (MoAIWD). Land O’Lakes will build capacity through Farmer Field Business Schools (FFBS) and demonstration plots (Yankho PlotTM) on topics such as orchard management, budding and grafting, nursery establishment, pruning, and weeding. Land O’Lakes will ensure that FFBS and Yankho PlotsTM build core climate smart agronomic and business skills of target producers.

Land O’Lakes will engage private sector partners to ensure increased utilization of irrigation technologies and increased access to improved inputs through micro distribution models such as village-based agents and micro-franchisees. Land O’Lakes will engage private sector partners to co-invest in Yankho Plots and to establish for-profit nursery enterprises. Land O’Lakes will introduce an ICT-based farm business planning


and performance tracking software.

2. Infrastructure: Post-harvest handling and storage Land O’Lakes will provide training and technical assistance, facilitate market linkages, and support access to finance to improve on-farm and off-farm post-harvest infrastructure and to increase the use of improved post-production processing and handling practices. Land O’Lakes will facilitate increased adoption of established standards by market actors, such as traders, processors, and producers.

Land O’Lakes will facilitate Farmer-Based Organizations (FBO) and private enterprises to develop low-cost cool storage, wholesale markets, and aggregation centers, and to utilize technology and practices such as refrigerated trucks, appropriately-sized profitable cold storage facilities, optimal packing technology, improved storage practices, and sorting, grading, and washing technology. Land O’Lakes will provide technical assistance to support these initiatives.

3. Training: Post-Harvest Processing Land O’Lakes will improve the efficiency and profitability of value-added manufacturing and promote the utilization of value-preserving practices and technologies throughout the fruit and vegetable value chains. Land O’Lakes will partner with TechnoServe and Partners in Food Solutions (TechnoServe/PFS) to provide targeted technical assistance to medium- and large-scale fruit and vegetable processors. Technical assistance will focus on processing line operational efficiency, improved packaging and labelling, modernization of physical equipment and plant facilities, and improved business and financial management practices. Land O’Lakes, through TechnoServe/PFS, will train fruit and vegetable processors on improved product quality standards and facilitate ISO and Hazard Analysis and Critical Control Points (HACCP) certification. Land O’Lakes will facilitate FBO access to improved post-harvest technology such as reusable crates, sorting and grading equipment, and washing stations. Land O’Lakes will also facilitate improved phytosanitary practices by agricultural enterprises and FBOs engaged in small-scale processing of fruits and vegetables into products such as jams, juices, and paste.

4. Capacity Building: Producer Groups/Cooperatives Land O’Lakes will apply cooperative development tools, including Land O’Lakes’s AgPrO products, to improve farm management and to increase the capacity of FBOs in agricultural production, post-harvest handling, and processing. AgPrO is a set of thirteen training modules, which serve as a guide for the step-by-step process of effective cooperative development. The guide covers everything from the first steps in cooperative establishment to profitability and growth. Land O’Lakes will link FBOs to business development service (BDS) providers and finance providers, and will collaborate with national-level producer associations and district-level FBOs to strengthen the delivery of member services and facilitate the transformation of FBOs into agricultural enterprises.

5. Market Access: Facilitate buyer-seller relationships Land O’Lakes will improve market access through facilitation of buyer-seller linkages by enabling increased access to market information and strengthening the capacity of key organizations in the trade sector. Specifically, Land O’Lakes will facilitate buyer-seller networking events, linkages to transporters and transport finance, the establishment and strengthening of equitable outgrower schemes between producers and processors stronger linkages to retail supermarkets for standards and specification trainings and procurement, promotion of village-based input sales agents, and targeted support for women- and youth-owned small and medium enterprises (SMEs) engaged in food processing.

6. Financial Services: Facilitate agricultural lending Land O’Lakes will stimulate increased agricultural lending by: establishing an Innovation, Investment, and Incentive Fund (I3 Fund); facilitating increased lending for climate smart irrigation; working with financial institutions to develop loan products appropriate for horticulture value-adding activities; facilitating the development of input credit relationships between processors and producers or FBOs; improving the financial literacy and credit-worthiness of FBOs and; facilitating the development of capital investment plans by fruit and vegetable processors for plant modernization and expansion, including improvement of business and financial management systems. Land O’Lakes will build the capacity of a local financial institution to conduct risk assessments and develop financial products for value addition in the fruit and vegetable sectors, such as working capital and trade finance for processing, aggregation, irrigation, export,


and transportation.

The I3 Fund is expected to consist of a Matching Grant Fund (MGF) and a Special Purpose Vehicle (SPV). The MGF will target FBOs and enterprises with the potential to unlock substantial value in the fruit or vegetable sector, but lack adequate credit history to qualify for loans from financial institutions. The SPV will make loans to financial institutions, SMEs, or both. Financial institutions that receive SPV loans will on-lend those funds to enterprises. The SPV will catalyze further investment in value-adding activities and trade, such as loans from the Overseas Private Investment Corporation (OPIC). Land O’Lakes will ensure that loans originating from the SPV, prior to the award’s date of completion, will be used for purposes such as increasing access to working capital for farmers and SMEs for input purchases, transport for space arbitrage of fruits and vegetables, market and trade infrastructure, processing equipment and equipment upgrades. After the award’s date of completion, Land O’Lakes-ID will ensure that loans originating from the SPV will be used to expand agricultural production and trade in African Least Developed Countries, as defined by the World Bank. For the life of the SPV, Land O’Lakes-ID will use principal and interest repaid to the SPV for continued lending and investment and for payments on the SPV’s loan from OPIC. The exact design and implementation will be shaped by the study and implantation plan undertaken in October 2016.

7. Facilitate Improved Enabling Environment Land O’Lakes will strengthen the enabling environment for production, value addition and processing of fruits and vegetables. Land O’Lakes will work with Michigan State University (MSU) to build the capacity of key government research and extension agencies, including MoAIWD, the Department of Agricultural Extension Services (DAES) and the Department of Agricultural Research and Technical Service (DARTS). Land O’Lakes, through MSU, will focus particularly on national research stations, which focuses on horticulture, through joint operational research. Land O’Lakes will ensure that MSU conducts research, in conjunction with DAES, DARTS, and private sector entities that target the needs of agricultural and commercial policymakers. Land O’Lakes, through MSU, will build the capacity of processors, traders, national associations, and FBOs to advocate for improved regulations and policies.

Land O’Lakes will work with TechnoServe/Partners in Food Solutions (TechnoServe/PFS) and the Malawi Bureau of Standards (MBS) to jointly establish and disseminate international quality standards. Land O’Lakes, through TechnoServe/PFS, will conduct a gap analysis and recommend concrete improvements to bring MBS’s standards, certification processes, and quality audit processes up to international levels. Land O’Lakes, through TechnoServe/PFS, will work with MBS to establish a one-year strategic action plan to address identified deficiencies with speed and efficiency, and will develop standards trainings for food processors.

Land O’Lakes, through MSU, will train trainers in government and industry responsible for food safety on improved processes, technologies, and practices. Land O’Lakes, in partnership with MSU, will support multi-stakeholder and public-private dialogues on issues of key importance to the agriculture production, processing and trade sector.

Evaluation Design 3.1 Purpose and Objective of the Midterm The purpose of midterm evaluation are to analyze and document the extent to which the program has achieved its goals and objectives and to explain any deviations from the plan. Specific objectives are listed below:

• Assess the relevance of the project strategy and approach as well as the validity of assumptions made during project design;

• Measure progress the project has made toward key results, including effectiveness and efficiency of interventions in achieving established targets;

• Assess operational aspects of the project, such as project management • Document lessons learned, challenges and unanticipated effects; • Identify enablers and constraints to progress (both internal and external factors) that have

supported or limited success of the project; • Assess sustainability efforts to date;


• Provide recommendations for necessary corrections to strengthen project performance, efficiency and sustainability; and

• Provide recommendations for areas of focus for the final evaluation, including reviewing and strengthening data collection systems and metrics in preparation for the final evaluation.

• Provide recommendations on the activities that could be discontinued without greatly affecting the impact of the project to account for a reduction in monetization proceeds.

3.2 Evaluation Questions The midterm evaluation will seek to answer the following key questions within the standard evaluation criteria:

Relevance • Does the results framework, assumptions, program design and project activities meet the needs of the participants and local conditions in the five target districts of Malawi?

• How aligned are the program strategy and activities with Government of Malawi (GoM) strategies and with USDA and USG development goals, objectives and strategies?

Effectiveness

• What internal and external factors have influenced the ability of the project to meet expected results and targets?

• To what extent are program targets and outcomes likely to be achieved by the end of the project?

Impact • What impacts are the project activities having on the program participants, both positive and negative, especially in relation to the expected results and strategic objectives?

o How has project training and access to finance affected the uptake of improved agricultural techniques, farm management practices, PHH, and use of improved infrastructure for farmers?

o How has the use of improved agricultural techniques and technologies and market linkages affected crop yield and farmer sales?

o How has the use of infrastructure for PHH affected post production losses and improved the quality of the product?

o How have producer group trainings affected the capacity of the groups?

o How has the project training and access to finance affected uptake of improved processing techniques, quality standards, and improved infrastructure for processors?

• What key successes should be replicated, or key improvements should be made to the implementation to maximize the results?

Efficiency • Were the resources and activities provided by the program carried out in a timely manner and with effective use of resources?

• How well has the project been managed and M&E data used to make programmatic decisions?

• How well does the project coordinate and complement existing value chain development projects to ensure efficient and effective use of resources and avoid duplication?

Sustainability • Which project activities and benefits are likely to be sustained or not, why?

• What evidence is there that the interventions are likely to scale up beyond the project life

• What activities could the project scale down or eliminate and not affect project impact to account for reduction in monetization proceeds?

3.3 Evaluation Methodology The midterm evaluation will use a non-experimental design, incorporating both primary quantitative and qualitative data collection, supplemented by project monitoring data. The midterm will collect information from registered participants, including farmers, FBOs and processors, to compare key metrics over time. A


separate contract was developed for a quasi-experimental design baseline and final evaluation to be conducted once participants are identified to allow for a rigorous evaluation approach with a comparison group. The midterm will remain non-experimental to allow for multiple baselines in the budget allotted.

The below text details how data will be collected from the key program participants and stakeholders to answer the key evaluation questions. A summary of the data collection methods can be found in the table below. The current sample frame is 170 FBOs, 15 processors, and about 26,000 farmers.

STAKEHOLDER DATA COLLECTION METHOD Midterm

Participant Farmers Household Survey X

Focus Group Discussions X

Farmer-based Organizations Financial Data X

Key Informant Interview X

Processors Financial Data X

Key Informant Interview X

Other Key Stakeholders Key Informant Interview X

Farmers: Quantitative data will be collected from participant farmers in each of the target value chains to assess their agricultural and post-production practices, crop yields, post-harvest losses, crop sales and use of finance. For each target value chain, the studies will collect information from a statistically relevant number of households that participate in that value chain at the 90 percent confidence level. The sample should be proportionally representative of the population of that value chain across the districts. Participant respondents will be selected randomly from the farmer participant list, the sample size proportionally spread across the target districts.

Qualitative data will also be collected from farmers in the targeted value chain through focus group discussions (FGD), in each of the districts. The FGDs will provide context to the quantitative data to describe why farmers are or are not changing their agricultural practices, successes and challenges in growing and selling their crops, participation in FBOs and feedback on how the project can be improved.

Farmer-based Organizations: The evaluation will utilize monitoring data collected at least every six months from each FBO. This data will include group agreements with input providers, processors and retailers, financial information on group sales and use of financing.

The evaluation will also collect quantitative and qualitative information from a purposive sample of project-supported FBOs through structured key informant interviews to verify the monitoring data, understand their relationships with input providers, processors, and retailers; the functioning of the group and value they provide to their members; their successes and challenges of working as a group, and solicit feedback on how the project could improve its activities. The evaluation will sample FBOs across the different districts and value chains that are 1) under performing; 2) of average performance; and 3) performing well, according to their monitoring data.

Processors: The evaluation will utilize monitoring data collected from processors at least every 6 months, including quantity of production and value of sales. The evaluation will also collect quantitative and qualitative information through structured key informant interviews with all processors to understand changes they have made in their processing practices, ways they are engaging with the FBOs, use of financial resources, successes and challenges in their current functioning, and suggestions for project improvement.

Other Key Stakeholders: The evaluation will conduct key informant interviews at the initial baseline and midterm with other key project stakeholders, including input supply distributers, trader/wholesalers, participating government staff, local leaders and program staff to understand how they have participated in the project, challenges and successes, and suggestions for improvement.


Evaluation Analysis

The quantitative data from farmers will be analyzed using pre-post comparison of key indicator between the impact baseline and the midterm evaluation. Since the impact baseline was conducted with project participants, we feel this will be a better comparison than the initial baseline values that were conducting with non-participants. The quantitative data from FBOs and processors will be compared over time and analyzed to understand profitability. Qualitative data from other stakeholders will be transcribed and analyzed by themes to provide context and explanation for the quantitative information.

Key Tasks The following activities will be carried out for the midterm: Review of Documents: Undertake review of the MSIKA program documents and other relevant documents that are available at the time, including, but not limited to, the following:

• Project agreement with USDA, including the MSIKA scope of work • The MSIKA Performance Management Plan • Semi-Annual farmer performance survey data • Semi-annual reports submitted by Land O’Lakes to USDA; • Initial and impact baseline report & data collection tools; • Any other program documents which will enable the evaluator to get acquainted with the project

progress including value chain and financial studies; • Relevant Government of Malawi reports and documents for background information and

establishing the socio-economic and political context in which MSIKA occurred.

Refinement of methodology and data collection tools: The evaluator, in close collaboration with the Land O’Lakes Global Monitoring, Evaluation, and Learning team, will do the following:

• Develop a finalized methodology to carry throughout the initial baseline and midterm, including a sampling frame, sampling technique and sample sizes for both quantitative and qualitative surveys. The sampling frame must use a minimum confidence level of 90 percent.

• Surveys should be comparable throughout the studies to ensure comparability of data over time, but additional questions should be added or tools created at MTE to explore participation in the project activities, and to ensure all evaluation questions, described above, are answered.

• Based upon a reading of the program documents, propose any additional topics or issues for analysis prior to conducting each evaluation.

Field Data Collection • Plan and coordinate the necessary logistics to collect the data in accordance with the selected

methodology. • Pre-test, edit, translate, finalize and reproduce the survey instruments. • Train and orient enumerators and data collection team. • Carry out the fieldwork using own transportation, including for household survey, focus group

discussion with farmers, and interviews with key informants such as farmer group committees, input services providers, district agriculture officers and Land O’Lakes program staff.

Data entry, analysis and reporting • Enter, clean, synthesize, analyze, and interpret data from both the quantitative surveys and the

qualitative protocols using approved statistical packages. • Prepare a draft evaluation report addressing the objectives and questions of this evaluation

outlined in this TOR and recommendations on the overall Land O’Lakes/FFPr MSIKA project for potential similar future project for review by Land O’Lakes staff and stakeholders.

• Develop a PowerPoint presentation of evaluation findings, present and submit to Land O’Lakes and stakeholders.

• Prepare a final evaluation report that includes revisions based on feedback on the draft report and presentation.


Timeframe The anticipate midterm evaluation timeline is provided below. The midterm evaluation is expected to commence May 27th, 2019.

Midterm Evaluation Activity Responsibility Due Date Review of relevant documents to prepare for inception meeting Evaluator May 27th – 30th, 2019 Inception meeting with Land O’Lakes to discuss protocol, methodology, sampling, tools and timeline

Evaluator and Land O’Lakes

May 30th, 2019

Develop an inception report and update data collection tools Evaluator May 30th – June 26th, 2019

Inception report and tools due to Land O’Lakes Evaluator June 26th, 2019

Land O’Lakes reviews report and tools and provides feedback, comments and suggestions to evaluator

Land O’Lakes June 27th – July 3rd, 2019

Prepare for field work, finalize tools based on Land O’Lakes feedback, test, refine and code instruments

Evaluator June 27th – July 12th, 2019

Enumerator training and data collection Evaluator July 15th – August 9th, 2019

Data cleaning, analysis and report writing Evaluator August 12th – September 18th, 2019

Draft Outline of midterm report for review Evaluator August 23rd, 2019 Draft midterm report and final cleaned data is submitted to Land O’Lakes

Evaluator September 18th, 2019

Land O’Lakes reviews draft final report and provides evaluator with comments and suggestions for revisions

Land O’Lakes September 19th – 25th, 2019

Presentation of evaluation findings to Land O’Lakes Evaluator Week of September 22nd, 2019 Finalize report based on Land O’Lakes feedback and prepare all deliverables

Evaluator September 26th – October 4th, 2019

All Final Deliverables Due (Final midterm report, clean data, and PPT presentation)

Evaluator October 4, 2019

Required Deliverables The deliverables under this assignment are listed in the table below.

Deliverable Due Date Description Inception Report June 26th, 2019 Report should describe the following:

i- Understanding of the project based on project documents and literature review

i- Finalized methodology including detailed sampling plan and field procedures

i- Quality control measures v- Communication protocol v- Finalized timeline (activities, responsible party, outputs, and timing)

vi- Draft Data collection tools

Outline of evaluation report

August 23rd, 2019

Submit a high-level outline of the report, including proposed sections for results and findings.

Draft midterm evaluation report

September 18, 2019

The report should be submitted in English addressing all the evaluation objectives and questions listed in the scope of work

Final Data Collection Tools

September 18, 2019

Electronic copies of all clean and final English-version of data collection tools

Final Cleaned Data September 18, 2019

Clean and final English versions of: - quantitative data sets in Microsoft-Excel and any other utilized

format (SPSS, STATA, etc) - qualitative transcripts, field and interview notes, complete list of key

informant interviews and FGDs in Microsoft-Word document PowerPoint Presentation

October 4, 2019 Presentation should include an abbreviated list of evaluation findings that can be presented to relevant internal and external stakeholders

Final version of the midterm evaluation report

October 4, 2019 Electronic copy of the midterm evaluation report should be submitted in English in both Microsoft- Word and PDF version. Report should include the following sections: a. Acknowledgements b. List of Acronyms and abbreviations c. Table of Contents d. Executive Summary (no longer than two pages) e. Background (Program description and purpose of baseline)


Deliverable Due Date Description f. Methodology and Implementation g. Results and Findings h. Recommendations i. Annex: Table of key program indicators with updated values in

comparison to baseline values for all required disaggregates in the PMP j. Annex: Scope of Work for the evaluation k. Annex: Inception Report for the evaluation l. Annex: Survey Instruments: questionnaire(s), survey(s), interview

protocol(s), focus group discussion protocol(s)

Relationship and Responsibilities The evaluator will be responsible for all tasks listed in this TOR with support from MSIKA staff members, assigned by the Chief of Party, to provide relevant documents and information and to locate participants and stakeholders. The evaluation team will communicate with the MSIKA Monitoring & Evaluation Specialist and the headquarters MEL Director, and keep them informed of their progress. Land O’Lakes staff and stakeholders will provide feedback on the inception report, data collection tools and draft report. During the evaluation, the evaluator may ask for additional advice or guidance from the above-mentioned Land O’Lakes staff, but the evaluator will conduct the evaluation with independence and impartiality. Land O’Lakes Global MEL team, based in the Land O’Lakes headquarter office, will give the final approval on all evaluation deliverables and will work closely with the Malawi MEL team to ensure the deliverables are reviewed and shared among relevant staff and stakeholders. Logistics The assignment will require travel to targeted program regions – Mchinji, Dedza, Ntcheu, Lilongwe, and Mangochi Districts. While MSIKA field staff in the districts will provide support and guidance to the evaluators around the geographical locations of the participants, all resources (e.g. interpreters, enumerators, transport, hotel reservations) should be arranged by the evaluator. Resulting Contract Land O’Lakes intends to issue a firm fixed-price contract for the midterm evaluation


Annex 3: Inception Report Introduction This inception report is the first stage of the Mid-Term Evaluation (MTE) process of the Malawi Strengthening Inclusive Markets for Agriculture (MSIKA) Program that is being implemented by Land O’Lakes (LOL) for the United States Department for Agriculture (USDA). This report provides LOL with our MTE implementation plan based on project documents from the MSIKA team, the inception discussion held on Tuesday 4th June 2019, and a follow up face to face meeting with the MSIKA team on 18-19th June 2019, further follow up meetings with the MSIKA team and email exchanges.

This report sets out how the MTE will be conducted, covering the consultants’ understanding of the program (section 2), an updated methodology including the sampling plan (section 3), quality control and logistics (section 4), communication protocol (section 5) and an updated work plan/timelines (section 6). The draft instruments are included in the annexes for the household survey (Annex 2), focus group discussions (FGDs) (Annex 3) and key informant interviews (KIIs) (Annex 4).

Understanding of the Program This section provides an update on our understanding of the MSIKA program.

From the Scope of Work (SoW) (Annex 1):

“MSIKA is a five-year value chain development project that will reach 36,000 smallholder farmers, 210 farmer-based organizations (FBOs), and 24 processors in south and central Malawi in the fruit and vegetable value chains. MSIKA will catalyze increased value addition and income for value chain actors by facilitating improved processing, increased crop productivity, improved post-harvest handling (PHH) and storage, expanded market linkages between farmers and processors, more efficient domestic trade, and increased potential exports of processed products in the long term. MSIKA interventions and market linkages will target generating a $18,681,943 million increase in value of sales by project participants, and leveraging $1,150,000 in new public or private investment by 2021.

MSIKA focuses on achieving the following objectives: • Increase agricultural productivity in the fruit and vegetable sector by increasing the availability of

improved inputs, improving infrastructure to support on-farm production, facilitating access to finance, and training farmers on improved agricultural techniques and technologies, as well as farm management.

• Expand trade of agricultural products in the fruit and vegetable sector by improving quality of post-production agricultural products, training producers and processors on improved post-production processes, facilitating improved linkages between buyers and sellers, improving market and trade infrastructure, and facilitating improved management of buyer/seller groups.”

MSIKA is implementing the following activities, which can be viewed as the components that constitute the program (see SoW in Annex 1 for more details):

1. Facilitate improved agricultural productivity 2. Infrastructure: Post-harvest handling and storage 3. Training: Post-harvest processing 4. Capacity building: Producer groups/cooperatives 5. Market access: Facilitate buyer-seller relationships 6. Financial Services: Facilitate SME (formerly agricultural) lending 7. Facilitate improved enabling environment

The MSIKA program seeks to achieve the following objectives:

1. “Increase agricultural productivity in the fruit and vegetable sector by increasing the availability of improved inputs, improving infrastructure to support on-farm production, facilitating access to finance, and training farmers on improved agricultural techniques and technologies, as well as farm management.

2. Expand trade of agricultural products in the fruit and vegetable sector by improving quality of post-production agricultural products, training producers and processors on improved post-production processes, facilitating improved linkages between buyers and sellers, improving market and trade infrastructure, and facilitating improved management of buyer/seller groups.”

The evaluation Team Leader and the Research Team received and reviewed the documents provided by MSIKA as an update on the MSIKA Program beyond the information in the SoWs.


Kadale has also drawn on its existing knowledge of MSIKA based on Kadale’s experience with the project baseline and the impact baseline, as well as our experience of working with previous Land O’ Lakes projects and smallholder focused projects in Malawi over the last twenty years.

Kadale’s original proposal made in 2016, was based on four districts, however, after the baseline, it was decided that MSIKA would be implemented across five districts: Dedza, Lilongwe, Mangochi, Mchinji and Ntcheu.

An important change in the MSIKA implementation over the baseline was that training focused on generic agricultural techniques and technologies applicable across the range of crops, rather than the crop specific ones that were presumed at the baseline. This implementation approach arguably better fits the reality of farming households that may be growing more than one of the target crops/trees, as well as other crops on which the generic techniques and technologies would be applied. MSIKA team report that they saw farmers moving from one crop to another in response to market demand so there was a need for training in production technologies that covered field and tree crops more generally. In addition, MSIKA’s priority was for this generic agricultural training, which has been followed more recently by additional training for farmers on harvest and post-harvest handling (PHH). At this point, at the outset of the MTE, MSIKA reports that all farmers in the database to 31st March provided to Kadale have been trained in the generic agricultural techniques and technologies, with some also trained in PHH.

The instruments and sample frame are based on the information that we have available applied to the instruments used in the impact baseline. These are presented separately in draft for input by the MSIKA and LOL teams prior to testing.

Methodology and Sampling From the SoW: “The contractor will conduct a mixed-methods evaluation with a non-experimental design, incorporating both primary quantitative and qualitative data collection, supplemented by project monitoring data. The mid-term will collect information from registered participants, including farmers, FBOs and processors, to compare key metrics over time…. The mid-term will remain non-experimental to allow for multiple baselines in the budget allotted.

“A summary of the data collection methods can be found in the table below. The current sample frame is 170 FBOs, 15 processors, and about 26,000 farmers, but the final sample frame will be determined in collaboration with the contractor.”

STAKEHOLDER DATA COLLECTION METHOD Midterm

Participant Farmers Household Survey X Focus Group Discussions X

Farmer-based Organizations Financial Data X Key Informant Interview X

Processors Financial Data X Key Informant Interview X

Other Key Stakeholders Key Informant Interview X Overall Approach The mid-term evaluation will adopt a mixed method approach using quantitative and qualitative methods to collect and report mid-term data on the output and outcome indicators for the program. A cross-sectional household survey will be conducted of qualifying farmer households that grow one or more of the target fruits or vegetables. Focus Group Discussions (FGDs) with farmers and Key Informant Interviews (KIIs) with FBOs, processors, project staff, and other stakeholders will be conducted to provide qualitative data to give additional insights.

LOL has specified that it wants 95% confidence overall and 90% confidence at value-chain level. This dual requirement makes the sampling more complex, as the final sample has to meet both criteria and the challenge is that there are relatively small numbers of guava, citrus and chili growers. Kadale’s view was originally that there would be a need for some purposive measures to ensure that the field team finds sufficient growers of these less common crops. However, this considerably complicates the sampling, so after multiple exchanges and discussion, LOL/MSIKA and Kadale agreed to aim for 90%, but not make it a requirement, and to drop the purposive element of EPA and village selection to target growers of particular crops.

To determine the sample frame for the farmers, processors and FBOs, the MSIKA team and Kadale discussed how long ago the participants would have to have engaged with the project to be included in the MTE. Kadale’s view was that the cut-off date should be such that value-chain farmers would have had time to implement the techniques and technologies for the survey to best capture the knowledge, use and outcome


of adopting these. A complicating factor is that the planting, growing and harvest times for crops differ and that the tree crops are long maturing, so only improvement and harvest techniques and technologies would have had any impact in the first 2.5 years of the project. A second complicating factor is that short-cycle tomato, onion, Irish potato and chili crops can be grown more than once in a year if there is access to dambo or irrigation water, compared to tree crops that are all annual.

Kadale’s initial suggestion was that a cut-off date around November 2018 would mean that farmers had complete cycles on which to report, including harvesting and sales, at the time of surveying. MSIKA team wanted to capture beneficiaries who had been trained in agricultural practices and others in PHH in the first quarter of 2019, particularly in January and February, so MSIKA proposed a 31st March 2019 cut-off date for inclusion of beneficiaries. MSIKA’s view was that these farmers would be able to report on growing, harvesting and sales for a survey conducted in July 2019 (as planned), when the short cycle crops would have been harvested and sold.

Kadale highlighted that a March 2019 cut-off date may result in lower reported adoption rate of farming practices and PHH practices, lower yields and sales as data might only be available for some farmers for the prior season and/or because harvest and sales were ongoing at the time of data collection.

The initial conclusion was that the 31st March 2019 cut-off date was accepted by Kadale, and where this cut-off affected results, Kadale would highlight this in the MTE report. This led to a slight delay in receiving the final cleaned database for beneficiaries up to 31st March 2019.

However, after submitting the draft inception report and following further discussions, the cut-off date for farmers who had completed their general agricultural training to be classed as beneficiaries, was set at 31st December 2018. It is expected that at least some of whom will also have received PHH training in the period after 1st January 2019, potentially providing some insights into PHH practices, while ensuring more farmers have had time to implement the new techniques and technologies learned. MSIKA team will therefore provide a revised database up to 31st December.

Farmer/Beneficiary Survey From the SoW: “The contractor will collect quantitative data from participant farmers in each of the target value chains to assess their agricultural and post-production practices, crop yields, post-harvest losses, crop sales and use of finance. For each target value chain, the studies will collect information from a statistically relevant number of households that participate in that value chain at the 90 percent41 confidence level. The sample should be proportionally representative of the population of that value chain across the districts. Participant respondents will be selected randomly from the farmer participant list, the sample size proportionally spread across the target districts.

The contractor will also collect qualitative data from farmers in the targeted value chain through focus group discussions (FGD), in each of the districts. The FGDs will provide context to the quantitative data to describe why farmers are or are not changing their agricultural practices, successes and challenges in growing and selling their crops, participation in FBOs and feedback on how the project can be improved.”

Farmer Beneficiary Survey and Sampling

Kadale proposes a sampling approach for the farmer beneficiary survey that should achieve the statistical rigor required at 95% for the overall sample, and seek a 90% confidence for each crop, within the available resources. To guarantee meeting both levels, the sample would need to be larger, but resources are constrained, as the original proposal was based on the former requirement only.

From the data available, that many farmers grow more than one of the seven target crops, so one interview could have information for multiple crops. This may of itself generate sufficient responses for each crop. Kadale will use a multi-stage cluster sampling strategy, using the project participant (‘beneficiary/farmer’) registration list. When reviewing the data base, there are many villages with relatively few beneficiaries. This will make it impractical to interview, as not all beneficiaries will be available on the day of interview, meaning that teams would have to spend more time moving between many villages. Based on a team of four enumerators and one supervisor, the enumerators would aim to do two interviews each in smaller villages, and three each in larger villages. As we need reserves, we propose the minimum number of beneficiaries for a village to be included at 10.

It is also the aim to get as many of the EPAs where there were beneficiaries into the sample. However, some EPAs have either none or only one village that had at least 10 beneficiaries. As the team has to plan for

41 The overall sample size will exceed the 95% confidence level for all horticulture, and aim for the 90% confidence level for each individual value chain. To compare some information, such as yield, at value chain level, 90% is set as the minimum level of confidence.


morning and afternoon interviews, going to an EPA with just one village could result in considerable travelling to get to another EPA for the other half of the day; so EPAs with one or no villages with at least 10 beneficiaries will be excluded.

The remaining villages will be given a random number and selected based on these. Within the selected villages, those with less than 15 beneficiaries will be allocated for two interviews per enumerator and those with 15 or over will be allocate for three interviews per enumerator, so the team can reach its required five interviews per day.

In the project and the impact baselines, there was a screening process that was based on producing one of the target crops, but also required a threshold/ minimum land area allocated to the target field crop(s) or a minimum number of trees for the target tree crop(s). It is possible that some of the trained farmers, when asked, may report that they grow on a smaller area of land or fewer trees than the original minima, possibly due to changes to their farming, or possibly original overstating to benefit from the program. After discussion with LOL/MSIKA, it was agreed that we should interview all farmers about the field crop irrespective of land size as these are market oriented crops, but not interview them about tree crops if they fell below minimum numbers, being less than seven for mango and less than four for guava and citrus as these trees are mostly not planted deliberately and not seen as a deliberately grown crop, so they are less likely to invest and improve in the trees if there are only a few of them.

From the cleaned database that MSIKA provided, the total number of beneficiary farmers that were trained as at 31st December 2019, was 8,468. As many of these farmers grow more than one of the target crops, the required confidence levels have been determined using Yamane’s sampling methodology.

The sample size calculation used the formula:

n = N*X / (X + N – 1), where X = Zα/22 *p*(1-p) / MOE2

Through this method, the minimum sample is calculated at 619 beneficiaries. The overall sample size has been increased to 630, to account for some responses that might be unusable/incomplete.

Based on the overall prevalence of each crop in the population, the required minimum sample per crop is:

Table 1: Minimum sample by crop to reach 90% confidence level

Crop Total Farmers per Crop Minimum Sample Required Tomato 5,648 259 Onion 2,819 247 Irish Potato 3,921 254 Chili 954 211 Mango 2,535 245 Guava 844 206 Citrus 271 136 Base 619

As noted earlier, it cannot be guaranteed that we will reach the minimum numbers for each of the crops, as our process involves random selection of interviewees.

The farmer beneficiary survey instrument is based on the impact baseline, as the aim is to compare progress against the baseline, so it has to remain close to the original. The impact baseline was based on crop specific practices that the MSIKA/LOL team provided to the research team. However, the MSIKA team has implemented training that is not crop specific; rather it is relevant for producing all of the seven field and tree crops and to be of value in all crop production. The MSIKA semi-annual questionnaire reflects this more generic agricultural training and fits the disaggregated indicators.

Therefore, the challenge for the MTE team in designing the instrument is to link back the stated practices, as actually taught to farmers, to the practices that were asked about in the impact baseline. There is clearly a high degree of overlap, but there are additional practices that farmers were trained in and some of the names and content have changed, such that it is not a perfect fit.

The MTE team has prepared a draft instrument that is based on the impact baseline instrument, but amended to use the wording of the practices that farmers were trained in. It also asks farmers to comment on each of the target crops they grow, rather than a general application of a new practices, so as to be comparable with the impact baseline which has this information per crop. It is important to specifically identify the actual practices the farmers know and used per crop, so that the MTE team can trace these back to the original lists in the impact baseline and determine what has changed.


Farmer Based Organisations From the SoW: “The contractor will utilize monitoring data collected at least every six months from each FBO. This data will include group agreements with input providers, processors and retailers, financial information on group sales and use of financing. The contractor will also collect quantitative and qualitative information from a purposive sample of project-supported FBOs through structured key informant interviews to verify the monitoring data, understand their relationships with input providers, processors, and retailers; the functioning of the group and value they provide to their members; their successes and challenges of working as a group, and solicit feedback on how the project could improve its activities. The evaluation will sample FBOs across the different districts and value chains that are 1) under performing; 2) of average performance; and 3) performing well, according to their monitoring data.”

To reach a statistically significant sample of the FBOs would be beyond the resources available, so the team will focus on a mixture of verification and qualitative data collection to enable insights to be generated. This requires that monitoring data provided by FBOs is checked, for the three categories of FBOs. This will be done through attempting to verify data for the FBOs that are interviewed. We are yet to determine which FBOs fall within each category, as that will be provided by MSIKA.

KIIs using a semi-structured tool will be conducted with FBOs that have been trained in multiple modules from the AgPrO manual, and that have been in the programme for at least 12 months, so that there has been time to implement and change. Per district, Kadale aims to interview one FBO that is under-performing, one of average performance and one that is performing well; according to their categorisation by LOL/MSIKA based on monitoring data. This makes it three per district and 15 for all the districts. The approach therefore blends reviewing the data that MSIKA has with verification of data with the selected FBOs, and obtaining qualitative insights during the KIIs.

Processors From the SoW: “The contractor will utilize monitoring data collected from processors at least every 6 months, including quantity of production and value of sales. The evaluation will also collect quantitative and qualitative information through structured key informant interviews with all processors to understand changes they have made in their processing practices, ways they are engaging with the FBOs, use of financial resources, successes and challenges in their current functioning, and suggestions for project improvement.”

Although the scope says interviews should be conducted with all processors, MSIKA has informed us that several of its processors have yet to start operations, and that it is in the process of working with them, so that it is too early to assess what has been done and what has changed. Kadale is also mindful to ensure that interviewing a lot of processors does not restrict the number of KIIs for other groups, by taking up too many KIIs leaving insufficient for other groups. Therefore, KIIs will be conducted with five processors selected by Kadale to give a range of perspectives.

Our view is that the locations of the processors is less important than getting diversity in size/scale of the operations and crops being processed. Some note will be taken of the inputs by MSIKA so that there is potential to get views on different types of intervention.

Other Key Stakeholders From the SoW: “The contractor will conduct KIIs at the initial baseline and mid-term with other key project stakeholders, including input supply distributers, trader/wholesalers, participating government staff, local leaders and program staff to understand how they have participated in the project, challenges and successes, and suggestions for improvement.”

Other key stakeholders for KIIs at district level will include government staff and one MSIKA supported agro-dealer. The focus for these KIIs will be the government DADOs and the Horticulture Specialists. The DADOs and Horticulture Specialists are important, because of their knowledge of the district, as well as the MSIKA program implementation. These two interviews per district gives a total of 10 KIIs. These district level KIIs will be conducted by the Kadale Research Team that will travel to each district during the data collection exercise and/or the Field Supervisors depending on logistic considerations.

As a courtesy, it is necessary to request to see the District Commissioner (DC) in each District, if they are available. We request a letter of introduction from MSIKA that explains Kadale’s assignment or a direct introduction by the MSIKA Field Staff.

In addition to these government level interviews, Kadale will also conduct interviews with other key stakeholders at national level that have been involved in MSIKA implementation. The national level other key stakeholder KIIs will be partners and collaborators, such as Technoserve/Partners in Food SolutionsI (TNS/PFS), Malawi Bureau of Standards (MBS), the micro finance institution (MFI) and the bank. The Malawi University (who have been working with Michigan State University (MSU)), an aggregator, and the Department of Agricultural Research Services (DARS).


Finally, Kadale plans to interview six Land O’Lakes staff at district and national levels to gather information and insights on the implementation and ideas for the future, as they have an in-depth knowledge of the MSIKA project.

Summary of KIIs In summary, the proposed KIIs are set out below.

Table 3: Proposed KIIs by category

Target Group KIIs Category Total KIIs

FBOs 3 per district Fruits and/or vegetables 15 Govt District Officials 2 per district As appropriate 10 Bank 1 As appropriate 1 MFI 1 As appropriate 1 DARS 1 As appropriate 1 Technoserve/PFS 1 As appropriate 1 Processors 5 Fruits and/or vegetables 5 Agro dealers 1 As appropriate 1 Malawi University 1 As appropriate 1 MBS 1 As appropriate 1 Aggregator 1 As appropriate 1 LOL staff 6 As appropriate 6 Total 44

These KIIs aim at understanding how they have participated in the program, the challenges and successes, and suggestions for improvement.

The KII instruments will be prepared as topic guides that will vary according to the role of the person to be interviewed. These will primarily be conducted by the Kadale Research Manager, starting with the DADOs in each District, as well as the Horticulture Specialist.

The instrument is sent under separate cover so that it is easier to edit/comment on.

USDA From the SoW: “Conduct an introductory phone call with USDA prior to conducting fieldwork for the MTE. If requested by USDA, conduct an in-person meeting or phone call with USDA after the draft report is completed to share key findings from the baseline and GA, MTE, and/or FE.”

We have confirmed with Land O’Lakes that it will put us in touch with USDA to have the discussion. This should take place by mid-July latest, but ideally in the first week of July if at all possible, before fieldwork commences. Focus Groups Additional qualitative data beyond the KIIs will be collected through 16 FGDs with participant farmers. The FGDs are intended to add insights from a range of perspectives, so the aim is to get the range of views, not to get a representative sample of beneficiaries, by district or value chain. Therefore, the structure seeks to have male and female respondents, respondents from each district and respondents from each value-chain.

FGDs are recorded for reference and play back later to get verbatim quotes as we write up the notes. Qualitative notes will not be verbatim notes of interviews, except for selected interesting quotes. Instead the notes will capture key themes and/or quotes which the research has identified as part of the FGD and/or KII. Kadale will prepare a matrix for capturing notes. Qualitative data will be triangulated with the farmer survey and KIIs to provide multi-sourced evidence for evaluation findings.


The FGDs are allocated as follows:

Table 4: Breakdown of FGDs by District and Crop

District Category Male Female Mixed FDG per crop FDG per District Dedza Irish

Potato 0 0 1 1

4 Onion 0 1 0 1 Guava 0 0 1 1 Tomato 1 0 0 1

Lilongwe Onion 1 0 0 1 2 Tomato 0 0 1 1 Mangochi Tomato 0 1 0 1

3 Citrus 0 0 1 1 Chili 1 0 0 1

Mchinji Mango 0 0 1 1

4 Chili 1 0 0 1 Citrus 0 0 1 1 Irish Potato 0 1 0 1

Ntcheu Mango 0 1 0 1

3 Irish Potato 1 0 0 1

Guava 0 1 0 1 Total 5 5 6 16 16

In summary, the breakdown is as follows:

Table 5: Summary by Category of FGD

Category Total Irish Potato 3 Tomato 3 Onion 2 Mango 2 Chili 2 Guava 2 Citrus 2 Total 16

The FGDs will use a topic guide with a menu of topics. These will contain a relatively long list of topics, some of which are mandatory, and some of which we will ask the facilitators to use for particular groups, as it will be too much for each FGD to discuss every topic. The facilitators will be encouraged to explore interesting comments that come out, as long as these are relevant to the evaluation. FGDs will be recorded using digital recorders to assist in recall of quotes and key points. The write-ups (in English) will be checked by the Team Leader, who will request clarifications and additional information/quotes if these are merited.

The instrument is sent under separate cover so that it is easier to edit/comment on.

Field Data Collection Kadale will use four data collection teams, each led by an experienced field supervisor, that will be trained at the Kadale offices in the survey instrument. The training will be for two days in the classroom, followed by one day field training to check that the enumerators are able to implement what they have learned. Training is planned to start on Wednesday 17th July and finish on Friday 19th July. Kadale will involve the MSIKA MEL and Technical team members in the training (see Quality section).

The allocation of the teams to Districts/EPAs/FBOs will depend on the final sample, as this is primarily a logistical task to ensure that the teams have a realistic workload and spend as little time as necessary on moving long distances. Kadale has planned many exercises of this nature before, so this should not present challenges.

The field teams will spend an estimated seven working days on data collection. Some teams may do more, others less depending on the final sample detail and logistical planning.


The field teams will focus mainly on the survey, but once Field Supervisors have made sure the team members are all working well, they will also undertake some KIIs and FGDs. It is expected that many of the FGDs will be conducted by one of the Research Managers, alongside the quality review work and conducting some of the district-level KIIs. National level KIIs will mainly be led by the Team Leader. In all of our work, we do have to take account of logistical issues, including if a key informant is not available when we have the window to interview them, which means we have to re-allocate the interview to another person or choose an alternative.

Field data collection will involve prior calling of the randomly selected respondents through the MSIKA Field Co-ordinators. Lists of the actual beneficiaries selected for interviewing will be provided by the Research managers to the Field Supervisors and the MSIKA Co-ordinators in advance, so that there is time for the latter to call the selected beneficiaries. The MSIKA Field Based Co-ordinators will advise on the best location to meet. Where possible, respondents will be called to come for morning or afternoon sessions, to reduce beneficiaries having to wait long.

Those who are substitutes, will be told that they might not be needed, but it will not take long to determine this, once we see who has turned up and they are verified as being the beneficiary and not a substitute decided by the beneficiary.

Analysis The Research Managers will lead on data analysis. This involves producing a data table for each question/sub-question, which is checked by, and discussed in the team and with the Team Leader. If there are unexpected or interesting results, these will be further analysed. This may involve different responses by different groups. Statistical significance will be checked. The data will be analysed against the impact baseline and set out against the indicators, so that it is clear what the findings were on each indicator on which data has been collected.

There will be large numbers of data tables, which can make it difficult for readers to find particular findings, to understand them and to see how they relate to the program indicators. Kadale will provide this summary of tables at the end of the MTE in Excel format for future reference for those that want to go into the data in more depth, as well as the final cleaned data set in SPSS and/or Excel.

Reporting Kadale will prepare a draft evaluation report addressing the objectives and questions of this evaluation outlined in this TOR and recommendations on the overall MSIKA project covering progress made in qualitative and quantitative terms against the indicators, key lessons from the implementation so far, and recommended changes that could be made to enable MSIKA to achieve its objectives by end of project. The report will follow the format that LOL has specified in the SoW.

In addition, Kadale will prepare a presentation of the draft report that will be made directly to the MSIKA team. This will be a good opportunity to review the key issues and findings, obtain additional information and clarifications, and discuss the recommendations made in the report so that these are refined or amended where appropriate, in line with Kadale maintaining its independence.

Based on feedback and questions, Kadale will address appropriate points and provide a final report within the agreed deadline (see workplan below).

Quality Control and Logistics Kadale adopts comprehensive quality control procedures to ensure data quality so that project implementers can make informed decisions based on quality evidence. Data quality will be ensured in the instrument design, piloting, training, incentives, data collection supervision and data entry protocols. The outcome of these is that we expect the analysis to be based on robust data.

Instrument design - We will seek input from LOL on the draft instruments to get clarifications/ corrections, and to align the instruments with the questions that need answering through the MTE. There are some challenges to align the instrument used in the impact baseline with the way the indicators have been revised and MSIKA is implemented, and the way semi-annual data has been collected. We have sought to ensure that questions in the MTE instruments can be related to the impact baseline where there is any conflict with the semi-annual tool.

Instrument piloting/testing – we will pilot test the draft instrument across two days in locations around Lilongwe that are implementing the MSIKA project, to determine if the questions are clear enough to get the expected responses, that the structure of the questionnaire flows as intended and to determine if there are unexpected responses that we have to cater for in the revised versions. In addition to the Field Supervisor, we will use at least one member of the team that has not been involved in preparing the instrument so that the testing more closely simulates using enumerators. During the piloting, we will amend and re-test questions on day two following any changes identified on day one, so that we have at least been able to see if the revised


questions work. If we find that further changes are needed, other than minor wording, then we will consider a further day of pilot testing.

Training enumerators – the training of enumerators is important to delivering quality. We will identify enumerators from our pool that we have used or that have been used by organisations we know and trust, prioritising those who were used by Kadale in MSIKA’s project baseline and impact baseline. We will call three more people for training than we intend to use, so that we can select the best and so we have a reserve if there are any problems with the selected individuals or any drop out, such as through illness. Our experience is that the enumerators generally have no major challenges with implementing the instruments, because they are designed to be clear and logical; however some individuals have attitudinal problems, such as turning up late, taking phone calls or not giving sufficient attention. In general our selection is based on attitude, and less commonly on a person’s limited capacity to deliver the work.

One aspect that we have found useful in getting enumerator to understand the instruments, is to go through the translated version of the tools as a team. This helps them to understand the particular meanings as they propose, discuss and agree on the best translation. This is particularly important where there are important nuances to get across to the team, for example what is meant by new agricultural ‘techniques’ and ‘technologies’. It also brings out ideas from the team on options that might have been missed and we encourage them to look for and identify logic flow errors in the instrument. Enumerators like to compete in the training to make suggestions that others have not seen. This all helps to further improve the instruments.

We have the translations of the technique and technology terms from MSIKA team; however, we also need to have an explanation of what the technique or technology involves, so that enumerators can relate what a farmer is saying to a particular technique or technology even if they do not use the name of it. To support this, we will need the MSIKA Technical Team to provide a short write up of what the techniques and technologies involve, to come to the training to explain them and to answer questions for the Field Supervisors and Enumerators.

Kadale uses tablet data collection, based on Open Data Kit (ODK). Using computer aided data collection requires enumerators to complete sections before they can move on and avoids errors in wrongly skipping sections, which improves questionnaire completeness. The data can also be reviewed each day to see if there are any patterns that suggest enumerators are filling it in themselves or making up data where it is missing. This review takes place centrally and the Research Team discuss issues that arise with the Field Supervisors each evening.

An additional part of the training is to do test interviews during training, where they practice in pairs or in bigger groups where they are observed interviewing and playing the role of respondents. This also brings out unanticipated responses and highlights the need to clarify points/questions/instructions. At the end of the training, the team will go to selected MSIKA sites near Lilongwe (that are not in the sample) to conduct test interviews in the field. By this point, we expect the enumerators to have understood the instrument, so this stage is to just ensure that we can observe them in operation and correct issues around how they conduct the interview (introductions, confidence, body language). It also enables the team to test how to identify and select respondents.

The team debrief following the field test and any final amendments are made. The final team is selected and provided with the final translated instruments.

Incentives – Kadale has a policy of paying a basic fee plus a quality bonus to enumerators. Our experience is that making a part of the payment dependent on quality provides an incentive to enumerators to do the job properly. We intend to contract enumerators with at least 25% of the payment linked to quality. Enumerators will be given clear warning if their quality is below the expected and that this will affect their bonus, if not corrected immediately. As they have seen us apply this in the past, we reasonably expect them to take what we say seriously.

Data collection – The role of the Field Supervisors for each of the teams is important for quality control. There will be extra training for the Field Supervisors in how to manage the team, and in their additional/different role compared to the enumerators. The Field Supervisor will accompany each enumerator in the field over the first 2-3 days to ensure that any issues are brought out and discussed with the enumerator and shared at the evening team meeting. This would normally represent around 5% of interviews observed with the enumerators to ensure that questions are asked and recorded properly, as designed. The Field Supervisor will back check a sample of households that have already been interviewed with a subset/extract of that are re-asked to ensure that the enumerators actually asked the questions and that the response is correctly recorded.

At the end of each day, Field Supervisors will check that all questionnaires have been completed by the enumerators to look for omissions or incorrect completion. This process will be easier given that data is collected on tablets where patterns and gaps can be identified. Each evening the team will debrief on


challenges faced, clarification on questions and responses given by respondents, and any other issues that arise. If there are quality issues, the Field Supervisor will talk to the individual concerned or to the whole team, if it affects all. The Field Supervisor will also speak to the Research Manager each day/evening to discuss any issues that need clarifying. If there is an issue that needs sharing with all the teams, the Research Manager will contact all the Field Supervisors, so that all are following the same approach.

At least one of the Kadale Research Managers will visit each team to observe the Field Supervisors and enumerators at work. They will also check completed questionnaires at random and communicate with all the teams over any issues that arise. They will check with each team daily on both progress and quality control issues.

Data entry – using tablet-based data collection saves on data entry time, errors and delays. It also enables the Research Managers to review the progress of the team and so be alerted to problems in the field that has led to slow collection that may not have been reported. Data will be exported daily to Excel and logic checks run by the Research Manager to ensure that collected and entered data makes sense. This will enable the Research Manager to identify if there are errors coming out. These are communicated with the Field Supervisors for immediate correction.

Overall supervision - The Research Managers report to the Kadale Team Leader daily over mail, phone and Skype during the training, fieldwork and data checking. The Team Leader will lead on the instrument design/review and will be involved in all decisions made by the team on piloting, sampling, training, fieldwork and data entry to ensure these are aligned with the MTE objectives and the quality standards required.

There will be a continued engagement with the MSIKA Monitoring, Evaluation and Learning (MEL) team to make sure that decisions made are consultative. If there are any technical, logistical or other issues encountered in the field, these will be reported to MSIKA-MEL for guidance.

We would be pleased to have members of the MSIKA-MEL and Technical Team to be integral at each stage of the design, testing, training and fieldwork. We would be happy if the MSIKA-MEL team can accompany our field teams to confirm that they are satisfied with the field work.

Communication Protocol It is our intention to keep the communication protocols simple. We will have considerable day to day interaction in the inception, design, testing and training stages between our two Research Managers and the MEL and Technical Teams at MSIKA. Where there is a question about a particular type of training, or a technology or an element of the programme, such as finance, then the communication will be direct to the relevant person to ensure rapid delivery of clear messages through direct discussion, while copying in the MSIKA-MEL team

Where there are bigger design issues, these will be channelled on our side via the Team Leader and picked up with the MSIKA-MEL team, copying in LOL MEL lead. If there are contract or financial issues, these will be addressed by the Kadale Team Leader with the Land O’Lakes HQ team.


Workplan Kadale understands the time constraints for this evaluation and is working with a firm end date fully in mind.

While there have been two working days’ delay in submitting the inception report, we still fully expect to meet the submission end dates per our workplan.

The updated workplan is set out below: Month: May June July August September Oct Week Commencing 27 3 10 17 24 1 8 15 22 29 5 12 19 26 2 9 16 23 30 7 14

Week Number 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Pre- Commencement

Contract Signed 28th Inception

Initial discussion/briefing

Documents made available by LOL

Document review & inception prep

Staff discussions Develop survey

method, sample & draft

Prepare and

submit inception report

26

Prepare detailed workplan and plan

logistics

Feedback from LOL on inception

report 5

Fieldwork preparations

Test instruments Refine

Instruments

Prepare sampling & logistics

planning

Mobilize/contract field & data team

Train enumerators and research

team

Undertake Beneficiary Surveys

Baseline Survey Undertake Stakeholder KIIs and FGDs

Conduct FGDs Conduct KIIs

Analysis and Report Writing

Data checking and cleaning

Data analysis Summary indicators 13

Draft Report & presentation 20

Feedback from LOL on draft

report 25

Final Report 30

Kadale Consultants (UK) Ltd. [email protected]



Annex 4: Research Instruments Household Instrument ID Module

Q# Question Response Logic* ID1 Enumerator Name ID2 Date of Interview ID3 Name of the FBO ID4 District Dedza

Lilongwe Mangochi Mchinji Ntcheu

ID5 EPA ID6 TA ID7 GVH ID8 Village

INTRODUCTION Hello, my name is [NAME] and I work for Kadale Consultants. Kadale is conducting a midterm evaluation for Land O’Lakes’ MSIKA program, which includes interviewing MSIKA beneficiary households in your community. Your household has been randomly selected to participate in this survey, which will ask questions about your household’s farming activities specifically in fruits and vegetable production and sales. The aim is to gather useful information that will be used to improve fruit and vegetable farming in this and other communities. The issues discussed in this interview will remain confidential and what you say will not be shared with anyone outside the research team. The survey will take about one-hour Taking part in this research is voluntary.

Consent Are you willing to participate in this survey? Consent Does not consent (Terminate interview and record the name and why they did not consent)

Screening Question

1. What is your name? (check that it is the selected person, not a person sent on their behalf) Yes No

2. Did you yourself or jointly with other family members grow onions, Irish potatoes, tomatoes, mangoes, oranges, tangerines and lemons, guava or chilies/Paprika/Red Cayenne in the past 12 months between July 2018 to June 2019? Yes No

3. Have you yourself received any training from the Land O’Lakes MSIKA project, including training by a LOL/MSIKA staff member or Land O’Lakes/MSIKA lead farmers, or at field days at Yankho plots or farmer field schools? Yes No

[If they are not the person listed, then ask if the person listed is available. If not, end the interview and tell the Field Supervisor. If they did not grow any of these crops in the last year (July 2018 to June 2019), then they may not be the person that is registered. Check to see if it is the person. If so, find out if they ever grew these. Note in what period they grew. If not in the last 12 months end the interview and tell the Field Supervisor. If they have not received any training or other services from Land O’Lakes (LOL)/MSIKA, end interview and tell the Field Supervisor. If you continue and it becomes clear that they have not been truthful on the screening, end the interview.]


A. General Information

Q# Question Response Logic A1a What is your surname? Check they are

the individual on the list; if not end interview

A1b What is your first name or the name you commonly use?

A2 [Enumerator to note sex of respondent] Male Female

A3 What is the highest level of education you reached? [Prompt if necessary]

Adult literacy Standard 1-8 Form 1-4 Further Education None

A4 What is your marital Status? Married Widowed Divorced Separated Never Marred

A5 What is your age? [Estimate age of person if don’t know] [If less than 17 terminate interview]

___ years

Phone1 What is your (respondent’s) phone number? A6 Are you the head of the household (HHH)? Yes

No

A7 What is your relationship to the HHH? Head of HH Spouse of Head of HH Daughter/Son of HHH Brother/Sister of HHH Father/Mother of HHH Other relation of HHH Friend of HHH

A8 BLANK [IF NOT HHH] A9 What is the sex of the HHH? Male

Female [IF A6=No]

A10 What is the highest level of education of the HHH? Adult literacy Standard 1-8 Form 1-4 Further Education None

[IF A6=No]

A11 What is the marital status of HHH? Married Widowed Divorced Separated Never Marred

[IF A6=No]

A12 What is the age of HHH [Ask them to estimate age if don’t know] ___ years [IF A6=No]

A13 Total number of adult males in the HH [Adult = 18 years or older]

A14 BLANK A15 Total number of male children in the HH [0 – 17

years]

A16 BLANK A17 Total number of adult females in the HH [18 years

or older]

A18 BLANK A19 Total number of female children in the HH [0 – 17

years]

A20 BLANK


Q# Question Response Logic A21 What are all the sources of your household’s

income? [SELECT ALL THAT APPLY]

Crop Farming Livestock production/sales Wage labour (casual) Formal work (salary) Business/self-employment Other

CHECK1 If Crop Farming was not selected, check, as they have to grow a target crop. If crop farming is really a source of income, return to previous screen and select “crop farming”. If not, select “end survey”. Tell supervisor and record reason.

End Survey

A22 Order these income sources on the importance to your household. 1 is the MOST important, and the last is the LEAST important.

Farming

Business/petty trading Skilled work Semi-skilled work Casual labor/Ganyu Formal work/employment Other A23 What is the total land area in acres that your HH

farmed between July 2018 and June 2019? [Total area = land owned + land rented] [One acre is 0.4 hectares, which is 40m x 100m. If they do not know the area, then one big pace is one metre; so how many paces by how many paces?] [ACRES] [

Cross check with S2

A23a [Record if the information above was an estimate] Estimate Respondent knows

A24 How much of the ${A23} acres is rented by you from another person?

A25 Are you a Lead Farmer trained by LOL/MSIKA? Yes No

A25a As a Lead Farmer, have you done any of the following?

Yes No

If A25=Yes a. Conducted trainings of other farmers b. Submitted training forms to LOL/MSIKA about

the training c. Established a demonstration plot d. Other (please specify) A26 Are you a Committee member of a LOL/MSIKA

Farmer Based Organisation? Yes No

A26a As a Committee member of an FBO, did you receive any governance training?

Yes No If A26= Yes

A27 Have you had any of the following activities from LOL/MSIKA? [READ OUT LIST]

a. Training in improved horticultural practices by LOL/MSIKA staff or Lead Farmer

Yes No

b. Training in horticultural post-harvest handling by LOL/MSIKA staff or Lead Farmer

c. Training in marketing d. Training in financial literacy e. Training in gender equality f. Training/membership of a village savings and

loan (VSL) group g. Linked to an MFI,


Q# Question Response Logic h. Linked to markets to sell horticulture products i. Participated in international/district trade fair j. Other (specify) A27b In total, how many trainings did you receive from

LOL/MSIKA staff or Lead Farmers? [DO NOT PROMPT]

PPI. Poverty Profile

Q# Question Response Logic PPI1 How many members does the

household have? Seven or more Six Five Four One, two or three

PPI2 Is the (oldest) female head/spouse able to read and write in Chichewa or English?

No Yes, only Chichewa Yes, English (regardless of Chichewa) No female head/spouse

PPI3 The floor of the main dwelling is mainly made of what material?

Smoothed mud, or sand Smooth cement, wood, tile, or other

PPI4 The outer walls of the main dwelling of the household are predominantly made of what material?

Mud (yomata), or grass Mud brick (unfired) Compacted earth (yamdindo), burnt bricks, concrete, wood, iron sheets, or other

PPI5 The roof of the main dwelling is predominantly made of what material?

Grass, plastic sheeting, or other Iron sheets, clay tiles, or concrete

PPI6 What kind of toilet facility does the HH use?

None, traditional latrine without roof shared with other households, or other Traditional latrine without roof only for household members Traditional latrine with roof shared with other households Traditional latrine with roof only for household members, VIP latrine, or flush toilet

PPI7 What is the household’s main source of lighting fuel?

Collected firewood, purchased firewood, grass, or gas Paraffin, or other Battery/dry cell(torch), candles, solar or electricity

PPI8 Do any HH members sleep under a bed net to protect from mosquitos at any time in the year?

No Yes

PPI9 Does the household own any tables?

No Yes

PPI10 Does the household own any beds?

No Yes

PPI Score

[CALCULATE] [Calculate] If < XX or > XX End


S. Crops and Land Area

Q# Question Response Logic S1 Which of these crops did you or anyone in your HH

harvest at least once in the past 12 months (July 2018 to June 2019)? [READ RESPONSES] [SELECT ALL THAT APPLY – it is very important to probe for all the crops they harvested]

Tomatoes Onions Irish potatoes Mangoes Oranges, tangerines & lemons Guavas Chilies (Birds eye, paprika & red cayenne)

S1b How many times did you harvest this field crop in the last 12 months (June 2018 to July 2019)?

[THIS CAN ONLY BE FOR FIELD CROPS NOT TREE CROPS]

Tomatoes One time Two times Three times

Onions Irish potatoes Chilies (Birds eye, paprika and red cayenne) S2a How many acres of these crops or trees did your household plant for the first full

harvest in the past 12 months (June 2018 to July 2019)? [If S1 = yes]

Tomatoes – first planting (acres) Onions – first planting (acres) Irish potatoes – first planting (acres) Mangoes – first planting (trees) If < 7 End Oranges, tangerines & lemons – first planting (trees) If < 4 End Guava – first harvest (trees) If < 4 End Chilies/paprika/cayenne – first harvest (acres) S2b How many acres of these crops did your household plant a second time in the

past 12 months (June 2018 to July 2019)?

Tomatoes – second planting (acres) [If S1b is two or three times]

Onions – second planting (acres) Irish potatoes – second planting (acres) Chilies/paprika/cayenne – second planting (acres) S2b How many acres of these crops did your household plant a third time in the past

12 months (June 2018 to July 2019)?

Tomatoes – third planting (acres) [If S1b is three times]

Onions – third planting (acres) Irish potatoes – third planting (acres) Chilies/paprika/cayenne – third planting (acres) S3 BLANK S4 BLANK S5 When was the first time you grew this crop?

[FOR TREE CROPS, THE TREE MAY HAVE BEEN THERE MANY YEARS SO RECORD THIS AS MORE THAN FIVE YEARS AGO]

Tomatoes One year ago Two years ago Three years ago Four years ago More than five years ago

Onions Irish potatoes Mangoes Oranges, tangerines & lemons Guavas Chilies, paprika & red cayenne


B. Crop Inputs [base crop relevance on question S2] Tomatoes (A)

Q# Question Response Logic B1_1 Have you used any of the following inputs for growing TOMATOES in the past 12

months (July 2018 to June 2019)? [READ LIST] [SELECT ALL THAT APPLY]

a. Recycled (non-OPV) seed from own crop

Yes No

b. Recycled (non-OPV) seed bought/got from another

source c. Certified hybrid or Open Pollinated Variety (OPV) seed d. Seedlings from another person/organization e. Insecticide (for killing insects & bugs) f. Fungicide (for killing moulds, rusts and other crop

diseases) g. Foliar feed/fertilizer (inorganic feed for the plants) h. Sprayer (for applying agro-chemicals) i. Compost/manure(organic) j. Water from a watering can k. Water from a gravity or a manual pump irrigation system l. Water from a motorized pump (e.g. electric or fuel) m. Herbicide (to kill weeds) n. Lime or other soil improvers (for acidic soil)

B1_2 Who supplied this input?

[READ OUT LIST] [DO NOT READ OUT RESPONSES. ENUMERATOR CLASSIFIES RESPONSES] [MULTIPLE RESPONSE]

a. BLANK

Agro-dealer (shop) Vendor (no shop)

LOL/MSIKA An NGO/project

Government FBO

Neighbor/friend Own/Personal

Communal source

b. Recycled (non-OPV) seed bought/ got from another

source c. Certified hybrid or Open Pollinated Variety (OPV) seed d. Seedlings from another person/organization e. Insecticide (for killing insects & bugs) f. Fungicide (for killing moulds, rusts & other crop diseases) g. Foliar feed/fertilizer (inorganic feed for the plants) h. Sprayer (for applying agro-chemicals) i. Compost/manure(organic) j. Watering can k. Gravity or a manual pump irrigation system l. Motorized pump (e.g. electric or fuel) m. Herbicide (to kill weeds) n. Lime or other soil improvers (for acidic soil)

B1_3 BLANK B1_4 BLANK B1_5 BLANK B1_6 Overall for TOMATOES, for the inputs you bought in the

past 12 months (July 2018 to June 2019) how easy was it to get them?

Very easy Somewhat easy Somewhat hard Very hard Did not buy inputs DNK

B1_7 Overall for TOMATOES, for the inputs you bought, how often were the types and quantities you needed in stock?

Always Not always Never DNK

[If B1_6 is did not purchase]


Onions (B)

Q# Question Response Logic B2_1 Have you used any of the following inputs for growing ONIONS in the past 12 months

(July 2018 to June 2019), on your crop? [READ LIST] [SELECT ALL THAT APPLY]

a. Recycled (non-OPV) seed from own crop

Yes No

b. Recycled (non-OPV) seed bought/ got from another source c. Certified hybrid or Open Pollinated Variety (OPV) seed d. Seedlings from another person/organization e. Insecticide (for killing insects & bugs) f. Fungicide (for killing molds, rusts and other crop diseases) g. Foliar feed/fertilizer (inorganic feed for the plants) h. Sprayer (for applying agro-chemicals) i. Compost/manure(organic) j. Watering can k. Gravity or a manual pump irrigation system l. Motorized pump (e.g. electric or fuel) m. Herbicide (to kill weeds) n. Lime or other soil improvers (for acidic soil) B2_2 Who supplied this input?


a. BLANK



Government FBO


Communal source

b. Recycled (non-OPV) seed bought/ got from another source c. Certified hybrid or Open Pollinated Variety (OPV) seed d. Seedlings from another person/organization e. Insecticide (for killing insects & bugs) f. Fungicide (for killing molds, rusts and other crop diseases) g. Foliar feed/fertilizer (inorganic feed for the plants) h. Sprayer (for applying agro-chemicals) i. Compost/manure(organic) j. Watering can k. Gravity or a manual pump irrigation system l. Motorized pump (e.g. electric or fuel) m. Herbicide (to kill weeds) n. Lime or other soil improvers (for acidic soil)

B2_3 BLANK B2_4 BLANK B2_5 BLANK

B2_6 Overall for ONIONS, for the inputs you bought in the past 12

months (July 2018 to June 2019) how easy was it to get them?

Very easy Somewhat easy Somewhat hard Very hard Did not purchase inputs DNK

B2_7 Overall for ONIONS, for the inputs you bought, how often were the types and quantities you needed in stock?


[if B2_6 is did not purchase]


Irish Potatoes (C)

Q# Question Response Logic B3_1 Have you used any of the following inputs for growing IRISH POTATOES in the past

12 months (July 2018 to June 2019)? [READ LIST] [SELECT ALL THAT APPLY]

a. Selected recycled seed potatoes from own production

Yes No

b. Selected recycled seed potatoes bought/got from another

person c. Certified seed potatoes d. Seedlings from another person/organization e. Insecticide (for killing insects & bugs) f. Fungicide (for killing molds, rusts and other crop diseases) g. Foliar feed/fertilizer (inorganic feed for the plants) h. Sprayer (for applying agro-chemicals) i. Compost/manure (organic) j. Water from a watering can k. Water from a gravity or a manual pump irrigation system l. Water from a motorized pump (e.g. electric or fuel) m. Herbicide (to kill weeds) n. Lime or other soil improvers (for acidic soil)



a. BLANK

Agro-dealer (shop) Vendor (no shop) LOL/MSIKA An NGO/project Government FBO Neighbor/friend Own/Personal Communal source

b. Selected recycled seed potatoes bought/got from another

person c. Certified seed potatoes d. Seedlings from another person/organization e. Insecticide (for killing insects & bugs) f. Fungicide (for killing molds, rusts and other crop diseases) g. Foliar feed/fertilizer (inorganic feed for the plants) h. Sprayer (for applying agro-chemicals) i. Compost/manure(organic) j. Watering can k. Gravity or a manual pump irrigation system l. Motorized pump (e.g. electric or fuel) m. Herbicide (to kill weeds) n. Lime or other soil improvers (for acidic soil)


B3_6 Overall for IRISH POTATOES, for the inputs you bought in

the past 12 months (July 2018 to June 2019)? how easy was it to get them?


B3_7 Overall for IRISH POTATOES, for the inputs you bought, how often were the types and quantities you needed in stock?




Mangoes (D)

Q# Question Response Logic B4_1 Have you used any of the following inputs for growing MANGOES in the past 12

months (July 2018 to June 2019)? [READ LIST] [SELECT ALL THAT APPLY]

a. Seedlings grown by self

Yes No

b. Seedlings bought/received from other person/

organization c. Certified/improved seedlings d. Grafting material for you to apply or applied by a

specialist e. Insecticide (for killing insects & bugs) f. Fungicide (for killing molds, rusts and other crop

diseases) g. Foliar feed/fertilizer (inorganic feed for the plants) h. Sprayer (for applying agro-chemicals) i. Compost/manure (organic) j. Watering can k. Gravity or a manual pump irrigation system l. Motorized pump (e.g. electric or fuel) m. Lime or other soil improvers (for acidic soil)



a. BLANK



Government FBO


Communal source






B4_6 Overall for MANGOES, for the inputs you bought in

the past 12 months (July 2018 to June 2019) how easy was it to get them?


B4_7 Overall for MANGOES, for the inputs you bought, how often were the types and quantities you needed in stock?




Orange, tangerines and lemons (E)

Q# Question Response Logic B5_1 Have you used any of the following inputs for growing ORANGES, TANGERINES

AND LEMONS in the past 12 months (July 2018 to June 2019)? [READ RESPONSES] [SELECT ALL THAT APPLY]


Yes No




diseases) g. Foliar feed/fertilizer (inorganic feed for the plants) h. Sprayer (for applying agro-chemicals) i. Compost/manure(organic) j. Water from a watering can k. Water from a gravity or a manual pump irrigation

system l. Water from a motorized pump (e.g. electric or fuel) m. Lime or other soil improvers (for acidic soil)






Government FBO


Communal source



specialist e. Insecticide (for killing insects & bugs) f. Fungicide (for killing molds, rusts & other crop



B5_6 Overall for Oranges, tangerines and lemons, for the

inputs you bought in the past 12 months (July 2018 to June 2019) how easy was it to get them?


B5_7 Overall for Oranges, tangerines and lemons, for types and quantities you needed in stock?




Guava (F)

Q# Question Response Logic B6_1 Have you used any of the following inputs for growing GUAVA in the past 12 months

(July 2018 to June 2019)? [READ QUESTIONS AND RESPONSES] [SELECT ALL THAT APPLY]


Yes No




diseases) g. Foliar feed/fertilizer (inorganic feed for the plants) h. Sprayer (for applying agro-chemicals) i. Compost/manure(organic) j. Watering can k. Gravity or a manual pump irrigation system l. Motorized pump (e.g. electric or fuel) m. Lime or other soil improvers (for acidic soil)



a. BLANK



Government FBO


Communal source



specialist e. Insecticide (for killing insects & bugs) f. Fungicide (for killing molds & rusts) g. Foliar feed/fertilizer (inorganic feed for the plants) h. Sprayer (for applying agro-chemicals) i. Compost/manure(organic) j. Watering can k. Gravity or a manual pump irrigation system l. Motorized pump (e.g. electric or fuel) m. Lime or other soil improvers (for acidic soil)


B6_6 Overall for GUAVA, for the inputs you bought in the past

12 months (July 2018 to June 2019), how easy was it to get them?


B6_7 Overall for GUAVA, for the inputs you bought, how often were the types and quantities you needed in stock?


[if B6_6 not did not purchase]


Chilies (G)

Q# Question Response Logic B7_1 Have you used any of the following inputs for growing CHILI in the past 12 months

(July 2018 to June 2019)? [READ QUESTIONS AND RESPONSES] [SELECT ALL THAT APPLY]


Yes No

b. Seedlings bought/received from other person/ organization c. Certified/improved seedlings d. Grafting material for you to apply or applied by a specialist e. Insecticide (for killing insects & bugs) f. Fungicide (for killing molds, rusts and other crop diseases) g. Foliar feed/fertilizer (inorganic feed for the plants) h. Sprayer (for applying agro-chemicals) i. Compost/manure(organic) j. Watering can k. Gravity or a manual pump irrigation system l. Motorized pump (e.g. electric or fuel) m. Lime or other soil improvers (for acidic soil)



a. [blank]



Government FBO


Communal source

b. Seedlings bought/received from other person/ organization c. Certified/improved seedlings d. Grafting material for you to apply or applied by a specialist e. Insecticide (for killing insects & bugs) f. Fungicide (for killing molds, rusts and other crop diseases) g. Foliar feed/fertilizer (inorganic feed for the plants) h. Sprayer (for applying agro-chemicals) i. Compost/manure(organic) j. Watering can k. Gravity or a manual pump irrigation system l. Motorized pump (e.g. electric or fuel) m. Lime or other soil improvers (for acidic soil)


B7_6 Overall for CHILI, for the inputs you bought in the past 12

months (July 2018 to June 2019) how easy was it to get them? Very easy Somewhat easy Somewhat hard Very hard Did not buy inputs DNK

B7_7 Overall for CHILI, for the inputs you bought, how often were the types and quantities you needed in stock?


[if B7_6 not did not purchase]


C. Growing Practices [base crop relevance on question S2] Tomatoes (A)

Q# Question Response Logic C1_1 What practices for growing TOMATOES do you know even if you do not practice them?

[DO NOT READ OUT]

a. Composting (use of rotted down plant and other organic matter)

Yes No

b. Manuring (use of animal urine or excrement) c. Ridging (banking up the soil to form a ridge for planting on) d. Mulching (cover soil with dead plant/ compost to keep moisture/ kill weeds) e. Crop rotation (grow different crops on same plot in successive seasons) f. Minimum tillage (making planting holes only rather than tilling all the soil) g. BLANK h. Planting seeds in a nursery before planting out i. Staking (adding stakes to enable plants to stay upright) j. Succession planting (plant part of plot one week, then other parts in

following weeks to spread harvesting) k. BLANK l. Using irrigation (application of water to crops using motorized or solar

pumps, treadle pumps, watering cans or buckets, river diversion or canalization)

m. Soil and water conservation (terracing, vetiver grass) n. Draining excess water o. Testing soil acidity p. Adding lime or ash to soil before planting to reduce acidity q. Choosing the variety (choosing different varieties for the growing

characteristics and market that aiming for) r. De-suckering or removing unwanted side-shoots s. Spraying for pests and disease t. Using recommended plant and ridge spacing u. Using recommended fertilizer and application rates v. Sterilizing nursery beds before planting (burning crop residues to kill weed

seeds, disease spores and pest eggs) w. Scouting for pests and diseases x. Uprooting infected plants and burning (to avoid spread of disease) y. Sowing seed in row/groove nursery z. Using fish soup / sugar solution as bait for insects aa. Planting in sunken beds bb. Hardening off before planting out (reduction of water to nursery seedlings) cc. Selecting the best seedlings for planting out

C1_3 From whom did you first learn about these practices?

[FOR LIST OF THOSE THEY KNOW – READ OUT] [DO NOT READ OUT RESPONSES – ENUMERATOR TO CLASSIFY] [MULTIPLE RESPONSE]


Always known GoM extension worker

LOL Extension Worker LOL/MSIKA Lead Farmer

NGO Extension Worker Radio

My Farmer organization Relative/other farmers

Saw drama Saw in newspaper Saw on television MSIKA Training

Training by other organisation School

Agro-dealer DNK/cannot remember

Other

C1_2 Which of the following practices did you use for your TOMATOES in the past 12 months (July

2018 to June 2019)? [READ OUT LIST]


Yes No

b. Manuring (use of animal urine or excrement) c. Ridging (banking up the soil to form a ridge for planting on) d. Mulching (cover soil with dead plant/ compost to keep moisture/ kill weeds) e. Crop rotation (grow different crops on same plot in successive seasons) f. Minimum tillage (making planting holes only rather than tilling all the soil) g. Blank h. Planting seeds in a nursery before planting out i. Staking (adding stakes to enable plants to stay upright) j. Succession planting (plant part of plot one week, then other parts in following

weeks to spread harvesting) k. Blank l. Using irrigation (application of water to crops using motorized or solar pumps,

treadle pumps, watering cans or buckets, river diversion or canalization) m. Soil and water conservation (terracing, vetiver grass) n. Draining excess water o. Testing soil acidity p. Adding lime or ash to soil before planting to reduce acidity q. Choosing the variety (choosing different varieties for the growing characteristics

and market that aiming for) r. De-suckering or removing unwanted side-shoots s. Spraying for pests and disease t. Using recommended plant and ridge spacing u. Using recommended fertilizer and application rates v. Sterilizing nursery beds before planting (burning crop residues to kill weed seeds,

disease spores and pest eggs) w. Scouting for pests and diseases x. Uprooting infected plants and burning (to avoid spread of disease) y. Sowing seed in row/groove nursery z. Using fish soup / sugar solution as bait for insects aa. Planting in sunken beds bb. Hardening off before planting out (reduction of water to nursery seedlings) cc. Selecting the best seedlings for planting out

C1_4 On how many acres of TOMATOES have you applied at least one of

these improved farming practices in the past 12 months (July 2018 to June 2019) over all of your planting cycles? [ACRES]

[Cross check to S2]

The first planting time The second planting time The third planting time


C1_5 Have you used this practice for the first time in the last 12

months? (July 2018- June 2019) [LIST FROM C1_2]

Yes No

C1_6 How many acres of TOMATOES did you apply at least one

of these improved practice on for the first time in the last 12 months over all of your planting cycles? (July 2018 to June 2019)


C1_7 Why did you not use the other practices?

[DO NOT READ RESPONSES] [ENUMERATOR CLASSIFIES] [MULTIPLE RESPONSE POSSIBLE]

Did not know it Expensive to use it Time consuming

Inefficient Not confident it will work

Not confident I can use it properly Too difficult for farmers to do it

Did not understand it Inadequate resources

Need did not arise Other

If C1_2= used on none of the crop

Onions (B)

Q# Question Response Logic C2_1 What practices for growing ONIONS do you know?

[DO NOT READ OUT]]


Yes No

b. Manuring (use of animal urine or excrement) c. Ridging (banking up the soil to form a ridge for planting on) d. Earthing up (adding soil on growing plants to stimulate tuber

development) e. Mulching (cover soil with dead plant/ compost to keep moisture/ kill

weeds) f. Crop rotation (grow different crops on same plot in successive seasons) g. Minimum tillage (making planting holes only rather than tilling all the soil,) h. Blank i. Planting seeds in a nursery before planting out j. Blank k. Using irrigation (application of water to crops using motorized or solar


l. Soil and water conservation (terracing, plant vetiver grass) m. Draining excess water n. Testing soil acidity o. Adding lime or ash to soil before planting to reduce acidity p. Choosing the variety (choosing different varieties for the growing

characteristics and market that aiming for) q. Spraying for pests and disease r. Clipping plant ends to allow seedlings make fresh sprouts s. Sterilizing nursery beds before planting (burning crop residues to kill

weed seeds, disease spores and pest eggs) t. Scouting for pests and diseases u. Using recommended plant and ridge/row spacing v. Using recommended fertilizer and application rates w. Uprooting infected plants and burning them (to avoid spread of disease) x. Sowing seed in row/groove nursery y. Using raised beds (rainy season)


Q# Question Response Logic z. Using fish soup / sugar solution as bait for insects aa. Planting in sunken beds (dry season) bb. Hardening off before planting out (reduction of water to nursery

seedlings) cc. Selecting the best seedlings for planting out

C2_3 From whom did you first learn about these

practices? [FOR LIST OF THOSE THEY KNOW – READ OUT] [DO NOT READ OUT RESPONSES – ENUMERATOR TO CLASSIFY] [SINGLE RESPONSE – QUESTION WHICH WAS FIRST]

Always known GoM extension worker LOL Extension Worker

LOL/MSIKA Lead Farmer NGO Extension Worker

Radio My Farmer organization Relative/other farmers




Other

C2_5 Which of the following practices did you use for your ONIONS in the past 12 months (July



Yes No

b. Manuring (use of animal urine or excrement) c. Ridging (banking up the soil to form a ridge for planting on) d. Earthing up (adding soil on growing plants to stimulate tuber development) e. Mulching (cover soil with dead plant/ compost to keep moisture/ kill weeds) f. Crop rotation (grow different crops on same plot in successive seasons) g. Minimum tillage (making planting holes only rather than tilling all the soil,) h. Blank i. Planting seeds in a nursery before planting out j. Blank k. Using irrigation (application of water to crops using motorized or solar pumps,

treadle pumps, watering cans or buckets, river diversion or canalization) l. Soil and water conservation (terracing, plant vetiver grass) m. Draining excess water n. Testing soil acidity o. Adding lime or ash to soil before planting to reduce acidity p. Choosing the variety (choosing different varieties for the growing characteristics

and market that aiming for) q. Spraying for pests and disease r. Clipping plant ends to allow seedlings make fresh sprouts s. Sterilizing nursery beds before planting (burning crop residues to kill weed seeds,

disease spores and pest eggs) t. Scouting for pests and diseases u. Using recommended plant and ridge/row spacing

C2_4 On how many acres of ONIONS have you applied at least one of these improved practices in the past 12 months (July 2018 to June 2019)? [ACRES]

[Cross check to S2]



v. Using recommended fertilizer and application rates w. Uprooting infected plants and burning them (to avoid spread of disease) x. Sowing seed in row/groove nursery y. Using raised beds (rainy season) z. Using fish soup / sugar solution as bait for insects aa. Planting in sunken beds (dry season) bb. Hardening off before planting out (reduction of water to nursery seedlings) cc. Selecting the best seedlings for planting out

C2_6 Have you used this practice for the first time

in the last 12 months? (July 2018- June 2019) [LIST FROM C2_2]

Yes No

C2_7 Why did you not use the other practices? [DO NOT READ RESPONSES] [ENUMERATOR CLASSIFIES] [MULTIPLE RESPONSE POSSIBLE]







Irish Potatoes (C)

Q# Question Response Logic C3_1 What practices for growing IRISH POTATOES do you know:

[DO NOT READ OUT]


Yes No

b. Manuring (use of animal urine or excrement) c. Ridging (banking up the soil to form a ridge for planting on) d. Earthing up (adding soil on growing plants to stimulate tuber

development) e. Mulching (cover soil with dead plant/ compost to keep moisture/ kill

weeds) f. Crop rotation (grow different crops on same plot in successive seasons) g. Blank h. Succession planting (plant part of plot one week, then other parts later) i. Blank j. Blank k. Using irrigation (application of water to crops using motorized or solar


l. Soil and water conservation (terracing, plant vetiver grass) m. Choosing the variety (choosing different varieties for the growing

characteristics and market that aiming for) n. Spraying for pests and disease o. Scouting for pests and diseases p. Using recommended plant and ridge spacing q. Using recommended fertilizer and application rates r. Uprooting infected plants and burning (to avoid spread of disease) s. Using fish soup / sugar solution as bait for insects t. Selection of seed potatoes u. Chitting (using diffused storage light to initiate sprouting of tubers)


C3_3 From whom did you first learn about these practices? [DO NOT READ OUT RESPONSES – ENUMERATOR TO CLASSIFY] [FOR LIST OF THOSE THEY KNOW – READ OUT] [SINGLE RESPONSE – QUESTION WHICH WAS FIRST]




Saw drama Saw in newspaper Saw on television MSIKA Training Training by other

organisation School


Other

C3_2 Which of the following practices did you use for your IRISH POTATOES in the past 12 months (July 2018 to June 2019)? [READ OUT LIST]


Yes No

b. Manuring (use of animal urine or excrement) c. Ridging (banking up the soil to form a ridge for planting on) d. Earthing up (adding soil on growing plants to stimulate tuber development) e. Mulching (cover soil with dead plant/ compost to keep moisture/ kill weeds) f. Crop rotation (grow different crops on same plot in successive seasons) g. Blank h. Succession planting (plant part of plot one week, then other parts later) i. Blank j. Blank k. Using irrigation (application of water to crops using motorized or solar pumps,

treadle pumps, watering cans or buckets, river diversion or canalization) l. Soil and water conservation (terracing, plant vetiver grass) m. Choosing the variety (choosing different varieties for the growing

characteristics and market that aiming for) n. Spraying for pests and disease o. Scouting for pests and diseases p. Using recommended plant and ridge spacing q. Using recommended fertilizer and application rates r. Uprooting infected plants and burning (to avoid spread of disease) s. Using fish soup / sugar solution as bait for insects t. Selection of seed potatoes u. Chitting (using diffused storage light to initiate sprouting of tubers)

C3_4 Excluding ridging, on how many acres of IRISH POTATOES have you applied at least one of these farming practices in the last 12 months? [ACRES]

[Cross check to S2]

The first planting time The second planting time The third planting season

C3_5 Have you used this practice for the first time in the last 12 months? (July 2018- June 2019) [LIST FROM C3_2]

Yes No



[DO NOT READ RESPONSES – ENUMERATOR CLASSIFIES]


Inefficient Not confident it will work Not confident I can use it

properly Too difficult for farmers to do

it Did not understand it Inadequate resources



Mangoes (D)

Q# Question Response Logic C4_1 What practices for growing MANGOES do you know?

[DO NOT READ OUT]


Yes No

b. Manuring (use of animal urine or excrement) c. Blank d. Pruning (cut back the canopy to let more light& air in; & to stop

trees growing too tall to harvest from) e. Mulching (cover soil with dead plant/ compost to keep moisture/

kill weeds) f. Spraying for pests and disease (use of crop protection chemicals) g. Blank h. Water capture (creating a basin around fruit trees) i. Using irrigation (application of water to crops using motorized or

solar pumps, treadle pumps, watering cans or buckets, river diversion or canalization)

j. Soil and water conservation (terracing, plant vetiver grass) k. Draining excess water l. Testing soil acidity m. Adding lime or ash to soil before planting to reduce acidity n. Blank o. Blank p. Blank q. Scouting for pests and diseases r. Blank s. Uprooting infected plants and burning (to avoid spread of

disease) t. Using fish soup / sugar solution as bait for insects u. Grafting (using stems of plants) v. Budding (using plant buds) w. Staking young trees x. Digging of planting holes prior to planting y. Deflowering of first flowers

C3_6 On how many acres of IRISH POTATOES did you apply at least one of these improved practices on for the first time in the last 12 months? (July 2018 to June 2019) [ACRES]



C4_3 From whom did you first learn about these practices? [FOR LIST OF THOSE THEY KNOW – READ OUT] [DO NOT READ OUT RESPONSES – ENUMERATOR TO CLASSIFY] [SINGLE RESPONSE – QUESTION WHICH WAS FIRST]





organisation School


Other

C4_2 Which of the following practices did you use for your MANGOES in the past 12 months (July



Yes No

b. Manuring (use of animal urine or excrement) c. Blank d. Pruning (cut back the canopy to let more light& air in; & to stop trees growing

too tall to harvest from) e. Mulching (cover soil with dead plant/ compost to keep moisture/ kill weeds) f. Spraying for pests and disease (use of crop protection chemicals) g. Blank h. Water capture (creating a basin around fruit trees) i. Using irrigation (application of water to crops using motorized or solar pumps,

treadle pumps, watering cans or buckets, river diversion or canalization) j. Soil and water conservation (terracing, plant vetiver grass) k. Draining excess water l. Testing soil acidity m. Adding lime or ash to soil before planting to reduce acidity n. Blank o. Blank p. Blank q. Scouting for pests and diseases r. Blank s. Uprooting infected plants and burning (to avoid spread of disease) t. Using fish soup / sugar solution as bait for insects u. Grafting (using stems of plants) v. Budding (using plant buds) w. Staking young trees x. Digging of planting holes prior to planting y. Deflowering of first flowers

C4_4 On how many MANGO trees have you applied at least one of these improved practices in the past 12 months? [TREES]

[Cross check to S2]


Yes No


C4_7 Why did you not use the other

practices? [DO NOT READ RESPONSES – ENUMERATOR CLASSIFIES]







Oranges, tangerines and lemons (E)

Q# Question Response Logic C5_1 What practices for growing Oranges, tangerines and lemons do you know?

[DO NOT READ OUT]


Yes No

b. Manuring (use of animal urine or excrement) c. Blank d. Pruning (cut back the canopy to let more light& air in; & to

stop trees growing too tall to harvest from) e. Mulching (cover soil with dead plant/ compost to keep

moisture/ kill weeds) f. Spraying for pests and disease (use of crop protection

chemicals) g. Blank h. Water capture (creating a basin around fruit trees) i. Using irrigation (application of water to crops using motorized

or solar pumps, treadle pumps, watering cans or buckets, river diversion or canalization)

j. Soil and water conservation (terracing, plant vetiver grass) k. Draining excess water l. Testing soil acidity m. Adding lime or ash to soil before planting to reduce acidity n. Blank o. Blank p. Blank q. Scouting for pests and diseases r. Blank s. Uprooting infected plants and burning (to avoid spread of

disease) t. Using fish soup / sugar solution as bait for insects u. Grafting (using stems of plants) v. Budding (using plant buds) w. Staking young trees x. Digging of planting holes prior to planting y. Deflowering of first flowers

C4_6 On how many MANGO trees did you apply at least one improved practice for the first time in the last 12 months? (July 2018 to June 2019) [TREES]









Other

C5_2 Which of the following practices did you use for your ORANGES, TANGERINES AND LEMONS in the past 12 months (July 2018 to June 2019)? [READ OUT LIST]


Yes No


trees growing too tall to harvest from) e. Mulching (cover soil with dead plant/ compost to keep moisture/

kill weeds) f. Spraying for pests and disease (use of crop protection chemicals) g. Blank h. Water capture (creating a basin around fruit trees) i. Using irrigation (application of water to crops using motorized or


j. Soil and water conservation (terracing, plant vetiver grass) k. Draining excess water l. Testing soil acidity m. Adding lime or ash to soil before planting to reduce acidity n. Blank o. Blank p. Blank q. Scouting for pests and diseases r. Blank s. Uprooting infected plants and burning (to avoid spread of disease) t. Using fish soup / sugar solution as bait for insects u. Grafting (using stems of plants) v. Budding (using plant buds) w. Staking young trees x. Digging of planting holes prior to planting y. Deflowering of first flowers

C5_4 On how many orange, tangerine or lemon trees have you applied at least one of these improved practices in the past 12 months? [TREES]

[Cross check to S2]


Guava (F)

Q# Question Response Logic C6_1 What practices for growing GUAVA do you know?

[DO NOT PROMPT]


Yes No


trees growing too tall to harvest from) e. Mulching (cover soil with dead plant/ compost to keep moisture/ kill

weeds) f. Spraying for pests and disease (use of crop protection chemicals) g. Blank h. Water capture (creating a basin around fruit trees) i. Using irrigation (application of water to crops using motorized or


j. Soil and water conservation (terracing, plant vetiver grass) k. Draining excess water l. Testing soil acidity m. Adding lime or ash to soil before planting to reduce acidity n. Blank o. Blank p. Blank q. Scouting for pests and diseases r. Blank s. Uprooting infected plants and burning (to avoid spread of disease) t. Using fish soup / sugar solution as bait for insects u. Grafting (using stems of plants) v. Budding (using plant buds) w. Staking young trees x. Digging of planting holes prior to planting y. Deflowering of first flowers

C5_6 On how many orange, tangerine or lemon trees did you apply at least one improved practice for the first time in the last 12 months? (July 2018 to June 2019) [TREES]

C5_7 Why did you not use the other practices? [DO NOT READ RESPONSES – ENUMERATOR CLASSIFIES]

Did not know it, Expensive to use it Time consuming, Inefficient Not confident it will work, Not confident I can use it properly Too difficult for farmers to do it, Did not understand it, Need did not arise

Inadequate resources Other



Yes No









Other

C6_2 Which of the following practices did you use for your GUAVA in the past 12 months (July 2018 to June 2019)? [READ RESPONSES]


Yes No

b. Manuring (use of animal urine or excrement) c. Blank d. Pruning (cut back the canopy to let more light& air in; & to stop trees growing

too tall to harvest from) e. Mulching (cover soil with dead plant/ compost to keep moisture/ kill weeds) f. Spraying for pests and disease (use of crop protection chemicals) g. Blank h. Water capture (creating a basin around fruit trees) i. Using irrigation (application of water to crops using motorized or solar pumps,

treadle pumps, watering cans or buckets, river diversion or canalization) j. Soil and water conservation (terracing, plant vetiver grass) k. Draining excess water l. Testing soil acidity m. Adding lime or ash to soil before planting to reduce acidity n. Blank o. Blank p. Blank q. Scouting for pests and diseases r. Blank s. Uprooting infected plants and burning (to avoid spread of disease) t. Using fish soup / sugar solution as bait for insects u. Grafting (using stems of plants) v. Budding (using plant buds) w. Staking young trees x. Digging of planting holes prior to planting y. Deflowering of first flowers


Yes No

C6_4 On how many guava trees have you applied at least one of these improved practices in the past 12 months? [TREES]

[Cross check to S2]





Inefficient Not confident it will

work Not confident I can use

it properly Too difficult for farmers

to do it Did not understand it Inadequate resources



Chilies (G)

Q# Question Response Logic C7_1 What practices for growing CHILI do you know?

[DO NOT READ OUT]


Yes No

b. Manuring (use of animal urine or excrement) c. Ridging (banking up the soil to form a ridge for planting on) d. Mulching (cover soil with dead plant/ compost to keep moisture/ kill weeds) e. Crop rotation (grow different crops on same plot in successive seasons) f. Minimum tillage (making planting holes only rather than tilling all the soil,) g. Blank h. Planting seeds in a nursery before planting out i. Blank j. Succession planting (plant part of plot one week, then other parts in

following weeks to spread harvesting) k. Blank l. Using irrigation (application of water to crops using motorized or solar


m. Soil and water conservation (terracing, plant vetiver grass) n. Draining excess water o. Testing soil acidity p. Adding lime or ash to soil before planting to reduce acidity q. Choosing the variety (choosing different varieties for the growing

characteristics and market that aiming for) r. Blank s. Spraying for pests and disease t. Blank u. Land Preparation (land clearing, tillage/ploughing) v. Using recommended plant and ridge spacing w. Using recommended fertilizer and application rates x. Blank y. Blank z. Uprooting infected plants and burning (to avoid spread of disease) aa. Sowing nursery seed in row/groove nursery bb. Using fish soup / sugar solution as bait for insects cc. Planting in sunken beds (dry season) dd. Using raised beds (rainy season) ee. Hardening off before planting out (reduction of water to nursery

seedlings)

C6_6 On how many orange, tangerine or lemon trees did you apply at least one improved practice for the first time in the last 12 months? (July 2018 to June 2019) [TREES]


Q# Question Response Logic ff. Sterilizing nursery beds before planting (burning crop residues to kill weed

seeds, disease spores and pest eggs) gg. Scouting for pest and disease hh. Selecting the best seedlings for planting out

C7_3 From whom did you first learn about these

practices? [FOR LIST OF THOSE THEY KNOW – READ OUT] [DO NOT READ OUT RESPONSES – ENUMERATOR TO CLASSIFY] [SINGLE RESPONSE – QUESTION WHICH WAS FIRST]







Other

C7_2 Which of the following practices did you use for your CHILI in the past 12 months (July

2018 to June 2019)? [READ LIST] Ask all


Yes No

b. Manuring (use of animal urine or excrement) c. Ridging (banking up the soil to form a ridge for planting on) d. Mulching (cover soil with dead plant/ compost to keep moisture/ kill weeds) e. Crop rotation (grow different crops on same plot in successive seasons) f. Minimum tillage (making planting holes only rather than tilling all the soil,) g. Blank h. Planting seeds in a nursery before planting out i. Blank j. Succession planting (plant part of plot one week, then other parts in following

weeks to spread harvesting) k. Blank l. Using irrigation (application of water to crops using motorized or solar pumps,

treadle pumps, watering cans or buckets, river diversion or canalization) m. Soil and water conservation (terracing, plant vetiver grass) n. Draining excess water o. Testing soil acidity p. Adding lime or ash to soil before planting to reduce acidity q. Choosing the variety (choosing different varieties for the growing

characteristics and market that aiming for) r. Blank s. Spraying for pests and disease t. Blank u. Land Preparation (land clearing, tillage/ploughing) v. Using recommended plant and ridge spacing w. Using recommended fertilizer and application rates x. Blank y. Blank z. Uprooting infected plants and burning (to avoid spread of disease) aa. Sowing nursery seed in row/groove nursery bb. Using fish soup / sugar solution as bait for insects cc. Planting in sunken beds (dry season) dd. Using raised beds (rainy season) ee. Hardening off before planting out (reduction of water to nursery seedlings)


ff. Sterilizing nursery beds before planting (burning crop residues to kill weed seeds, disease spores and pest eggs)

gg. Scouting for pest and disease hh. Selecting the best seedlings for planting out




Inefficient Not confident it will work Not confident I can use it

properly Too difficult for farmers to

do it Did not understand it Inadequate resources



C7_4 On how many acres of CHILI have you applied two or more of these farming practices in the last 12 months? [ACRES]

[Cross check to S2]

C7_5 Have you used this improved practice for the first time in the last 12 months? (July 2018- June 2019) [LIST FROM C7_2]

Yes No

C7_6 On how many acres of CHILI did you apply improved practices on for the first time in the last 12 months? (July 2018 to June 2019) [ACRES]


D. Harvest and Post-Harvest Handling Practices [base crop relevance on question S2] Tomatoes (A)

Q# Question Response Logic D1_1 What improved harvest & post-harvest practices for handling TOMATOES do you

know? [DO NOT READ RESPONSES] [SELECT ALL THAT APPLY]

a. Timely harvesting not too early so immature and not too late so over-ripe

Yes No

b. Handling carefully to avoid damage c. Storing in cool places out of the sun prior to sale d. Grading by color, size and shape j. Sorting by variety, size/maturity, damaged, ripeness e. Packing - Loading into appropriate containers for

storage or transportation f. Not over-filling baskets & bags or using very big bags,

as this damages product at the bottom g. BLANK h. Not stacking more than three containers high to avoid

crushing tomatoes in lower containers. i. Adding soft dry grass as soft litter

D1_3 From whom did you first learn about these

improved harvest and post-harvest practices? [FOR LIST OF THOSE THEY KNOW – READ OUT] [DO NOT READ OUT LIST – ENUMERATOR TO CLASSIFY RESPONSES]







Other

[If D1_1 = yes]

D1_2 Which improved harvest & post-harvest practices did you use for handling TOMATOES

the last 12 months (July 2018 – June 2019? [READ LIST]

a. Timely harvesting not too early so immature and not too late so over-ripe

Yes No

b. Handling carefully to avoid damage c. Storing in cool places out of the sun prior to sale d. Grading by color, size and shape j. Sorting by variety, size/maturity, damaged, ripeness e. Packing - Loading into appropriate containers for storage or transportation f. Not over-filling baskets & bags or using very big bags, as this damages product

at the bottom g. BLANK h. Not stacking more than three containers high to avoid crushing tomatoes in

lower containers. i. Adding soft dry grass as soft litter

D1_2b Have you used this practice for the first time in the last 12 months? (July 2018-

June 2019) [LIST FROM D1_2]

Yes No


Onions (B) Q# Question Response Logic

D2_1 What improved harvest & post-harvest practices for handling ONIONS do you know? [DO NOT READ LIST] [SELECT ALL THAT APPLY]

a. Harvesting & selling green/fresh onions with stalks on

Yes No

b. Lifting when at least 20% of tops have bent over & dried; leave until

outer is dry & brown (cured)- delete red text Drying (curing) onions in the sun or under a shade before storing to

improve in keeping quality d. Trimming dried stems and roots before packing e. Grading by variety, size and appearance f. Packing - Loading commodities into appropriate bags/containers for

storage or transportation g. Not over-filling baskets & bags or using very big bags, as this damages

product at the bottom h. Sorting by variety, dryness, removing damaged etc

D2_3 From whom did you first learn about these improved harvest and post-harvest

practices? [FOR LIST OF THOSE THEY KNOW – READ OUT] [DO NOT READ OUT LIST – ENUMERATOR TO CLASSIFY RESPONSES]

[If D2_1 = yes]







Other

D2_2 Which improved harvest & post-harvest practices did you use for handling ONIONS in the

last 12 months (July 2018 – June 2019)? [READ LIST]

[If D2_1 = yes]

a. Harvesting & selling green/fresh onions with stalks on

Yes No

b. Lifting when at least 20% of tops have bent over & dried; leave until outer is

dry & brown (cured)- delete red text Drying (curing) onions in the sun or under a shade before storing to improve in

keeping quality d. Trimming dried stems and roots before packing e. Grading by variety, size and appearance f. Packing - Loading commodities into appropriate bags/containers for storage or

transportation g. Not over-filling baskets & bags or using very big bags, as this damages product

at the bottom h. Sorting by variety, dryness, removing damaged etc

D2_2b Have you used this practice for the first time in the last 12 months? (July 2018-


Yes No


Irish Potatoes (C) Q# Question Response Logic

D3_1 What improved harvest & post-harvest practices for IRISH POTATOES do you know? [DO NOT READ LIST] [SELECT ALL THAT APPLY]]

a. Harvesting when plant tops wilt and start to wither; slashing tops close to soil to cure in soil a day before harvesting

Yes No

b. Lifting potatoes with a fork/prong, not a hoe c. Rubbing off surface dirt after harvest; or after drying & curing d. Blank e. Keeping clean potatoes in a cool dry place to cure or heal surface

damage, before packing, storage & selling f. Handling carefully to avoid damage – remove all damaged potatoes to

avoid rotting g. Grading by color, size and shape k. Sorting by variety, maturity, removing damages, other materials etc h. Packing - Loading commodities into appropriate bags/containers for

storage or transportation i. Using night vented sheds for short term storage for selling outside main

season

D3_3 From whom did you first learn about these improved harvest and post-harvest practices? [FOR LIST OF THOSE THEY KNOW – READ OUT] [DO NOT READ OUT RESPONSES – ENUMERATOR TO CLASSIFY

[If D3_1 = yes]







Other

D3_2 Which improved harvest & post-harvest practices did you use for handling IRISH

POTATOES the last 12 months (July 2018 – June 2019? [READ LIST]

[If D3_1 = yes]

a. Harvesting when plant tops wilt and start to wither; slashing tops close to soil to cure in soil a day before harvesting

Yes No

b. Lifting potatoes with a fork/prong, not a hoe c. Rubbing off surface dirt after harvest; or after drying and curing d. Blank e. Keeping clean potatoes in a cool dry place to cure or heal surface

damage, before packing, storage & selling f. Handling carefully to avoid damage – remove all damaged potatoes to

avoid rotting g. Grading by color, size and shape k. Sorting by variety, maturity, removing damages, other materials etc h. Packing - Loading commodities into appropriate bags/containers for

storage or transportation


i. Using night vented sheds for short term storage for selling outside main season

D3_2b Have you used this practice for the first time in the last 12 months? (July

2018- June 2019) [LIST FROM D1_2]

Yes No

Mangoes (D) Q# Question Response Logic

D4_1 What improved harvest & post-harvest practices for MANGOES do you know? [DO NOT READ LIST] [SELECT ALL THAT APPLY]

a. Harvesting when ripening just started and color begins to change

Yes No

b. Harvesting by climbing the tree c. Harvesting by using a special harvesting pole d. Catching fruit before it falls using bags or spreading sheets e. Handling carefully to avoid damage f. Storing in cool places and out of the sun prior to sale g. Grading by color, size, and shape l. Sorting by variety, size/maturity, ripeness, removing damaged h. Packing - Loading commodities into appropriate containers for

storage or transportation i. Storing in crates to prevent damage in transporting j. Washing to improve appearance k. Not over-filling baskets & bags or use very big bags as this damages

product at the bottom


[If D3_1 = yes]







Other

D4_2 Which improved harvest & post-harvest practices did you use for handling MANGOES

the last 12 months (July 2018 – June 2019? [READ LIST]

[If D4_1 = yes]

a. Harvesting when ripening just started and color begins to change

Yes No

b. Harvesting by climbing the tree c. Harvesting by using a special harvesting pole d. Catching fruit before it falls using bags or spreading sheets e. Handling carefully to avoid damage f. Storing in cool places and out of the sun prior to sale g. Grading by color, size, and shape l. Sorting by variety, size/maturity, ripeness, removing damaged


h. Packing - Loading commodities into appropriate containers for storage or transportation

i. Storing in crates to prevent damage in transporting j. Washing to improve appearance k. Not over-filling baskets & bags or use very big bags as this damages


D4_2b Have you used this practice for the first time in the last 12 months? (July 2018- June 2019) [LIST FROM D1_2]

Yes No

Oranges, Tangerines and Lemons (E)

Q# Question Response Logic D5_1 What improved harvest & post-harvest practices for ORANGES and LEMONS do you

know? [DO NOT READ LIST] [SELECT ALL THAT APPLY]

a. Blank

Yes No

b. Blank c. Timely harvesting not too early so immature and not too late so over-ripe d. Harvesting by climbing the tree e. Harvesting by using a special harvesting pole f. Catching fruit before it falls using bags or spreading sheets g. Handling carefully to avoid damage h. Storing in cool places and out of the sun prior to sale i. Grading by color, size, and shape n. Sorting by variety, size/maturity, ripeness, removing damaged j. Packing - Loading commodities into appropriate containers for storage or

transportation k. Storing in crates to prevent damage in transporting l. Washing to improve appearance m. Not over-filling baskets & bags or use very big bags as this damages



[If D5_1 = yes]


LOL/MSIKA Lead Farmer




organisation School


Other


D5_2 Which improved harvest & post-harvest practices did you use for handling ORANGES and LEMONS the last 12 months (July 2018 – June 2019? [READ LIST]

[If D5_1 = yes]

a. Blank

Yes No

b. Blank c. Timely harvesting not too early so immature and not too late so over-ripe d. Harvesting by climbing the tree e. Harvesting by using a special harvesting pole f. Catching fruit before it falls using bags or spreading sheets g. Handling carefully to avoid damage h. Storing in cool places and out of the sun prior to sale i. Grading by color, size, and shape n. Sorting by variety, size/maturity, ripeness, removing damaged j. Packing - Loading commodities into appropriate containers for storage or

transportation k. Storing in crates to prevent damage in transporting l. Washing to improve appearance m. Not over-filling baskets & bags or use very big bags as this damages

product at the bottom D5_2b Have you used this practice for the first time in the last 12 months? (July 2018-


Yes No

Guava (F)

Q# Question Response Logic D6_1 What improved harvest & post-harvest practices for GUAVA do you know?

[DO NOT READ LIST] [SELECT ALL THAT APPLY]

a. Blank

Yes No

b. Harvesting when ripening just started and color begins to change c. Harvesting by climbing the tree d. Harvesting by using a special harvesting pole e. Catching fruit before it falls using bags or spreading sheets f. Handling carefully to avoid damage g. Storing in cool places and out of the sun prior to sale h. Grading by color, size, and shape m. Sorting by variety, size/maturity, ripeness, removing damaged i. Packing - Loading commodities into appropriate containers for storage

or transportation j. Storing in crates to prevent damage in transporting k. Washing to improve appearance l. Not over-filling baskets & bags or use very big bags as this damages



[If D6_1 = yes]






organisation School


Other

D6_2 Which improved harvest & post-harvest practices did you use for handling GUAVA the

last 12 months (July 2018 – June 2019? [READ LIST]

[If D6_1 = yes]

a. Blank

Yes No

b. Harvesting when ripening just started and color begins to change c. Harvesting by climbing the tree d. Harvesting by using a special harvesting pole e. Catching fruit before it falls using bags or spreading sheets f. Handling carefully to avoid damage g. Storing in cool places and out of the sun prior to sale h. Grading by color, size, and shape m. Sorting by variety, size/maturity, ripeness, removing damaged i. Packing - Loading commodities into appropriate containers for

storage or transportation j. Storing in crates to prevent damage in transporting k. Washing to improve appearance l. Not over-filling baskets & bags or use very big bags as this damages


D6_2b Have you used this practice for the first time in the last 12 months? (July 2018- June 2019) [LIST FROM D1_2]

Yes No

Chilies (G)

Q# Question Response Logic D7_1 What improved harvest & post-harvest practices for CHILIES do you know?

[DO NOT READ LIST] [SELECT ALL THAT APPLY]

a. Blank

Yes No

b. Timely harvesting not too early so immature and not too late so over-ripe c. Handling carefully to avoid damage d. Storing in cool places out of the sun prior to sale e. Grading by color, size and dryness Sorting by variety, maturity and freshness, also removing foreign matter f. Packing - Loading commodities into appropriate bags for storage or

transportation n. Store chilis in a cool dry and well-ventilated place away from moisture to

avoid damaging the produce g. Blank h. Blank i. Blank j. Blank


k. Blank l. Blank m. Blank

D7_3 From whom did you first learn about these improved

harvest and post-harvest practices? [FOR LIST OF THOSE THEY KNOW – READ OUT] [DO NOT READ OUT RESPONSES – ENUMERATOR TO CLASSIFY

[If D7_1 = yes]


LOL/MSIKA Lead Farmer




organisation School


Other

D7_2 Which improved harvest & post-harvest practices did you use for handling CHILIES the

last 12 months (July 2018 – June 2019? [READ LIST]

[If D7_1 = yes]

a. Blank

Yes No

b. Timely harvesting not too early so immature and not too late so over-ripe c. Handling carefully to avoid damage d. Storing in cool places out of the sun prior to sale e. Grading by color, size and dryness Sorting by variety, maturity and freshness, also removing foreign matter f. Packing - Loading commodities into appropriate bags for storage or

transportation n. Store chilis in a cool dry and well-ventilated place away from moisture to

avoid damaging the produce g. Blank h. Blank i. Blank j. Blank k. Blank l. Blank m. Blank D7_2b Have you used this practice for the first time in the last 12 months? (July 2018-


Yes No


Q# Question Response Logic D8 Where do you store these crops?

[READ RESPONSES] [SELECT ALL THAT APPLY]

Inside my house In a separate building/store /shed In a Nkhokwe/outside store In baskets/bags outside or in a pile FBO Warehouse Other

D8_b Did you build or restore any storage building or storeroom or shed in the last 12 months? (June 2018 to July 2019)

Yes No

If Yes D9 What is new or restored building/store/shed’s floor area?

[square meters] [1 LARGE STEP = 1 PACE = 1 METER]

D10 How much did you spend on it? [MK]

E. Gender [base crop relevance on question S2] Tomatoes (A)

Q# Question Response Logic E1 Who in your household does the following work for TOMATOES?

[READ RESPONSES]

a) Land preparing Only by Men More by men than by women By men & women equally More by women than by men Only by women DNK

b) Planting c) Managing the crop while growing d) Harvesting the crop e) Handling crop after harvest, but before selling,

including preparing it for selling f) Selling the crop, whether to neighbors, traders,

processor or at a marketplace

g. Managing proceeds(cash) from selling farm produce

Q# Question Response Logic E1b “Is the breakdown of tasks by men and women different

from the answers on <first crop> Yes No

If no, skip to Section F

Onions (B)

Q# Question Response Logic E2 Who in your household does the following work for ONIONS?

[READ RESPONSES]





g. Managing proceeds(cash) from selling farm produce Irish Potatoes (C)

Q# Question Response Logic E3 Who in your household does the following work for IRISH POTATOES?

[READ RESPONSES]






g. Managing proceeds(cash) from selling farm produce Mangoes (D)

Q# Question Response Logic E4 Who in your household does the following work for MANGOES?

[READ RESPONSES]





g) Managing proceeds(cash) from selling farm produce Oranges, tangerines and lemons (E)

Q# Question Response Logic E5 Who in your household does the following work for Oranges, tangerines and

lemons? [READ RESPONSES]





g) Managing proceeds(cash) from selling farm produce Guava (F)

Q# Question Response Logic E6 Who in your household does the following work for GUAVA?

[READ RESPONSES]





g) Managing proceeds(cash) from selling farm produce Chilies (G)

Q# Question Response Logic E7 Who in your household does the following work for CHILIES?

[READ RESPONSES]

a) Land preparing Only by Men


b) Planting More by men than by women By men & women equally More by women than by men Only by women DNK

c) Managing the crop while growing d) Harvesting the crop e) Handling crop after harvest, but before selling,



g) Managing proceeds(cash) from selling farm produce

F. Production & Sales [base V.C. relevance on question S2] Tomatoes (A)

Q# Question Response Logic F1_0 Do you have farm records for your production and sales

of TOMATOES? Yes No

Request Can we use them now to check production and sales? Yes No

F1_1 What volume/Weight of TOMATOES did your household harvest in the past 12 months (July 2018 to June 2019)? [20 LITRE PAIL HAS THE SAME VOLUME AS NDOWA] [OTHER FARMERS USE 40 LITRE or 60 LITRE VOLUME PAILS FOR ONIONS AND TOMATOES, CONVERT TO KGS BASED ON 20 LITRE PAIL]

First harvest in the last 12 months (July 2018-June 2019) # of 40Lt Basins (dish) (weight=32kg) # of 20lt Pails (weight=16kg)

Total KG Second harvest in the last 12 months (July 2018-June

2019)

# of 40Lt Basins (dish) (weight=32kg) # of 20lt Pails (weight=16kg)

Total KG Third harvest in the last 12 months (July 2018-June 2019) # of 40Lt Basins (dish) (weight=32kg) # of 20lt Pails (weight=16kg)

Total KG

F1_2 From July 2018 to June 2019, what quantity did you sell of TOMATOES from your first harvest? [ASK FOR EACH HARVEST IN THE LAST 12 MONTHS]

[<= production]

# of 40Lt Basins (dish) (weight=32kg) # of 20lt Pails(weight=16kg) Total KG From July 2018 to June 2019, what quantity did you sell of TOMATOES from your

second harvest?

# of 40Lt Basins (dish) (weight=32kg) # of 20lt Pails(weight=16kg) Total KG From July 2018 to June 2019, what quantity did you sell of TOMATOES from your

third harvest?

# of 40Lt Basins (dish) (weight=32kg) # of 20lt Pails(weight=16kg) Total KG

F1_3 From July 2018 to June 2019, what volume/weight of your first TOMATO harvest

was spoiled [ASK FOR EACH HARVEST]

[<= production minus sales]

# of 40Lt Basins (dish) (weight=32kg)


# of 20lt Pails(weight=16kg) Total KG From July 2018 to June 2019, what volume/weight of your second TOMATO

harvest was spoiled

# of 40Lt Basins (dish) (weight=32kg) # of 20lt Pails(weight=16kg) Total KG From July 2018 to June 2019, what volume/weight of your third TOMATO harvest

was spoiled

# of 40Lt Basins (dish) (weight=32kg) # of 20lt Pails(weight=16kg) Total KG

F1_4 Why were the TOMATOES spoiled/lost?

[SELECT ALL THAT APPLY]

Damage at harvest Damaged at farm (storage) Damaged during transport Damaged at market Could not be sold prior to spoil DNK

F1_4b

Why did you not sell any TOMATOES? No surplus to sell Could not get a buyer Prices were too poor Quality of my crop was too poor Other DNK

If F1_2 = 0

F1_5 BLANK F1_6 BLANK F1_7 Did you primarily sell TOMATOES?

[READ RESPONSES]

With farmers in my club/group With farmers not in my club/group On my own as an individual Other DNK

Only if Sales > 0

F1_8 Did you sell TOMATOES at any of the following locations? [READ RESPONSES] [SELECT ALL THAT APPLY]

Neighbors Local market Traders who came to me Traders that I delivered to Distant market Direct to Processor/large buyer NGO/Farmer group Other

Only if Sales > 0

F1_9 Rank these markets from 1 [MOST IMPORTANT TO INCOME FROM THIS CROP) to last (LEAST IMPORTANT TO INCOME FROM THIS CROP]

Neighbors Local market Traders who came to me Traders that I delivered to Distant market Direct to Processor/large buyer NGO/Farmer group Other F1_10 How was the weather for growing TOMATOES

in the past 12 months (July 2018 to June 2019)?

Favorable Moderate Bad DNK

F1_10a Was your TOMATO production affected by the heavy rains and flooding in March 2019 [Cyclone Idai]

Yes No

If yes go to F1_10b


F1_10b What was the impact of this on your TOMATO crop?

A small amount of my crop was lost About half my crop was lost Nearly all my crop was lost All my crop was lost

F1_10b What other effects did it have on your household?

Lost other crops Lost livestock Lost house/buildings Lost other assets (e.g. bicycle, radio, etc) Lost a relative No other effects

Onions (B)


of ONIONS? Yes No


F2_1 What volume/Weight of ONIONS did your household harvest in the past 12 months (July 2018 to June 2019)? [DO NOT DOUBLE COUNT]

# of 50kgs bags(weight= 55 kgs) # of 20Lt Basins (dish) (weight=22kg) Total KG Second harvest in the last 12 months (July 2018-June 2019) # of 50kgs bags(weight= 55 kgs) # of 20Lt Basins (dish) (weight=22kg) Total KG Third harvest in the last 12 months (July 2018-June 2019) # of 50kgs bags(weight= 55 kgs) # of 20Lt Basins (dish) (weight=22kg) Total KG

F2_2 From July 2018 to June 2019, what quantity did you sell of ONIONS from your

first harvest? [FOR EACH HARVEST]

[<= production]

# of 50kgs bags(weight= 55 kgs) # of 20Lt Basins (dish) (weight=22kg) Total KG From July 2018 to June 2019, what quantity did you sell of ONIONS from your

second harvest?

# of 50kgs bags(weight= 55 kgs) # of 20Lt Basins (dish) (weight=22kg) Total KG From July 2018 to June 2019, what quantity did you sell of ONIONS from your

third harvest?

# of 50kgs bags(weight= 55 kgs) # of 20Lt Basins (dish) (weight=22kg) Total KG

F2_3 From July 2018 to June 2019, what volume/weight of your first ONION harvest

was spoiled? [DO NOT DOUBLE COUNT]

[<= production]



From July 2018 to June 2019, what volume/weight of your second ONION harvest was spoiled?

# of 50kgs bags(weight= 55 kgs) # of 20Lt Basins (dish) (weight=22kg) Total KG From July 2018 to June 2019, what volume/weight of

your third ONION harvest was spoiled?


F2_4 Why were the ONIONS spoiled/lost?



F2_4b Why did you not sell any ONIONS? No surplus to sell Could not get a buyer Prices were too poor Quality of my crop was too poor Other DNK

If F2_2 = 0

F2_5 BLANK F2_6 BLANK F2_7 Did you primarily sell ONIONS?

[READ RESPONSES]

With farmers in my club/group With farmers not in my club/group On my own as an individual Other DNK (do not read out)

Only if Sales > 0

F2_8 Did you sell ONIONS at any of the following locations? [READ RESPONSESS] [SELECT ALL THAT APPLY]


Only if Sales > 0

F2_9 Rank these markets from 1 (MOST IMPORTANT TO INCOME FROM THIS CROP) to last (LEAST IMPORTANT TO INCOME FROM THIS CROP)

Neighbors Local market Traders who came to me Traders that I delivered to Distant market Direct to Processor/large buyer NGO/Farmer group Other F2_10 How was the weather for growing ONIONS in the

past 12 months (July 2018 to June 2019)?


F2_10a Was your ONION production affected by the heavy rains and flooding in March 2019?

Yes No


F2_10b What was the impact of this on your ONIONS?


F2_10c What other effects did it have on your household? Lost other crops Lost livestock Lost house/buildings Lost other assets (e.g. bicycle, radio, etc) Lost a relative No other effects

Irish Potatoes (C)

Q# Question Response Logic F1_0 Do you have farm records for your production and sales of

IRISH POTATOES? Yes No


F3_1 What volume/Weight of IRISH POTATOES did your household harvest in first harvest in the past 12 months (July 2018 to June 2019)? [EACH HARVEST]

12 (50Kg bags) of Irish potatoes = 1 Ngolo One (50Kg Bag) of Irish potatoes=70kgs One (20 Litre pail) of Irish potatoes = 19 Kgs Total KG Second harvest in the last 12 months (July 2018-June 2019) 12 (50Kg bags) of Irish potatoes = 1 Ngolo One (50Kg Bag) of Irish potatoes=70kgs 3 (20 litre pails) of Irish potatoes = 1 (50 Kg bag) of Irish One (20 Litre pail) of Irish potatoes = 19 Kgs Total KG Third harvest in the last 12 months (July 2018-June 2019) 12 (50Kg bags) of Irish potatoes = 1 Ngolo One (50Kg Bag) of Irish potatoes=70kgs One (20 Litre pail) of Irish potatoes = 19 Kgs Total KG # of 20lt Pails (weight=16kg)

Total KG

F3_2 From July 2018 to June 2019, what quantity did you sell of your first harvest of IRISH POTATOES? [EACH HARVEST]

[<= production]

12 (50Kg bags) of Irish potatoes = 1 Ngolo One (50Kg Bag) of Irish potatoes=70kgs One (20 Litre pail) of Irish potatoes = 19 Kgs Total KG From July 2018 to June 2019, what quantity did you sell of your second harvest

of IRISH POTATOES? [EACH HARVEST]

12 (50Kg bags) of Irish potatoes = 1 Ngolo One (50Kg Bag) of Irish potatoes=70kgs One (20 Litre pail) of Irish potatoes = 19 Kgs Total KG


From July 2018 to June 2019, what quantity did you sell of your third harvest of IRISH POTATOES? [EACH HARVEST]


F3_3 From July 2018 to June 2019, what volume/weight of your first IRISH POTATO harvest was spoiled? [EACH HARVEST]

[<= production]

12 (50Kg bags) of Irish potatoes = 1 Ngolo One (50Kg Bag) of Irish potatoes=70kgs One (20 Litre pail) of Irish potatoes = 19 Kgs Total KG From July 2018 to June 2019, what volume/weight of your second IRISH

POTATO harvest was spoiled? [EACH HARVEST]

12 (50Kg bags) of Irish potatoes = 1 Ngolo One (50Kg Bag) of Irish potatoes=70kgs One (20 Litre pail) of Irish potatoes = 19 Kgs Total KG From July 2018 to June 2019, what volume/weight of your third IRISH POTATO

harvest was spoiled? [EACH HARVEST]


F3_4 Why were the IRISH POTATOES spoiled/lost?



Why did you not sell any IRISH POTATOES? No surplus to sell Could not get a buyer Prices were too poor Quality of my crop was too poor Other DNK

If F3_2 = 0

F3_5 BLANK F3_6 BLANK F3_7 Did you primarily sell IRISH POTATOES?

[READ RESPONSES]

With farmers in my club/group With farmers not in my club/group On my own as an individual Other DNK (do not read out)

Only if Sales > 0


F3_8 Did you sell IRISH POTATOES at any of the following locations? [READ RESPONSES] [SELECT ALL THAT APPLY]


Only if Sales > 0


Neighbors

Local market Traders who came to me Traders that I delivered to Distant market Direct to Processor/large buyer NGO/Farmer group Other F3_10 How was the weather for growing IRISH

POTATOES in the past 12 months (July 2018 to June 2019)?


F3_10a Was your IRISH POTATO production affected by the heavy rains and flooding in March 2019?

Yes No

F3_10b What was the impact of this on your IRISH POTATOES?



Mangoes (D)


of MANGOS? Yes No


F4_1 What volume/Weight of MANGOES did your household harvest in the past 12 months (July 2018 to June 2019)? [DO NOT DOUBLE COUNT]

One (20 litre pail) of Mangoes= 16.3 Kgs One (15 Litre pail) of Mangoes= 13.9Kgs Total KG

F4_2 From July 2018 to June 2019, what quantity did you sell of MANGOES?

[<= production]

One (20 litre pail) of Mangoes= 16.3 Kgs One (15 Litre pail) of Mangoes= 13.9Kgs Total KG

F4_3 From July 2018 to June 2019, what volume/weight of MANGOES was spoiled?

[<= production]

One (20 litre pail) of Mangoes= 16.3 Kgs


One (15 Litre pail) of Mangoes= 13.9Kgs Total KG

F4_4 Why were the MANGOES spoiled/lost?



Why did you not sell any MANGOES? No surplus to sell Could not get a buyer Prices were too poor Quality of my crop was too poor Other DNK

If F4_2 = 0

F4_5 BLANK F4_6 BLANK F4_7 Did you primarily sell MANGOES with?

[READ RESPONSES]

Farmers in my club/group Farmers not in my club/group On my own as an individual Other DNK (do not read out)

Only if Sales > 0

F4_8 Did you sell MANGOES at any of the following locations? [READ RESPONSES] [SELECT ALL THAT APPLY]


Only if Sales > 0


Neighbors Local market Traders who came to me Traders that I delivered to Distant market Direct to Processor/large buyer NGO/Farmer group Other F4_10 How was the weather for growing MANGOS

in the past 12 months (July 2018 to June 2019)?


F4_10a Was your MANGO production affected by the heavy rains and flooding in March 2019?

Yes No

F4_10b What was the impact of this on your MANGO TREES?

Lost one or more trees Other (specify)

F4_10c What other effects did it have? Lost other crops Lost livestock Lost house/buildings Lost other assets (e.g. bicycle, radio, etc) Lost a relative No other effects


Oranges, Tangerines and Lemons (E)


of ORANGES, TANGERINES and LEMONS? Yes No


F5_1 What volume/Weight of ORANGES TANGERINES and LEMONS did your household harvest in the past 12 months (July 2018 to June 2019)?

# 20 liter Pails (14 kgs) Other Total KG

F5_2 From July 2018 to June 2019, what quantity did you sell of ORANGES

TANGERINES and LEMONS? [<= production]


F5_3 What volume/weight of your ORANGES TANGERINES and LEMONS was

spoiled in the past 12 months (July 2018 to June 2019)? [<= production]


F5_4 Why were the ORANGES TANGERINES and

LEMONS spoiled/lost? [SELECT ALL THAT APPLY]


Why did you not sell any ORANGES TANGERINES and LEMONS?

No surplus to sell Could not get a buyer Prices were too poor Quality of my crop was too poor Other DNK

If F5_2 = 0

F5_5 BLANK F5_6 BLANK F5_7 Did you primarily sell ORANGES TANGERINES

and LEMONS with: [READ RESPONSES]


Only if Sales > 0

F5_8 Did you sell ORANGES TANGERINES and LEMONS at any of the following locations? [READ RESPONSES] [SELECT ALL THAT APPLY]


Only if Sales > 0

F5_9 Rank these markets from 1 (MOST IMPORTANT TO INCOME FROM THIS CROP)

to last (LEAST IMPORTANT TO INCOME FROM THIS CROP)

Neighbors Local market Traders who came to me Traders that I delivered to Distant market


Direct to Processor/large buyer NGO/Farmer group Other F5_10 How was the weather for growing ORANGES,

TANGERINES and LEMONS in the past 12 months (July 2018 to June 2019)?


F5_10a Was your ORANGE, TANGERINE and LEMON production affected by the heavy rains and flooding in March 2019?

Yes No

F5_10b What was the impact of this on your crop?

Lost one or more trees Other (specify)


Guava (F)


GUAVA? Yes No


F6_1 What volume/Weight of GUAVA did your household harvest in the past 12 months (July 2018 to June 2019)?


F6_2 From July 2018 to June 2019, what quantity did you sell of GUAVA?

[DO NOT DOUBLE COUNT]

[<= production]


F6_3 What volume/weight of your GUAVA was spoiled in the past 12 months (July

2018 to June 2019)? [<= production]


F6_4 Why were the reasons GUAVA

spoiled/lost?



Why did you not sell any GUAVA? No surplus to sell Could not get a buyer Prices were too poor Quality of my crop was too poor Other DNK

If F6_2 = 0

F6_5 BLANK F6_6 BLANK


F6_7 Did you primarily sell GUAVA with: [READ RESPONSES]


Only if Sales > 0

F6_8 Did you sell GUAVA at any of the following locations? [READ RESPONSES] [SELECT ALL THAT APPLY]


Only if Sales > 0

F6_9 Rank these markets from 1 (MOST IMPORTANT TO INCOME FROM THIS

CROP) to last (LEAST IMPORTANT TO INCOME FROM THIS CROP)

Neighbors Local market Traders who came to me Traders that I delivered to Distant market Direct to Processor/large buyer NGO/Farmer group Other F6_10 How was the weather for growing GUAVA in

the past 12 months (July 2018 to June 2019)?


F6_10a Was your GUAVA production affected by the heavy rains and flooding in March 2019?

Yes No

F6_10b What was the impact of this on your GUAVA crop?

None Lost one or more trees Other (Specify)

F6_10c What other effects did it have on your household?


Chilies (G)


CHILIES? Yes No


F7_1 What volume/Weight of CHILIES did your household harvest in the past 12 months (July

2018 to June 2019)? First harvest

# of 50kgs Bags (37.5kgs) Other Total KG What volume/Weight of CHILIES did your household harvest in the past 12

months (July 2018 to June 2019)? Second harvest

# of 50kgs Bags (37.5Kgs) Other Total KG


What volume/Weight of CHILIES did your household harvest in the past 12 months (July 2018 to June 2019)? Third harvest

# of 50 kgs Bags (37.5Kgs) Other Total KG

F7_2 From July 2018 to June 2019, what quantity did you sell of CHILIES?

First Harvest [<= production]

# of 50 kgs Bags (37.5Kgs Other Total KG From July 2018 to June 2019, what quantity did you sell of CHILIES?

Second Harvest

# of 50 kgs Bags (37.5 Kgs) Other Total KG From July 2018 to June 2019, what quantity did you sell of CHILIES?

Third Harvest

# of 50 kgs Bags (37.5 Kgs) Other Total KG

F7_3 From July 2018 to June 2019, what volume/weight of your first CHILIES harvest

What ONIONS was spoiled? First Harvest [<= production]

# of 50 kgs Bags (37.5 Kgs) Other Total KG From July 2018 to June 2019, what volume/weight of your second CHILIES

harvest was spoiled? Second Harvest

# of 50 kgs Bags (37.5 Kgs) Other Total KG From July 2018 to June 2019, what volume/weight of your third

CHILIES harvest was spoiled? Third Harvest

# of 50 kgs Bags (37.5 Kgs) Other Total KG

F7_4 Why were the reasons CHILIES spoiled/lost?



Why did you not sell any CHILIES? No surplus to sell Could not get a buyer Prices were too poor Quality of my crop was too poor Other DNK

If F7_2 = 0

F7_5 BLANK F7_6 BLANK F7_7 Did you primarily sell CHILIES with:

[READ RESPONSES]


Only if Sales > 0


F7_8 Did you sell CHILIES at any of the following locations? [READ RESPONSES] [SELECT ALL THAT APPLY]


Only if Sales > 0


Neighbors Local market Traders who came to me Traders that I delivered to Distant market Direct to Processor/large buyer NGO/Farmer group Other F7_10 How was the weather for growing CHILIES in

the past 12 months (July 2018 to June 2019)?


F7_10a Was your CHILIES production affected by the heavy rains and flooding in March 2019?

Yes No

F7_10b What was the impact of this on your CHILIES crop?

A small amount of my crop was lost About half my crop was lost Nearly all my crop was lost All my crop was lost Lost one or more trees

F7_10c What other effects did it have on your household?


H. Farm Management [base crop relevance on question S2]

Q# Question Response Logic H1 What are the farm management best

practices you know of? [DO NOT PROMPT]

a. Keep good farm records b. (blank) c. Sell together with other farmers d. Do costings for growing crops e. Calculate profits after selling f. Plan production for upcoming season g. Plan production for a specific market h. Understand the specifications for specific buyers i. Separating commodities into the different grades j. Timely buying of inputs

H1b What records did you keep? If yes to H1

a. keep good farm record

Input instructions (type, usage -- mostly pesticides for dilution) Costs (inputs bought, hired labor, rent, etc.) Production (area planted, date planted, chemicals used, volume harvested)


Sales (each sales transaction - volume and value) Seasonal Calendar for each crop (farmers should keep record of when should do

different things for each type of crop, i.e. planting, sowing, weeding, nursery, and also the spacing that crops should be etc.)

Loan records (re-payment tracking) History of crops grown in previous season (to inform the type of crops grown) If H1 = Yes H3 From whom did you first learn about each practice?

[READ LIST] [SELECT ONE RESPONSE]

a. Keep good farm records Parent/neighbor LOL/MSIKA Trainer LOL/MSIKA Lead Farmer Other Lead farmer Govt ext’n officer Other NGO/Project Farmer Organisation Other DNK

b. (blank) c. Sell together with other farmers d. Do costings for growing crops e. Calculate profits after selling f. Plan production for upcoming season g. Plan production for a specific market h. Understand the specifications for specific buyers i. Separating commodities into the different grades

j. Timely buying of inputs

H2 Have you used these management practices in the last 12 months? [READ PRACTICES]

a. Keep good farm records Yes – In all ways Yes – In some ways No DNK

b. (blank) c. Sell together with other farmers d. Do costings for growing crops e. Calculate profits after selling f. Plan production for upcoming season g. Plan production for a specific market h. Understand the specifications for specific buyers i. Separating commodities into the different grades j. Timely buying of inputs

H4 Did you employ or hire anyone for more than four weeks in the

last 12 months (July 2018-June 2019)? Yes No

H5 How many men did you employ or hire? H6 How many women did you employ or hire? H7 How many weeks in the last 12 months (July 2018-June 2019)

did you employ or hire men

H8 How many weeks in the last 12 months (July 2018-June 2019) did you employ or hire women

H9 Did you process any of your harvest in the last 12 months (July

2018-June 2019) into other products to sell? Yes No

H10 Which crops did you process? Tomatoes

Onions Irish potatoes Mangoes Oranges, tangerines and lemons Guava Chilies (Birds eye and Paprika), red cayenne


H11 For the crops you processed, what did you do? Made sauce Made jam Made juice Made a snack product Cooked for eating by others (e.g. fries/chips) Made another product

H12 What was the total Kwacha value of your sales of this/these products in the last 12 months?

MK _______

H13 What was the total volume of sales of these products in the last 12 months?

H14 Did you buy any equipment or land in the last 12 months? Yes, No H15 What was the total amount you spent on equipment and land

in the last 12 months? If yes in

H14

I. Information [base crop relevance on question S2]

Q# Question Responses Logic I2 Where do you get information regarding the

following: [READ LIST] [ENUMERATOR TO CLASSIFY RESPONSES] [Select all that apply]

Neighbor/friend Govt ext’n officer Land O’Lakes/MSIKA extension worker LOL/MSIKA lead farmer Private buyer ext’n officer NGO/Project ext’n officer Vendors/traders Radio SMS FBO Leadership Fellow FBO member Posters Agricultural shows Trade fairs Other sources Did not get this info

a. Locations of markets to sell your crops

b. Names of possible buyers c. Where to buy inputs d. Where to access to finance e. Buyers’ product requirements

(volumes, grades, quality etc) f. Weather which can affect your crops g. Market prices for your crops

I3 Which is the best source of information for each

of the following? [READ LIST] [ENUMERATOR TO CLASSIFY RESPONSES]

Neighbor/friend Govt ext’n officer Land O’Lakes/MSIKA extension worker LOL/MSIKA lead farmer Private buyer ext’n officer NGO/Project ext’n officer Vendors/traders Radio SMS FBO Leadership Fellow FBO member Posters Agricultural shows Trade fairs Other sources Did not get this info

Locations of markets to sell your crops Names of possible buyers Where to buy inputs Where to access to finance Buyers’ product requirements (volumes,

grades, quality etc) Weather which can affect your crops Market prices for your crops

I1a Have you received any of the following information from

LOL/MSIKA in the past 12 months (July 2018 to June 2019)?

Locations of markets to sell your crops

Yes No

Names of possible buyers Where to buy inputs Where to access to finance Buyers’ product requirements (volumes, grades, quality etc) Weather which can affect your crops Market prices for your crops


J. Financial Services Q# Question Response Logic J1 Do you or anyone in your HH currently have a bank

account?

Yes No DNK

J2 If yes, is the account mainly for saving or for making payments to others?

Savings Payments Other reason DNK

If J1 = Yes

J3 Do you or anyone in your household CURRENTLY have savings with a Savings & Credit Co-operative Organization (SACCO) or MFI?

Yes No DNK

J4

Do you or anyone in your household CURRENTLY have a savings with a village savings and loan group?

Yes, a LOL/MSIKA organised group Yes, a group organised by another organisation No DNK

J5a Did you or anyone in your household take a loan from a bank, SACCO, or MFI in the PAST 12 MONTHS? [Multiple response possible]

Yes, from a bank Yes, from an MFI Yes, from a SACCO No DNK

J6-8 Blank J9a Did you or anyone in your HH take farm inputs on loan

from a buyer or an agro-dealer in in the PAST 12 MONTHS? [multiple response]

Yes, a processor, Yes, an agro-dealer No

J9b What is the name of processor? What is the name of the agro-dealer? J10 Blank J11 Blank J12a Did you or anyone in your HH have a loan in cash from

a Land O’Lakes village savings & loan group in the past 12 months?

Yes No

J13 Blank J14 Blank J15 Blank J16 Blank J17 Did you or anyone in your HH receives any training in

how to manage your finances in the PAST 12 MONTHS?

Yes from LOL/MSIKA Yes from my Farmer Organisation Yes from another organisation No

FGD Topic Guide – Farmer Beneficiaries SPACING REMOVED Complete this prior to starting the group discussion using information from MSIKA/LOL.

Name of Facilitator: __________________________ Date held:______________ 2019

FBO name: __________________________ District:___________________

EPA:_____________________________ Village: ______________________

FGD is: Male only Female only Mixed (Circle)

Main crop focus (circle one): Tomato Onion Irish potato Mango Chili Citrus Guavas

Other target crops grown (circle all that apply): Tomato Onion Irish potato Mango Chili Citrus Guava (For the discussion, focus only on the main crop that the group is growing). (When recruiting, check that the participants are the actual beneficiaries that attended agricultural production training, not a spouse or a proxy, otherwise they cannot contribute well).

A. Introduction and Consent: “Hello, my name is <name> and I am working for Kadale Consultants. Kadale has been asked to talk to fruit and vegetable farmers by MSIKA project, which is run by Land O’Lakes, a US organization that works with farmers. Your names have been given to us as people who have been trained by MSIKA project.

We would like to discuss your experiences as <crop> growers and the training you received from Land O’ Lakes MSIKA program.

The discussion will take about an hour and a half. What you tell us will not be attributed to any of you. Anything said by any of you should not be shared outside of the research team.

Are you willing to take part in the discussion?” (If any say no, ask why and recruit again)

Reason(s) for not giving consent: ____________________________________________________

List the participants who consented to be part of the group

Name Land area (acres)

or # of trees for <target crop>

Sex

(M/F)

Age (approx. if not known)

Add more lines

(Note: If respondent has not been trained by LOL/ MSIKA in agricultural production and/or post-harvest handling, then recruit an alternative who has).

B. Background Information (to get the group talking) 1. How long have you been growing <the crop>?) Were you growing this before being trained by

LoL/MSIKA? C. Agricultural production training from MSIKA (nb. This can include training by MSIKA directly

or by Lead Farmers trained by MSIKA. Post-Harvest Handling training is covered in the next section, so come back to that after discussing agricultural production training)

1. What agricultural production training did you receive from LOL /MSIKA, including by Lead Farmers trained by LOL/ MSIKA? What did you like about the training? How could it be improved?

2. Which of the agricultural production practices that you learnt from LOL/MSIKA have you been able

to apply/use?

Probe: How many in the group applied each practice from the list they say

Practices that have been applied How many apply it?

1. – 10.

Probe: Why apply these? Why not apply others? What stops you from using other practices that you learned about? This is a key question so probe extensively.

3. Did Land O’Lakes/MSIKA link you to input suppliers/agro-dealers during field days or at other times? How has this helped you access inputs? Overall, how easy/difficult has it been so far to get the farm inputs that you learned about? Note: Farmers may talk about inputs for production (seed, crop protection chemicals, fertilizer, equipment), and harvest and post-harvest chemicals for storage and equipment for storing – it is okay to discuss both if they come up, but try to separate responses about crop production from harvesting and post harvesting handling/storage..42

4. What difference has the agricultural production training by Land O’Lakes/MSIKA made to the amount of crop you produce and the quality of your crop, if any? Why do you think that is? Probe: This is trying to see what difference it has made to the amount and the quality of their production – ask them to compare with the seasons before they applied the practices, and ideally some quantification, even if it is “about half more than before.”

5. Did Cyclone Idai affect your crop’s production in any way? (Probe: If yes, how did it affect your crops? What did you do to mitigate? Were the trainings that were offered by MSIKA useful in mitigating some of the impacts? Ask respondents to be specific about which trainings/techniques?)

D. Post-harvest handling (If the members of this FBO have not had training in PHH, then skip this section)

1. What training on post-harvest handling did you receive from Land O’Lakes/MSIKA including that from Lead Farmers trained by LOL/ MSIKA? What did you like about the approach? How can it be improved? (Note: this includes practices about handling the crop at 1. harvesting, 2. storage on the farm and 3. at the market, so ensure they consider practices for all three areas)

2. Which of the harvest and post-harvest handling practices that you learnt from Land O’Lakes/MSIKA (list from previous question) have you been able to apply/use?

Probe: Ask how many in the group applied each practice that someone gives you.

Practices that have been applied How many know it?

1. – 8.

Probe: Why did they apply these?

Why not apply others? What stops you from using other post-harvest and handling practices that you learned about?

This is a key question so probe extensively.

3. Have you seen any change in losses at harvest, on the farm or at the market? Probe: If so, what has been the change?

E. Markets 1. How do you sell your <target crop>?

Probe: Do you sell on your own? With others? Where do you sell your crops? Why do you sell it in this way?

2. How has Land O’ Lakes/MSIKA better helped you to find markets for your crop? What works well and what does not work so well? How could MSIKA’s support to help farmers sell their crops be improved?

Probe this to get different responses from group members and see how many get each type (e.g. market identification, market prices, market information).

F. Any questions or comments from the group? G. Other observations from the facilitator Thank you all.

42

FBO KII Guide Potentially applicable indicators.

1. Percent of producers who have access to current market information through their cooperatives or producer associations

2. Value of sales by project beneficiaries 3. Percent of registered processing firms in target sectors that obtain certification with Malawi

Bureau of Standards related to product quality 4. Total increase in installed storage capacity (dry or cold storage) as a result of USDA assistance 5. Number of sales agreements with FBOs 6. Number of FBOs using improved financial management practices and systems as a result of

USDA assistance 7. Percentage of FBOs who are aware of international production and handling standards as a result

of USDA assistance 8. Number of FBOs trained in improved financial and organizational management)

Prior to the FBO interview, gather and insert the following info:

Name of FBO: Year Established: District: Target crops grown: EPA: GVH location: Village location: # of MSIKA farmers: Chairperson name Contact number: Secretary name Contact number: Treasurer name Contact number: Does the FBO process? If so what/how?

Is this certified to MBS standards?

How many sales agreements has the FBO made with the help of MSIKA?

2018 ___________ 2017 ___________ 2016 ___________

What value of FBO member sales are claimed by MSIKA?

2018 – MK _________ 2017 – MK _________ 2016 – MK _________

Has this FBO been trained in improved financial and organizational management?

How many improved financial mgt practices and systems has MSIKA helped the FBO to implement?

___________

Is this FBO reported as being aware of international production and handling standards due to MSIKA’s support?

What is the increase in storage capacity claimed by MSIKA

___________ cu. mtrs

Data from MSIKA/LOL in advance (columns 1-3):

Training module /name When held (month/yr)

# of members trained

Verified by interviewer Comments

1. – 8. Wholly/partially/ not verified

Opening – interview with the committee members. My name is <name>. We are conducting an independent review of Land O’Lakes’ MSIKA program which has been training farmer member organisations like yours, and training farmers in agricultural techniques and technologies. We want to ask you some questions that will help us to learn more about the work of Land O’Lakes/MSIKA. What you tell us will not be shared with Land O’Lakes/MSIKA, so they will not find out what you say about them. Are you happy to continue? (Yes/No)

1. Please introduce the members of the committee:

Mr/Mrs /Ms Name Position Phone #

1. Tell me a bit about this FBO, such as when it was established, how many members it has and the crops your members grow. (This question aims to just start the conversation and make them comfortable)

2. When did you first meet Land O’Lakes/MSIKA? What did they say to you that encouraged you to start working with them?

3. What were your expectations of Land O’Lakes/MSIKA? What did they expect of you?

4. What training have you received about running a farmer member-based organisation such as <name of the FBO>

Training module /name When held (month/yr)

# of members trained

Verified by interviewer Comments

1. – 8. Wholly/partially /not verified

5. What have been the most useful training the leadership of the FBO has received? Why do you say that? What has been the least useful? Why do you say that? In what ways could the training be improved?

Has this FBO been trained in improved financial and organizational management?

How many improved financial mgt practices and systems has MSIKA helped the FBO to implement?

___________

6. What have been the most useful training the members of the FBO received? Why do you say that? What has been the least useful? Why do you say that? What changes have you seen as a result of these trainings? Can you give examples? In what ways could the training be improved?

7 What improved crop practices have you seen many members now adopting? Why these ones and not others? What could help to increase the uptake of techniques and technologies by members? What have been the results on production volumes and quality from the training? (push for numbers and/or examples of change).

8. What market information has the FBO received and what does it pass on to members? What training in accessing markets has the FBO and its members received? How have these trainings helped the FBO and its members to access markets? Can you give some examples of these new markets?

9. How does the FBO help its members to sell their crops? What records do you keep of members sales? What was the volume of sales for your members in 2018? And 2017? And 2016?

How many sales agreements has the FBO made with the help of MSIKA?

2018 ___________ 2017 ___________ 2016 ___________

What value of FBO member sales are claimed by MSIKA?

2018 – MK _________ 2017 – MK _________ 2016 – MK _________

10. Do you process any of your members’ crops in any way? What do you do? Is this certified by MBS? Who

Is this FBO reported as being aware of international production and handling standards due to MSIKA’s support?

Is the processing certified by MBS? Was that due to MSIKA help?

_____________ _____________

11. Do you have any warehousing or storage capacity? Did MSIKA help you to increase this? When/how did you do this?

What is the increase in storage capacity claimed by MSIKA ________ cubic. Mtrs (width x length x height

12. Did cyclone Idai affect the FBO activities in any way? What were these? Any other comments you want to share about working with MSIKA/Land O’ Lakes. Thanks

KII Topic Guide Processors – MSIKA MTE

Prior to each processor interview, obtain key information from the LOL/MSIKA team on the background to the processor and the work that has been done with them

Background of the processor (what they process, products, markets, scale, plans, etc.)

Background to the relationship (key people on both sides, when started, what challenges in progressing, etc)

What LOL/MSIKA has done with them (training, technical help, financial support/access, MBS certification, quality stds training), etc.

Results claimed - investment leveraged, certification achieved, employment attributed, change in sales, new markets, new products, etc.

Other information relevant for the interview?

Introduction: My name is XXXX, and I am working for Kadale Consultants. Kadale has been asked by LOL’s MSIKA project to talk to fruit and vegetable processors. Your name and business have been given to us as an organization that MSIKA has been working with on a number of things. We would like to discuss your experience of working with them. The discussion will take about an hour and a half. What you tell us will not be directly attributed to you, so Land O’Lakes will not know what you have said about them. Are you willing to take part in the discussion?” Consent given – Yes / No 1. Tell me about your business?

Probe: When started, what processing, what products made, what markets, what opportunities to grow, how many employees (now and a year ago – FT/PT, Temp, M/F, if possible), etc.

2. How did you first came into contact with the LoL/MSIKA team?

Probe: approached MSIKA or approached by MSIKA? What were your expectations, what encouraged you to develop a working relationship, has the relationship worked well, why/not, etc.?

3. What have been the areas of support from, and collaboration with, LOL/MSIKA?

Probe: explore each of the areas they mention. Check for any areas provided by LOL/MSIKA, but not mentioned by the processor. What has progressed well, more slowly? Why, what could have been done to speed it up?

4. How has the support/collaboration assisted the business?

Probe: Get detail, examples, any quantification?

5. (If LOL/MSIKA indicate that these are relevant):

Have you increased your warehousing/storage capacity (dry or cold) with LOL/MSIKA help? Probe for details – when, by how much, in what way, why, etc.?

Have you obtained any certification for your products from MBS? Probe for details – at what stage is it, when achieved, what products, how did MSIKA help, etc.?

Have your staff been trained in quality standards? Probe for details – when, covering what, how many, how useful/how applied, etc.

Name of organization: Name of interviewee: Contact # or email: Position of interviewee: Conducted by: Date conducted:

Have you formed any public-private partnerships? Probe for details – with whom, what does the partnership cover, etc.?

What are the improved good manufacturing practices that you have learnt via LOL e.g. food safety?

6. Has working with LOL/MSIKA resulted in any increase in investment by you in your business? Probe: What investment was made, value of it, why, in what way was it linked to LOL/MSIKA, etc.?

7. Has working with LOL/MSIKA had an effect on your sales and/or employment?

Probe: quantify if possible. What led to the increase, how much did the work with LOL/MSIKA result in this, what is the likely change in the coming year, etc.?

Probe: have you been helped to make any sales agreements with FBOs or aggregators or supermarkets?

Do you have any points you would like to feedback to LOL/MSIKA? Do you have any questions for me? Thank you.

KII Topic Guide DADO/Horticulture Specialist – MSIKA MTE

Introduction: “My name is <name>, and I am working for Kadale Consultants. Kadale has been asked by LOL’s MSIKA project to talk to key stakeholders, such as Government Officers, who know about the implementation of its programme.

We would like to discuss your experience of working with them.

The discussion will take about an hour and a half. What you tell us will not be directly attributed to you, so Land O’Lakes will not know what you have said about them.

Are you willing to be interviewed?” Consent given – Yes / No

1. When did you first come into contact with Land O’Lakes MSIKA team? What did they tell you about the program? What expectations did you have at the start?

2. What are the activities of the MSIKA program that you have seen being implemented in this District? Please tell me more about each of these

Probe: Get the list of activities first, before getting the detail - Ask so that you get them all (“any other elements?)” – agricultural production training, post-harvest handling training, demonstration plots, formation and training of crop-based Farmer Business Organisations (FBOs), working with any processors or agro-dealers, etc.

Probe to get details of each component per the table. We are looking for process issues – e.g. how did the co-ordination with govt. go; how involved are govt. in the identifying areas, making links etc.; how well organised were the activities, etc. We are also looking for content issues: how appropriate was the training content for these types of farmers, were these useful activities, etc.

Probe for suggestions on ways to improve it and why they think that. While not seeking criticisms, explore any areas where there is some weakness to address, what contributes to this, how has it been addressed since being identified, etc.

District: Name of interviewee: Contact # or email: Position of interviewee: Conducted by: Date conducted:

Activity Interviewee confirms

What involvement have you had in the trainings and other activities run

by LOL/MSIKA?

What are your comments on how the activities were

run (timely, how organised, DADO/HCS

involved, etc.)?

What are your observations on the ‘content’ of the

activities (relevant, useful, something smallholders can

implement, etc.)?

Any improvements or suggestions?


Yes/No

PHH Training Yes/No

Demo plot establishment Yes/No

Formation of, and training of Farmer Based Organisations

Yes/No

3. What changes have you seen as a result of the MSIKA program, such as changes in farmer knowledge and understanding, adopting agricultural practices, lower harvest losses, more sales, etc.

Activity Include if

mentioned in

Q2 above

What changes have you seen knowledge and use

of practices?

Have you seen any changes for farmers, such as higher production, higher quality, lower losses, more sales?

Have you seen any changes in the

operations of the FBOs?

Any improvements or suggestions?


Yes/No

PHH Training Yes/No

Demo plot establishment Yes/No

Formation of, and training of Farmer Based Organisations

Yes/No

4. How has MSIKA improved your and/or other government staff, such as your extn officers, knowledge and skills in relation to the seven target horticultural crops?

Probe: In what ways, who benefited, how great was the change in knowledge, skill, etc.

5. What was the impact of Cyclone Idai on horticultural crops and value-chain activities?

Probe for specifics of where in the District, in what ways (flooding, water logging, wind), to what extent/how severe, affecting how many farmers in these value-chains, etc.

Do you have any points you would like to feedback to LOL/MSIKA?

Do you have any questions for me?

Thank you.

Annex 5: FGD and KII details

Table 70: Focus Group Discussions – Detailed Breakdown District Crop(s) Sex of

Participants Location District Total Variations from plan

1

Dedza

Irish Potato 4F/6M Kanyama EPA, GVH Chiphazi, Kanyama village

4

None

2

Tomato 4F/6M Lobi EPA, GVH Kauma

Not enough male tomato producers, so make into a mixed group

3

Onion (and other field crops)

3F/3M Kanyama EPA, GVH Chiphazi, Kanyama village

Not enough female onion producers, so mixed sex and crop was done

4

Mixed (mango, onion, tomato, lemons, guava)

8F/4M Lobi EPA, Kamala village

Not enough hGuava producers, so mixed egetable and fruit producers

5

Lilongwe

Tomato 5F/5M Chileka EPA, Nyanja village

3

None

6

Onion 7F/1M Chileka EPA, Kadzani village

Not enough male producers, so mixed male and female was done

7 Chillies 6F/2M Mkwinda EPA,

Chikunkhwiro village

Was just added

8

Manogchi

Tomato 10F/0M Lungwena EPA, Malamya village

2

None

9

Fruits (mixed)

4F/4M Nansenga EPA, Mpondasi village

Was supposed to be for citrus but citrus farmers were too few in numbers so mixed was done

10

Mchinji

Chillies 4F/5M Chiosya EPA, Galeta village

4

Not enough male producers, so mixed male and female, chilie and field crops

11 Irish Potatoes

7F/0M Zulu EPA, Chithoda village

None

12 Citrus 5F/5M Madziakayenda village

None

13 Mango 4F/3M Zulu EPA, Kayesa village

None

14

Ntcheu

Irish Potato 2F/7M Tsangano EPA, TA Mpando

3

Not enough male producers, so male and female was done

15 Guava 8F/0M Njolomole EPA, TA Njolomole

None

16 Mango 6F/6M Nsipe EPA,

Makwangwala Not enough female farmers, so mixed male and female was done

Total 87F/57M 16

Table 71: KIIs – Detailed Breakdown REDACTED

Annex 6: Additional Tables Table 72: Practices Used by Crop, Baseline v Mid-term

# Practices Used Tomato Onion Potato Mango Citrus Guava Chili B’line MTE Dif B’line MTE Dif B’line MTE Dif B’line MTE Dif B’line MTE Dif B’line MTE Dif B’line MTE Dif

1 Composting 45.3 65.3 20.0 59.2 73.0 13.8 29.8 54.2 24.4 17.9 63.7 45.8 42.3 71.4 29.1 29.9 50.0 20.1 35.5 46.3 10.8 2 Manuring 64.9 68.5 3.6 73.5 61.3 -12.2 42.9 50.8 7.9 14.5 47.3 32.8 53.5 85.7 32.2 19.7 47.9 28.2 48.4 68.7 20.3 3 Ridging 51.4 70.3 18.9 36.7 61.3 24.6 84.1 84.6 0.5 n/a n/a n/a n/a n/a n/a n/a n/a n/a 54.8 92.5 37.7 4 Mulching 66.7 82.8 16.1 63.3 77.4 14.1 19.0 25.1 6.1 6.0 27.5 21.5 8.5 28.6 20.1 n/a n/a n/a 22.6 71.6 49.0 5 Crop Rotation 45.6 58.4 12.8 50.0 59.9 9.9 53.6 62.5 8.9 n/a n/a n/a n/a n/a n/a n/a n/a n/a 29.0 53.7 24.7 6 Minimum tillage 14.1 20.7 6.6 7.1 13.9 6.8 5.5 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 3.2 19.4 16.2 7 Planting seeds in a nursery before plant

out 86.2 97.0 10.8 85.7 92.7 7.0 13.8 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 67.7 98.5 30.8

8 Staking 80.8 95.9 15.1 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 29.0 n/a n/a 9 Succession planting 29.1 48.7 19.6 n/a n/a n/a 29.8 42.1 12.3 n/a n/a n/a n/a n/a n/a n/a n/a n/a 3.2 52.2 49.0 10 Using irrigation 88.9 98.3 9.4 81.6 91.2 9.6 68.5 84.6 16.1 17.0 40.7 23.7 36.6 85.7 49.1 21.2 43.8 22.6 48.4 55.2 6.8 11 Soil and water conservation 24.0 43.3 19.3 25.5 47.4 21.9 n/a 46.2 n/a 3.4 39.6 36.2 5.6 57.1 51.5 9.5 35.4 25.9 22.6 40.3 7.7 12 Draining excess water 15.9 31.3 15.4 17.3 35.0 17.7 n/a n/a n/a 2.1 17.6 15.5 1.4 n/a n/a 5.8 20.8 15.0 6.5 19.4 12.9 13 Testing soil acidity 0.3 7.5 7.2 1.0 5.8 4.8 n/a n/a n/a n/a 7.7 n/a n/a 14.3 n/a n/a 6.3 n/a 3.2 9.0 5.8 14 Adding lime or ash to soil to reduce acidity 0.6 8.8 8.2 1.0 21.9 20.9 n/a n/a n/a n/a 13.2 n/a n/a n/a n/a n/a 1.4 n/a 3.2 19.4 16.2 15 Choosing the variety 34.5 68.5 34.0 39.8 67.2 27.4 n/a 68.2 n/a 7.7 n/a n/a n/a n/a n/a 5.8 n/a n/a 22.6 68.7 46.1 16 De-suckering/removing unwanted side

shoots 63.7 89.4 25.7 n/a n/a n/a n/a n/a n/a 15.3 n/a n/a 21.1 n/a n/a 20.4 n/a n/a 45.2 n/a

17 Spraying for pests and disease 67.3 87.9 20.6 67.3 78.1 10.8 n/a 80.9 n/a 3.0 29.7 26.7 15.5 28.6 13.1 8.0 33.3 25.3 38.7 65.7 27.0 18 Using recommended plant and ridge

spacing n/a 93.5 n/a n/a 95.6 n/a n/a 95.3 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 91.0 n/a

19 Using recommended fertilizer & application n/a 91.4 n/a n/a 92.7 n/a n/a 95.3 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 51.6 67.2 n/a 20 Sterilizing nursery beds before planting n/a 76.5 n/a n/a 70.8 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 79.1 n/a 21 Scouting for pests and diseases n/a 70.5 n/a n/a 68.6 n/a n/a 64.9 n/a n/a 25.3 n/a n/a 28.6 n/a n/a 35.4 n/a n/a 65.7 n/a 22 Uprooting infected plants and burning n/a 78.0 n/a n/a 84.7 n/a n/a 72.6 n/a n/a 41.8 n/a n/a 14.3 n/a n/a 43.8 n/a n/a 74.6 n/a 23 Sowing seed in row/groove nursery n/a 89.0 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 94.0 n/a 24 Using fish soup/sugar solutions as insect

bait n/a 8.4 n/a n/a 8.0 n/a n/a 7.0 n/a n/a 9.9 n/a n/a - n/a n/a 10.4 n/a n/a - n/a

25 Planting sunken beds n/a 78.2 n/a n/a 75.9 n/a n/a n/a n/a n/a - n/a n/a n/a n/a n/a n/a n/a n/a 52.2 n/a 26 Hardening off before planting out n/a 79.7 n/a n/a 80.3 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 74.6 n/a 27 Selecting the best seedlings for planting

out n/a 84.4 n/a n/a 83.2 n/a n/a 88.6 n/a n/a n/a n/a 19.7 n/a n/a 11.7 n/a n/a 41.9 83.6 41.7

28 Green manuring 45.9 n/a n/a 49.0 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 35.5 n/a n/a 29 Water capture 25.8 n/a n/a 19.4 n/a n/a 22.1 n/a n/a 4.3 54.9 50.6 2.8 85.7 82.9 4.4 52.1 47.7 6.5 n/a n/a 30 Earthing up n/a n/a n/a 79.6 85.4 5.8 83.0 92.3 9.3 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 31 Clipping plant ends n/a n/a n/a n/a 63.5 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 32 Using raised beds n/a n/a n/a n/a 75.2 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 74.6 n/a 33 Chitting n/a n/a n/a n/a n/a n/a n/a 54.8 n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 34 Pruning n/a n/a n/a n/a n/a n/a n/a n/a n/a 62.1 86.8 24.7 47.9 71.4 23.5 48.9 83.3 34.4 n/a n/a n/a 35 Grafting n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 14.3 n/a n/a 28.6 n/a n/a 10.4 n/a n/a n/a n/a 36 Budding n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 18.7 n/a n/a 28.6 n/a 26.3 12.5 -13.8 n/a n/a n/a 37 Staking young trees n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 38.5 n/a n/a 42.9 n/a n/a 35.4 n/a n/a n/a n/a 38 Digging of planting holes prior to planting n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 41.8 n/a 52.1 42.9 -9.2 35.0 39.6 4.6 n/a n/a n/a 39 Deflowering of first flowers n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 28.6 n/a n/a 14.3 n/a 4.4 27.1 22.7 n/a n/a n/a 40 Top working n/a n/a n/a n/a n/a n/a n/a n/a n/a 23.4 n/a n/a 15.5 n/a n/a n/a n/a n/a 51.6 n/a n/a 41 Land preparation n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 94.0 n/a 42 Planting n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 43 Drying n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 44 Tree rejuvenation n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a n/a 29.0 n/a n/a Sample size/difference 333 464 131 98 137 39 289 299 10 235 91 -144 71 7 -64 137 48 -89 31 67 36