Pushing the frontiers in ag- nutrition methods and metrics · 2019-05-17 · effective labor is a function of nutrient intake. Due to methodological pitfalls such as: 1) Unobserved

Funded by:

Pushing the frontiers in ag-nutrition methods and metrics:

Round 1 Fellows share their research

26 October 2016

Tufts University Friedman School of Nutrition

London School of Hygiene & Tropical Medicine

Funded by:

Nutritional Barriers to Agricultural Productivity in

Smallholder Farm Households: Panel Data Evidence from

Uganda

Round 1 Wrap-Up webinar

Oct 26, 2016

Rosemary IsotoInnovative Metrics and Methods for

Agriculture and Nutrition Actions

Introduction

47 %US$ 1.25 26%

206 million

807 million

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World Bank(2015) Poverty

World Bank(2015) Poverty

FAO (2014)Malnutrition



FAO (2013)Malnutrion,

Uganda

Poverty and Malnutrition in SSA

Objectives of the Study

• 1) Assess the impact of nutritional intake on

agricultural productivity and, hence, labor

productivity.

• 2) Examine gender differences in deficiency of

these nutrients and the resulting effect on

productivity.

• 3) Assess the threshold levels for macronutrient

and micronutrient intake needed to achieve some

level of agricultural productivity.

Methodology: Data

• The study utilizes a panel dataset from Uganda

National Panel Surveys (UNPS) that has four

waves; 2005/06, 2009/10, 2010/11 and

2011/12.

• Balanced panel with 1,634 observations per

wave

• Total – 6,536 observations.

Methodology: Empirical Models

• Objective one: We estimate a Cobb Douglas production function in which effective labor is a function of nutrient intake. Due to methodological pitfalls such as:

1) Unobserved fixed effects such as genetic endowment, high metabolic rate of some individuals that may be problematic in estimating the nutrition productivity relationship.

2) Causality that can run in both directions.

• Fixed Effects Instrumental Variable (FEIV) approach is used.

• Objective 2: We disaggregate the data based on gender and estimate the C-D function using FEIV.

• Objective 3: First use multivariate analysis specifically, cluster analysis, to characterize the population using nutrient intake profiles. The dissimilarity measure used in the classification was Euclidean distance between these quantitative variables.

• Thereafter, instrumental variable threshold regression is used after obtaining threshold values (Ward’s 1963).

Results: Descriptive Statistics for the Waves

2005/06 2009/10 2010/11 2011/12 Panel

Mean (S.D) Mean (S.D) Mean (S.D) Mean (S.D) Mean (S.D)

Socio-demographic

Age of head43.90

(15.02)

47.87

(14.90)48.54 (14.84)

49.42

(14.55)

47.43

(14.97)

Education of head 3.22 (3.47) 2.79 (3.22) 3.17 (3.56) 5.39 (4.12) 3.64 (3.75)

Sex of head 0.73 (0.44) 0.72 (0.45) 0.69 (0.46) 0.69 (0.46) 0.71 (0.45)

Household size 6.13 (2.96) 6.93 (3.18) 7.58 (3.46) 8.17 (3.74) 7.20 (3.43)

Productivity

Cropland (acres)11.38

(14.99)8.66 (9.80) 9.33 (11.70) 6.95 (6.87)

9.08

(11.34)

Hired labor26.26

(75.66)

38.62

(370.82) 21.69 (42.41)

17.73

(40.75)

26.08

(191.62)

Family labor 138 (153)871

(1225)

817

(878)

1606

(10605)

858.25

(5380)

41648 48743 39287 43117 43198

Annual Percentage Changes in Daily Nutrient Intake in Uganda in 2005/06-2011/12

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2005/06 2009/10 2010/11 2011/12

Wave

Calories Average calories=-4.9%

Calcium Average calcium=86%

Iron Average Iron=-7.4%

Proteins Average proteins=42.3%

Vitamin C Average vitamin C=59.8%

Years:2005/06-2011/12

Annual percentage change in daily per capita nutrient intake in Uganda

Econometric Results: FEIV Estimates for Productivity and

Nutrient Intake ( Objective 1)

Variables Calories Proteins Calcium Iron Vitamin

C

Vitamin

A

Nutrients 3.541*** 3.880*** 3.092*** 6.156*** 2.038*** 3.723***

(0.422) (0.484) (0.442) (0.874) (0.394) (1.349)

Cropland (ln) 1.233*** 1.165*** 1.207*** 1.310*** 1.294*** 1.221***

(0.093) (0.091) (0.096) (0.095) (0.091) (0.117)

Family labor (ln) 1.291*** 1.321*** 1.492*** 1.332*** 1.323*** 1.205***

(0.074) (0.073) (0.082) (0.074) (0.069) (0.097)

Hired labor (ln) 0.347*** 0.302*** 0.244*** 0.394*** 0.445*** 0.579***

(0.047) (0.049) (0.056) (0.047) (0.043) (0.074)

Input cost (ln) 0.055*** 0.049*** 0.047** 0.060*** 0.064*** 0.107***

(0.018) (0.018) (0.019) (0.018) (0.017) (0.027)

Constant -23.58*** -12.53*** -17.20*** -12.38*** -6.788*** -16.97**

(3.145) (1.924) (2.860) (2.159) (1.847) (7.094)

R2 overall 0.074 0.053 0.051 0.039 0.130 0.06

Prob > chi2 0.000 0.000 0.000 0.000 0.000 0.000

Rho 0.157 0.238 0.123 0.275 0.106 0.234

Observations 6,536 6,536 6,536 6,536 6,536 6,536

Number of year 4 4 4 4 4 4

Production Function Estimation Results after Gender Disaggregation (Objective 2)

Variable Male Model Estimates Female Model Estimates

Calories 2.980*** 4.960***

(0.473) (0.849)

Proteins 3.354*** 5.588***

(0.549) (0.926)

Calcium 2.433*** 4.893***

(0.437) (0.890)

Iron 5.686*** 10.08***

(1.035) (1.827)

Vitamin C 1.186** 3.676***

(0.481) (0.674)

Vitamin A -1.660*** -0.468

(0.640) (0.996)

Objective 3: Visualizing Clusters0

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G1G2G3G4G5G6G7G8G9G10G11G12G13G14G15G16G17G18G19G20G21G22G23G24G25G26G27G28G29G30G31G32G33G34G35G36G37G38G39G40G41G42G43G44G45G46G47G48G49G50Households

Dendogram for 2005/06

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Classification of households based on nutrient intake and productivity in Uganda

Threshold Values and Estimates from IV Threshold

Regression

Nutrient Threshold value Below the

threshold

estimates

Above the

threshold

estimates

calories 2635.85 (18.88) 2.996*** (0.759) 5.419*** (1.113)

Proteins 79.30 (0.79) 3.101*** (0.767) 8.534*** (1.943)

Calcium 931.68 (12.14) 1.993*** (0.718) 1.522*** (0.636)

Iron 15.41 (0.27) 4.395*** (1.227) 1.396 (1.365)

Vitamin C 149.83 (2.19) 0.890 (0.967) 1.124* (0.576)

Vitamin A 449.10 (11.36) 2.518*** (0.86) 9.512 (9.239)

Conclusion and Recommendations

• The study finds strong significant effect of calories, proteins, calcium, iron and vitamin C and vitamin A intake on productivity and hence, labor productivity.

• Women’s productivity more than doubles with additional nutrient intake.

• An IV threshold estimation shows that households were better off when their nutrient intake was above the threshold value.

• Therefore, policies that focus on enhancing intake of these nutrients have a great potential for delivering considerable benefits to SHF.

• In addition, policies that take into consideration gender will greatly benefit especially women who provide the bulk of agricultural labor.

Funded by:

Women’s Time Use, Agriculture and Nutrition in Rural Zambia

Cynthia R. Matare, PhD

IMMANA Research Dissemination Webinar

26 October, 2016

© ActionAid

Women’s Time Matters!

- “We don’t know how to measure what we care about, so we care about what we measure.” -

1. To explore women’s time use in rural Zambia

1. To develop a context-specific and rapid tool for measuring women’s time burdens.

Research Aim

Methods

Focus Group Discussions To determine the activities that women participate in within

their households and communities.

To determine HOW women perceive time scarcity/stress

Cognitive Interviews To identify any problems with comprehension of and/or

response to questions

Household Observations To obtain an objective assessment of women’s time use

© C.R. Matare

“… we have a lot of things to do, and one cannot

even sit like we have sat here for an hour.” LFP5

“… a woman does not have time for resting; she works as long as it is day. What makes her rest is

night. ” LMP10

Cooking Sweeping

Laundry Caring for children

Fetching water

Fetching firewood

Laundry

Dishes Farming GardeningProcessing grain and

other crops

Focus Group Discussions

© C.R. Matare

“We men are lazy…”

“Playing soccer”

“… our role is to make sure that the mother is

near the child.” CMP3

“We can cook, but our tradition says

the woman should do the housework.”

LMP10

“Talking to friends.”

“Listening to the radio.” “Drinking

beer with friends.”

Focus Group Discussions

© C.R. Matare

Cognitive Testing Results

1 month recall “How(often) many times did you do...?”

Misunderstood words and phrases “Working for wages” vs. “Piecework”

“Buying and selling goods”

Perceived Time Scarcity Likert -type questions First person statements

5-point Likert scale

Q6: I have had to wake up much earlier to give me more time to finish my activities/work.

- Rephrased to “… wake up earlier than you

usually do…”

Q3: My work often gives me stress.

© C.R. Matare

Household Observations

Funded by:

Nawonga chomene!

Funded by:

Strengthening the monitoring and evaluation of the Ghana School Feeding

Programme

Matilda Essandoh Laar, MPH, PhD

26th October, 2016Innovative Metrics and Methods for


The Ghana School Feeding Program

Government initiative started in 2005 under the CAADP

GSFP: Nationwide program focused on schools in poor communities

Modeled after the “home-grown” approach to link SFP to local agriculture

GSFP: Objectives

Monitoring and Evaluation Plan

Tools: Summary

• Daily monitoring tool for the headteacher

• Daily monitoring tool for the caterer

• Procurement tool for caterer (daily, weekly, monthly, etc.)

• School term monitoring tool (termly)

• Spot checks using the School Sanitation and Facilities monitoring tool

GSFP: Handy measures• Use of food scales is not a common practice in Ghana• PCD (Partnership for Child Development): Tools and menu

planner

Study: IMMANA ProjectObjective:• To conduct a pilot study to assess the feasibility of using

the new GSFP tools in monitoring and evaluating the selected outputs and outcomes of the GSFP.

Specific objectives:• To determine the feasibility of using the new tools in

monitoring and evaluating the nutritional quality of the meals.

• To determine the feasibility of using the new tools in monitoring and evaluating the link of the GSFP to local agriculture.

Study: Research Site

•2 districts per selected region

•3communities/schools per selected district

•Used M&E tools for a school term

Study Design

Quantitative

• Use of daily M&E tools for a school term (trainings and implementation)

• Spot checks using sanitation tools

• School term monitoring tool

Qualitative

• Interviews to understand experiences with using the new tools for M&E

Statistical Analysis

• N=720 school days; 18 clusters (schools)

• Outcomes: meal diversity, nutrient content of meal

(protein, vit A, Iron)

• Independent variables: recent payment of caterers,

(%) of local procurement, school enrolment,

number of food vendors in school, presence of local

farmers in community, use of district menu,

number of SIC meetings, rural/periurban, student drop-out rates

• Linear mixed models (adjusted for cluster)

Key challenges: Data Collection1. Caterer labour strikes led to delays in our scheduled

trainings for the project (8 weeks instead of 12 weeks data collection period)

2. Training: Challenges with the completion of the daily feeding monitoring tools• Caterer form: Tinned tomatoes, fresh vegetables

(tomatoes, onions)• Headteacher form: Impossible to report amount

of fishpowder used

3. District Desk Officers: Weakest link in the chain of transfer of monitoring information

Lessons learned: Data Collection

1. Tools need to be developed in consultation with the users to help address practical concerns earlier in the process

2. It is important to address issues identified from pre-tests before implementation

3. Need for a paid M&E coordinator at district level to replace the desk officers

Key challenges: Data Analysis

1. GSFP M&E MIS (Management information Systems) has not been developed

2. Structure of caterer and headteacher tools do not allow easy analysis of food composition needed access nutrient quality

Lessons learned: Data Analysis

• MIS is needed to ensure sustainability of monitoring activities

• MIS will be needed for data entry at district and regional levels

• Need to develop a simplified tool to verify use of meal plans and recipes developed by nutrition officers

Findings: Qualitative Analysis

1. High respondent burden for both caterers and headteachers

2. Daily monitoring not achieved (completed weekly or every other day

3. Amounts allotted for a meal per child (80 pesewas, about 25 cents)is inadequate for preparation of nutritious meals

4. Delayed reimbursement of caterers affected the nutritional quality of meals

5. Headteachers believed they should be compensated for collecting GSFP M&E data.

Recommendations1. Introduce meal planning and portion sizes to assist

with preparation of nutritious meals

2. Increase amounts allotted for a meal per child (80 pesewas, about 25 cents)is inadequate for preparation of nutritious meals

3. Daily monitoring not sustainable. Develop simplified tools (checklist) for routine monitoring and use detailed tools in a subgroup at defined time points

4. MIS is essential for collating monitoring data to inform program decisions. Summaries by district and regional personnel are prone to error.

Recommendations5. Better communication and strategies to promote ownership of the GSFP by all collaborating ministries (Health, Agriculture, local Government and Rural

Development)

“You see, personally, it’s a passion. I have worked with children all my life… I care about the welfare of children…It wouldn’t be so bad if they (the government) could pay us on time”

(Caterer, GSFP)

Acknowledgement• Dr Grace Marquis- McGill University

• Dr Esi Colecraft- University of Ghana

• Ghana School Feeding National Secretariat

• Partnership for Child Development- Ghana Office

Funded by:

Determinants of food insecurity among young

children: an analysis of intra household allocation of

food in Rural Bihar, India

Nitya Mittal

Mentors – Prof. P. A. Chiappori, Columbia University,

Prof. J. V. Meenakshi, Delhi University

26th October, 2016

Intra-household allocation of resources- I

• Availability is not the only factor that affect allocation of food

to young children.

• Undernourished children found not just in households with

inadequate food, but also in households with adequate access

to food in India and elsewhere in the world.

• The objective is to identify the pathways that affect allocation

of calories to young children, and whether the role played by

these pathways differ depending on total resources available.

Intra-household allocation of resources - II

• Data – primary survey conducted in 300 households in 4

villages in Bihar

• Methodology – Switching regression models

• Preliminary analysis suggest that the pathways indeed differ,

investment motive determines allocation in low resource

households, while other pathways (nutrition knowledge and

mother’s bargaining power) are important in resource

sufficient household.

Measuring Nutrition: validation exercise - I

• Different tools available to measure nutritional intake. Differ

by accuracy of measure and the cost involved.

• For instance – dietary recall surveys are the gold standard, but

extremely costly. On the other hand, food frequency and

consumption expenditure are relatively cheap to administer but

do not give as detailed information.

• The objective of the study is to validate the use of food

frequency and consumption expenditure to measure diet

quality with the gold standard (24-hour dietary recall).

Measuring Nutrition: validation exercise - II

• Comparison of food frequency with dietary recall for young

children

• Two Dietary Diversity Measures constructed – Food

Consumption Score (WFP) and DDS (FANTA) and compared

with calorie, vitamin and iron intakes

• Positive correlation between DD measures and intakes, but not

good at identifying child below required level of intake.

Funded by:

Food system dynamics, animal health, and child nutrition in Tanzania

26th October 2016

IMMANA fellowship webinar

Mieghan Bruce

System Dynamicsa methodology and mathematical modelling technique

for framing, understanding, and discussing

complex issues and problems

Household dynamics and

decision-making

Individual nutrition outcomes

Livestock production system

• Food• Income• Asset store

• Labour

• Animal husbandry

System Dynamics processIterative phases of System Dynamics methodology

1. PROBLEM IDENTIFICATION: identifying and articulating the issue to be addressed

2. MODEL CONCEPTUALIZATION: developing a causal theory about the issue

3. MODEL FORMULATION: quantifying the model

4. MODEL SIMULATION and ANALYSIS: testing the model to assess whether it is fit for purpose and scenario simulation

5. MODEL USE: communication of results to (hopefully) design better nutrition-sensitive food system programmes and policies

1. PROBLEM IDENTIFICATION• Child stunting in Tanzania 42% (NBS, 2011)

• Agricultural interventions - potential to improve nutrition of farming households

• BUT farm-level link (or HH-level link) is hard to document

2. MODEL CONCEPTUALTISATIONFour broad pathways:1. Income from agriculture2. Consumption of own production

AFFECTED BY3. Socio-cultural factors including gender4. Food environment (prices and availability)

Pathways are interlinked!(Ruel and Alderman, 2013; Carletto et al., 2015)

2. MODEL CONCEPTUALTISATION

POULTRY FLOCK

MODEL

Home consumption

HOUSEHOLD INDICATORS

Egg and meat consumption

Sale

Gift

Keep to increase flock size

Newcastle Disease

Vaccination

Mortality

NUTRITION INDICATORS

HAZWAZ

AnaemiaVitamin A

DIETARY INDICATORSEgg consumption

Meat consumptionDietary Diversity

CHILD NUTRITION

INTRA-HOUSEHOLD DYNAMICS

Decision-makingHousehold size

Head of HH genderSocial capital

Time use and allocation

Purchases

Exchange for other

livestock

3. MODEL FORMULATION• Strengthening food and nutrition security through family poultry

and crop integration in Tanzania and Zambia

• Primary data collection of data on small ruminants

4. MODEL SIMULATION AND ANALYSIS• Positive link between number of chickens and BOTH home

consumption and sales rates

• Positive link between HH food security indicators and child dietary indicators (in small household size <6 people)

• BUT Weak link between offtake rates and child nutrition indicators

• Reducing poultry mortality increases home consumption and sales BUT Newcastle disease vaccination had varied effect on mortality rates

5. MODEL USE??

• Intermediary indicators can be assessed (own consumption versus income effects)

• Simulate “what if” scenarios (change frequency and timing of vaccination)

• Could be used as a predictive tool during development phase

• Model can be adapted for different areas using local data

• Can be extended…

Thank you for listening!

ACKNOWLEDGEMENTS

Jonathan Rushton – RVC, UK

Furaha Mramba – TVLA, Tanzania

Robyn Alders – University of Sydney, Australia

Wende Maulaga – Nkuku4U, Tanzania

Julia de Bruyn - University of Sydney, Australia

Elpidius Rukambile - Nkuku4U, Tanzania

Msafiri Kalloka - Nkuku4U, Tanzania

Nkuku4U team and staff at TVLA

REFERENCES

Alders, R.G., Bagnol, B. and Young, M.P. (2010): Technically sound and sustainable Newcastle disease control in village chickens: lessons learnt over fifteen years. World’s Poultry Science Journal 66(03), 433-440.

Carletto G., Ruel M., Winters P. & Zezza A. (2015). – Farm-Level Pathways to Improved Nutritional Status: Introduction to the Special Issue. Journal of Development Studies, 51 (8), 945-957. doi: 10.1080/00220388.2015.1018908.

NBS and ICF Macro. (2011): Tanzania Demographic and Health Survey 2010. National Bureau of Statistics, Dar es Salaam, Tanzania, and ICF Macro, Maryland, USA.

Ruel, M., and H. Alderman. 2013. “Nutrition-Sensitive Interventions and Programmes: How Can They Help to Accelerate Progress in Improving Maternal and Child Nutrition?” The Lancet 382 (9891): 536–551.

Funded by:

Milk consumption, nutrition

and health in southern Ethiopia

Bekele Megersa

October 26, 2016, Hawassa EthiopiaInnovative Metrics and Methods for


Introduction • Animal Source Foods (ASFs) are rich in energy,

proteins and essential micronutrients that promote:

growth, health, cognitive functioning of children

(Neumann et al. 2003)

• As pastoralists have a limited access to diverse

food sources, milk is the major ASF that contributes

to child nutrition.

• This study explores the contribution of milk

consumptions to nutritional outcome of study

children

Methods...(1)Questionnaire survey (406 households),

(2) Dietary intake (diversity and amount)

(3) Anthropometry (538 children aged 6 -59m).

(4) Repeated record on dietary intake (60 households Sept 2015 to March 2016 in Borana, southern Ethiopia).

Dietary diversity is very low (2.66) and highly variable (CV:

33%) with milk and cereal consumption predominating their

dietary intake.

Few children fulfilled minimum acceptable diet(5%), minimumdietary diversity (8%), minimum meal frequency (31.2%) of theWHO.

Consumption of other ASFs such as meat (7.4%) and eggs (4.5%)were very low, though egg is relatively available

0102030405060708090

100

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Fig1. Proportions of dietary diversity by tercils of food groups

Variables No Underweight Stunting Wasting

severe global severe

(< -3SD)

global severe global

(< -3SD)(< -2SD) (< -2SD)(< -3SD)(< -2SD)

Overall 538 7.4 28.3 14.7 41.1 2.2 9.8

Boys 270 7.4 30.7 15.2 43.3 2.2 10.7

Girls 268 7.5 25.7 14.2 38.8 2.2 8.6

Chi-square 1.7 1 1

6-23 months 157 3.2 19.7 11.5 31.8 3.2 7.6

24-59 months 381 9.2 31.8 15.5 44.6 1.8 10.8

Chi-square 5.8* 7.9* 1.5 7.5* 0.9 1.2

The result is comparable to the national level figure of 44% stunting, 29% underweight among under-five children (EDHS 2011).

Malnutrition was more prevailing in among children aged 24 to 59 months

Nutrients Total Milk Others

NAR %Below

RDA

NAR NAR

Energy 76.6 84 51.3 25.4

Protein 113 39.6 81 32

carbohydrate 61.2 87.5 21.1 40.1

Vitamin A 60.1 92 54.8 5.3

Vitamin B1 41.9 99.4 31.6 10.3

Vitamin B2 193.2 4.1 174.4 18.7

Vitamin B6 108.9 51.1 56.2 52.6

Folic acid 48.5 95.5 15.8 32.7

Vitamin C 22.7 98.9 16.2 6.5

Calcium 105.3 49.3 98.3 6.9

Iron 59.6 86.4 4.7 54.9

Zinc 76.5 80.5 52 24.5

MAR 66.3 46.2 24.7

NAR (%), MAR (%) and contributions of milk and other food sources to nutrient

requirements of study children

Milk still contributed to higher proportion of MAR, energy and other nutrients including calcium, protein, vitamins B2, A, B6 and zinc requirements

Indicators waz haz whz Illness

age groups 6 - 23 24 - 59 6 - 23 24 - 59 6 - 23 24 - 59 6 - 23 24 - 59

DDS terciles1 :1

2 0.04 0.25* -0.17 0.45* 0.15 -0.01 0.53 0.27

3 0.25 0.63* -0.1 0.74* 0.43 0.29 0.77 0.37

Meal terciles2: 1

2 0.1 -0.02 -0.44 0.22 0.49 -0.2 0.22 -1.12*

3 0.27 0.22 -0.54 0.63* 0.76 -0.12 -0.35 -1.39*

Milk terciles3: 1

2 0.37* 0.06 0.14 0.11 0.41* -0.04 -0.1 0.22

3 0.57* 0.57* 0.16 0.58* 0.66 0.36* 0.21 0.11

Age in months -0.03* -0.02* -0.02* -0.02 -0.02* -0.01 -0.02 -0.01

Effects of dietary intake indicators on nutritional and health outcomes

Dietary intake indicators had positive effects on nutrition outcome of children while only meal frequency had negative relationship with illness.

Mean adequacy ratio versus dietary intake indicators and their predicted values

Combination of DDS, milk and meal frequencies had higher R² values (0.30 compared DDS and milk (R²=0.27), DDS and meal (R²=0.28), and DDS alone (R²=0.26).

This suggests using more than one proxy measure may better predict the actual nutrient intakes specially for pastoral areas

Malnutrition is widely prevalent among the

study children

Study children had level of dietary diversity

Milk remains an integral part of children’s

diets, and contributed to a higher proportion

of their nutrient requirements.

Results showed that dietary intake indicators

had positive effects on nutrition outcome of

children.

However, the co-occurrence of very high prevalence of stunting with high milk consumption is surprising

Funded by:

Documents

Pushing the frontiers in ag- nutrition methods and metrics · 2019-05-17 · effective labor is a function of nutrient intake. Due to methodological pitfalls such as: 1) Unobserved