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Seeing is Believing? Evidence from an Extension
Network Experiment in Mozambique
Florence Kondylis
Development Research Group (DIME), World Bank
Valerie Mueller
Development Strategy and Governance Division
International Food Policy Research Institute
Siyao Zhu
Development Research Group (DIME), World Bank
Grantee Final Report
Accepted by 3ie: August 2014
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Suggested citation: Kondylis, F, Mueller, V and Zhu, S, 2014. Seeing is
Believing? Evidence from an Extension Network Experiment in Mozambique, 3ie
Grantee Final Report. New Delhi: International Initiative for Impact Evaluation
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3ie Final Report: Seeing is Believing? Evidence from an Extension Network Experiment
Florence Kondylis∗
Development Research Group (DIME)World Bank
Valerie MuellerDevelopment Strategy and Governance DivisionInternational Food Policy Research Institute
Siyao ZhuDevelopment Research Group (DIME)
World Bank
June 27, 2014
∗Corresponding authors' emails are: [email protected]; [email protected] discussed in this publication has been funded by the International Initiative for Impact Evaluation, Inc.(3ie) through the Global Development Network (GDN), the Mozambique o�ce of the United States Agency for In-ternational Development, the Trust Fund for Environmentally and Socially Sustainable Development, the BelgianPoverty Reduction Partnership and the Gender Action Plan, and the CGIAR Research Program on Policies, Insti-tutions, and Markets (PIM) led by the International Food Policy Research Institute (IFPRI) and �nanced by theCGIAR Fund Donors. The authors bene�ted from comments provided by Jenny Aker, Madhur Gautam, and MarkusGoldstein and during presentations at the CSAE (Oxford), the Mid-Western Economic Development Conference,the Development Impact Evaluation Seminar Series at the World Bank, and the IFPRI Seminar Series. The viewsexpressed in this article do not re�ect those of the World Bank, 3ie or its members. The authors would like tothank Pedro Arlindo, Jose Caravela, Destino Chiar, Isabel Cossa, Beatriz Massuanganhe, and Patrick Verissimo fortheir collaboration and support throughout the project. John Bunge, Ricardo da Costa, and Cheney Wells providedexcellent �eld coordination; Siobhan Murray impressive research assistance. Usual disclaimers apply.
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Contents
1 Introduction 6
2 Agricultural Extension Constraints in Mozambique and Intervention 9
2.1 Extension Network in Mozambique's Zambezi Valley . . . . . . . . . . . . . . . . . . 102.2 External Validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3 Experimental Design, Data and Identi�cation 11
3.1 SLM Trainings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.3 Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.4 Measuring Information Di�usion and Behavioral Change . . . . . . . . . . . . . . . . 173.5 Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193.6 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4 Results 21
4.1 CF Adoption and Learning-by-doing . . . . . . . . . . . . . . . . . . . . . . . . . . . 214.2 CF Substitution of Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.3 Others' Knowledge and Adoption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.4 Farmers' Perceptions of Cost Savings . . . . . . . . . . . . . . . . . . . . . . . . . . 244.5 CF and Others' Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
5 Discussion 26
Appendix A: Additional Figures and Tables 45
Appendix B: Sample Design 65
Appendix C: Household Survey Instrument 67
Appendix D: Power Calculations 69
Appendix E: Study Design and Implementation 73
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List of Figures
1 Timeline of Trainings and Contact Farmer and Household Surveys . . . . . . . . . . 322 Geographical Distribution of (Non-CF) Households . . . . . . . . . . . . . . . . . . . 333 E�ect of SLM Training Intervention on Contact Farmers . . . . . . . . . . . . . . . . 34E.1 Randomized Multi-Arm Treatment Design . . . . . . . . . . . . . . . . . . . . . . . . 76
List of Tables
1 Advantages of SLM Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352 Contact Farmers' Characteristics in Treated and Control Communities . . . . . . . . 363 Other Farmers' Characteristics in Treated and Control Communities . . . . . . . . . 374 Characteristics Comparison between Contact Farmers and Other Farmers . . . . . . 385 E�ect of SLM Training Intervention on Contact Farmers . . . . . . . . . . . . . . . . 396 E�ect of SLM Training Intervention on Contact Farmers' SLM Adoption . . . . . . . 407 E�ect of SLM Training Intervention on Contact Farmers' Adoption Controlling for
Previous Adoption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 418 E�ect of SLM Training Intervention on Other Farmers . . . . . . . . . . . . . . . . . 429 Selective Attrition, Balanced Sample & Lee's Bounds . . . . . . . . . . . . . . . . . . 4310 Heterogeneity of ITT on Other Farmers' Adoption of Micro-Catchments . . . . . . . 44A.1 Advantages of Being a Contact Farmer in Treated and Control Communities . . . . . 46A.2 Extension Agents' Characteristics in Treated and Control Communities at Midline . 47A.3 E�ect of SLM Training Intervention on Contact Farmers (Includes Intercropping) . . 48A.4 E�ect of SLM Training Intervention on Other Farmers (Includes Intercropping) . . . 49A.5 SLM Learning before 2010 in Treated and Control Communities (Recall) . . . . . . . 50A.6 SLM Adoption before 2010 in Treated and Control Communities (Recall) . . . . . . 51A.7 Gender Barriers to Adoption (Mean Di�erences within the Control Group) . . . . . . 52A.8 Other Farmers' Characteristics between Attrition Groups . . . . . . . . . . . . . . . 53A.9 Attrition of CFs and Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54A.10 Probability of CF and Household Attrition . . . . . . . . . . . . . . . . . . . . . . . . 55A.11 Descriptive Statistics of Contact Farmers' Characteristics . . . . . . . . . . . . . . . 56A.12 Descriptive Statistics of Other Farmers' Characteristics . . . . . . . . . . . . . . . . . 57A.13 E�ect of SLM Training Intervention on Contact Farmers' SLM Knowledge . . . . . . 58A.14 E�ect of SLM Training Intervention on Other Farmers' SLM Adoption . . . . . . . . 59A.15 From Whom Other Farmers Claim to Learn SLM Techniques . . . . . . . . . . . . . 60A.17 Other Farmers' Perceptions of Technique's Labor Savings Compared to Traditional
Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62A.18 Contact Farmers' Perceptions of SLM Techniques . . . . . . . . . . . . . . . . . . . . 63A.19 E�ect of SLM Training Intervention on Contact Farmers' Labor Time . . . . . . . . 64D.1 Power Calculations for Row Planting . . . . . . . . . . . . . . . . . . . . . . . . . . . 70D.2 Power Calculations for Crop Rotation . . . . . . . . . . . . . . . . . . . . . . . . . . 71D.3 Reverse Power Calculations for Micro-Catchments . . . . . . . . . . . . . . . . . . . . 72
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Abstract
Farmers' adoption of existing, improved techniques is a central challenge in Sub-SaharanAfrica. Extension agents (EAs) are commonly used to disseminate agricultural techniques andtechnologies. There is no evidence on what activities and incentive mechanisms can make ex-tension services work for farmers. Within an existing extension network in Mozambique, wecompare di�usion of sustainable land management (SLM) practices in the classic training andvisit (T&V) model and a revised T&V model. Both models rely on extension agents (EAs)to transmit information about new technologies to contact farmers (CFs), model farmers whoserve as points-of-contacts between EAs and other farmers within their communities. The re-vised T&V model o�ers CFs a direct, centralized training on SLM of similar content and breadthas the EA training. The direct training program avoids two pitfalls in the classic model: i) lowtransmission of information due to infrequent EA visits, and ii) receipt of poor quality informa-tion, e.g., through EA �lters on information. Two hundred communities in central Mozambiquewere randomly assigned to the classic or modi�ed T&V arms. We track information transmissionthrough two nodes: from EAs to CFs, and from CFs to others. Knowledge does not propagatee�ciently under the classic T&V model. Directly trained CFs are more likely to demonstrateand adopt techniques and learn-by-doing. Subsequent di�usion within the community is limited:only the technique with perceived labor savings is practiced by other males, with an e�ect sizeof 75 percent.
The results imply that the e�ectiveness of even a revised T&V model continues to depend onthe pro�le of the contact farmer and fails to address the demand-side constraints of the averagefarmer. First, female farmers' responses were particularly una�ected by the revised T&V model.Only when we strati�ed the treatment e�ects by whether the CF had similar crop portfolios asthe farmer did we witness an e�ect on women's adoption rates. These �ndings are consistentwith previous work which shows farmers are more likely to learn from �seed adopters� withsimilar characteristics. Providing messengers with amenable farming conditions may improvethe targeting of female farmers in the provision of extension services. Second, in spite of theCF rendering three SLM techniques valuable enough to adopt after receiving the training, theaverage farmer chose to adopt only one of the three techniques, micro-catchments. Our �ndingssuggest that the farmer might have been in�uenced by the immediate labor savings of thetechnique. Farmers exposed to the intervention were more likely to perceive micro-catchmentsas labor saving. Furthermore, trained, CFs on average spent 4-hours less preparing land theweek prior to the interview. Although the labor saving estimates for CFs are inclusive of alltechniques they adopted, the descriptive �ndings are consistent with farmer's beliefs updatingin response to the intervention following their adoption of micro-catchments or after observingthe CF's demonstration activities.
The Market-led Smallholders Development in the Zambezi Valley Project (MSDVP) wasimplemented by the Government of Mozambique (GoM) in the context of a large nationalagricultural reform, with technical supervision from the World Bank (WB). Its main objectivewas to rebuild the agricultural extension network in 5 districts of the Zambezi Valley. Theproject provides extension services at 2 levels: (1) 2 extensionists at the level of the postoadministrativo, each provides services to 8-10 communities; and (2) a contact farmer (CF)in each community. Yet, the structure of the extension network implemented by the projectdoes not necessarily guarantee a �ow of information from CFs to farmers. The GoM workedwith a team of researchers from the World Bank and IFPRI to use the MSDVP as a learningvehicle and set up a Randomized Controlled Trial (RCT) to test the e�ectiveness of providinga centralized, direct training and performance-based incentives to CFs in boosting farmers'adoption of sustainable land practices (SLM).
The design of the evaluation consists of 3 overlaid treatment arms: (1) the revised T&Vmodel, where existing male CFs receive a direct SLM training; (2) additional women are desig-nated to receive direct SLM training; and (3) additional performance-based incentives for CFs.The various treatment arms were randomly assigned at the village level (150 treatment, 50 con-trol), using a strati�ed procedure at the district level. The report focuses on treatment 1 andcomments on the challenges in implementing treatment 3.
4
The project team collected baseline, midline, and endline CF surveys as well as largeComputer-Assisted Personal Interview (CAPI) household surveys (midline and endline) in all�ve project districts of the Zambezi valley. The survey design included household, CF, commu-nity and extension worker instruments and was administered in all 200 treatment and controlvillages. One principal male and female farmer was interviewed in each of the randomly sampled3,600 households, allowing for heterogeneous program e�ects by gender to be captured. Thesurvey was implemented in two rounds: one after planting, one after harvest of the main seasonof interest. The �rst visit was used to perform actual adoption checks in each farmer's main�eld. Field measurements included total area cultivated under SLM, allowing us to documentadoption patterns both at the extensive and intensive margins. Placebo questions were insertedto circumvent measurement error from self-reported outcomes.
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1 Introduction
Agricultural innovation is a necessary condition to accelerate productivity and achieve food security
in Africa (Hazell, 2013). Recent e�orts focus on designing mechanisms to overcome constraints
on farmer's adoption, such as underdeveloped input delivery systems (Shiferaw, Kebede, and You,
2008), high acquisition costs (Suri, 2009), and time inconsistency (Du�o, Kremer, and Robinson,
2011). A growing literature recognizes the role of information failures in the agricultural technolog-
ical di�usion process, focusing on conditions for e�ective communication between peers (Munshi,
2004; Bandiera and Rasul, 2006; Conley and Udry, 2010; Magnan et al., 2012, McNiven and Gilligan,
2012; BenYishay and Mobarak, 2013). Despite its formative e�ect on di�usion (Feder, Just, and
Zilberman, 1985; Davis, 2008), evidence on the e�cacy of extension to help farmers overcome infor-
mation failures is mixed (Bindlish and Evenson, 1997; Purcell and Anderson, 1997; Gautam, 2000;
Anderson and Feder, 2007; Benin et al., 2007; Davis et al., 2010; Waddington and White, 2014).
Recent experiments show potential in improving learning and adoption through participatory ap-
proaches in extension, e.g., �eld trials, farmer �eld schools, and innovation platforms (Agyei-Holmes
et al., 2011; Du�o, Kremer, and Robinson, 2011; Pamuk, Bulte, and Adekunle, 2013; Du�o, Kenis-
ton, and Suri, 2014). We focus on the e�ectiveness of the Training and Visit (T&V) system, applied
in several developing countries, to encourage the adoption of sustainable land management (SLM)
practices (Gautam, 2000; BenYishay and Mobarak, 2013).
Garden variety T&V models try to expand the geographic coverage of extension by engaging
extension agents with a village point-of-contact (contact farmer). The model relies on extension
agents (EAs) to transmit information about new technologies to contact farmers (CFs). In central
Mozambique, the government randomly assigned 50 communities to the classic T&V model and 150
communities to a modi�ed T&V model to disseminate information on SLM. The modi�ed T&V
model o�ers CFs a direct, centralized training on SLM practices, with similar content and breadth
to the EA training. By centralizing and standardizing the training, the intervention addresses two
pitfalls in the classic T&V system: i) low transmission of information due to infrequent EA visits,
and ii) receipt of poor quality information, e.g., through EA �lters on information. There were
no other changes in the infrastructure or composition of extension services across treatment and
control groups. Comparing CFs' SLM knowledge and adoption across treatment arms provides a
6
direct test of the assumption that a T&V model is conducive to EA-to-CF knowledge transfers, as
measured against a direct, central CF training.
We next measure how exogenous variations in CF training quality a�ect other farmers' SLM
learning and adoption. Both models ignore constraints on CF outreach 1and demand-side constraints
on adoption in the context of social learning.2 Given these limitations, we anticipate farmers to
be a�ected by the revised T&V model twofold. First, farmers may be exposed to information on
additional SLM practices with enhanced frequencies of exposure, if directly, trained CFs alter their
demonstration activities and investment portfolio. Second, the direct training may both enlighten
the CF and improve the quality of information available. Under those circumstances, risk-averse
farmers may be inclined to adopt given reduced uncertainty of the bene�ts of SLM (Rosenzweig
and Binswanger, 1993; Rosenzweig and Wolpin, 1993; Ghadim, Pannell, and Burton, 2005; Dercon
and Christaensen, 2011).
The conservation agriculture technologies examined in this study are not akin to the input
and crop adoption trials most commonly depicted in the literature (Munshi, 2004; Bandeira and
Rasul, 2006; Conley and Udry, 2010; McNiven and Gilligan, 2012). Seven SLM techniques were
advocated in the CF training: mulching, strip tillage, micro-catchments, contour farming, crop
rotation, improved fallowing, and row planting. On average, each of these techniques are not
viewed productive. The time and labor allocation required to implement the techniques can be cost
prohibitive. Other known bene�ts (such as improvements in soil quality) are realized over a longer
time horizon which may be less desirable to subsistence farmers facing high discount rates. Despite
these barriers to adoption, some techniques may be considered bene�cial to farmers facing tough
agronomic conditions. Water saving technologies, such as contour farming and micro-catchments,
may render farmers on sloped or arid land more resilient to losses from erosion and drought. Given1First, the original criticism of the T&V model of a lack of accountability and incentive structures to encourage
outreach remains in both models (Gautam, 2000). Second, transaction costs associated with communicating tohouseholds in remote areas or outside of their social network may continue to limit the di�usion process.
2Common demand-side constraints may inhibit the intervention's success. First, farmers may be inclined to freeride on the learning of others and delay the adoption until pro�table (Foster and Rosenzweig, 1995). Second, withina community, farmers may be heterogeneous along dimensions of crop choice, soil conditions, and management style,which can a�ect the di�usion process (Munshi, 2004; Conley and Udry, 2010). Third, the characteristics of theprimary adopter (CF) are likely to a�ect how other farmers internalize the information. For instance, farmers maybe more inclined to learn from others' with similar characteristics (Feder and Savastano, 2006; Bandiera and Rasul,2006; BenYishay and Mobarak, 2013), while CFs may be quite dissimilar than their communal peers. Social in�uencemay be more relevant to a community of passive learners, requiring interventions that improve the ties and visibilityof previous adopters (Hogset and Barrett, 2010).
7
high levels of risk aversion with regard to their cultivation decision, the intervention may change the
behavior of some farmers by reducing the uncertainty of their most relevant technology's bene�ts.
Demonstration activities as well as CFs' private adoption were much higher in communities
where the CFs had been centrally trained 15 months after the training. This increase in adoption
augmented knowledge of the techniques adopted 27 months after the initial training. Taken together,
our results suggest that this wedge in CF knowledge likely corresponds to learning-by-doing in this
treatment arm, as the actual bene�ts of the techniques are exposed through increased practice.
Exploiting this exogenous information shock at the village level to measure the impact of boost-
ing CFs' demonstration, knowledge, and adoption activities on practices within the community
provides mixed results. While CFs' activities and knowledge in the revised T&V model successfully
increased others' adoption of one of the techniques adopted by the CFs, this is not the case for
all demonstrated techniques and all farmers. Only men increased their micro-catchments' adoption
by 3 percentage points (an e�ect size of 75%) 15 months after the intervention. Of the additional
techniques adopted by the trained CFs (strip tillage, micro-catchments, and contour farming) in
response to the intervention, the adoption of micro-catchments is more likely to achieve positive net
short-term bene�ts as it does not require major investments in tools and labor in its implementa-
tion (Liniger et al., 2011). In our study, other farmers exposed to the intervention only perceived
micro-catchments as labor-saving technique and CFs spent less time on agricultural tasks. The
indirect evidence suggests that farmers are likely to act on the information they received, when the
technology requires little up-front investment and short-term cost savings are expected.
In what follows, we summarize the limitations of the agricultural extension network in Mozam-
bique and improvements provided by the intervention (Section 2). We then describe the evaluation
design and empirical strategies used to identify the impact of the modalities used to deliver infor-
mation to the contact and other farmers (Section 3). Section 4 presents estimates of the impact
of the intervention on the contact and other farmer's knowledge and adoption of SLM practices.
Section 5 discusses the implications of this study for future adoption studies.
8
2 Agricultural Extension Constraints in Mozambique and Interven-
tion
Mozambique's agricultural extension network was created in 1987 and began to operate in 1992 after
the peace agreement. During the past two decades, the Ministry of Agriculture (MINAG) promoted
the development of extension networks (Eicher, 2002). This expansion is set to continue moving
forward (Gêmo, Eicher, and Teclemariam, 2005 ). Extension agents (EAs) are employed by the
District Services for Economic Activities (Serviços Distritais de Actividades Económicas) and oper-
ated at the sub-district level to disseminate information and new techniques. The system assumes
that information �ows are a linear process: agricultural innovations are created by researchers, then
distributed by extension workers, and lastly adopted by producers (Pamuk, Bulte, and Adekunle,
2013).
Country-wide, coverage is as low as 1.3 EAs per 10,000 rural people (Coughlin, 2006). Given
this shortage, EAs are inclined to visit the same villages every year based on their achievements
and potentials (Coughlin, 2006). Only 15 percent of farmers report receiving extension services
(Cunguara and Moder, 2011). To address these supply-side bottlenecks, the World Bank promoted
the T&V model of extension. In practice, a communal representative, the CF, is designated to
receive information on improved techniques from the EA and disseminate it to his community
through village-level demonstration activities. Under this model, increased frequencies in training
and visits would be made by the EAs to a select group of CFs (Feder and Anderson, 2004).
The present National Plan for Agricultural Extension (PRONEA 2007-2014) and Extension
Master Plan (2007-2016) aim to develop the decentralization of services at the district level and
expand the T&V model, increase participation of targeted groups (women and marginal farmers),
and enhance partnerships with other actors, such as private sector and NGOs (Callina and Childia-
massamba, 2010). Despite the importance placed on extension services and, particularly, the T&V
model by the national government, there are no rigorous studies that validate this policy action.
The literature in which evaluates the T&V system is conducted using non-experimental methods
(Gautam, 2000). Recent work attempts to correct for the non-random assignment of extension
services (Cunguara and Moder, 2011) and �nds a positive impact of extension on farm income in
9
Mozambique. In what follows, we describe the details of the extension network and the T&V model
present at baseline.
2.1 Extension Network in Mozambique's Zambezi Valley
Our experiment is set in �ve districts of central Mozambique, Mutarara (Tete province), Maríngue
and Chemba (Sofala province), Mopeia and Morrumbala (Zambézia province). This area receives
support from a large, World Bank-Government of Mozambique (GoM) investment that aims to
support the development of the extension network. The project provides three levels of agricultural
technical assistance: each district has a facilitator, an environmental specialist, and eight EAs. A
district is sub-divided into four administrative posts (posto administrativo) that include about 8-10
communities (aldeia). Each community has a designated CF who receives direct assistance from the
two EAs placed in his administrative post,3 who in turn receive direct assistance from the district-
level technical sta�. CFs are expected to provide advice to their peers within the community through
demonstration activities, as well as being responsive to farmers' demands for technical assistance.
We examine whether the T&V model is as e�ective in getting CFs to demonstrate and learn new
technologies as a direct training program. The classic T&V model was adapted from the historical
comprehensive model, since monthly EA trainings were di�cult to sustain �nancially (Gautam,
2000). Instead, EAs periodically received an extensive training (2010 and 2012). Moreover, in the
classic T&V model, the majority of CFs receive visits from EAs monthly rather than twice a month
(as planned under the historical, comprehensive T&V model) (Anderson and Feder, 2004).
The conditions underlying most extension networks raise concerns about the e�cacy of the
classic T&V model. If EAs are challenged to reach communities in the �rst place, designating CFs
in these communities may not adequately address the supply issue of extension services. Moreover,
information may get diluted from the central level to the CFs. EAs may not su�ciently train the
CFs to ensure know-how is transmitted. There is also no guarantee that EAs transmit a clear to-do
list to CFs to conduct dissemination activities in the communities. By comparing the T&V model to3EAs can choose which CFs to work with, and do not necessarily split responsibilities. Hence, a given CF may
interact with both EAs in his administrative post. CFs are typically chosen by the community. In 2010, CFs wereon average in their position 3 years with a standard deviation of 3. This indicates the majority of CFs were alreadycommissioned by the system prior to the intervention.
10
a central training of the CFs, keeping the extension system and infrastructure constant, we provide
a direct test of the �rst modality of the T&V model, which assumes EAs will successfully train all
CFs (with no information �lters).
2.2 External Validity
As mentioned above, our study area is limited to �ve districts of Mozambique's Zambezi valley.
While this is a large-scale program, it is not immediately clear that our results would hold in other
contexts. Our study likely provides an upper-bound estimate of the T&V model relative to the
impact of central training. Even though the ratio of EAs per administrative post is on par with the
national average of 1.89 (Gêmo and Chilonda, 2013),4 our study districts are receiving enhanced
support from the local Services for Economic Activities o�ce. Hence, it is unclear that EAs in our
sample face a smaller set of demands on their time, leading them to be more available to train CFs.
A competing assumption is that as EAs receive more attention from the district services in our
study area, they may be exposed to more trainings and, therefore, too busy to provide assistance
to their communities. We provisionally rule this out, as we did not hear of additional trainings to
EAs over our study period.
3 Experimental Design, Data and Identi�cation
At baseline, CFs and EAs were operating in all communities of our �ve study districts. From
these districts, we randomly selected 200 communities (with 200 CFs) in 16 administrative posts,
to which 30 EAs are assigned. All EAs received SLM training. We randomly assigned CFs in 150
(�Treatment�) communities to a similar, centrally-administered training on SLM, which we describe
in more detail in the next sub-section. CFs in the remaining 50 (�Control�) communities were
supposed to receive SLM training from their EAs, which corresponds to the �status quo� T&V4This ratio is calculated using the 2010 �gures from the Direçåo Nacional de Extenså Agraria (DNEA), available
at the following URL: http://www.worldwide-extension.org/africa/mozambique/s-mozambique.
11
modality.5 The randomization was strati�ed at the district level.
To test for e�ective knowledge di�usion in the T&V model and isolate the additional e�ect of
a central training of CFs, we hold constant all other typical T&V interventions across treatment
and control communities. Speci�cally, and in line with the status quo modality, all CFs in the
experimental sample are supposed to receive assistance from their EAs as well as a toolkit to create
a demonstration plot within the community (aldeia). These demonstration plots are then used by
(1) EAs to teach and assist each CF in implementing at least one of the agricultural practices of
the CF's choice, and (2) the CF to demonstrate the elected new techniques to farmers in their
community.
It is important to note that all CFs in treatment and control communities are encouraged to
maintain demonstration plots within the project areas.6 Usage is quite high and not statistically
di�erent across treatment and control communities: 82 (83) percent of the CFs in treated (control)
communities maintained a demonstration plot in 2012. By 2013, the use of demonstration plots
increased to 90 (93) percent. Our experiment allows us to compare information di�usion across two
modalities: direct, centrally administered training, vs. second-hand, EA-led training. The extent of
information di�usion across the two modalities can then be measured through observed variations
in the technique-mix learned and adopted across treatment status, not at the extensive margin of
CFs' demonstration activities.
Our design implies that each EA will work with both �Treatment� and �Control� CFs in his
administrative post.7 A threat to our identi�cation stems from the fact that CFs may request
di�erent levels of attention from their EAs across treatment assignments, displacing EA's time
away from the other treatment status. For instance, �Treatment� CFs may be more engaged with5The full design consists of multiple treatment arms. A second treatment arm was overlaid to our central training
that randomly assigned 75 of the 150 treated communities to have an additional trained female. This second treatmentis the subject of a separate study. In the present study, we pool the two treatments together, to examine the impactof having at least 1 CF trained on SLM in the community on farmer outcomes. A third treatment arm was overlaid tothe �rst two that attempted to provide di�erent performance-based incentives for the CFs to reach farmers. Althoughwe do not measure this e�ect explicitly, the third treatment arm is controlled for in the regression analysis.
6There is no instruction, however, as to what type of plot should be used for demonstration. CFs can elect touse their own, private plot or use a communal land. Hence, we present the adoption results for any plot (own ordemonstration).
7A limitation of working with an existing extension network is that we could not withhold information froma random group of CFs by shutting down their interactions with their assigned EAs. Given the small number ofextension workers (30), reasonable levels of statistical power cannot be reached by assigning the intervention at theEA level. We do verify that extension agent characteristics are balanced across treatment and control communitiesat midline (Table A.2). We further include EA-team indicators in our regression analysis (noting that each CF isassisted by two EAs (not one EA) within the administrative post).
12
the techniques they have learned and request more follow-up visits from their EAs. Reassuringly, we
�nd that �Control� and �Treatment� CFs received equal amounts of attention from their EAs in the
year after the training, both at the extensive and intensive margins.8 We account for unobserved
heterogeneity across EAs using EA �xed e�ects.
3.1 SLM Trainings
Our training intervention encompasses seven9 SLM techniques: Mulching, Crop Rotation, Strip
Tillage, Micro-catchments, Contour Farming, Row Planting, and Improved Fallowing. Mulching
covers the soil with organic residues to maintain soil humidity, suppress weeds, reduce erosion,
and enriches the quality of the soil cover. Crop rotation rotates crops on a given plot to improve
soil fertility and reduce the proliferation of plagues. Strip tillage prevents opening the soil, such
as through plowing, harrowing, or digging on land surrounding the seed row. Micro-catchments
(approximately 15-cm deep permanent holes) are constructed around the base of a plant, such
as maize, to aid water and nutrient accumulation. Contour farming is the use of crop rows along
contour lines forti�ed by stones (or vegetation) to reduce water loss and erosion on sloped land. Row
planting can improve productivity by improving access to sunlight and facilitates weeding and other
cultivation practices (e.g., mulching and intercropping) by providing space between rows. Improved
fallowing reduces the productivity losses from fallowing land by targeted planting of species that
enrich soil in a shorter time frame than traditional fallowing.
Table 1 summarizes the functionality of each of these techniques (Liniger et al., 2011).10 In
8While access to the EA was also surveyed at endline, the statistics are contaminated by the fact that EAs visited�Treatment� CFs to advertise the second SLM training. Hence, reported access to EA mechanically goes up in thetreatment at endline. Thus, Table A.2 focuses on midline observations only to prove balance across extension agentcharacteristics. It is also important to note that the fact that there were no EA di�erences across groups at midlinehas implications on the interpretation of the intervention's impacts. One interpretation is that EAs were diligentabout visiting all CFs irrespective of the intervention which would imply that the main advantage of the treatment isimproving the quality of the information delivered. An alternative interpretation is that EAs might be irresponsiveto the increase in the demand for services that a centralized training might have produced. This would motivatethe centralized training at increasing frequency to address the low transmission of information due to infrequent EAvisits.
9Intercropping was included in the curriculum, which allows for the cultivation of several crops at once. Weexclude this technique from the analysis as it was already widely adopted at the time of the intervention by CFs (98percent) and other farmers (76 and 81 percent of women and men, respectively). Including the technique bears littleconsequence on our point estimates (Tables A.3-A.4).
10 As row planting is often used to reinforce some of the above practices (e.g., mulching and strip tillage), the
13
sub-Saharan Africa, mulching and crop rotation o�er the greatest number of advantages in terms
of water e�ciency, soil fertility, and improving plant material. They were also the most common
techniques applied by farmers in the Zambezi Valley at baseline, though adoption rates were far
from universal (Tables A.5-A.6) . Use of strip tillage, micro-catchments, and contour farming is
also deemed e�ective at improving water e�ciency and soil fertility.
While the long-term bene�ts of conservation agriculture motivate the proliferation of the tech-
niques over 10-15 years, the short-term bene�ts are quite mixed (Giller et al., 2009). In their review
of the literature, Giller et al. (2009) suggest conservation agriculture can improve yields in the short-
term particularly in areas where moisture is limited. It can also have quite devastating impacts in
humid areas, in terms of waterlogging and increased pest incidence. For each SLM technique, we
asked the CFs in our study whether they believed the practice a�ected productivity, the land prepa-
ration e�ort, the planting seed e�ort, and harvesting e�ort in our midline survey. The greatest
percentages of CFs (in the control group) report mulching and crop rotation techniques increase
productivity (60% and 36%) and reduce land preparation e�orts (45% and 26%). As mentioned,
these techniques were also the most popular at baseline. The percentage of CFs (in the control
group) that perceive strip tillage and micro-catchments to be productive only slightly lags behind
the percentage reported for crop rotation (29% and 24%, respectively). This suggests that some
(but not all) SLM technologies pose as reasonable instruments to test knowledge di�usion under
the T&V model in the Zambezi valley.
We worked with technical sta� from the Ministry of Agriculture (MINAG) to develop an edu-
cational agenda for the EAs and CFs on these SLM practices. The EAs were given two three-day
training courses in SLM techniques in October 2010 and November 2012 (prior to the main planting
season), delivered by their district technical sta� with support from the central MINAG project
team. Half of the training sessions were devoted to in-class lectures, and the other half consisted
of hands-on plot demonstrations. The syllabus included a thorough review of the advantages of
each technique over less-environmentally desirable ones. The weeks that followed those two train-
ings, �Treatment� CFs were invited to attend the same course, delivered by the same district-level
independent number of advantages of row planting are undocumented.
14
technical sta�11 with support from MINAG sta�12.
After the �rst training, all CFs (�Control� and �Treatment�) received a new toolkit13 (bicycle,
tools to plow the land, and smaller articles) and the mandate to disseminate the techniques most
pertinent to their local area on their demonstration plots.14A second toolkit with similar items
(including a bicycle) was provided to all CFs again in July 2012 before the second training. The only
di�erence between our �Treatment�and �Control� CFs is the modality through which they received
training on the selected seven SLM techniques.15 EAs were told to transfer their knowledge to
�Control� CFs and assist both �Treatment� and �Control� CFs in setting up demonstration activities.
Inviting �Treatment� CFs to the district-level trainings was left to each EA team, at the admin-
istrative post level. EAs were given the list of randomly chosen �Treatment� CFs, and the district
sta� explained the physical impossibility of training all CFs at once and that a lottery had been
used to select the participating CFs. An attendance sheet was taken at training by the district sta�.
In 2010, only four �Treatment� CFs did not attend the training, and all are in the Mopeia district.16
Since district sta�s may have an incentive to misreport attendance, we performed independent au-
dits. First, we veri�ed that the attendance list re�ected the (randomly assigned) eligibility, and
found no contamination of the control group. Second, we showed up unannounced at the trainings
in all �ve districts. Finally, attendance lists were back-checked: a random set of listed participants
were visited in November and December of 2010 and asked whether they attended the SLM training.
Our back-checks indicate that attendance was genuine.
Similar checks were performed on the 2012 training. While the attendance list fully lines up
with our back-checks, participation was not universal, and contamination was quite substantial.11In some districts, district sta� relied on their EAs to help during the hands-on sessions. This could contaminate
our results by lowering the amount of on-farm attention �Treatment� CFs subsequently received from their EAs. Ifanything, this implies that we will underestimate information �ow in the centrally-ran training arm, and overestimateit in the T&V model.
12Given the low literacy of farmers, a �lm covering all techniques substituted the initial lecture format in the secondtraining of the CFs.
13The toolkit distribution was planned, regardless of our intervention, by the project sta�, as the previous distri-bution had been done in 2007 and the items were deemed too old to function in 2010.
14The project had started to disseminate mulching, strip tillage, row planting, and crop rotation as early as 2008.However, the formal practice was sparse at the time of the intervention and most EAs and CFs had not received aformal training on SLM techniques or been instructed to transfer their knowledge to their peers.
15It is possible that in attending the centralized training, CFs might �feel special�. Our treatment e�ect mayincorporate how this feeling may empower CFs to implement their lessons in practice. Although this was beyond thescope of the evaluation, we did ask CFs to report their state of happiness. We found 91 percent of the CFs (in thecontrol group) and 85 percent of the CFs (in the treatment group) were happy and there is not statistically signi�cantdi�erence in those proportions (Table A.1).
16These CFs were trained by the EA on an individual basis, and the follow-up training was veri�ed.
15
Sixty-three (sixteen) percent of the treated (control) communities had at least one CF attend the
training.17 As these �gures signal signi�cant exposure of �Control� CFs to the treatment in 2012,
they foreshadow our weakened ability to statistically di�erentiate the two training models in the
2013 (second follow-up) survey round.
3.2 Data
We conducted two follow-up surveys, a 2012 (midline) round, and a 2013 (endline), which form a
panel of households and CFs in the study area.18 A baseline census survey was administered to all
CFs in August 2010, before the district-level randomization. Figure 1 illustrates the timing of the
surveys and CF trainings over the course of four years.
Midline and endline surveys collected household demographics, individual and plot-level SLM
adoption, and household production information for approximately 4,000 non-CF households in 200
communities (aldeias, that mostly overlap with Mozambique's enumeration areas) (Figure 2). A
listing of households residing in each community was performed, from which we drew a random
sample of 18 non CF-households per community. Our �eld work included �ve survey instruments:
a household questionnaire; a household agricultural production questionnaire; a CF questionnaire;
an extension agent questionnaire; and a community questionnaire. The household survey was also
administered to CF households, in addition to the speci�c CF survey. The present analysis exploits
the information from the household and CF surveys.
Both midline and endline surveys were conducted during the primary planting season. In each
survey round, households were visited twice: pre- and post-harvest. This is because SLM practices
are most visible just after planting (pre-harvest, from February to April), while production data can
only be obtained after harvest (May-June). Hence, all household surveys were administered during
February�April, with the exception of the agricultural production module. The agricultural pro-
duction (and CF, community, and extension agent) surveys were administered post-harvest during
17The contamination likely was caused by a combination of EA and self-selection. CFs in the control group couldhave easily learned about the trainings from peers located elsewhere. Clearly, the EAs were involved in organizing thetraining. A politically connected CF might have been admitted by the EA or district o�cer as the project fosteringthe evaluation was ending.
18Following McKenzie (2012), we optimize our probability of detecting a signi�cant impact under a budget con-straint by conducting two follow-up data collections rather than a baseline and a follow-up.
16
May and June in 2012 and June through August in 2013.
3.3 Balance
We use data from the baseline CF survey and retrospective information collected in the 2012 house-
hold survey to check for balance across treatments. Table 2 indicates minor di�erences between
CFs in the treatment and control communities. �Treatment� CFs spent almost four more hours a
week working as a CF (pre-intervention) with slightly more recent training when we condition on
being formally trained. �Control� CFs were exposed to a greater number of techniques prior to the
intervention.19 In spite of these di�erences, (recalled) pre-intervention adoption rates among CFs
in control and treated communities are similar.20 Farmers' (recalled) baseline SLM learning and
adoption rates are also similar across treatments (Table 3).21
Taken together, these results suggest that we will provide a conservative measure of the relative
impact of directly training CFs. Our estimates might understate the impact of direct training, and
overestimate the impact of T&V model. In addition, the fact that CFs are more knowledgeable in
SLM than the average farmer at baseline further suggests that the impact estimates of the training
program are likely not generalizable to the average farmer.
3.4 Measuring Information Di�usion and Behavioral Change
Central to identifying variations in information di�usion is measuring changes in agricultural prac-
tices. Our study rests on the reliability of our markers of individual SLM knowledge and adoption
outcomes. We focus on three outcomes: a knowledge score (see Kondylis, Mueller, and Zhu (2014)
for details of the exam), the number of techniques the respondent identi�ed by name, and the num-
19Given CFs in treatment villages spend more hours a week working as a CF at baseline, we will include the variableas a control in the regression analysis.
20Balance tests for the CF and other farmers' knowledge and adoption of individual SLM techniques at baselineare reported in Tables A.5-A.6. Because these values are based on recall data, the tests should be interpreted withcaution.
21Even though mean comparisons indicate there are no statistically signi�cant di�erences, recall bias may bepresent. We therefore do not exploit the recalled information beyond balance checks.
17
ber of techniques the respondent claims to adopt on any plot.22 Objective adoption measures were
also collected for two plots per household and largely corroborate the self-reported outcomes (see
Kondylis, Mueller, and Zhu (2014) for a detailed comparison).23
Since the CFs were encouraged to choose the most relevant techniques to their local conditions,
we focus on aggregate measures of knowledge and adoption for our main results. However, restricting
the analysis to aggregate measures of knowledge and adoption may lead us to overlook patterns of
substitution across techniques attributable to the intervention. For example, we may underestimate
the impact of the intervention if CFs substitute away from already-disseminated technologies to
the bene�t of some �newer� techniques within the proposed package. We therefore also present how
knowledge and adoption of speci�c techniques indicators are a�ected by the intervention. Technique-
speci�c knowledge is captured by whether the respondent answers correctly at least 1 of 3 knowledge
questions pertaining to the practice.
Knowledge, adoption and perception of the SLM techniques were collected at the individual
level from the household questionnaire. Two respondents were interviewed: the household head
and his/her partner or spouse. If a polygamous household was encountered, the main spouse was
interviewed.24 Thus, our sample of CFs and other farmers consists of those who reported their
personal information, participated in an agricultural knowledge exam with questions related to
each speci�c SLM practice, and self-reported their SLM adoption rates. Speci�cally, 179 and 172
villages were interviewed for the contact farmer survey in 2012 and 2013, respectively; 2,536 male
and 3,716 female non-CFs were surveyed in 2012, and 3,115 female and 2,175 male non-CFs in
2013.25 Selective sample attrition is of de�nite concern, and we detail below how selective attrition22For CFs, the majority of the analysis rests on their adoption of techniques on any plot which includes their own
and demonstration plots. There are slight di�erences between adoption measures which include and exclude thedemonstration plot for a minority of CFs who's demonstration plots rest on communal land (29%). We verify theresults are not driven by communal propriety of the demonstration plot and report those robustness checks in somebut not all of our CF tables of results.
23Our decision to focus on the knowledge score and self-reported adoption outcomes is motivated by the conclusionsof Kondylis, Mueller, and Zhu (2014). Using the Smallholders' midline survey data, we �nd that learning outcomesbased on knowledge exams provide more precision when compared to know-by-name questions, as they reveal thetrue knowledge of those individuals less familiar with the name of the technique yet more familiar with its purposeand usage. In our triangulation of the self-reported vs. observed adoption, we �nd that false reporting is negligible.Since objective measures of adoption are only collected for a subset of plots in the sample (one per respondent), weinstead focus on a more inclusive measure of adoption provided by self-reports of men and women surveyed in thesample.
24A polygamous household is characterized by the household head having more than one spouse or partner. Only2.7 percent of the households in our sample are polygamous.
25For analysis, we restrict the sample to farmers with complete information on household and individual charac-teristics. We have 179 and 168 CFs in 2012 and 2013, respectively; 2,475 male and 3,592 female non-CFs in 2012,
18
is addressed in our speci�cations.
3.5 Empirical Strategy
We measure information di�usion through a direct training model relative to the traditional, T&V
extension network: from EA to CF, and CF to others. A particularly attractive feature of our design
is that we track information di�usion through an existing network. We �rst measure to what extent
directly training CFs a�ects CFs' and others' knowledge and adoption.26 We causally estimate the
intent-to-treat e�ects (ITT) of a community being assigned to a central CF training (relative to a
status quo T&V information di�usion modality) on the SLM knowledge and adoption of CFs27 and
others in the community, Y, using a simple reduced-form speci�cation:
Yi,h,j = β0 + β1Tj + β2Xi,h,j + εi,h,j (1)
T takes the value 1 for each community j with a centrally trained CF. Individual i, household h, and
administrative post indicator variables are included in the vector X to improve the precision of the
estimated coe�cients. 28 Since CFs were exposed to a team of two EAs within each administrative
post, we identify the ITT from within-EA-team variations, by including administrative post �xed
e�ects in all regressions . We also use the Huber-White heteroskedasticity-robust estimator to
calculate the standard errors when using the sample of CFs. For the other farmer regressions, we
cluster the standard errors at the community level to allow for arbitrary correlation of treatment
e�ects within the community. Gender-di�erentiated e�ects are presented throughout to allow for
di�erent functional form, as women cultivate their own plots separate from their husbands' and may
face varying constraints on their time, input use, crop choice, and plot characteristics (Table A.7).
and 3,098 female and 2,141 male non-CFs in 2013.26CFs have the �exibility to decide which SLM techniques to adopt on the demonstration plot. As the marginal
value of adopting a technique will vary with the predominant crops grown, soil quality, topography, and other localconditions, demonstrated technique-mix is unlikely to be uniform across communities.
27CF-level regressions are run using community-level CF outcomes and characteristics. In those communities wherewe (randomly) assigned a second woman CF, we measure increased village-level exposure by regressing the maximum(mean) value of binary (continuous) outcomes of CFs within the village on the maximum (mean) value of binary(continuous) covariates.
28We address omitted variable bias by including variables that re�ect CF (or other farmers') demographic char-acteristics, the number of hours worked by the CF at baseline, administrative post indicators, and indicators fortreatment arms not analyzed in the present study.
19
We separate speci�cations by round for a few reasons. First, nine percent of households attrited
between 2012 and 2013. We �nd evidence of selective attrition across household survey rounds, as
individuals present in both midline and endline rounds appear statistically di�erent than individuals
only present during the midline and endline surveys (Table A.8).29 Second, in spite of attrition being
uncorrelated with the treatment (Table A.10), evolution in the realities of the program on the ground
compels us to split the sample by survey year. For instance, as mentioned above, contamination
was quite large in 2012, while absent in 2010. Hence, results from the 2013 survey will likely
underestimate the impact of the intervention, and our inferences draw heavily on the estimates
provided by the 2012 survey.
We perform two robustness checks to examine the sensitivity of our results to attrition. The �rst
diagnostic estimates (1) using the balanced panel. We show that the inclusion of individuals only
present in one round a�ects the precision of our point estimates rather than their magnitude and
sign. The second check bounds the treatment e�ect for selective attrition using a method proposed
by Lee (2009). Upper (lower) bounds of the treatment e�ect are produced non-parametrically
by trimming the tail of the distribution of the outcome variable in the treatment group below
quantile p (and above quantile 1-p), where p is the di�erence in the proportions of non-missing
observations between the treatment and control groups divided by the total number of observations
in the treatment group.
The technologies we disseminate are somewhat novel in the sense that baseline adoption is
low. However, awareness of the techniques is quite high (Tables A.5-A.6). While there are large
potential gains in knowledge and adoption as a result of the SLM training, farmers' responses are
less likely to be driven by the �freshness� of the material. A caveat to the low novelty content of SLM
training is that, should adoption prove low both at the CF and farmer levels, we will not be able to
rule out demand-side from supply-side constraints without further investigation. We �rst provide
information on the potential costs savings associated with others' technological adoption (both
in terms of changes in perceptions and realized labor savings). We additionally assess whether CF
29Attrition rates at the household and CF level are not statistically di�erent nor correlated across treatment groups(Tables A.9-A.10). The characteristics of CFs do not vary on average over time (Table A.11). Household attritionrates appear consistent with other studies in the same region (de Brauw, 2014). A probit regression in which re�ectsthe probability of hte household attriting indicates the percent of household members that were away in 2012 increasesthe probability of moving out of the sample (Table A.10). Age and the number of children of the household headreduces the probability of moving out of the sample.
20
pro�le and CF similarity in production habits with others' in�uenced the impact of the intervention,
di�erentiating ITT estimates by variants in CF and farmer characteristics.
3.6 Summary Statistics
To understand the socioeconomic conditions in the project area, we brie�y describe the character-
istics of the average farmer in our sample (drawing from statistics in Table A.12). The majority of
individuals are women, due to the high prevalence of female headship (approximately 30 percent)
in the region (TIA, 2008) . The average plot owner was 38 years old with only 2 years of schooling.
Most plot owners were married with 3 children, living in a single-room house made of mud and
sticks, with palm or bamboo roofs (not reported). They possess 2 hectares of land on average with
a standard deviation of 1.8.
CFs are more knowledgeable (Tables 2 and 3), educated and wealthier (Table 4) than the average
farmer. Communicator pro�le has been shown to a�ect the di�usion process in ambiguous ways
(Munshi, 2004; Bandiera Rasul, 2006; Feder and Savastano, 2006; Conley and Udry, 2010; BenY-
ishay and Mobarak, 2013). While CFs are positively selected in attributes, they are also well-known
in their communities: 81 and 90 percent of male and female farmers in the control group declare
knowing them personally. However, only 79 and 67 percent of males and females report knowing
that these individuals assume a role as CF in their community. Thus, barriers to knowledge di�usion
may stem from a lack of transparency in the roles of CFs rather than their dissimilarities with those
they intend to serve. We explore the latter possibility explicitly by estimating heterogeneous e�ects
of the treatment.
4 Results
4.1 CF Adoption and Learning-by-doing
Table 5 provides the ITT estimates of aggregate measures of knowledge and adoption. Despite the
variety of techniques adopted among control CFs at midline, we detect that CFs adopt an additional
21
technique in response to the training. These e�ects are not driven by di�erential access to extension
agents (row 1, Table 5).
We further disaggregate ITT estimates by the adoption of speci�c techniques (Table 6). Directly
trained CFs are more likely to adopt techniques that were most uncommon at baseline, and perceived
productive as outlined is Section 3.1. E�ect sizes range from 24 to 41 percent. Adoption signi�cantly
trended downward in both treatment and control villages. In fact, at endline, the impact of the
intervention on adoption is insigni�cant for all techniques.30
We next examine changes in CF knowledge scores to test whether direct training corrected for
any loss in EA-to-CF information di�usion associated with a pure T&V approach. By additionally
comparing the adoption and knowledge gains, we can observe whether increased training helped lift
a genuine information constraint to adoption, or whether the gains are purely achieved through in-
creased salience of the techniques. Figure 3 graphs the e�ect sizes of the treatment on the knowledge
and adoption of each SLM technique.31 The left panels of Figure 3 indicate that directly training
CFs did little to increase CFs' knowledge scores on the techniques relative to a pure T&V regimen
at midline. Hence, the gains in adoption observed under the direct training modality at midline are
attributable to increased salience of information rather than an actual learning e�ect. This is not
surprising since CFs were su�ciently aware of the techniques from the outset.
Tracking adoption and knowledge scores across years allows us to document CFs' learning-by-
doing. In contrast to the adoption decay in control and treatment communities, CF knowledge of
SLM signi�cantly expands in treatment areas (see Table A.13 for knowledge score point estimates).
Treated CFs' knowledge scores associated with the adopted techniques go up one year after we detect
a signi�cant increase in adoption. Moreover, the order of magnitude of these gains is remarkably
similar to those achieved on adoption: the largest gains are experienced for contour farming, with
slightly lower gains in strip tillage and micro-catchments. Taken together, the pro�le of this one-year
lag from demonstration to learning suggests that CFs acquired knowledge through a learning-by-
doing process.30The maintenance costs o�er one explanation for the disadoption in the subsequent year of the study. For example,
Liniger et al. (2011) indicate for micro-catchments the long-term bene�ts appear less positive than the short-termbene�ts.
31The probability of detecting statistical signi�cance due to the intervention increases with the number of outcomesused. Our results are not robust according to the �idàk (p-value=0.015) and Bonferonni (p-value=0.014) adjustments(Abdi, 2007). However, micro-catchment adoption at midline and contour farming knowledge at endline are robust tothe adjusted p-values in a regression model that substitutes district indicators for the administrative post indicators.
22
We show direct training has the potential to increase adoption of innovative practices both at
the intensive and extensive margins. Although formal training on its own did not appear to lift any
knowledge constraint among relatively skilled CFs, it increased adoption through added salience.
This intimates a weakness of the T&V model, where EAs are not as e�ective in getting farmers to
devote time to adopting new activities as a direct training.
4.2 CF Substitution of Techniques
We next try to formalize whether the training caused CFs to modify their practices towards newer
techniques brought to their attention by the direct training. To gauge the potential substitution
e�ects, we exploit the (recall) baseline adoption measures to estimate the following regression,
suppressing all subscripts in (1) but those that re�ect time:32
Yt = β0 + β1T + β2Yt−1T + β3Yt−1 + β4X + ε (2)
where t signi�es the midline and t-1 baseline. The results in Table 7 are suggestive that the training
might have encouraged but not substituted techniques (e.g., improved fallowing). Although the signs
of the parameter of the variables interacting the treatment and previous adoption (β3) are negative
for mulching and crop rotation, their magnitudes are similar to the ITT estimate. We err on the
cautious side in the interpretation of these results, as clearly there appears to be a greater, though
statistically insigni�cant, recall bias among treatment farmers for some techniques.
4.3 Others' Knowledge and Adoption
We now turn to CFs' ability to di�use knowledge to others. We exploit the random, positive shock
introduced by the intervention in CF activity to measure the extent of CF-to-others knowledge
transmission. Table 8 provides the mean aggregate knowledge and adoption rates of other farmers
in the control communities, as well as the ITT estimates of changes in knowledge and adoption32Note that we focus on the adoption practices on CFs' own plots here, since information on the demonstration
plots were not collected for baseline through recall.
23
outcomes attributable to our intervention. Even though the margin for gains from receiving the
information was larger than that of CFs, other farmers' aggregate SLM knowledge and adoption
remained the same. This is in spite of farmers' reporting learning techniques, such as contour
farming, explicitly from CFs (Table A.15). The absence of adoption is robust to balancing the panel
at midline and accounting for selective attrition at endline (Table 9). These qualitative results
indicate that demand-side constraints may continue to hinder farmers' adoption.
4.4 Farmers' Perceptions of Cost Savings
Our midline survey asked farmers whether they perceived each technique to require more labor
e�ort, equivalent labor e�ort, or less labor e�ort than the use of traditional cultivation practices.
Farmers in the control group perceive all techniques to be labor intensive, with a range of less
than 1 percent to 18 percent of farmers declaring the techniques decrease the amount of labor
required (Table A.16). We �nd that increasing exposure to SLM information through the trained
CF a�ected farmers' perceptions of the adoption costs for micro-catchments only. The intervention
signi�cantly increased the proportion of farmers who perceive micro-catchments to be labor-saving
by 2 percentage points for men, amounting to a 100-percent increase relative to the control for male
farmers.
The changes in farmers' perceptions are complementary to the ITT estimates for others' adoption
by technique. We observe male farmers exposed to the intervention were more likely to adopt micro-
catchments by 3 percentage points (an e�ect size of 75% according to Table A.14).33 Women do not
act on the information they receive. Though complementary, the above inferences are not indicative
of a causal relationship between perceptions and adoption.
We lastly explore whether farmers were motivated by the demonstrated, short-term cost savings
of the technology. In particular, we examine whether farmers' adoption rates coincide with CF
labor savings in terms of the number of hours spent last week devoted to di�erent agricultural tasks
and the total number of weeks devoted to farming in the last year. Noting that our measure of33The results are not robust to adjustments in the familywise error rates. However, the micro-catchment adoption
of male farmers at midline is robust to the Sidak and Bonferonni p-value adjustments in a regression that substitutesdistrict for administrative post indicators.
24
labor e�orts is inclusive of all techniques adopted by the CF (not exclusive to micro-catchments),
we provide ITT estimates of the CF labor e�orts for various agricultural tasks (Table A.18). The
results in the Appendix indicate trained, CFs spent four fewer hours preparing land last week, and
saved a total of one week spent on farming in the last year at midline.
Thus, male farmers may be particularly motivated to adopt micro-catchments by the immediate
gains in labor savings. Although we cannot make claims de�nitively using cost data, bene�t-
cost assessments of other SLM studies in Africa suggest micro-catchments o�er an additional cost-
advantage. Unlike strip tillage and contour farming, additional tools are not required to create
micro-catchments (Liniger et al., 2011).34
4.5 CF and Others' Heterogeneity
We lastly explore whether CFs' characteristics provoke heterogeneous responses among farmers.
Working with an existing network of CFs, we could not exogenously vary their education, age,
wealth, or cropping patterns. The results that follow cannot be interpreted as causal linkages
but as descriptive evidence. Speci�cally, it must be noted that, as CFs are, on average, of higher
status than other farmers we do not have a counterfactual where average farmers are placed in
a communicator role. We simply interact the treatment variable with CF characteristics, while
controlling for both CFs and farmers' characteristics, following (1).
Table 10 displays the results from regressions using farmers' adoption of micro-catchments as
outcomes, since the adoption for other techniques remained unchanged by the treatment (Table
A.14). Estimates of the treatment e�ect as well as the combined e�ect of the treatment and the
treatment interacted with whether the CF completed his secondary education, was older than the
median age, had above median landholdings, or produced the same two primary crops as the farmer
are provided. We assume having similar primary crops to the CF is an exogenous variable, i.e.
cropping decisions are �xed before adoption decisions and cropping decisions are independent of
the treatment.35 For male farmers, exposure to increased CF activity yields larger point estimates34Farmers may adopt micro-catchments in order to increase their resilience. We however do not witness any
signi�cant changes in the ITT estimates when stratifying the sample by below and above median 30-year cumulativerainfall averages.
35Although not shown here, whether the farmer grew the same primary two crops as the CF is not a�ected by the
25
when CFs are older and more educated. Only when we distinguish the female sample by whether
they have similar crop portfolios as their CF do we observe a positive association between their
adoption and treatment. This is somewhat consistent with the idea that social learning is more
prevalent in homogeneous farming conditions (Munshi, 2004). Delays in women's adoption may stem
from gendered di�erences in production technologies and an inability to extrapolate demonstrated
activities to their own plot.
5 Discussion
Our study aims to reveal which mechanisms implementable within an existing extension network
can improve the limited knowledge and adoption of novel agricultural practices in Mozambican rural
communities. We examine innovation di�usion through two nodes of an existing extension network:
EA-to-CF and CF-to-others interactions. We �nd that both modalities come short of e�ectively
propagating innovative SLM techniques. Directly training CFs on SLM dominates a pure T&V
approach to extension, as it is conducive to more demonstration, private adoption and learning-by-
doing among CFs. This demonstrates that SLM techniques were in fact valued by sophisticated
farmers, and that in-depth knowledge, not awareness, of the techniques constituted a barrier to
adoption among CFs. Although the point estimates on the learning and adoption gains from direct
training are small, the e�ect sizes are large. Running small-scale, low-cost trainings of designated
communicators can provide a more e�cient solution to enhance agricultural knowledge and practices
than relying on extension workers to provide ad hoc training.
Training a few �seed adopters� in a community may not be enough to boost adoption of a new
technique. Studying the impact of an exogenous increase in CFs' activities shows that demonstration
is not su�cient to create learning within a community and to get others to adopt on a large scale.
Male farmers choose to adopt one of three SLM techniques that the trained, CFs adopted. Looking
at the multiplier e�ect of CFs' demonstration activities, these results imply that a one percentage
point increase in CF demonstration of micro-catchments induce other male farmers to increase their
treatment at midline. Male farmers exposed to the intervention are more likely to grow the same primary two cropsas the CF at endline. The intervention has no e�ect on the crop decisions of women, however.
26
adoption by 0.1 percentage points.
Farmers' perceived costs of SLM techniques pose one obvious demand-side constraint to adop-
tion. Adoption of micro-catchments increased 3 percentage points among male farmers exposed to
the intervention with no changes in adoption for the other two techniques covered by the trained
CFs. The average male farmer exposed to the intervention in our study is more likely to perceive
micro-catchments as labor saving. Their CFs realized labor savings in the form of a 4-hour reduction
in land preparation the week prior to the interview and a 1-week savings of working on the farm
over the last year (although these are inclusive of all techniques CFs adopted). The descriptive
evidence is consistent with farmers' beliefs updating in response to the intervention following their
adoption of micro-catchments or after observing the CF's demonstration activities.
The pro�le of the �seed adopters� in�uences whether woman act on the information they receive.
When focusing on the women who have similar cropping patterns as the CF, we observe social learn-
ing is more prevalent within this demographic. Women are more likely to act on the information
they receive: their micro-catchments' adoption increases by 3 percentage points at midline and 9
percentage points at endline. Providing messengers with amenable farming conditions may improve
the targeting of female farmers in the provision of extension services.
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30
Figures and Tables
31
Figure 1: Timeline of Trainings and Contact Farmer and Household Surveys
32
Figure 2: Geographical Distribution of (Non-CF) Households
33
Figure 3: E�ect of SLM Training Intervention on Contact Farmers
34
Table 1: Advantages of SLM Techniques
Technique Water E�ciency Soil Fertility Improve Plant Material Improve Micro-ClimateMulching X X X XStrip tillage X XMicro-catchments X XContour farming X XCrop rotation X X XImproved fallowing XRow planting - - - -Source: Sustainable Land Management in Practice, 2011.
35
Table 2: Contact Farmers' Characteristics in Treated and Control Communities
Variables
Treated
Control
Di�.
Mean
SDN
Mean
SDN
ofMean
BaselineSurvey
CFage
38.858
9.348
148
40.160
10.559
50-1.302
Everbeingform
ally
trained
0.350
0.479
140
0.447
0.503
47-0.097
Num
berof
yearssinceform
altraining
2.157
2.239
513.409
3.202
22-1.252*
Exp
erienceas
CFin
years
2.243
2.401
144
2.653
2.570
49-0.410
#of
farm
ersassisted
inlast
7days
18.034
16.095
147
19.100
14.333
50-1.066
#of
malefarm
ersassisted
inlast
7days
10.871
9.659
147
10.860
9.064
500.011
#of
farm
ersassisted
inlast
30days
37.060
28.320
133
38.370
26.441
46-1.309
#of
malefarm
ersassisted
inlast
30days
22.480
15.145
148
22.240
17.203
500.240
Hours
workedas
CFin
last
7days
14.813
12.726
144
12.340
11.573
502.473
Hours
norm
ally
working
asCFperweek
16.322
12.498
143
12.960
12.034
503.362
Total
acreageof
cultivated
land
3.184
1.619
144
3.070
1.542
500.114
#of
households
inthecommun
ity
284.421
267.037
126
244.548
265.410
4239.873
#of
plotsin
thecommun
ity
459.269
430.130
108
436.063
426.578
3223.206
MidlineSurvey
(recall)
Num
berof
techniques
learnedbefore
2010
2.839
2.362
137
3.286
2.255
42-0.446
Num
berof
techniques
adoptedbefore
2010
1.409
1.210
137
1.167
0.935
420.242
Num
berof
observations
137
42179
Source:
Contact
Farm
erBaselineSurvey,2010;Household
Survey,2012.
Note:
***,
**,and*indicate
signi�canceat
the1,
5,and10
percentcritical
levelfortstatistics.
36
Table 3: Other Farmers' Characteristics in Treated and Control Communities
Variables Treated Control Di�erenceMean SD Mean SD of Mean
Midline Survey
Is the head of household 0.585 0.493 0.588 0.493 -0.003Male 0.420 0.493 0.414 0.493 0.005Age 37.764 19.980 37.843 20.093 -0.079Years of schooling completed 2.057 4.866 1.844 4.905 0.213Single 0.063 0.504 0.058 0.509 0.005Married 0.844 0.546 0.855 0.550 -0.011Divorced, separated, or widowed 0.091 0.366 0.085 0.368 0.006Number of children (ages < 15 years) 2.756 3.406 2.843 3.432 -0.087Landholdings (hectares) 2.004 3.995 1.880 4.033 0.124Number of rooms in the house 1.427 2.116 1.444 2.138 -0.017Housing walls made of brick 0.100 0.777 0.096 0.785 0.004Housing roof made of tinplate 0.079 0.718 0.079 0.725 0.000
Midline Survey (recall)
Number of techniques learned before 2010 1.236 4.514 1.303 4.563 -0.066Number of techniques adopted before 2010 0.509 2.024 0.554 2.045 -0.045Number of observations 4,385 1,499 5,884Source: Household Survey, 2012.
Note: T test inferences are based on standard errors clustered at the community level.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
37
Table 4: Characteristics Comparison between Contact Farmers and Other Farmers
Variables Midline EndlineCFs Others Di�erence CFs Others Di�erenceMean Mean of Mean Mean Mean of Mean
Household Characteristics
Is the head of household 1.000 0.586 0.414*** 0.988 0.594 0.394***Age 41.425 37.784 3.640** 43.341 38.775 4.566***Years of schooling completed 5.469 2.003 3.467*** 5.494 2.114 3.380***Single 0.017 0.062 -0.045 0.006 0.048 -0.042**Married 0.978 0.847 0.131*** 0.971 0.851 0.120***Divorced, separated, or widowed 0.045 0.089 -0.044 0.07 0.101 -0.031Number of children (ages < 15 years) 3.779 2.778 1.001*** 3.706 2.89 0.816***Landholdings (hectares) 3.233 1.972 1.261*** 3.654 2.401 1.253***Number of rooms in the house 1.777 1.432 0.345** 1.748 1.412 0.336**Housing walls made of brick 0.168 0.099 0.068Housing roof made of tinplate 0.207 0.079 0.128**
Production
Grew maize 0.699 0.636 0.064 0.738 0.642 0.096Grew sorghum 0.139 0.240 -0.101 0.157 0.274 -0.117Grew cotton 0.202 0.097 0.105** 0.064 0.052 0.012Grew sesame 0.243 0.161 0.082 0.337 0.148 0.189***Grew cassava 0.069 0.171 -0.102 0.041 0.143 -0.102Grew cowpea 0.225 0.354 -0.128 0.331 0.349 -0.018Grew pigeon pea 0.202 0.189 0.013 0.186 0.216 -0.030
Farm Characteristics
Plot size (hectares) 1.151 0.951 0.201* 1.301 1.166 0.135Plot was �at 0.807 0.639 0.167** 0.640 0.594 0.046Plot was burnt 0.063 0.240 -0.178** 0.064 0.249 -0.185**Used herbicides/pesticides/fungicides 0.156 0.062 0.094** 0.087 0.021 0.067***Used natural fertilizer 0.358 0.269 0.090 0.622 0.438 0.184Used chemical fertilizer 0.127 0.008 0.119*** 0.052 0.005 0.047***Number of observations 179 5,884 6,063 172 5,076 5,248Sources: Household Survey, 2012, 2013.
Note: T test inferences are based on standard errors clustered at the community level.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
38
Table 5: E�ect of SLM Training Intervention on Contact Farmers
Midline EndlineControl ITT N Adjusted Control ITT N AdjustedMean(SD) R2 Mean(SD) R2
Access to EAs
EA visited CF 0.595 -0.079 179 -0.083at least 1/month (0.093)EA visited CF 0.738 -0.088 179 -0.129at least 1/half year (0.098)EA visited CF 0.786 0.021 179 -0.127at least 1/year (0.093)
Performance
Knowledge Score 0.625 -0.002 179 -0.096 0.641 0.093*** 168 0.022(0.201) (0.040) (0.142) (0.027)
# of techniques 4.214 0.524 179 -0.079 4.048 1.320*** 168 0.040known by name (1.601) (0.369) (1.667) (0.387)# of techniques 1.214 0.788*** 179 0.117 2.357 0.654** 168 0.032adopted on own plot (1.001) (0.247) (1.340) (0.308)# of techniques 4.452 0.817* 179 -0.072 3.024 0.551 168 -0.029adopted on any plot (1.928) (0.415) (1.569) (0.360)Source: Household Survey and Contact Farmer Survey, 2012, 2013.
Note: Regressions include the following variables: a constant, age, completed at least primary schooldummy, single dummy, number of children, total landholdings, the number of rooms in the household,baseline CF's number of years since formal training, missing dummy, posto indicators, and incentivetreatment. ***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
39
Table 6: E�ect of SLM Training Intervention on Contact Farmers' SLM Adoption
Adoption on Midline EndlineAny Plot Control ITT N Adjusted Control ITT N Adjusted
Mean R2 Mean R2Mulching 0.929 0.032 179 -0.130 0.929 0.031 168 -0.048
(0.051) (0.053)Strip Tillage 0.619 0.152� 179 -0.061 0.476 0.172 168 -0.060
(0.094) (0.114)Micro-catchments 0.643 0.216** 179 -0.087 0.476 0.057 168 -0.129
(0.099) (0.114)Contour Farming 0.405 0.171^ 179 -0.109 0.048 0.070 168 -0.133
(0.105) (0.071)Crop Rotation 0.905 0.050 179 -0.073 0.548 0.049 168 -0.037
(0.062) (0.100)Row Planting 0.524 0.097 179 -0.073 0.357 0.138 168 -0.091
(0.098) (0.103)Improved Fallowing 0.429 0.097 179 -0.044 0.190 0.034 168 -0.100
(0.103) (0.084)Source: Household Survey and Contact Farmer Survey, 2012, 2013.
Note: Regressions include the same explanatory variables as models in Table 5.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.�: P-value is 0.106. ^: P-value is 0.104.
40
Table 7: E�ect of SLM Training Intervention on Contact Farmers' Adoption Controlling for PreviousAdoption
Adoption on MidlineOwn Plot Control ITT Adopted ITT * N Adjusted Treatment
Mean Before Adopt Bef. R2 E�ect (PV)Mulching 0.405 0.254** 0.537*** -0.235 179 0.141 0.103
(0.105) (0.131) (0.143)Strip Tillage 0.286 -0.035 0.769*** 0.159 179 0.656 0.165
(0.060) (0.104) (0.114)Micro-catchments 0.119 0.162** 0.662*** -0.061 179 0.346 0.674
(0.068) (0.131) (0.146)Contour Farming 0.000 0.022 0.981*** 0.000 179 0.428 .
(0.017) (0.081) .Crop Rotation 0.262 0.196** 0.812*** -0.243 179 0.398 0.115
(0.089) (0.138) (0.153)Row Planting 0.119 0.077 0.810*** -0.157 179 0.488 0.242
(0.050) (0.121) (0.134)Improved Fallowing 0.024 -0.011 0.022 0.484** 179 0.161 0.033
(0.042) (0.205) (0.225)Source: Household Survey, 2012.
Note: Regressions include the same explanatory variables as models in Table 5.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
41
Table 8: E�ect of SLM Training Intervention on Other Farmers
Midline EndlineControl ITT N Adj. Control ITT N Adj.Mean(SD) R2 Mean(SD) R2
Access to CF
Has access to any Female 0.080 0.028 3423 0.012 0.214 0.028 2951 0.004contact farmer (0.025) (0.033)in the last half year Male 0.135 0.048 2461 0.016 0.301 0.001 2120 0.001
(0.037) (0.042)
Performance
Knowledge score Female 0.290 0.004 3423 0.006 0.369 -0.019 2951 0.009(0.157) (0.016) (0.236) (0.026)
Male 0.316 0.006 2461 0.016 0.416 -0.019 2120 0.013(0.161) (0.016) (0.221) (0.024)
# of techniques Female 1.457 0.079 3423 0.025 1.581 0.016 2951 0.007known by name (1.485) (0.135) (1.457) (0.195)
Male 1.709 0.018 2461 0.014 2.025 -0.171 2120 0.010(1.588) (0.144) (1.610) (0.196)
# of techniques Female 0.659 -0.055 3423 0.005 0.912 0.111 2951 0.003adopted (0.765) (0.072) (0.932) (0.109)
Male 0.749 -0.034 2461 0.011 1.175 -0.009 2120 -0.001(0.820) (0.080) (1.002) (0.118)
Source: Household Survey, 2012, 2013.
Note: Regressions include the following variables: a constant, age, completed at least primary schooldummy, single dummy, widow dummy, number of children, total landholdings, the number of roomsin the household, baseline CF's number of years since formal training, missing dummy, posto indicators,and incentive treatment.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
42
Table 9: Selective Attrition, Balanced Sample & Lee's Bounds
Midlin
e(BalancedSample)�
End
line(Lee'sBound
s)Control
ITT
NAdjusted
TreatmentBound
Con.Interval
#of
Sel.Obs.
Trimming
Mean(SD
)R2
Low
erUpp
erLow
erUpp
er#
ofObs.
Proportion
Accessto
CFs
Has
access
toany
Female
0.083
0.018
2706
0.010
0.027
0.042*
-0.006
0.084
2706
0.015
contactfarm
er(0.026)
(0.019)
(0.024)
3423
inthelast
halfyear
Male
0.135
0.047
1873
0.017
-0.013
0.007
-0.067
0.053
1874
0.019
(0.041)
(0.031)
(0.026)
2461
Perform
ance
Knowledgescore
Female
0.297
0.002
2706
0.005
-0.005
0.008
-0.025
0.031
2706
0.015
(0.160)
(0.018)
(0.012)
(0.014)
3423
Male
0.314
0.006
1873
0.018
-0.020
-0.008
-0.046
0.018
1874
0.019
(0.162)
(0.017)
(0.015)
(0.015)
2461
#of
techniques
Female
1.524
0.087
2706
0.025
-0.024
0.068
-0.148
0.255
2706
0.015
know
nby
name
(1.505)
(0.145)
(0.073)
(0.110)
3423
Male
1.783
-0.052
1873
0.020
-0.268**
-0.149
-0.470
0.019
1874
0.019
(1.609)
(0.157)
(0.120)
(0.100)
2461
#of
techniques
Female
0.681
-0.036
2706
0.006
-0.020
0.029
-0.096
0.129
2706
0.015
adopted
(0.776)
(0.077)
(0.045)
(0.059)
3423
Male
0.785
-0.062
1873
0.017
-0.130*
-0.058
-0.255
0.046
1874
0.019
(0.830)
(0.084)
(0.074)
(0.062)
2461
Source:
Household
Survey,2012,2013.
Note:
�Regressions
includ
ethesameexplanatoryvariablesas
modelsin
Table8.
***,
**,and*indicate
signi�canceat
the1,
5,and10
percentcritical
levelfortstatistics.
43
Table 10: Heterogeneity of ITT on Other Farmers' Adoption of Micro-Catchments
Adoption of MidlineMicro-catchments Ctrl. ITT T+T* T+T* T+T* T+T* N Adj.
Mean Ed>7years Age≥41 Land≥2.75 Same Crop R2Female 0.039 -0.012 0.024 3239 0.002
(0.014) (0.017)Male 0.039 0.017 0.056�� 2348 0.008
(0.020) (0.025)Female 0.039 0.007 -0.004 3239 0.000
(0.017) (0.014)Male 0.039 0.036 0.038� 2348 0.007
(0.023) (0.021)Female 0.039 0.000 0.006 3239 0.000
(0.016) (0.015)Male 0.039 0.039* 0.027 2348 0.006
(0.021) (0.024)Female 0.039 0.002 0.029� 3237 -0.001
(0.013) (0.017)Male 0.039 0.041** -0.001 2342 0.009
(0.018) (0.039)Adoption of EndlineMicro-catchments Ctrl. ITT T+T* T+T* T+T* T+T* N Adj.
Mean Ed>7years Age≥43 Land≥3.5 Same Crop R2Female 0.082 0.019 0.004 2474 -0.002
(0.031) (0.033)Male 0.137 -0.016 -0.044 1775 -0.001
(0.038) (0.048)Female 0.082 0.047 -0.019 2474 0.003
(0.035) (0.028)Male 0.137 0.001 -0.053 1775 0.001
(0.048) (0.035)Female 0.082 -0.006 0.038 2474 0.000
(0.028) (0.036)Male 0.137 -0.029 -0.016 1775 0.001
(0.038) (0.045)Female 0.082 0.001 0.089� 2546 0.002
(0.025) (0.048)Male 0.137 -0.038 -0.007 1843 0.001
(0.033) (0.068)Source: Household Surveys, 2012, 2013.
Note: Regressions include the same explanatory variables as models in Table 8.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics. Interactioncolumns re�ect the combined values of the treatment and treatment interacted with the CFcharacteristics coe�cients. ���, ��, and � indicate values are signi�cant based on the treatment andthe treatment interacted with the CF characteristics variable at the 1, 5, and 10 percent critical levels.
44
Appendix A: Additional Figures and Tables
45
Table A.1: Advantages of Being a Contact Farmer in Treated and Control Communities
Variables
Midlin
eEnd
line
Treated
Control
Di�.
Treated
Control
Di�.
Mean
Mean
ofMean
Mean
Mean
ofMean
CFfeeled
happy
0.854
0.905
-0.051
0.868
0.884
-0.016
Being
CF:to
help
commun
ity
0.905
0.810
0.096*
0.984
0.977
0.008
Being
CF:to
learnnewtechniques
0.562
0.452
0.110
0.736
0.767
-0.031
Being
CF:prestige
oftheposition
0.285
0.238
0.047
0.171
0.186
-0.016
Being
CF:curiosityforfarm
ingtechniques
0.190
0.167
0.023
0.302
0.302
0.000
CFused
thedemoplot
inthepast
12months
0.796
0.857
-0.062
0.837
0.837
0.000
CFow
nedthedemoplot
0.686
0.786
-0.100
0.713
0.721
-0.008
EAsgave
CFagricultureinpu
ts0.540
0.524
0.016
0.566
0.512
0.054
EAsgave
CFnaturalfertilizers
0.146
0.119
0.027
0.171
0.163
0.008
EAsgave
CFchem
ical
fertilizers
0.380
0.381
-0.001
0.357
0.302
0.054
EAsgave
CFfarm
ingtools
0.175
0.119
0.056
0.372
0.419
-0.047
EAsgave
CFanynon-technicalsupp
ort
0.577
0.548
0.029
0.628
0.535
0.093
CFreceived
conservation
agriculturetrainings
0.961
0.907
0.054
Num
berof
CAtrainingsCFreceived
2.535
2.488
0.047
Trainingwas
easy
toattend
0.775
0.721
0.054
Paidfortransportation
toattend
thetraining
0.178
0.093
0.085
Num
berof
observations
137
42179
129
43172
Source:
Contact
Farm
erSurvey,2012,2013.
Note:
***,
**,and*indicate
signi�canceat
the1,
5,and10
percentcritical
levelfortstatistics.
46
Table A.2: Extension Agents' Characteristics in Treated and Control Communities at Midline
Variables Treated Control Di�.Mean SD Mean SD of Mean
EA age 35.415 4.646 34.925 4.962 0.489EA years of schooling completed 7.192 0.534 7.263 0.601 -0.071# of years worked as EA 6.388 5.919 5.355 4.329 1.033# of years worked in agricultural section, before became an EA 4.451 2.893 4.412 2.994 0.038# of training received over the past 5 years 9.624 5.265 9.645 5.563 -0.021Received training from MINAG (government) 0.344 0.477 0.289 0.460 0.055Received training from Smallholder project 0.752 0.434 0.816 0.393 -0.064# of weeks in training during the last 12 months 1.244 0.601 1.276 0.601 -0.032One of the main topic of trainings was conservation agriculture 0.944 0.231 0.974 0.162 -0.030Number of observations 125 38 163Source: Extension Agent Survey, 2012; Contact Farmer Survey, 2012.
Note: ***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
47
Table A.3: E�ect of SLM Training Intervention on Contact Farmers (Includes Intercropping)
Midline EndlineControl ITT N Adj. Control ITT N Adj.Mean(SD) R2 Mean(SD) R2
Knowledge Score 0.642 -0.007 179 -0.111 0.661 0.067*** 168 -0.014(0.152) (0.033) (0.121) (0.023)
# of techniques 5.214 0.487 179 -0.086 5.024 1.343*** 168 0.042known by name (1.601) (0.370) (1.689) (0.389)# of techniques 2.048 0.799*** 179 0.080 3.310 0.653** 168 0.038adopted on own plot (1.125) (0.269) (1.352) (0.315)# of techniques 5.429 0.824* 179 -0.078 4.000 0.532 168 -0.021adopted on any plot (1.965) (0.421) (1.562) (0.366)Source: Household Survey and Contact Farmer Survey, 2012, 2013.
Note: Regressions include the same explanatory variables as models in Table 5.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
48
Table A.4: E�ect of SLM Training Intervention on Other Farmers (Includes Intercropping)
Midline EndlineControl ITT N Adj. Control ITT N Adj.Mean(SD) R2 Mean(SD) R2
Knowledge score Female 0.339 0.005 3423 0.010 0.410 -0.012 2951 0.012(0.143) (0.013) (0.178) (0.018)
Male 0.358 0.008 2461 0.022 0.449 -0.012 2120 0.013(0.148) (0.014) (0.162) (0.016)
# of techniques Female 2.377 0.083 3423 0.022 2.486 0.006 2951 0.003known by name (1.525) (0.138) (1.533) (0.186)
Male 2.652 0.025 2461 0.015 2.941 -0.159 2120 0.009(1.622) (0.141) (1.666) (0.191)
# of techniques Female 1.415 -0.045 3423 0.005 1.757 0.096 2951 0.002adopted (0.879) (0.086) (1.052) (0.107)
Male 1.560 -0.045 2461 0.012 2.044 -0.006 2120 -0.001(0.924) (0.087) (1.100) (0.114)
Source: Household Survey, 2012, 2013.
Note: Regressions include the same explanatory variables as models in Table 8.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
49
Table A.5: SLM Learning before 2010 in Treated and Control Communities (Recall)
Variables Treated Mean Control Mean Di�erence of MeanContact Farmers
Learned mulching 0.620 0.762 -0.141*Learned strip tillage 0.321 0.429 -0.107Learned micro-catchments 0.504 0.524 -0.020Learned contour farming 0.307 0.381 -0.074Learned crop rotation 0.591 0.690 -0.099Learned row planting 0.285 0.238 0.047Learned improved fallowing 0.212 0.262 -0.050Number of observations 137 42 179
Other Farmers�
Learned mulching 0.306 0.337 -0.031Learned strip tillage 0.182 0.227 -0.045Learned micro-catchments 0.145 0.113 0.032Learned contour farming 0.039 0.048 -0.009Learned crop rotation 0.360 0.360 0.000Learned row planting 0.104 0.114 -0.010Learned improved fallowing 0.101 0.104 -0.003Number of observations 4,385 1,499 5,884Sources: Household Survey, 2012.
Note: �T test inferences are based on standard errors clustered at the community level.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
50
Table A.6: SLM Adoption before 2010 in Treated and Control Communities (Recall)
Variables Treated Mean Control Mean Di�erence of MeanContact Farmers
Adopted mulching 0.489 0.405 0.084Adopted strip tillage 0.248 0.214 0.034Adopted micro-catchments 0.190 0.167 0.023Adopted contour farming 0.007 0.000 0.007Adopted crop rotation 0.314 0.262 0.052Adopted row planting 0.124 0.095 0.029Adopted improved fallowing 0.036 0.024 0.013Number of observations 137 42 179
Other Farmers�
Adopted mulching 0.181 0.203 -0.022Adopted strip tillage 0.087 0.118 -0.031Adopted micro-catchments 0.059 0.036 0.023Adopted contour farming 0.002 0.000 0.002Adopted crop rotation 0.121 0.132 -0.011Adopted row planting 0.055 0.059 -0.005Adopted improved fallowing 0.005 0.005 0.000Number of observations 4,385 1,499 5,884Sources: Household Survey, 2012.
Note: �T test inferences are based on standard errors clustered at the community level.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
51
Table A.7: Gender Barriers to Adoption (Mean Di�erences within the Control Group)
Variables Midline EndlineMale Female Di�erence Male Female Di�erenceMean Mean of Mean Mean Mean of Mean
Production
Grew maize 0.650 0.601 0.048 0.753 0.583 0.170***Grew sorghum 0.122 0.305 -0.183*** 0.179 0.332 -0.153**Grew cotton 0.188 0.024 0.164*** 0.096 0.014 0.082***Grew sesame 0.248 0.092 0.156*** 0.181 0.093 0.088**Grew cassava 0.198 0.154 0.044 0.166 0.128 0.038Grew cowpea 0.265 0.394 -0.129 0.351 0.347 0.004Grew pigeon pea 0.204 0.167 0.036 0.233 0.180 0.053
Farm Characteristics
Plot size (hectares) 1.015 0.821 0.194*** 1.292 0.983 0.309***Plot was �at 0.606 0.633 -0.027 0.570 0.551 0.019Plot was burnt 0.243 0.268 -0.025 0.249 0.253 -0.003Used herbicides/pesticides/fungicides 0.124 0.018 0.106*** 0.048 0.002 0.046***Used natural fertilizer 0.278 0.296 -0.018 0.51 0.42 0.09Used chemical fertilizer 0.009 0.001 0.007 0.01 0.00 0.01Number of observations 565 675 1,240 481 657 1,138Sources: Household Survey, 2012, 2013.
Note: T test inferences are based on standard errors clustered at the community level.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
52
Table A.8: Other Farmers' Characteristics between Attrition Groups
Variables Both Midline Endline Mean Di�. Mean Di�. Mean Di�.Rounds Only Only B-ML B-EL ML-EL
Is household head 0.609 0.506 0.460 0.102 *** 0.149 *** 0.046 *Age 38.321 35.898 36.287 2.423 *** 2.034 ** -0.389Years of schooling completed 1.934 2.242 2.559 -0.308 ** -0.624 *** -0.316Single 0.059 0.071 0.113 -0.012 -0.054 ** -0.042 **Married 0.843 0.861 0.792 -0.019 0.050 * 0.069 ***Divorced, widow, or separated 0.097 0.060 0.095 0.038 *** 0.003 -0.035 **Total number of children 2.776 2.788 2.784 -0.012 -0.009 0.003Total number of rooms 1.414 1.493 1.395 -0.079 0.019 0.098Total landholdings 1.999 1.879 2.292 0.120 -0.294 * -0.414 ***Number of observations 4580 1304 496Sources: Household Survey, 2012, 2013.
Note: T test inferences are based on standard errors clustered at the community level.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
53
Table A.9: Attrition of CFs and Households
Variables Treated Control Di�.Mean SD Mean SD of Mean
CFs attrited from Midline 0.109 0.313 0.048 0.216 0.062# of Obs. 137 42 179Household attrited from Midline� 0.090 0.372 0.087 0.374 0.003# of Obs. 2750 935 3685Source: Household Survey and Contact Farmer Survey, 2012, 2013.
Note: �T test inferences are based on standard errors clustered at the community level.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
54
Table A.10: Probability of CF and Household Attrition
CF Village HouseholdTreatment 1 0.074 Treatment 1 0.013
(0.069) (0.014)Treatment 3 0.003 Treatment 3 -0.011
(0.056) (0.012)Age -0.006** Age -0.001**
(0.003) (0.000)Completed at least 0.052 HH head completed at -0.004primary school (0.064) least primary school (0.012)Single -0.097 HH head Single 0.023
(0.199) (0.021)HH head divorced, 0.043**widow, or separated (0.017)
Total number -0.010 Total number -0.006**of children (0.013) of children (0.003)Total landholding 0.004 Total landholding -0.004(hectares) (0.012) (hectares) (0.003)Total number of rooms -0.022 Total number of rooms -0.002
(0.035) (0.008)Number of years -0.040* Number of years 0.005since formal training (0.024) since formal training (0.004)Missing dummy -0.138 Missing dummy 0.027
(0.112) (0.020)Household head -0.033 Household head 0.000was female (0.096) was female (0.013)% of household -0.394 % of household 0.131**members was away (0.368) members was away (0.066)HH has non-own 0.033 HH has non-own -0.013farming work (0.080) farming work (0.012)HH has outside -0.017 HH has outside 0.003employment (0.078) employment (0.017)2012 precipitation 0.000 2012 precipitation -0.001shock (0.002) shock (0.000)Constant 0.352 Constant 0.030
(0.436) (0.081)N 178 N 3656Adj. R-sq (0.078) Adj. R-sq 0.004Source: Household Survey and Contact Farmer Survey, 2012, 2013.
Note: Regressions include posto �xed e�ect.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
55
Table A.11: Descriptive Statistics of Contact Farmers' Characteristics
Variables Pooled Men Women Di�erenceMean SD Mean Mean of Mean
Midline
Is the head of household 0.970 0.171 1.000 0.727 0.273***Age 41.080 10.362 41.599 36.909 4.690**Years of schooling completed 5.487 2.872 5.672 4.000 1.672***Single 0.015 0.122 0.011 0.045 -0.034Married 0.945 0.229 0.972 0.727 0.244***Divorced, separated, or widowed 0.040 0.197 0.017 0.227 -0.210***Number of children (ages < 15 years) 3.759 2.151 3.847 3.045 0.802*Landholdings 3.218 2.284 3.248 2.984 0.264Number of rooms in the house 1.769 0.919 1.780 1.682 0.098Number of observations 199 177 22
Endline
Is the head of household 0.938 0.242 1.000 0.690 0.310***Age 43.311 10.703 43.515 42.500 1.015Years of schooling completed 5.431 2.824 5.754 4.143 1.612***Single 0.005 0.069 0.006 0.000 0.006Married 0.938 0.242 0.982 0.762 0.220***Divorced, separated, or widowed 0.057 0.233 0.012 0.238 -0.226***Number of children (ages < 15 years) 3.646 2.177 3.772 3.143 0.630*Landholdings 3.680 2.296 3.772 3.314 0.459Number of rooms in the house 1.760 0.935 1.744 1.857 -0.113Number of observations 209 167 42Source: Household Survey, 2012, 2013.
Note: ***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
56
Table A.12: Descriptive Statistics of Other Farmers' Characteristics
Variables Pooled Men Women Di�erenceMean SD Mean Mean of Mean
Midline
Is the head of household 0.586 0.493 0.944 0.329 0.615***Age 37.784 14.507 40.312 35.967 4.345***Years of schooling completed 2.003 2.802 3.351 1.033 2.319***Single 0.062 0.241 0.074 0.054 0.020Married 0.847 0.360 0.904 0.806 0.098***Divorced, separated, or widowed 0.089 0.285 0.021 0.138 -0.117***Number of children (ages < 15 years) 2.778 2.014 2.920 2.677 0.243***Landholdings 1.972 1.772 2.110 1.873 0.236**Number of rooms in the house 1.432 0.730 1.491 1.389 0.102*Number of observations 5,884 2,461 3,423
Endline
Is the head of household 0.594 0.491 0.919 0.360 0.559***Age 38.775 14.322 41.193 37.038 4.155***Years of schooling completed 2.114 2.793 3.608 1.041 2.567***Single 0.048 0.214 0.056 0.042 0.014*Married 0.851 0.356 0.916 0.804 0.112***Divorced, separated, or widowed 0.101 0.301 0.028 0.153 -0.125***Number of children (ages < 15 years) 2.890 2.071 3.072 2.760 0.312***Landholdings 2.401 2.336 2.602 2.257 0.346***Number of rooms in the house 1.412 0.718 1.461 1.377 0.083Number of observations 5,076 2,122 2,954Source: Household Survey, 2012, 2013.
Note: T test inferences are based on standard errors clustered at the community level.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
57
Table A.13: E�ect of SLM Training Intervention on Contact Farmers' SLM Knowledge
Knowledge Midline EndlineScore Control ITT N Adjusted Control ITT N Adjusted
Mean(SD) R2 Mean(SD) R2Mulching 0.833 0.038 179 -0.099 0.952 0.030 168 -0.126
(0.258) (0.047) (0.139) (0.022)Strip Tillage 0.460 0.051 179 -0.102 0.563 0.122* 168 -0.076
(0.345) (0.072) (0.270) (0.066)Micro-catchments 0.798 -0.031 179 -0.068 0.798 0.111* 168 -0.001
(0.399) (0.082) (0.332) (0.057)Contour Farming 0.524 0.030 179 -0.094 0.516 0.181** 168 -0.039
(0.369) (0.073) (0.405) (0.077)Crop Rotation 0.540 -0.063 179 -0.102 0.595 0.067 168 -0.076
(0.329) (0.070) (0.271) (0.049)Row Planting 0.476 -0.155 179 -0.039 0.143 0.118 168 -0.102
(0.505) (0.103) (0.354) (0.088)Improved Fallowing 0.738 0.008 179 -0.124 0.643 0.024 168 -0.148
(0.276) (0.060) (0.229) (0.054)Source: Household Survey, 2012, 2013.
Note: Regressions include the same explanatory variables as models in Table 5.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
58
Table A.14: E�ect of SLM Training Intervention on Other Farmers' SLM Adoption
Adoption Midline EndlineCtrl. ITT N Adj. Ctrl. ITT N Adj.Mean R2 Mean R2
Mulching Female 0.248 -0.051 3423 0.009 0.390 0.056 2951 0.009(0.044) (0.049)
Male 0.248 -0.022 2461 0.004 0.512 0.005 2120 0.000(0.041) (0.051)
Strip Tillage Female 0.153 -0.008 3423 0.029 0.200 -0.004 2951 0.002(0.039) (0.038)
Male 0.161 -0.007 2461 0.023 0.225 -0.003 2120 0.006(0.040) (0.044)
Micro-catchments Female 0.041 0.000 3423 -0.001 0.084 0.022 2951 0.002(0.013) (0.024)
Male 0.039 0.033* 2461 0.006 0.139 -0.020 2120 -0.001(0.017) (0.029)
Contour Female 0.000 0.002 3423 0.000 0.007 -0.006 2951 0.010Farming (0.001) (0.005)
Male 0.000 0.002 2461 -0.001 0.017 -0.014 2120 0.016(0.002) (0.010)
Crop Rotation Female 0.131 0.005 3423 0.002 0.151 0.039 2951 0.003(0.020) (0.028)
Male 0.182 -0.013 2461 0.004 0.160 0.035 2120 -0.002(0.024) (0.033)
Row Planting Female 0.073 0.012 3423 0.010 0.061 0.002 2951 0.001(0.020) (0.021)
Male 0.093 -0.008 2461 0.011 0.095 -0.023 2120 0.003(0.023) (0.026)
Improved Female 0.014 -0.014* 3423 0.019 0.019 0.002 2951 0.005Fallowing (0.008) (0.006)
Male 0.026 -0.020* 2461 0.018 0.027 0.012 2120 0.002(0.011) (0.013)
Source: Household Survey, 2012, 2013.
Note: Regressions include the same explanatory variables as models in Table 8.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
59
Table A.15: From Whom Other Farmers Claim to Learn SLM Techniques
Midline EndlineCtrl. Mean ITT N Adj. R2 Ctrl. Mean ITT N Adj. R2
Any SLM extension 0.054 0.008 5884 0.023 0.043 -0.012 5071 0.007technique agent (0.015) (0.013)
contact 0.122 0.026 5884 0.011 0.285 -0.007 5071 0.013farmer (0.028) (0.037)other 0.803 0.005 5884 0.002 0.789 -0.019 5071 0.008farmer (0.027) (0.032)
Mulching extension 0.041 -0.007 5884 0.017 0.034 -0.012 5071 0.009agent (0.011) (0.011)contact 0.091 0.008 5884 0.009 0.232 -0.011 5071 0.012farmer (0.022) (0.034)other 0.296 -0.023 5884 0.021 0.301 -0.003 5071 0.003farmer (0.041) (0.039)
Strip Tillage extension 0.007 0.004 5884 0.005 0.003 0.005 5071 0.002agent (0.006) (0.004)contact 0.023 0.003 5884 0.003 0.056 -0.010 5071 0.001farmer (0.009) (0.019)other 0.166 -0.014 5884 0.036 0.162 0.024 5071 0.004farmer (0.031) (0.036)
Micro-catchments extension 0.007 0.013** 5884 0.011 0.005 0.001 5071 -0.001agent (0.005) (0.003)contact 0.041 0.020 5884 0.006 0.089 -0.011 5071 0.012farmer (0.018) (0.022)other 0.095 0.021 5884 0.004 0.078 0.012 5071 0.005farmer (0.018) (0.023)
Contour extension 0.002 0.002 5884 0.006 0.003 -0.002 5071 0.000Farming agent (0.002) (0.002)
contact 0.005 0.010** 5884 0.002 0.015 0.001 5071 0.007farmer (0.005) (0.007)other 0.037 0.003 5884 0.013 0.013 -0.008 5071 0.004farmer (0.011) (0.006)
Crop Rotation extension 0.021 0.004 5884 0.011 0.012 -0.004 5071 0.002agent (0.010) (0.005)contact 0.030 0.012 5884 0.006 0.115 -0.021 5071 0.011farmer (0.011) (0.028)other 0.302 -0.004 5884 0.006 0.253 0.035 5071 0.002farmer (0.025) (0.035)
Row Planting extension 0.004 0.002 5884 0.001 0.003 0.000 5071 0.000agent (0.003) (0.003)contact 0.010 0.000 5884 0.000 0.030 0.001 5071 0.000farmer (0.004) (0.014)other 0.099 0.018 5884 0.007 0.048 0.003 5071 0.002farmer (0.025) (0.018)
60
Continued.Midline Endline
Ctrl. Mean ITT N Adj. R2 Ctrl. Mean ITT N Adj. R2Improved extension 0.007 0.001 5884 0.002 0.005 -0.004 5071 0.001Fallowing agent (0.006) (0.003)
contact 0.011 -0.002 5884 0.001 0.025 -0.003 5071 0.004farmer (0.005) (0.009)other 0.073 -0.020 5884 0.007 0.083 -0.007 5071 0.004farmer (0.019) (0.022)
Source: Household Survey, 2012, 2013.
Note: Regressions include the same explanatory variables as models in Table 8.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
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Table A.17: Other Farmers' Perceptions of Technique's Labor Savings Compared to TraditionalMethod
Technique Saves MidlineLabor Time Control Mean ITT N Adjusted R2Mulching Female 0.133 -0.025 3423 0.008
(0.035)Male 0.142 -0.015 2461 0.018
(0.034)Strip Tillage Female 0.156 -0.003 3423 0.018
(0.036)Male 0.177 -0.030 2461 0.006
(0.040)Micro-catchments Female 0.008 0.006 3423 0.002
(0.005)Male 0.008 0.019** 2461 0.001
(0.009)Contour Farming Female 0.005 0.009 3423 0.005
(0.006)Male 0.008 -0.006 2461 0.002
(0.005)Crop Rotation Female 0.063 0.007 3423 0.000
(0.017)Male 0.090 0.011 2461 0.005
(0.021)Row Planting Female 0.041 0.010 3423 0.003
(0.014)Male 0.055 0.009 2461 0.009
(0.018)Improved Fallowing Female 0.034 -0.010 3421 0.009
(0.014)Male 0.043 -0.011 2461 0.008
(0.016)Source: Household Survey, 2012.
Note: Regressions include the same explanatory variables as models in Table 8.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
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Table A.18: Contact Farmers' Perceptions of SLM Techniques
Variables Increase Reduce land Reduce Reduce Nproductivity preparation planting harvesting
e�ort seed e�ort e�ortMulching Treated 0.569 0.423 0.307 0.234 137
Control 0.595 0.452 0.310 0.238 42Strip tillage Treated 0.314 0.270 0.321 0.139 137
Control 0.286 0.238 0.286 0.190 42Micro-catchments Treated 0.212 0.088 0.124 0.073 137
Control 0.238 0.143 0.119 0.071 42Contour farming Treated 0.095 0.022 0.044 0.036 137
Control 0.071 0.048 0.071 0.071 42Crop rotation Treated 0.401 0.255 0.190 0.182 137
Control 0.357 0.262 0.214 0.190 42Row planting Treated 0.182 0.117 0.168 0.073 137
Control 0.167 0.095 0.095 0.071 42Improved fallowing Treated 0.124 0.036 0.036 0.044 137
Control 0.095 0.071 0.048 0.048 42Source: Household Survey, 2012.
Note: ***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
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Table A.19: E�ect of SLM Training Intervention on Contact Farmers' Labor Time
Midline EndlineControl ITT N Adj. Control ITT N Adj.Mean(SD) R2 Mean(SD) R2
Hours spent on 5.762 -4.124** 178 -0.064 6.429 -2.167 168 -0.070preparation of land (13.879) (1.977) (15.353) (2.857)Hours spent on seeding 6.071 -1.183 178 -0.036 10.357 -6.386** 168 -0.083
(13.767) (2.717) (17.862) (3.081)Hours spent on 3.476 -2.105 178 0.020 1.738 -0.751 168 -0.082transplantation (9.094) (1.613) (6.666) (1.516)Hours spent on irrigation 0.000 -0.214 178 -0.130
(0.000) (0.380)Hours spent on sacha 15.333 0.258 178 -0.050 5.833 -0.093 168 -0.082
(15.550) (3.176) (14.252) (2.539)Hours spent on protection 0.000 1.061 178 -0.131 0.000 0.656 168 -0.027
(0.000) (0.805) (0.000) (0.658)Hours spent on harvesting 6.214 -0.988 178 -0.132 15.810 -3.314 168 -0.011
(15.645) (2.391) (19.573) (3.711)Total weeks spent on 26.143 -1.233 178 -0.114 30.381 -9.298** 168 -0.056farming in last year (17.512) (3.615) (19.196) (3.720)Source: Household Survey, 2012, 2013.
Note: Regressions include the same explanatory variables as models in Table 5.***, **, and * indicate signi�cance at the 1, 5, and 10 percent critical level for t statistics.
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Appendix B: Sample Design
In our initial proposal, the 3ie funds were solicited to fund a midline survey. The Governmentof Mozambique was responsible for the baseline survey, in which they would hire a survey �rmthrough an international bidding process. Despite the fact that the procurement process startedin March 2010, the contract was only signed more than a year later due to several bureaucratichurdles the Government of Mozambique faced. This delay would have turned the activity into a�rst follow-up survey instead of a baseline. The contract was signed in April 2011 and the �rmconsultants arrived in Mozambique in July. However, the �rm which was provided the contract didnot have the capacity to implement the survey. At the same time, the World Bank could not legallyinterfere nor cancel the �rm's contract given that the legal document was between the governmentand the �rm. Nevertheless, the World Bank provided assistance to the government in cancelling thecontract given the striking failure of the �rm. The resources intended for the baseline survey couldbe utilized to hire a di�erent �rm. Unfortunately, the cancellation of the contract coincided withthe implementation timing of our 3ie survey. In short, the 3ie survey was the �rst household surveyround, 15 months after the training of the EAs and CFs. We refer to this survey as the midlinesurvey.
The World Bank team managed to collect a small, baseline survey that was administered to theexisting CFs in August, 2010. The data from the baseline CF survey in addition to the data fromretrospective questions asked to the respondents of the household survey in 2012 are used to performbalancing tests. While a baseline survey is an ideal scenario, a recent paper by McKenzie (2012)sorts through why such a loss may not interfere with the estimation of treatment e�ects. He arguesfor highly variable outcomes that have relatively low autocorrelation it is more important to increasethe number of follow-up surveys to improve statistical power. McKenzie provides arguments drawingfrom the literature and using analytical tools to explain why agricultural productivity and incomemight fall into this category of outcomes. Given this recent work and the inability to administer thebaseline survey as intended, the project team used the baseline survey funds for a second, follow-upsurvey in 2013 referred to as the endline survey.
We commissioned the National Statistics Institute (INE) of Mozambique to collect the midlineand endline data in which is used in this report, hereby called the Smallholders' Survey. Theycollected a listing of households for each of 200 communities (enumeration areas), where 18 non-CFhouseholds were selected randomly in each community. The �eld work consisted of two phases: pre-and post-harvest. There were �ve survey instruments: a household survey; a household agriculturalproduction survey; a CF survey; an extension agent survey; and a community survey. Since SLMpractices are most visible prior to planting (pre-harvest), we conducted the household survey duringthe months of February through April in 2012. The information was collected for approximately4,000 households including the households of the male and female CFs. To improve the precisionof reported agricultural production, we administered the household agricultural production survey(and the remaining surveys) post-harvest during May and June in 2012. During the post-harvestphase in the midline survey, �ve communities were not surveyed from the initial sample of 200communities (3 control communities and 2 treatment 1 communities). The analysis in this reportuses available information from the household and CF surveys.
The household survey asked several questions related to household demographics, individualknowledge of SLM and non-SLM agricultural practices, individual time use, labor allocation, em-ployment and income, and plot-speci�c characteristics. The respondents consist of (at most) twofarmers per household: the household head and his/her partner/spouse. If a polygamous householdwas encountered, the main spouse was interviewed. During the indoor interview, the male farmerand the female farmer reported their personal information, agriculture knowledge and self-reported
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SLM adoption separately. During the �eld survey, enumerators took objective measurements onone plot per farmer that he/she was mainly responsible for in the 2011/2012 rainy season. The enu-merators would identify which SLM techniques were adopted on the plot, if any, and then measurethe total area of each plot that uses each SLM technique using a GPS. In order to select a plotmanaged by the man and woman of the household, each was asked which plot is most important tohimself/herself and primarily his/her responsibility. The enumerators were instructed to report twodistinct plots. If either male or female claimed they did not have any responsibility to a particularplot, they were asked to indicate on which plot they spent the most of their time. If a householdhad one single plot, that plot was only measured once. Similarly, if the household was headed by afemale (or male) without a partner or spouse present, a single plot was measured.
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Appendix C: Household Survey Instrument
The majority of survey questions are asked about the 2011/2012 and 2012/2013 rainy seasons (15and 27 months after the �rst training). There are several modules in each questionnaire and variousquestionnaires for di�erent units of analysis. Each questionnaire is also only available in Portuguese.We provide the English translation of questions for the modules in the household survey, in whichour self-reported adoption and knowledge variables are based below.
A list of eight sustainable land management (SLM) techniques, including a placebo technique,is on the questionnaire. The enumerator asks the following:
� Do you know of any agricultural conservation techniques? (The enumerators checks o� thelist each SLM the respondent names by memory.)
� Have you heard of any of these agricultural conservation techniques? (The enumerator readsthe names of the SLM techniques that were not already mentioned by the respondent in the previousquestion.)
For each of the eight sustainable land management (SLM) techniques that the respondent hasheard of, including the placebo, the respondent is asked the following questions with respect to SLMknowledge and adoption:
� Do you use this technique on any part of your plot?� On how many of your plots, do you use this technique?� Does the technique reduce the hours spent working on the plot in comparison to the use of a
traditional method?� Did the respondent apply this technique last season in 2010/2011 (midline)?� Did the respondent apply this technique before the last season in 2010/2011 (midline)?� In what year, did you learn this technique?� From whom did you learn this technique?� Did you discuss this technique with any person/people?� Did you teach this technique to others?� To whom, did you teach this technique?� In what year did you teach this technique to others?� Did you discuss this technique with these people?� Do you know of other people that apply this technique?� How many people do you know that apply this technique?
We then ask a series of questions regarding the source of their agricultural information, whichis not technique speci�c:
� In the last three years, aside from agricultural conservation techniques, did you receive adviceregarding other agricultural techniques, like cultivation techniques, new crops, or fertilizers?
� From who, did you learn the above applications?� Did you approach the person to get the advice?� In the last three years, aside from agricultural conservation techniques, did you give oth-
ers advice regarding other agricultural techniques, such as cultivation techniques, new crops, orfertilizers?
� To whom, did you teach the above applications?� Did the person approach you to receive your advice?
Our knowledge score is based on the following twenty questions:� What materials can you use to cover the soil?
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� What are the bene�ts of covering the soil?� What are the bene�ts of burning crops?� If you decide to intercrop or mix crops at once and have decided to plant maize, which other
crops can be grown at the same time?� What are the bene�ts of mixing crops at the same time?� Imagine that you are mixing maize, sesame, pigeon pea and cowpeas, What is the distance,
in cm, you should keep between maize rows?� Imagine you are mixing pigeon peas and maize on the same plot. Should you plant the two
crops at the same time, or after seeding one crop let some time pass before sowing the other crop?� Which crop would you need to seed �rst?� Can you tell me how many weeks after sowing maize must you sow pigeon pea?� Which crops can cassava be mixed with?� Imagine that in this last season, maize crops su�ered a strong plague. In this season, you want
to intercrop maize with another crop that should help ward o� pests. Which crop could you use forthis purpose? (Write no more than two crops.)
� What are the bene�ts of crop rotation from year to year?� Imagine the following two scenarios of crop rotation: 1) First time: Maize, Second time:
Sesame, Third time: Sorghum, Fourth time: Maize; 2) First time: Sesame, Second time: Cowpeas,Third time: Maize, Fourth time: Cowpeas. Which of the two scenarios is better for the fertility ofthe soil?
� What are the advantages of micro-basins?� Should you dig up the micro-basins before or after the rain?� Should contour farming be applied on mountainous terrain or plains?� Imagine that you are applying the technique of contour lines. A row of seedlings must go up
and down or be in the same level as the slope?� What are the bene�ts of contour farming?� What tools can be used to seed directly?� Plow only where sows worsen water conservation in the soil?
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Appendix D: Power Calculations
We provide tables of power calculations which are computed using the 2008 National AgriculturalSurvey and the Optimal Design software as recommend by 3ie. The take up rates presented therewere 100%, 89%, 72%, 50%, 39% and 22%. Di�erent take up rates were modelled as di�erent clustersizes (18, 16, 13, 9, 7 and 4, respectively), i.e., only considering compliers. For each take up rate, the�Prob of Success Treatment group� presents the adoption rate that we are able to detect (obtainedfrom OD software) given the input values. The minimum detectable e�ect (MDE) lines presentthe di�erence in percentage points between that �gure and the corresponding �gure for the controlgroup, at di�erent take up rates. We assume three di�erent combinations of signi�cance levels andpower (respectively - 1% and 0.96; 2.5% and 0.9; 5% and 0.8). Tables D.1 and D.2 matches those,per intervention, with di�erent take-up levels.
The adoption rates and e�ects attributable to the treatment are much smaller (than thoseinitially calculated using the National Agricultural Survey) for the majority of the techniques coveredin our survey. We therefore provide reverse power calculations using the sampsi and sampcluscommands in Stata in Table D.3 in this Annex for the adoption of mulching and micro-basins. We�nd that we would have needed to collect over 600 and 16,000 clusters to pick up a statisticallysigni�cant e�ect of mulching and micro-basins' adoption on farmers' main plot, respectively.
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Table D.1: Power Calculations for Row Planting
T1 T1 T1 T2 T2 T2 T3 T3 T3Alpha 0.001 0.025 0.05 0.001 0.025 0.05 0.001 0.025 0.05Beta (1-Power) 0.004 0.1 0.2 0.004 0.1 0.2 0.004 0.1 0.2Number of clusters 200 200 200 150 150 150 100 100 100Cluster size 18 18 18 18 18 18 18 18 18Total observations 3600 3600 3600 2700 2700 2700 1800 1800 1800
Take up POS control 0.523 0.523 0.523 0.523 0.523 0.523 0.523 0.523 0.523Full (18) POS treatment 0.696 0.629 0.609 0.721 0.646 0.621 0.758 0.671 0.641
MDE 0.173 0.106 0.086 0.198 0.123 0.098 0.235 0.148 0.11889% (16) POS treatment 0.699 0.631 0.609 0.728 0.648 0.621 0.763 0.674 0.644
MDE 0.176 0.108 0.086 0.205 0.124 0.098 0.240 0.151 0.12172% (13) POS treatment 0.706 0.637 0.614 0.733 0.651 0.626 0.773 0.679 0.649
MDE 0.183 0.114 0.091 0.210 0.128 0.103 0.250 0.156 0.12650% (9) POS treatment 0.719 0.644 0.619 0.748 0.661 0.634 0.791 0.691 0.659
MDE 0.196 0.121 0.096 0.225 0.138 0.111 0.268 0.168 0.13639% (7) POS treatment 0.731 0.652 0.627 0.766 0.671 0.641 0.810 0.701 0.666
MDE 0.208 0.129 0.104 0.243 0.148 0.118 0.287 0.178 0.14322% (4) POS treatment 0.769 0.674 0.644 0.805 0.696 0.661 0.858 0.733 0.691
MDE 0.246 0.151 0.121 0.282 0.173 0.138 0.335 0.210 0.168Source: 2008 National Agricultural Survey.Notes: MDE de�ned in percentage points. POS refers to probability of success.
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Table D.2: Power Calculations for Crop Rotation
T1 T1 T1 T2 T2 T2 T3 T3 T3Alpha 0.001 0.025 0.05 0.001 0.025 0.05 0.001 0.025 0.05Beta (1-Power) 0.004 0.1 0.2 0.004 0.1 0.2 0.004 0.1 0.2Number of clusters 200 200 200 150 150 150 100 100 100Cluster size 18 18 18 18 18 18 18 18 18Total observations 3600 3600 3600 2700 2700 2700 1800 1800 1800
Take up POS control 0.308 0.308 0.308 0.308 0.308 0.308 0.308 0.308 0.308Full (18) POS treatment 0.497 0.415 0.393 0.527 0.432 0.405 0.577 0.462 0.427
MDE 0.189 0.107 0.085 0.219 0.124 0.097 0.269 0.154 0.11989% (16) POS treatment 0.502 0.417 0.393 0.532 0.435 0.425 0.582 0.465 0.43
MDE 0.194 0.109 0.085 0.224 0.127 0.117 0.274 0.157 0.12272% (13) POS treatment 0.512 0.42 0.398 0.542 0.44 0.427 0.594 0.47 0.435
MDE 0.204 0.112 0.090 0.234 0.132 0.119 0.286 0.162 0.12750% (9) POS treatment 0.522 0.43 0.402 0.557 0.45 0.437 0.621 0.482 0.445
MDE 0.214 0.122 0.094 0.249 0.142 0.129 0.313 0.174 0.13739% (7) POS treatment 0.539 0.437 0.41 0.582 0.457 0.425 0.644 0.495 0.455
MDE 0.231 0.129 0.102 0.274 0.149 0.117 0.336 0.187 0.14722% (4) POS treatment 0.582 0.46 0.427 0.626 0.487 0.447 0.689 0.532 0.482
MDE 0.274 0.152 0.119 0.318 0.179 0.139 0.381 0.224 0.174Source: 2008 National Agricultural Survey.Notes: MDE de�ned in percentage points. POS refers to probability of success.
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Table D.3: Reverse Power Calculations for Micro-Catchments
Measure Scenario E�ect size Intra-cluster correlation No. of clusters requiredUse on main plot observed 1% 0.39 16162
desired 10% 0.39 163desired 15% 0.1 25
Use on any plot observed 1% 0.11 1053desired 10% 0.11 43desired 15% 0.11 5
Source: Smallholders' Household Survey 2012
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Appendix E: Study Design and Program Implementation Challenges
From the beginning of the program, the impact evaluation involved local and state governmentcounterparts. The concept was �rst presented in May, 2010 to the district facilitators, environmen-tal technicians, environmental and natural resources specialists, and �eld operations' coordinatorsfor the Smallholders' project in Chemba and Morrumbala. The main issues discussed were therandomization of activities, delivering incentives to the CFs to teach other farmers, monitoring per-formance of CFs, women-led activities, dependence of demonstration plot activities on input factorsother than technical training, and a �eld team plan for SLM training.
The Smallholders' project team, over the same period, undertook extensive �eld visits focusing onthe plots of CFs. A total of 13 agricultural sites were visited; 8 of which already had demonstrationplots. The demonstration plots were not being used at the time since the main planting season hadpassed. Most of the plots visited were cultivating horticulture or maize. Since the Smallholders'project had already begun in 2007, some CFs prior to the SLM intervention had already receivedtrainings even though there was no o�cial record of such trainings. The main activities on thedemonstration plots were the adoption of new varieties and plot organization. In terms of SLM,only some techniques had been disseminated by a subset of the CFs that were interviewed.
While in the �eld, the Smallholders' project team interviewed 38 CFs and 15 EAs about theirinvolvement in agricultural technology dissemination activities, and investigated the role of femalefarmers as bene�ciaries of the agricultural extension network. From the interviews, it becameevident that the dissemination of SLM techniques was limited, as was the basic SLM knowledgeof the CFs. The overall impression was that intercropping and mulching were relatively populartechniques (about a 50 percent adoption rate) while the adoption of other techniques was scarce.The dissemination of those few widely used techniques came from the communities themselves notthe project team or EAs. The assistance to female farmers was lower than the assistance to malefarmers in some communities, while there appeared to be no di�erences in assistance reported inother communities. Based on the feedback from the local stakeholders, the individual and focusgroup interviews, and a �nal consultation with the agricultural extension network of the Ministryof Agriculture in all 5 study districts (Mutarara, Maríngue and Chemba, Mopeia, and Morrumbala)in August-September of 2010, the impact evaluation concept note was updated and the randomizedassignments of control and treatment groups constructed.
From the pool of all communities in the project area with an active CF a group of 200 commu-nities was randomly selected for the analysis. Each community has one CF. As such, the impactevaluation covers in total 200 CFs. From these 200 communities/CFs, a sub group was randomlyassigned to the demonstration plot intervention. In addition, among the communities with thedemonstration plot intervention, female-led demonstration plots and di�erent incentive structuresfor the CFs were allocated to a randomly selected subset of these communities. The design ofthe proposed evaluation consists of 3 treatment arms, allowing the Government of Mozambique tomeasure (Figure E.1): (1) the impact of Sustainable Land Management (SLM) demonstration plotswithin a community on farmers' adoption, productivity, SLM knowledge and other farming prac-tices; (2) the impact of additional demonstration plots managed by women in mainstreaming SLMpractices to women; and (3) the additional e�ect of providing material and social performance-basedincentives to CFs.
First, three quarters of the villages were randomly assigned to receive training in SLM techniques.Those male CFs from communities selected also received support from their assigned EAs to set upSLM demonstration plots.
Second, half of the villages assigned to demonstration plots were randomly selected to nominatea woman farmer who, along with the male CF, would be trained on SLM techniques and invited to
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set up another demonstration plot. The selected woman, or female CF, would also receive supportfrom the EA and the male CF throughout the process. In return, she was asked to mimic the maleCF's role within the community, with a special mandate to support other female farmers adoptSLM techniques and other improved technologies.
Third, some communities were randomly assigned to performance-based material and socialincentives to test their impact on CFs' e�ort and farmers' adoption. Both the group of communitieswith only one demonstration plot and the group of communities with an additional female-led plotwere eligible and were randomly divided into three sub groups consisting each of 25 communities,respectively. In the �rst sub group the material incentive scheme was installed, in the second groupthe social incentive scheme, and communities in the last group received neither form of incentives.Hence, a total of 50 communities received the `material' and another 50 communities the `social'treatment, while the remaining 50 communities acted as control communities for the incentiveintervention.
Performance was to be monitored against a simple adoption target: to get over 15 farmers toadopt correctly at least one newly demonstrated SLM technique on a quarter of their own plots.Farmers who were subjected to the incentive schemes received clear instructions on the incentivescheme, i.e. on the target, the monitoring, the payo�, and the timing of the intervention. Thistarget was planned to be surveyed by independent enumerators after planting, in the �rst andsecond seasons.
The material incentive bene�ts were the following: a rain coat and a number of bicycle accessories(2 tires, one pair of pedals, 10 spokes, 2 inner tubes and a repair toolkit).
The social incentive bene�t was the following: a t-shirt with the inscription �I, Mr. (Ms.) `X.Y',was distinguished by the Government of Mozambique for my good work in the dissemination ofmodern farming practices in `community Z� '.
The randomization was based on the census of 291 existing CFs and their communities from�ve districts, conducted in August 2010. Within each district, the communities were grouped bygeographic proximity (7 km or less). Then, one community was selected from each group randomly40 times. Of the 40 communities, 10 communities were randomly assigned into the control group,and 30 were randomly assigned into treatment 1. Of the 30 communities in treatment 1, 15 wererandomly assigned into treatment 2.
While the implementation of the �rst and second treatment arms went rather smoothly, the socialand material performance-based incentives (treatment 3) proved di�cult to implement. Issues withthe understanding and willingness to cooperate with this aspect of the program arose as early asOctober 2010. Compliance to the study design di�ered by district. Prior to the training, listsof which CFs were eligible for the social or material performance schemes were personally givento the EAs in each district. In Chemba, EAs agreed to the conditions and had not raised anyissues. In Mutarara, Morrumbala, and Mopeia, many were not aware of the incentives aspect ofthe intervention nor had they known eligibility would be based on a lottery. In fact, in these threedistricts, there was resistance against the lottery. The IE �eld coordinator at the time provided anexplanation of why administering a lottery was crucial for the impact evaluation. In Maringue, theEAs seemed most resistant to the idea of the lottery because it was perceived as inequitable. TheEAs also expressed their concerns with making promises to CFs that they could not keep. Whilethe project team was in the �eld after the training in November and December 2010 to interviewa random group of CFs, they learned none of the randomly picked 3 male and 3 female CFs wereaware of the performance incentives.
Field conditions did not allow the team to adhere to the intended measurement of CF perfor-mance and prize delivery in the �rst year. Originally, CF performance would be assessed prior tothe harvest, around February and March 2011. The team planned to hire university students in
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each community to visit each of the 15 farmers that the CF claimed were adopting the SLM tech-niques. This proved di�cult to mobilize while overseeing the 3ie-�nanced follow-up survey whichhad multiple instruments and phases, and mobilized a lot of the M&E team's supervision capacity.The practical solution developed by the research team at the time was to rely on self-reports builtinto the follow-up survey. The award was to be delivered soon after having the information of thewinners (between June and September 2011). According to the CF survey, few CFs met the target:1 in Mopeia district, 4 in Chemba district, 6 in Morrumbala district, 5 in Mutarara district, and0 in Maringue district. After several attempts of validating prize delivery, we can only state withcertainty that prizes were delivered in Mutarara district in 2012. Although an attempt to reinforcetreatment 3 was made in the second SLM training in November 2012, we faced similar resistanceand therefore the implementation of the treatment was unsuccessful. We nevertheless control foreligibility to receive this treatment in all regressions in case it might a�ect outcomes.
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Figure E.1: Randomized Multi-Arm Treatment Design
76
