metadata: authors: 0: author: Kondylis,Florence
1: author: Mueller,Valerie
2: author: Zhu, Siyao Jessica
count: Africa,Mozambique
docna: 0: docna: Seeing is believing ? evidence from an
            extension network experiment
docty: Policy Research Working Paper
theme: Rural policies and institutions,Rural markets,Rural services and infrastructure,Other environment and natural resources management
majtheme: Environment and natural resource management,Rural development
subtopic: Climate Change and Agriculture,Agricultural Extension,Crops and Crop Management Systems,Demographics,Hydrology,Food Security
teratopic: Environment,Agriculture
abstracts: cdata!: Extension is designed to enable
            lab-to-farm technology diffusion. Decentralized models
            assume that information flows from researchers to extension
            workers, and from extension agents to contact farmers (CFs).
            CFs should then train other farmers in their communities.
            Such a modality may fail to address informational
            inefficiencies and accountability issues. The authors run a
            field experiment to measure the impact of augmenting the CF
            model with a direct CF training on the diffusion of a new
            technology. All villages have CFs and access the same
            extension network. In treatment villages, CFs additionally
            receive a three-day, central training on the new technology.
            They track information transmission through two nodes of the
            extension network: from extension agents to CFs, and from
            CFs to other farmers. Directly training CFs leads to a
            large, statistically significant increase in adoption among
            CFs. However, higher levels of CF adoption have limited
            impact on the behavior of other farmers.
geo_reg_key: 517195,517191
repnme: repnme: Seeing is believing ? evidence from an
            extension network experiment
display_title: Seeing is believing evidence from an
            extension network experiment
listing_relative_url: /research/2014/08/20028274/seeing-believing-evidence-extension-network-experiment
topicv3: Agriculture Extension m1325967 1009,Agriculture and Food Security m1326205 435,Water m1326522 137,Crop Production m1326627 174,Demographic Change m1327040 1448
publishtoextweb_dt: 2016-11-07T00:00:00Z
historic_topic: Environment,Agriculture
no_of_pages: 59
author_keys: 0: author_key: 797414
1: author_key: 1865225
model_extractions: WPS7000 Policy Research Working Paper 7000

# Seeing Is Believing?

Evidence from an Extension Network Experiment _Florence Kondylis Valerie Mueller Siyao Zhu_ Development Research Group Impact Evaluation Team August 2014
Policy Research Working Paper 7000

# **Abstract**

Extension is designed to enable lab-to-farm technology diffusion. Decentralized models assume that information flows from researchers to extension workers, and from extension agents to contact farmers (CFs). CFs should then train other farmers in their communities. Such a modality may fail to address informational inefficiencies and accountability issues. The authors run a field experiment to measure the impact of augmenting the CF model with a direct CF training on the diffusion of a new technology. All villages have CFs and access the same extension network. In treatment villages, CFs additionally receive a three-day, central training on the new technology. They track information transmission through two nodes of the extension network: from extension agents to CFs, and from CFs to other farmers. Directly training CFs leads to a large, statistically significant increase in adoption among CFs. However, higher levels of CF adoption have limited impact on the behavior of other farmers.

This paper is a product of the Impact Evaluation Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at fkondylis@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Seeing Is Believing? Evidence from an Extension Network Experiment Florence Kondylis<sup>*</sup> Development Research Group The World Bank Valerie Mueller* Development Strategy and Governance Division International Food Policy Research Institute Jessica Zhu Agricultural and Applied Economics University of Wisconsin, Madison JEL Classifications: O1, O3, Q1, D8 Keywords: information failure; technology diffusion; agriculture; Africa.

> *Corresponding authors' emails are fkondylis@worldbank.org; v.mueller@cgiar.org.

This research was 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 International Development; the Trust Fund for Environmentally and Socially Sustainable Development; the Belgian Poverty Reduction Partnership and the Gender Action Plan; and the CGIAR Research Program on Policies, Institutions, and Markets (PIM) led by the International Food Policy Research Institute (IFPRI) and nanced by the CGIAR Fund donors. The authors beneted from comments provided by Jenny Aker, Luc Behagle, Madhur Gautam, Markus Goldstein, Maria Jones, Rashid Lajaaj, Mark Lundell, Mushq Mobarak, Tewodaj Mogues, Glenn Sheri, David Spielman; and seminar participants at the CSAE (Oxford), the Mid-Western Economic Development Conference, the NEUDC at Boston University, The Ohio State University, the Paris School of Economics, the University of Georgia, the World Bank, and IFPRI. The views expressed in this article do not reect those of the World Bank, 3ie, or their members. The authors would like to thank Pedro Arlindo, Jose Caravela, Destino Chiar, Isabel Cossa, Beatriz Massuanganhe, and Patrick Verissimo for their collaboration and support throughout the project. John Bunge, Ricardo da Costa, and Cheney Wells provided excellent eld coordination; Siobhan Murray and João Rodrigues impressive research assistance.

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# 1 Introduction

Agricultural innovation is necessary to accelerate growth and achieve food security in Africa (Hazell, 2013). Despite availability of yield-enhancing technologies, adoption rates in Sub-Saharan agriculture remain low (Gollin et al., 2005). A growing literature identies information failures as an impediment to the technological diusion process in agriculture (Bandiera and Rasul, 2006; Conley and Udry, 2010; Munshi, 2004). Less documented are the modalities through which information can best diuse and boost adoption of productive farming practices.

Agricultural extension services are designed to facilitate the diusion of innovations from lab to farm. In developing countries, they account for large shares of government expenditures on agriculture (Akroyd and Smith, 2007). These substantive investments are seldom supported by causal evidence regarding their eectiveness as a whole, or of a particular modality (Anderson and Feder, 2004). Contact farmers (CFs), who serve as points of contact between extension agents (EAs) and other farmers, are ubiquitously used as messengers of information in developing countries. Ecacy of the CF modality rests on two key assumptions. First, EAs will eectively train CFs to adopt and demonstrate new technologies to peers. Frequent EA visits are supposed to elicit a process of experiential learning among CFs. Second, other farmers' exposure to CFs will encourage wider adoption in the community, through a peer learning process. Despite some evidence of implementation and accountability constraints, and perhaps for lack of a viable policy alternative, the CF model persists across Africa (Gautam, 2000). Formally documenting returns to additional low-cost, scalable interventions to help leverage these large investments in agricultural extension services could signicantly aect the path of technology diusion.

We exploit a large-scale, government-run randomized controlled trial (RCT) to measure the impact of augmenting the CF model with a direct training on the diusion of a new technology in central Mozambique. Our treatment consists of adding a direct CF training to an existing CF model, holding everything else constant. In practice, CFs in treatment villages receive two three-day trainings (one in 2010, one in 2012) on a yield-enhancing technology at district headquarters, by the same experts and using the same curriculum as provided to EAs. All EAs were trained on sustainable land management (SLM) and were expected to train their local CFs to demonstrate the technology to other farmers in 200 villages. All CFs were provided demonstration kits to encourage 2
adoption and diusion of information to other farmers. We augmented the CF model by centrally training CFs on SLM in 150 randomly selected (treatment) communities. The training format was part lectures, part hands-on, with similar content and breadth as the EA training. The central training is the only dierence between treatment and control, and all 200 villages adhere to the status quo CF model. We use two rounds of follow-up survey data on 200 CFs and a random sample of over 5,000 other farmers to examine the impact of adding a central training on knowledge and adoption of the technology, as well as agricultural production.

The CF model enables a process of experiential learning among CFs through the use of demonstration activities and regular on-site feedback from EAs. This practice is similar to on-the-job, learning-by-doing processes in other labor markets. Neoclassical growth theories suggest learningby-doing may be of equal importance to formal training in explaining human capital formation as a production input (Lucas, 1988). While learning-by-doing theories are supported in the context of rm or plant-level studies (Levitt et al., 2012; Thompson, 2010), empirical evidence of the signicance of learning through extension programs on agricultural growth is mixed (Bindlish and Evenson, 1997; Purcell and Anderson, 1997; Gautam, 2000; Anderson and Feder, 2007; Benin et al., 2007; Davis et al., 2012; Waddington et al., 2014). We contribute to this research agenda by formally documenting the impact of augmenting an existing, decentralized extension model with a relatively low-cost centralized training modality.

Adding a direct training may aect technology adoption among CFs through three broad categories of mechanisms: increased quantity of information, enhanced learning experience, and channels other than knowledge. First, the curriculum in a direct training may increase the quantity of information transferred (e.g., number of techniques taught). Central trainings are oered in an enclosed setting under the supervision of project sta, and extension agents present the material from the course manual. This plausibly increases the chance that the intended curriculum is covered.

Second, the centralized format of the training may enhance the learning experience. For instance, the formal setting may add credibility to the information. While the use of course materials hardly aects learning indicators in other settings (Tan et al., 1999; Glewwe et al., 2004, 2009), use of computing technology as a complementary input to a standard curriculum has been shown to have a positive eect on learning (Banerjee et al., 2007; Linden, 2008). For these reasons, the information set shared during a training held at district headquarters may be (perceived to be) of higher quality 3
than what is given during eld visits from the EAs. Peer learning will also likely be more pronounced during a centralized training, as CFs with similar characteristics get to share information and jointly interact with the material.

Third, a direct training could increase CFs' adoption through other channels than knowledge. For instance, the training may improve EA-to-CF accountability. Directly trained CFs may demand more information from EAs (Björkman and Svensson, 2009; Banerjee et al., 2010). Being formally trained could also build empowerment, reinforcing the identity of the CF as community messenger and their propensity to lead village-level demonstration activities. Similarly, attending a training in the district town for a few days may make CFs feel special relative to control CFs. Alternatively, a centralized training could create a momentum among peers to adopt the new practices, akin to herd behavior (Banerjee, 1992; Karlan et al., 2014).

We nd a statistically signicant increase in CF adoption of SLM when CFs have access to a direct training in addition to the status quo extension modality. Private returns in the form of labor savings and yield benets in dry years accompany increases in adoption. However, CFs' knowledge scores on the SLM curriculum are unaected by the direct training.

Increasing demonstration of SLM practices could reinforce the perceived benets of SLM among peers by increasing knowledge and reducing the uncertainty of SLM benets (Foster and Rosenzweig, 1995). Yet, boosting CF demonstration and adoption through a direct training does not aect other farmers' practices within the community in our context. Patterns of CF-farmer interactions suggest that the direct training did not additionally stimulate CFs to fulll their role as village messengers. Interestingly, variations in treatment eects by CF characteristics and similarity in cropping patterns indicate that relevance of expected cost-benet margins aect the diusion process. For example, pit planting adoption rates increase when a CF's crop portfolio matches the farmers'. Hence, our results corroborate the idea that the proximity of the source of information may be the primary constraint on changes in farmer behavior (Munshi, 2004; Feder and Savastano, 2006; Bandiera and Rasul, 2006; Conley and Udry, 2010) and, therefore, that the process of CF selection may aect the pace of diusion (Beaman et al., 2014; BenYishay and Mobarak, 2014).

Overall, our ndings suggest augmenting decentralized extension programs with a direct training modality can improve their eectiveness in getting CFs to demonstrate new technologies. Our costbenet analysis shows net private returns of up to USD 76 per CF. Yet, a direct CF training leads 4
to modest diusion to others in the community. Taken together, these results imply that adding a direct training modality on its own may not be enough to reform the speed of technology diusion. Further study is needed to build up the evidence base, using larger samples, improved measurement techniques, and testing complementary policy actions to make extension services work for farmers.

In what follows, we detail the Mozambique extension policy and network at baseline (Section 2). We then describe the evaluation design and empirical strategies used to identify the impact of adding a direct CF training on technology diusion (Section 3). Section 4 presents estimates of impact on CF knowledge, adoption, and productivity, other farmers' adoption and knowledge, as well as measures of cost-eectiveness. Section 5 discusses implications of this study for policy and future research.

# 2 Agricultural Extension Constraints in Mozambique

## 2.1 National Extension Coverage

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) has promoted and expanded extension networks (Eicher, 2002; Gemo et al., 2005). EAs are employed by the District Services for Economic Activities (Serviços Distritais de Actividades Económicas) and operate at the subdistrict level to disseminate information and new techniques. The system assumes that information ows linearly: agricultural innovations are created by researchers, then distributed by extension workers, and nally adopted by producers (Pamuk et al., 2014). Countrywide, 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 set of villages every year based on their achievements and potentials (Coughlin, 2006). Only 15 percent of farmers report receiving extension services (Cunguara and Moder, 2011).

At the time our study was designed, the present National Plan for Agricultural Extension and Extension Master Plan aimed to develop the decentralization of services at the district level; increase participation of targeted groups (women and marginal farmers); and enhance partnerships with other actors, such as the private sector and nongovernmental organizations (Gallina and Chidia5
massamba, 2010). Given the importance the government places on decentralized extension services and the lack of rigorous evidence to date, formally documenting the impact of this policy action seems warranted (Gautam, 2000).<sup>1</sup> In what follows, we describe the details of the status quo extension model operating at baseline in our study area.

# 2.2 Study Area

We worked in ve districts of central Mozambique: Mutarara (Tete Province), Maríngue and Chemba (Sofala Province), and Mopeia and Morrumbala (Zambézia Province; Figure 1). This area receives nancing from a large World BankGovernment of Mozambique investment to support the development of the extension network (Smallholder project ). The project provides three levels of agricultural technical assistance: each district has a facilitator, an environmental specialist, and eight EAs. A district is subdivided into four administrative posts (posto administrativo) that include about 810 communities (aldeia). EAs periodically receive training from the district specialists.<sup>2</sup> Each community has a designated contact farmer (CF) who receives direct assistance from the two EAs placed in his administrative post.<sup>3,4</sup> CFs receive visits from EAs monthly. They were instated to respond to other farmers' demands for technical assistance and provide advice through demonstration activities.

A CF model of extension may not foster learning and adoption among CFs. EAs are typically challenged to reach the communities they serve. Designating CFs may therefore not adequately address the supply-side constraints of extension services. Another concern is that information may get diluted from the central level to CFs. For instance, EAs may not cover all techniques, suciently train the CFs, nor adhere to the expected format. Since CFs do not know what curriculum their EA should follow, accountability may be low. Finally, periodic visits from the EA may not be sucient

> 1Recent work has employed a quasi-experimental design to evaluate the impact of extension and found a positive impact of extension on farm income in Mozambique (Cunguara and Moder, 2011)

> 2In October 2010 and November 2012, these trainings were dedicated to SLM.

> 3The ratio of EAs per administrative post in our study area is on par with the 2013 national average of 1.89 (Gêmo and Chilonda, 2013). This 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.

> 4EAs 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 had been in their position for three years on average, with a standard deviation of 3. This indicates the majority of CFs were already commissioned by the project prior to our intervention.

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in getting CFs motivated to demonstrate to others in their community.

The underlying assumption of the CF model is that, through peer learning, a change in CF demonstration eort should aect the process of diusion to other farmers in the community. By exogenously aecting CFs' adoption of a new technology, our experiment directly tests whether the CF model is suited to promoting technology adoption on a large scale. Allowing the ITT estimates to vary by CF characteristics provides qualitative evidence of existing barriers to knowledge transfer.

# 3 Experiment and Data

We run a large eld experiment to test for eective knowledge diusion under the CF model of extension, and isolate the additional impact of directly training CFs. A new technology, SLM, was disseminated through the extension network for the rst time in 2010. Our study started in October 2010 and ended after the main 2013 cropping season, thus spanning three main agricultural seasons (Figure 2). We collected three rounds of data: a rapid CF baseline and two CF and household-level follow-ups, respectively, 15 and 27 months after the rst SLM demonstration season. By baseline, we refer to September 2010 and earlier. The initial demonstration season in our study was 2011. Our surveys captured the 2012 and 2013 adoption seasons. This section details the experiment and data sources.

## 3.1 Sustainable Land Management

Sustainable land management (SLM, or conservation farming) is a yield-enhancing farming technology that consists of a bundle of techniques adapted to local crops and agro-ecological conditions (Haggblade and Tembo, 2003; Thierfelder et al., 2015).<sup>5</sup> In the Zambezi valley, the recommended SLM technology package encompasses seven SLM techniques: Mulching, Crop Rotation,

> 5A direct implication is that, while positive yield eects of SLM are relatively well documented for Southern Africa (Haggblade and Tembo, 2003; Thierfelder et al., 2015), there is little evidence on the returns of individual SLM techniques.

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Strip Tillage, Pit Planting, Contour Farming, Row Planting, and Improved Fallowing.<sup>6</sup> Mulching covers the soil with organic residues to maintain soil humidity, suppress weeds, reduce erosion, and enrich 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. Pit planting consists of constructing permanent holes 15 cm deep 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 improves productivity by improving access to sunlight and facilitating weeding and other cultivation practices (for instance, mulching and intercropping) by providing space between rows. Improved fallowing reduces temporary productivity losses from fallowing through targeted planting of species that recharge the soil in a shorter time frame.

There are important complementarities across these techniques, which are expected to generate savings in labor time during the main season. For instance, combining strip-tillage with pit planting will ensure that pits do not need to be excavated every year. This should save labor at the seeding stage over the traditional methods of tillage and planting in ridges. Similarly, strip-tillage and contour farming combined will save time, since the terraces will not have to be prepared every year for seeding. Combining mulching and pit planting can also help maximize the nutrient retention of the soil around the maize crop and minimize the need for weeding.

We asked CFs to recall their familiarity with these techniques at baseline (Table A.1). SLM exposure varied widely across techniques and farmer types. Twenty-one percent of CFs had heard of improved fallowing relative to 10 percent of other farmers. In contrast, 76 percent of CFs knew of mulching, compared to 34 percent of other farmers. This suggests that some, if not all, SLM technologies taught in the CF training and disseminated by EAs pose as reasonable instruments to track knowledge diusion in the Zambezi valley.<sup>7</sup>

> 6Intercropping was included in the curriculum, but is excluded from the analysis as it was already widely adopted at the time of the intervention by CFs (98 percent) and other farmers (76 and 81 percent of women and men, respectively). Including the technique bears little consequence on our point estimates (not reported).

> 7The 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 a formal training on SLM techniques, or been instructed to transfer their knowledge to their peers.

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# 3.2 Training

We now describe the trainings delivered to EAs and CFs in the context of our study. First, all EAs serving administrative posts within our study area received two three-day training courses on SLM techniques in October 2010 and November 2012 (prior to the main planting season). Technical sta from the Ministry of Agriculture (MINAG) developed the educational agenda on SLM practices, and the training was delivered by MINAG's district technical sta with support from one sta from the central project team. Half of the training sessions were devoted to in-class lectures, and the other half consisted of hands-on demonstrations. The syllabus included a thorough review of the advantages of each SLM technique over less-environmentally desirable ones.<sup>8</sup> The EA training also highlighted good practices in fostering interactions between EAs and CFs.

The centralized CF trainings were held a few weeks after those of the EAs. The content of the CF training was similar to that received by EAs, and was delivered by the same district-level and central MINAG sta.<sup>9</sup><sup>_,_10</sup> The cost of a direct training per CF per year was 74 USD. Over three agricultural seasons, this represents a modest 12.8 percent increase in the total salary and training cost of the extension network.<sup>11</sup> After these trainings were completed, all EAs worked with their CFs to disseminate the SLM techniques most pertinent to their local area on their (own or communal) demonstration plots, regardless of their CFs' treatment status. All CFs received a new toolkit (a bicycle, tools to plow the land, and smaller articles).<sup>12</sup> A second toolkit with similar items (including a bicycle) was provided to all CFs again in July 2012. The only dierence between treatment and control CFs is

> 8The main charts from the class can be found here: http://siteresources.worldbank.org/INTDEVIMPEVAINI/Resources/Flipchart_deAC_anonymized.pdf. The general curriculum used by the MINAGRI sta is provided on this site: http://siteresources.worldbank.org/INTDEVIMPEVAINI/Resources/Manual_AC_FINAL.pdf. The hands-on component of the training was not recorded but followed closely the techniques discussed in class.

> 9In 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. This may lead us to underestimate information ow in the central training arm, and overestimate it in pure CF model. Reassuringly, as mentioned above, we do not nd that EAs devoted more time to visiting CFs in treatment communities, relative to control.

> 10Given the low literacy of farmers, a lm covering all techniques substituted the initial lecture format in the second training of the CFs in 2012.

> 11The monthly EA salary costs were at USD 210 per EA from data provided by the DNPDR and the Smallholders project team, and EA training costs ran at USD 370 per training. Each EA supervised on average 5 treated CFs over the course of 36 months. There are obviously other, non-wage costs to running an extension network. To the extent that we do not account for these additional costs, we overestimate the relative cost of adding a direct CF training.

> 12The toolkit distribution was planned, independently of our intervention, by the project sta. The previous distribution had been done in 2007 and, by 2010, the items were deemed too old to function.

9
that treated CFs received an additional direct training on SLM.

# 3.3 Experimental design

At baseline, CFs and EAs in our ve study districts operated under the CF model of extension in all communities. From these districts, we randomly selected 200 communities (with 200 CFs) in 16 administrative posts, to which 30 EAs were assigned. All EAs received SLM training. We randomly assigned CFs in 150 treatment communities to the augmented version of the CF model (treatment), stratifying the assignment at the district level. Control (50) and treatment (150) CFs received SLM training during visits from their EAsthe status quo CF extension modality. Treatment CFs additionally received the direct CF training described above.<sup>13</sup> This design allows us to isolate the additional eect of a direct training, implicitly testing for eective knowledge diusion under the CF model. For this purpose, we held constant all other extension interventions across treatment and control communities. Specically, in line with the status quo modality, all CFs in the study area receive assistance from their EAs and a tool kit to set up and maintain a demonstration plot within the community. These demonstration plots are used by (1) EAs to teach and assist CFs in implementing at least one of the agricultural practices of the CF's choice, and (2) CFs to demonstrate the new techniques to other farmers in their community.

In practice, the CF-level random assignment was implemented as follows. Each EA team at the administrative post level was in charge of inviting treatment CFs to the central SLM trainings. During the EA trainings on SLM, district sta explained the physical impossibility of training all CFs at once and that a lottery had been used to select the participating CFs. EAs were then given the list of randomly chosen treatment CFs. An attendance sheet was taken at CF training by the district sta. In October 2010, only four treatment CFs did not attend the training (all in the Mopeia district), and there was no contamination to control CFs.<sup>14</sup> Since district sta may have

> 13The full design consists of multiple treatment arms. A second treatment arm was overlaid on our central training that randomly assigned 75 of the 150 treated communities to have an additional trained female. This second treatment is the subject of a separate study. In the present study, we pool the two treatments together, to examine the impact of having at least one CF in the community trained on SLM on farmers' outcomes. A third randomized treatment arm was overlaid on the rst two that attempted to provide dierent performance-based incentives for the CFs to reach farmers in both villages that were assigned to the direct training and control communities. These incentives were never announced to the CFs, and we show that they did not have any statistically signicant eect on our outcomes of interest (not reported). Nonetheless, we control for this third treatment arm in the regression analysis.

> 14These CFs were trained by the EA on an individual basis, and the follow-up training was veried.

10
an incentive to misreport attendance, we performed independent audits. 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 and veried attendance verbally. 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 results from these audits indicate that attendance was genuine.

Similar checks were performed on the 2012 training. While the attendance list was equally validated, participation was not universal and contamination was quite substantial. Of the treated communities, 63 percent had at least one CF attend the training, and 16 percent of control communities had a CF attend.<sup>15</sup> These gures signal statistically signicant exposure of control CFs to the treatment in 2012. While this may hamper our ability to statistically dierentiate the two training models on CF behavior in the 2013 (second follow-up) survey round, our results on other farmers at endline are arguably robust to this contamination. Increased demonstration by control CFs in the 2013 growing season is unlikely to have aected farmers' adoption in that same season.

There are two important limitations to our identication strategy: one concerns the intensive margin of EA support to treatment CFs relative to control CFs, while the other relates to the extensive margin of EA attention. First, direct training and EA support are likely complementary inputs. Therefore our estimates capture the overall eect of augmenting the CF model with a direct training. We cannot disentangle the impact of learning during the central training from that of improved learning during regular EA-to-CF tutorials as a result of the direct training.

Second, our design implies that each EA will work with both treatment and control CFs in his administrative post.<sup>16</sup> 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 time away from the other treatment statusthe extensive margin of EA attention across treatments. For instance, treatment CFs may request more follow-up visits from their EAs, cutting into the time

> 15The contamination likely was caused by a combination of self-selection and EA oversight. CFs in the control group could have easily learned about the trainings from peers in other communities. Since EAs and district sta were involved in organizing the training, it is easy to see how a well-connected CF might have been invited in.

> 16A limitation of working with an existing extension network is that we could not withhold information from a random group of CFs by shutting down their interactions with their assigned EAs. Given the small number of extension workers (30), reasonable levels of statistical power cannot be reached by assigning the intervention at the EA level. We do verify that extension agent characteristics are balanced across treatment and control communities at midline (Table A.2).

11
EAs devote to control CFs. Reassuringly, we nd that control and treatment CFs received equal amounts and types of attention from their EAs in the year after the training (Table A.3).

# 3.4 Data

We conducted two follow-up surveys after the rst training and demonstration season (October 2010 to April 2011). A 2012 (midline) round and a 2013 (endline) round form a panel of households and CFs in the study area.<sup>17</sup> We randomly sampled 18 non-CF households in each community from a full listing performed by our enumerators ahead of the survey. At both midline and endline, households were visited twice: pre- and postharvest. This allows us to observe SLM practices when they are most visible, just after planting (preharvest, from February to April), and to record production data after harvest (from mid-May on). Hence, our eldwork ran from February to April and May to August in 2012 and 2013.

Midline and endline surveys gathered longitudinal CF and household information on the two main agricultural seasons that followed the rst demonstration season. Our eldwork included ve survey instruments: a household questionnaire, a household agricultural production questionnaire, a CF questionnaire, an EA questionnaire, and a community questionnaire. The household survey was also administered to all 200 CF households, in addition to the specic CF survey. These surveys provide household demographics, SLM knowledge for the two main agricultural producers in the household, individual and plot-level SLM adoption, and production information for approximately 3,600 non-CF households in 200 communities. Since the plot roster identies the adult in charge of making agricultural decisions for each cultivated plot, we obtain individual measures of knowledge and adoption for a sample of 5,884 and 5,071 individuals at midline and endline, respectively.

A rapid baseline survey was administered to all CFs in August 2010, before the district-level randomization. This provided data to perform balance tests on the success of the randomization, using the pre-intervention characteristics of CFs by treatment status. Figure 2 illustrates the timing of the surveys and CF trainings over the course of the four-year study.

> 17Operational constraints precluded us from conducting a household survey at baseline.

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# 3.5 Descriptive Statistics

We briey describe the average characteristics of farming individuals in our sample (Table A.4). More than half of the individuals are women, and the prevalence of female headship is consistently high (approximately 30 percent) for the region (TIA, 2008). The average farmer is 38 years old with two years of schooling. Most plot owners are married with three children, and live in a single-room house made of mud and sticks with a palm or bamboo roof (not reported). Farmers possess 2.2 hectares of land on average, with a standard deviation of 2.1.

CFs are more knowledgeable (Tables 1 and 2), more educated, and wealthier (Table 3) than other farmers. While CFs are positively selected in attributes, they are also well known in their communities: 84 percent of farmers in the control group declared knowing them personally. However, only 72 percent of the same group of farmers reported knowing that these individuals assumed a role as CF in their community.

We also note that usage of demonstration plots was quite high and not statistically dierent across treatment and control communities (Table A.3). Of the CFs in treatment and control communities, 85 percent maintained a demonstration plot.<sup>18</sup> Thus, changes in patterns of CFto-other-farmers information diusion across the two modalities can be interpreted as resulting from variations at the intensive margin of CFs' activities (e.g., number of techniques demonstrated, quality of the demonstration).

# 3.6 Balance

We use data from the baseline CF survey as well as time-invariant and retrospective information collected in the 2012 household survey to check for balance across treatments. Table 1 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) and had slightly more recent training when we condition on being formally trained. Control CFs were exposed to a greater number

> 18There was no instruction, however, as to what type of plot should be used for demonstration activities. CFs could choose to use their own, private plot or communal land. Hence, we present the demonstration results for any plot (own or not).

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of techniques prior to the intervention.<sup>19</sup> In spite of these dierences, (recalled) pre-intervention adoption rates among CFs in control and treated communities were similar, as were other farmers' (recalled) baseline SLM learning and adoption rates (Table 2).<sup>20</sup>

# 3.7 Measuring Information Diusion and Behavioral Change

Central to identifying variations in information diusion is measuring changes in learning and agricultural practices. Our study rests on the reliability of our markers of individual SLM knowledge and adoption. We focus on three outcomes: a knowledge score, the number of techniques the respondent identied by name, and the number of techniques the respondent reported having adopted on any plot.<sup>21</sup> The knowledge score is a continuous measure based on the number of correct responses provided in the 23-question exam, covering all SLM techniques. For CFs, the majority of the analysis rests on their self-reported adoption of techniques on any plot (demonstration or not).<sup>22</sup> Since the CFs were encouraged to choose the techniques most relevant to their local conditions, our main results focus on unweighted aggregate measures of knowledge and adoption. However, we create a second set of weighted knowledge and adoption outcomes as a robustness check. Prior to aggregation, we multiply the technique by a weight based on its relative importance to maize revenue. This is done as follows. First, we compute a vector of weights, based on a regression of maize revenue on adoption of the seven individual practices. Second, we compute adoption and knowledge

> 19Given that CFs in treatment villages spend more hours a week working as a CF at baseline, we will include the variable as a control in the regression analysis.

> 20Balance tests for the CFs' and other farmers' knowledge and adoption of individual SLM techniques at baseline are reported in Tables A.1 and A.5. Because these values are based on recalled data, the tests should be interpreted with caution. Even though mean comparisons indicate there are no statistically signicant dierences, recall bias may be present. We therefore do not exploit the recalled information beyond balance checks.

21Our decision to focus on the knowledge score and self-reported adoption outcomes is motivated by the conclusions in Kondylis et al. (2015). Using the midline survey data, they nd learning outcomes based on knowledge exams provide more precision than know-by-name questions, inasmuch as they reveal the true knowledge of those individuals less familiar with the name of the technique yet more familiar with its purpose and usage. Objective adoption measures were also collected for two plots per household and largely corroborate the self-reported outcomes. In our triangulation of the self-reported versus observed adoption, we nd that false reporting is negligible. Since objective measures of adoption are collected for only a subset of plots (one per respondent) at midline and a subset of the sample at endline, we instead focus on a more inclusive measure of adoption provided by self-reports of interviewed men and women.

> 22There are slight dierences between adoption measures which include and exclude the demonstration plot. This is due to the fact that some CFs demonstrate on communal land (29%). We verify the results are not driven by communal propriety of the demonstration plot (not reported). Since 71% of demonstration activities are carried out on CFs' own plots, we choose to use pooled adoption on and o demonstration plots as our main marker of adoption. This improves our precision but does not aect our conclusions.

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indices of practices weighted by these correlations between adoption and maize production.

We additionally explore patterns of knowledge and adoption specic to individual techniques. We use responses from the same knowledge exam to quantify farmer knowledge of individual SLM techniques, categorizing questions by technique. The knowledge of a specic technique is a [0,1] continuous variable that depicts the share of questions pertaining to the practice that the respondent has answered accurately. The adoption of a technique is captured by a binary variable that indicates whether the farmer adopted the technique on at least one of his plots.

Knowledge, adoption, and perception of the SLM techniques were collected at the individual level from the household questionnaire. Two respondents were interviewed: typically, the household head and the head's partner or spouse.<sup>23</sup> 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. Our nal regressions samples consist of 347 CF-year observations and 10,955 person-year observations.<sup>24</sup> Selective sample attrition is of concern, and we address it in the next section.

# 3.8 Empirical Strategy

We causally estimate the intent-to-treat (ITT) eects of a community being assigned to a direct CF training (relative to a status quo CF modality) on the SLM knowledge and adoption of CFs<sup>25</sup> and other farmers in the community, Y, using a simple reduced-form specication:

> 23In the case of polygamous households, the main spouse was interviewed. Only 2.7 percent of our sampled households are polygamous.

> 24The number of villages that were administered the CF survey were 179 and 172 in 2012 and 2013, respectively. The number of farmers interviewed in 2012 were 6,252, and 5,290 in 2013. Sample sizes vary in descriptive statistic tables and some regression tables, due to the addition of variables excluded from the main analysis.

> 25CF-level regressions control for community-level CF outcomes and characteristics. In those communities where we (randomly) assigned an additional woman farmer to be trained, we measure increased village-level exposure by regressing the maximum value of CF outcomes within the village on the maximum (mean) value of binary (continuous) covariates. Switching to mean of outcomes, and controlling for mean and max of all covariates does not aect our conclusions.

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_T_ takes the value 1 for each community j with a trained CF. Individual i, household h, and community characteristics are included in the vector _X_ to improve the precision of the estimated coecients. An indicator for the second follow-up survey, _νt_ , is also included to capture the eect of time-specic events on behavior.<sup>26</sup> We estimate all main regression models on the pooled sample, controlling for survey-year xed eects. 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.<sup>27,28</sup>

# 4 Results

## 4.1 CF Learning and Adoption

We rst examine the ITT estimates of a direct training on unweighted aggregate measures of CFs' knowledge and adoption (Panel A, Table 4). While control CFs adopted on average 3.74 techniques, we detect that CFs adopt on average a 0.73 additional technique in response to the training (statistically signicant at the 5 percent level). The associated eect size is large at 0.39 standard deviations in the control group, or a 19.6% increase relative to the mean in the control. Next, we run similar specications with the weighted versions of the outcome.<sup>29</sup> Control CFs adopt on average

> 26We include variables that reect CF (or other farmer) demographic characteristics: age, primary school completion, whether the individual is single (and a separate widow dummy for the other farmer sample), number of children, total landholdings, the number of rooms in the house, the number of hours worked by the CF at baseline, an indicator for a missing response for the baseline CF variable, district indicators, and indicators for treatment arms not analyzed in the present study. Our results are robust to specications that omit the demographic characteristics (Tables A.6 and A.7) or replace district with administrative post xed eects (Tables A.8 and A.9).

> 27Attrition rates at the household and CF level are not statistically dierent (Table A.10) nor correlated across treatment groups (Table A.11). Household attrition rates appear consistent with those of other studies in the same region (De Brauw, 2014). A probit regression reveals that the greater the percentage of household members away in 2012 and the incidence of being single produces a greater probability of the household moving out of the sample (Table A.11). Age, the number of children of the household head, and exposure to a precipitation shock reduce the probability of moving out of the sample.

> 28We perform two additional robustness checks to examine the sensitivity of our results to attrition (not reported). The rst diagnostic estimates (1) using the balanced panel. We show that the inclusion of individuals present in both rounds 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). This check conrms that selective attrition is unlikely to aect our conclusions.

> 29The linear model of maize revenue controls for adoption of each SLM technique, as well as household demographics, as in Table 4, and production inputs. The regression estimates are presented in Table A.12. The vector of weights

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60.1% of the practices, and directly trained CFs increased adoption by about 10.6 percentage points (statistically signicant at the 5% level; Panel B, Table 4). This eect is similar in magnitude to that obtained on our unweighted index, with a similar eect size of 0.40 standard deviation in the control group, or a 17.6% increase relative to the mean in the control. Weighting our knowledge index conrms that directly training CFs did not aect their knowledge scores. Overall, unweighted and weighted results suggest that a direct training was eective in raising CFs' adoption of SLM practices, with little eect on knowledge scores.

To shed light on changes in the technique mix, we disaggregate the ITT estimates of adoption by technique (Table 5). Despite positive point estimates for all practices, statistical signicance is achieved for only strip-tillage, pit planting and contour farming (statistically signicant at the 10%, 1%, and 10% levels, respectively).<sup>30</sup> The magnitude of these eects is substantial, ranging from 28.3% to 65% increases relative to the control mean. To account for multiple hypothesis testing, we adjust our inferences for familywise error rates (idák, p-value = 0.015; Bonferroni, p-value = 0.014), following Abdi (2007). The eect on pit planting adoption is robust to multiple hypotheses testing (Figure 4), with a 28 percent increase in adoption relative to the control mean.<sup>31</sup> Finally, we examine changes in technique-specic knowledge as a result of the direct training. In line with our aggregate measures of knowledge, Table 6 indicates that adding a direct training to the CF model did little to increase CFs' knowledge scores.

consists of the estimated regression parameter for each technique divided by the sum of the parameters over all seven techniques. Since some regression coecients are negative, we use improved fallowing as the reference weight. Thus, mulching takes value 0.227, strip tillage, 0.177, pit planting, 0.157, contour farming, 0.201, crop rotation, 0.193, and row planting, 0.045.

> 30We note that adoption trended downward in both treatment and control villages (not reported). However, these changes are fully attributable to a fall in demonstration of SLM from midline to endline, while adoption on nondemonstration plots actually increases from midline to endline (not reported). We additionally use rainfall data to provide partial evidence that this trend cannot be explained by climatic conditions (NASA 1/2x 1/21981/2013 precipitation data available at http://power.larc.nasa.gov/cgi-bin/cgiwrap/solar/hirestimeser.cgi?email=daily@larc.nasa.gov). Indeed, a dry shock during the rainy season prior to our midline survey (2011) and endline (2012) surveys could aect adoption. Figure 3 displays yearly standardized cumulative rainfall in the rainy season over the 1981/2012 period, and their 95% condence intervals. We observe that rainfall in the study years (2010/2012) are within normal range. Nonetheless, we test whether adoption decisions vary by exposure to a dry spell over the rainy season (where dry is dened as whether the cumulative rainfall during the growing season was below the 31-year 25th percentile). We nd that rainfall anomalies do not explain variations in adoption (Table A.13).

> 31We also follow Anderson (2008) and address multiple inference in two additional ways (not reported): (1) using a free step-down resampling method to our p-values for familywise error rate, and (2) employing the false discovery rate control methodology proposed by Benjamini and Hochberg (1995). All yield the same results.

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# 4.2 Private Returns on SLM

Farmers will adopt a technique only if it demonstrates (public or private) positive returns. Recent observational and experimental evidence documents positive maize yield eects of SLM techniques in southern Africa, as well as substantial labor savings (Beaman et al., 2014; BenYishay and Mobarak, 2014; Haggblade and Tembo, 2003; Thierfelder et al., 2015). We examine private returns to SLM to explain the observed increase in adoption among CFs, beyond their willingness to comply with the training. In practice, we modify (1) to estimate the ITT eects on maize yields and revenue, input use, and on-farm labor allocation.

Table 7 (Panel A) presents maize yields (revenue per hectare) and total revenue accrued from a direct SLM training. Given our low level of statistical power, we present results on the full sample and winsorizing yields at 1% to account for outliers.<sup>32</sup> Results show positive though imprecise point estimates, indicating eect sizes on the order of 0.13-0.24 standard deviations.

Since most SLM practices disseminated have water-conserving properties (Liniger et al., 2011), we further account for the possibility that rainfall patterns in the main growing season may mediate the impacts of the intervention. In practice, we add a control variable to distinguish eects by whether the community experienced a dry spell during the survey round: Dry Year, which takes value one if the cumulative precipitation in a given location fell below the 30-year rst quartile, zero otherwise.<sup>33</sup> In line with recent experimental evidence on pit planting (Beaman et al., 2014), we nd that training CFs on SLM has positive and large, if noisy, eects on maize yields and revenues during drier spells (Table 7, Panel B). The magnitude of the eects, on the order of 0.35 standard deviations, or 37% increase in both yield and total revenue, are in line with ndings from the literature that claim increases of 50 to 100% (Haggblade and Tembo, 2003). The measured yield eects from receiving a SLM training corroborate the notion that farmer adoption of SLM technologies is motivated by short-term yield advantages. In the absence of any statistically signicant dierences in input use as a result of the direct training (Table 8), these yield eects in dry conditions are credibly attributable to SLM adoption.

> 32Winsorizing yields at 1% does not aect the control group mean as the entire top 1% of the distribution is in the treatment group.

> 33For these computations, we use NASA 1/2x 1/21981/2013 Precipitation data available at http://power.larc.nasa.gov/cgi-bin/cgiwrap/solar/hirestimeser.cgi?email=daily@larc.nasa.gov. This measure of weather event is used by others in the literature, see for instance Jayachandran (2006).

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An additional economic benet of applying SLM is in the form of large labor savings that follow from rening tillage operations and herbicide applications (Mazvimavi et al., 2011). These gains are expected to materialize from the second adoption season onward, since the rst year requires at least equal amount of land preparation as traditional practices (Haggblade and Tembo, 2003). Building on this literature and recent large-scale experimental evidence (BenYishay and Mobarak, 2014), our ndings support a delayed contribution of SLM to labor savings. We witness a substantial reduction in the number of hours spent seeding over the week preceding the interview and the total weeks spent farming at endline (Table 9). In particular, CFs spent 6.6 fewer hours seeding in the last week (a relative eect size of 0.63 standard deviations) and 7.1 fewer weeks farming over the last year (a relative eect size of 0.37 standard deviations).<sup>34</sup> While large, these point estimates are in line with the magnitude of eects mentioned in the literature (30 days per year reported in Haggblade and Tembo 2003). Since use of herbicides remained constant across treatment arms (Table 8), labor savings at endline are plausibly attributable to increased SLM adoption and complementary usage of the tools provided in the kit to minimize tillage operations.

# 4.3 Others Farmers' Knowledge and Adoption

We now turn to CFs' ability to spread knowledge and adoption among other farmers in the community. We exploit the exogenous, positive shock in CFs' demonstration of SLM induced by the direct training to measure the extent of CF-to-others knowledge transmission. Since we cannot exclude the possibility that our treatment aected other farmers' adoption of SLM through channels other than demonstration, we adapt (1) and estimate the ITT of directly training CFs on adoption and knowledge on a random sample of other farmers in the community.

Table 10 reports the ITT estimates of directly training CFs on CF-farmer interactions and other farmers' aggregate knowledge and adoption of SLM. First, we note that the direct training did not increase farmers' access to CFs. Second, other farmers' SLM knowledge and adoption are unaected by their exposure to a directly trained CF, despite the margin of gains in SLM awareness being larger than for CFs. These zero eects are robust to balancing the panel at midline, accounting for

> 34These eects are robust to 1% top and bottom winsorizing.

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selective attrition at endline (not reported), and cannot be explained by anomalous precipitations over the study period (Figure 3; FEWS 2012, 2013). Looking at ITT eects by technique conrms this general pattern (Table A.14).

Recall the direct training led to a 15.9 percentage point increase in CF adoption of pit planting. Other farmers in treated communities were more likely to adopt pit planting by 2.8 percentage points (albeit a non-signicant eect). Placing a 95% condence interval around this point estimate allows us to rule out adoption rates higher than (0 _._ 028 + 1 _._ 96 _×_ 0 _._ 018 =)6 _._ 3% for pit planting among other farmers. A back-of-the-napkin calculation on this (weak) pit planting result rules out a propagation rate higher than (6 _._ 3 _/_ 15 _._ 9 =)39 _._ 6% . Adoption of pit planting by a CF would, at best, inspire less than half of a farmer in the community to adopt pit planting. This implies low and slow CF-to-other-farmers technology diusion in the augmented CF model: increasing demonstration of SLM has little eect on other farmers' behavior.

Learning about other farmers' perceptions of labor savings associated with each SLM practice may shed light on the mechanisms underlying adoptionor, in our context, a lack thereof. We asked farmers whether they perceived each technique to require more labor eort, equivalent labor eort, or less labor eort than traditional cultivation practices. Farmers in the control group perceived all techniques to be labor intensive, with a range of less than 1 percent to 16 percent of farmers declaring the techniques to decrease the amount of labor required (not reported). Exposure to a trained CF does not favorably aect farmers' perceptions of adoption costs (not reported). These measures of communication and perceptions indicate that a direct training did not contribute to increasing CF-other farmers interactions, and that CFs' increased demonstration and use of SLM had little impact on other farmers' perceptions of these techniques.

Lastly, we explore whether CFs' characteristics provoke heterogeneous responses among farmers. We focus on four CF indicators: above median educational attainment, above median age, above median landholdings, and production of the same two crops as the farmer.<sup>35</sup> Each regression separately adds an interaction of the treatment variable with one of the four indicators and the interacted indicator on its own. Working with an existing network of CFs, we could not exogenously

> 35Our specication implies that having similar primary crops as the CF is an exogenous decision. Specically, we assume cropping decisions are made before adoption decisions, and cropping decisions are independent of the treatment. While we cannot verify the order of the respective planting decisions, we nd that other farmers' propensity to grow the same primary two crops as the CF is not aected by the treatment (not reported).

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vary their education, age, wealth, or cropping patterns. Thus, the results that follow cannot be interpreted causally, but as descriptive evidence. In addition, CFs are, on average, of higher status than other farmers, which reduces the number of variations we have access to in establishing a counterfactual. Finally, to yield interpretable results, we need this exercise to focus on a single technique rather than on an aggregate measure of adoption. We focus on the adoption of pit planting among other farmers as the outcome, since it is the only single practice which was statistically signicantly adopted by CFs, when adjusting our inference for multiple hypothesis testing.

Table 11 displays the results from interacted regression models accounting for farmer heterogeneity in the treatment eects, with the additive eect of the treatment and its interaction with the CF characteristic reported in the last row. Overall, we nd CFs with above median total landholdings were 4.4 percentage points more likely to convince other farmers to adopt pit planting, a 64.7% increase relative to the control (signicant at the 10% level). Credibility in the source of information appears to inuence all farmers, CFs with larger farms perhaps commanding more trust and respect within the community. More interestingly, similarities in crop portfolios between CFs and other farmers appear to inuence adoption rates. Other farmers' adoption grew an additional 5.2 percentage points when they had access to a directly trained CF who grew similar crops to theirs. This is consistent with the idea that homogeneous farming conditions are conducive to social learning (Munshi, 2004). Delays in adoption may stem from dierences in production technologies and an inability to extrapolate demonstrated activities to their own plot.

# 4.4 Cost-Benet Analysis

To provide perspective on the cost-eectiveness of the program, we compare the average annual costs of directly training each CF to the average annual benets realized by the CF. We consider three scenarios where the private returns to a direct CF training are in the form of maize revenue, labor earnings, and both.<sup>36</sup> To compute the value of labor savings, we multiply the estimate of the intervention's impact on farm labor savings by the shadow value of labor.<sup>37</sup> Benets to other farmers

> 36This approach ignores the social benets produced by the technologies which cannot be quantied over a shortterm horizon, such as soil and water quality.

> 37We price the shadow value of labor at the minimum agricultural wage oered in Mozambique. Minimum wage rates are provided by the U.S. State Department: http://www.state.gov/e/eb/rls/othr/ics/2013/204700.htm. Agriculture is the lowest wage rate at 74 USD per month.

21
are excluded from all three scenarios given the non-signicant (negative) ITT point estimates on adoption.

Calculations for the three scenarios are presented in Table 12. Positive net benets over one adoption season exist when we account for the labor benets only, and when we account for both labor and yield benets. The costs of training represent 12.8 percent of the total annual costs of running the extension system,<sup>38</sup> with benets ranging from USD -55 to USD 76 per CF per season.

Although we fail to measure diusion to other farmers in our context, the predicted returns from CF's labor savings justify scaling a program of this nature. This motivates further research in extension modalities to improve the delivery of information to other farmers to augment the pool of program beneciaries. For instance, providing performance-based incentives to CFs and tempering the selection of CFs does appear to achieve greater rates of technological adoption within communities (Beaman et al., 2014; BenYishay and Mobarak, 2014; BenYishay et al., 2015).

# 5 Discussion

Decentralized extension modalities continue to garner support in Africa despite criticism, often anecdotal, of their inecacy in reaching most farmers and providing relevant information. We designed an experiment in Mozambique to examine whether adding an in-depth centralized training on a new technology improves the knowledge and adoption of innovative agricultural practices. We show that adding a direct training to an existing CF model increases adoption of SLM among CFs. Net private returns come mostly in the form of labor savings associated with the new practices.

Despite these gains in adoption, adding a direct training to the CF modality had little impact on CF knowledge scores. This could of course be the result of poor quality testing and measurement error (Laajaj and Macours, 2015). Alternatively, relative to the status quo extension modality, the direct training may not have changed adoption by increasing CFs' knowledge. Knowledge is not a necessary condition for adoption of a new technology, as manifestations of herd behavior indicate (Banerjee, 1992; Karlan et al., 2014). Instead, the direct training intervention may have gotten more

> 38We focus on the costs specic to the SLM intervention, which include the annual per community cost of an extension agent and per community cost of the SLM training.

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CFs to adopt SLM by strengthening their sense of identity as communicators in their community. The absence of dierences in the use of a demonstration plot, interactions with other farmers, and subjective happiness (not reported) across treatment arms however suggests CFs' dedication and esteem were unaected. Another possibility is that adding a centralized CF training may have heightened the quality and credibility of the information, beyond the scope of our knowledge test. The participatory nature of the training may have helped CFs convert the information into productive behavior, and fostered higher peer learning.

Although adding a direct CF training successfully encouraged adoption of a new technology at the village level relative to the status quo model of extension, it failed to encourage higher diusion to other farmers in the community. There are a number of reasons why this may be the case. First, increased demonstration may not eectively address other barriers to adoption. Having access to a demonstration plot may need to be complemented by other learning inputs, such as CF time or other farmers' time. Adding a direct CF training does not address the fact that CFs' opportunity costs of time may limit interactions with peers. For instance, adding a performance-based incentive payment for contact farmers is shown to positively aect their impact in Malawi (BenYishay and Mobarak, 2014). Similarly, increasing demonstration of a yield-enhancing practice may not address other demand-side ineciencies, such as the tendency to delay adoption until protable (Foster and Rosenzweig, 1995), heterogeneity in farming conditions (Conley and Udry, 2010; Munshi, 2004), and social distance between messengers and peers (Feder and Savastano, 2006; Beaman et al., 2014; BenYishay and Mobarak, 2014).

An alternative explanation is that farmers may learn more from their own experience than from their peers (Foster and Rosenzweig, 1995; Bryan et al., 2014; Dupas, 2014). Failing to notice a gap between knowledge and actual practice, and not the information set itself, may also pose a key barrier to learning. Hanna et al. (2014) nd that seaweed farmers in Indonesia acted on the information received only when it included descriptions of the relationship between yield and pod size from their own plot. If the main constraint to adoption of a protable practice such as SLM is not a lack of exposure or knowledge, but a failure to notice its benets, then augmenting the CF model will have little eect on the pace of diusion within the community.

While we cannot reject that adding a direct training to a decentralized extension model is a cost eective intervention, more work is needed to understand the potential of community-level demon23
stration activities on technology diusion. The prole of the seed adopters inuences whether farmers act on the information they receive. When focusing on farmers with similar cropping patterns as their CF, we observe modest (yet statistically signicant) technology diusion. Complementary interventions, such as assigning dierent types of seed adopters (Beaman et al., 2014; BenYishay and Mobarak, 2014; BenYishay et al., 2015) or encouraging experiential learning in the community (Jones et al., 2015), may increase the pace of technology diusion in the context of decentralized extension services.

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# Figures and Tables

27
# a 2 .-
Figure 2: Timeline of Training and Survey <!-- Start of picture text --> Household and CF Household and CF<br>Midline Survey Endline Survey<br>Feb.2012-Apr.2012 Feb.2013-Apr.2013<br>CF (preharvest) (preharvest)<br>Baseline Survey May.2012-Aug.2012 May.2013-Aug.2013<br>Jun.2010 (postharvest) (postharvest)<br>2010 2011 2012 2013 2014<br>Demonstration season Adoption season Adoption season<br>Rainy season Rainy season Rainy season<br>Oct.2010-Apr.2011 Oct.2011-Apr.2012 Oct.2012-Apr.2013<br>EAs and CFs EAs and CFs<br>First training Second training<br>Oct.2010 Nov.2012<br><!-- End of picture text --> Notes: EA = extension agent. CF = contact farmer.

29
<!-- Start of picture text --> o)<br>2000<br>a ar1s88<br>Cc<br>©o16—<br>= E2008<br>=me1S83 a“.tes = = deme<br>=<br>ce aa - iss =O M2<br>_— T1382 583<br>' 1988 wriss4<br>ise<br>1980 1985 1990 1995 2000 2005 2010 2015<br>7 ear<br><!-- End of picture text -->
<!-- Start of picture text --> CF adoption<br>ae}<br>4<br>@ co .<br>oe<br>o<br>7<br>on F<br>5<br>| . 6<br>; * 4 2<br>-.15 -1 -.05 0 05 A 5 2 25<br>Point Estimate<br>TheNotes:green Techniques:line indicates1: mulching,the standard2: strip-tllage,critical value3: pitof planting,0.10. The 4:red-dotted contour farming.line refers5: cropto the rgtation,Sidak critical6: rowvalue plantingof 0.015.fF: improved fallowing.<br>CF = contact farmer; SLM = sustainable land management.<br><!-- End of picture text -->
Table 1: Contact farmers' characteristics by treatment status

|Dierence<br>in mean|-1.302|-0.097<br>-1.252*<br>-0.410|-1.066<br>0.011<br>-1.309<br>0.240<br>2.473<br>3.362<br>0.047<br>39.873<br>23.206<br>-0.446<br>0.242||
|---|---|---|---|---|
|N|50|47<br>22<br>49|50<br>50<br>46<br>50<br>50<br>50<br>50<br>42<br>32<br>42<br>42||
|SD|10.559|0.503<br>3.202<br>2.570|14.333<br>9.064<br>26.441<br>17.203<br>11.573<br>12.034<br>0.624<br>265.410<br>426.578<br>2.255<br>0.935|statistics.|
|Control<br>Mean|40.160|0.447<br>3.409<br>2.653|19.100<br>10.860<br>38.370<br>22.240<br>12.340<br>12.960<br>1.242<br>244.548<br>436.063<br>3.286<br>1.167|level for t|
|N|148|140<br>51<br>144|147<br>147<br>133<br>148<br>144<br>143<br>144<br>126<br>108<br>137<br>137|.<br>ritical|
|SD|9.348|0.479<br>2.239<br>2.401|16.095<br>9.659<br>28.320<br>15.145<br>12.726<br>12.498<br>0.655<br>267.037<br>430.130<br>2.362<br>1.210|vey, 2012<br> percent c|
|Treated<br>Mean|38.858|0.350<br>2.157<br>2.243|18.034<br>10.871<br>37.060<br>22.480<br>14.813<br>16.322<br>1.289<br>284.421<br>459.269<br>2.839<br>1.409|ousehold sur<br> 1, 5, and 10|
||||days<br> last 7 days<br>0 days<br> last 30 days<br>eek<br>unity<br> before 2010<br> before 2010|rvey, 2010; H<br>cance at the|
|||raining<br> as CF|assisted in last 7<br>mers assisted in <br> assisted in last 3<br>mers assisted in <br>F in last 7 days<br>ked as CF per w<br>ltivated land<br>lds in the comm<br> the community<br>alled<br>hniques learned <br>hniques adopted|rmer baseline su<br> * indicate signi<br>tact farmer.|
|||l t<br>ce|rs <br>ar<br>rs <br>ar<br> C<br>or<br> cu<br>ho<br>in <br>Rec<br>tec<br>tec|fa<br>nd <br>on|
||survey|trained<br>ce forma<br>experien|of farme<br>of male f<br>of farme<br>of male f<br>rked as <br>rmally w<br>tares of <br>of house<br>of plots <br>urvey: <br>of SLM <br>of SLM|Contact <br>*, **, a<br>viates c|
|able|line|ally <br>s sin<br>s of|ber <br>ber <br>ber <br>ber <br>s wo<br>s no<br>l hec<br>ber <br>ber <br>ine s<br>ber <br>ber|ces: <br>s: **<br>bbre|
|ri|se<br>e|rm<br>ar<br>ar|um<br>um<br>um<br>um<br>ur<br>ur<br>ta<br>um<br>um<br>idl<br>um<br>um|ur<br>te<br>F a|
|Va|Ba<br>Ag|Fo<br>Ye<br>Ye|N<br>N<br>N<br>N<br>Ho<br>Ho<br>To<br>N<br>N<br>M<br>N<br>N|So<br>No<br>C|

32
Table 2: Other farmers' characteristics by treatment status

|Variable|Treated||Control||Dierence|
|---|---|---|---|---|---|
||Mean|SD|Mean|SD|in mean|
|Midline survey: 2012||||||
|Is the head of household|0.585|0.493|0.588|0.493|-0.003|
|Male|0.420|0.493|0.414|0.493|0.005|
|Age|37.764|19.980|37.843|20.093|-0.079|
|Years of schooling completed|2.057|4.866|1.844|4.905|0.213|
|Single|0.063|0.504|0.058|0.509|0.005|
|Married|0.844|0.546|0.855|0.550|-0.011|
|Divorced, separated, or widowed|0.091|0.366|0.085|0.368|0.006|
|Number of children (ages < 15 years)|2.756|3.406|2.843|3.432|-0.087|
|Total hectares of owned land|2.004|3.995|1.880|4.033|0.124|
|Number of rooms in the house|1.427|2.116|1.444|2.138|-0.017|
|Housing walls made of brick|0.100|0.777|0.096|0.785|0.004|
|Housing roof made of tinplate|0.079|0.718|0.079|0.725|0.000|
|Midline survey: Recalled||||||
|Number of SLM techniques learned before 2010|1.236|4.514|1.303|4.563|-0.066|
|Number of SLM techniques adopted before 2010|0.509|2.024|0.554|2.045|-0.045|
|Number of observations|4,385||1,499||5,884|

Source: Household survey, 2012.

Notes: 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. SLM=sustainable land management.

33
Table 3: Socioeconomic and farming characteristics of contact farmers and other farmers

|||Means|Dierence in mean|
|---|---|---|---|
||CFs|Other farmers||
|Household Characteristics: In the curr|ent year|||
|Is the head of household|0.994|0.590|0.405***|
|Age|42.364|38.243|4.121***|
|Years of schooling completed|5.481|2.054|3.427***|
|Single|0.011|0.056|-0.044*|
|Married|0.974|0.849|0.126***|
|Divorced, separated, or widowed|0.057|0.095|-0.038|
|Number of children (ages < 15 years)|3.744|2.830|0.913***|
|Total hectares of owned land|3.439|2.171|1.269***|
|Number of rooms in the house|1.763|1.423|0.340**|
|Housing walls made of brick|0.168|0.099|0.068|
|Housing roof made of tinplate|0.207|0.079|0.128**|
|Production: In the current rainy seaso|n|||
|Grew maize|0.725|0.637|0.088|
|Grew sorghum|0.139|0.255|-0.116|
|Grew cotton|0.133|0.076|0.058|
|Grew sesame|0.270|0.156|0.113*|
|Grew cassava|0.058|0.157|-0.099|
|Grew cow pea|0.278|0.347|-0.069|
|Grew pigeon pea|0.191|0.200|-0.009|
|Farm characteristi<br>|cs: In th|e current rainy s|eason|
|Plot size (hectares)|1.215|1.047|0.167|
|Plot was at|0.727|0.616|0.111*|
|Plot was burnt|0.060|0.244|-0.184***|
|Used herbicides/pesticides/fungicides|0.133|0.042|0.091***|
|Used natural fertilizer|0.484|0.351|0.133|
|Used chemical fertilizer|0.099|0.006|0.092***|
|Number of observations|351|10,960|11,311|

Sources: Household survey, 2012, 2013. Notes: 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. CF abbreviates contact farmer.

 This variable is only available in Midline.

34
Table 4: Eect of a direct SLM training on contact farmers' adoption and knowledge

||Ctrl.Mean<br>[SD]|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|
|Panel A: CFs' Knowledge and Adoption, un|weighted||||
|Knowledge score|0.633<br>[0.173]|0.052<br>[0.055]|347|0.102|
|Number of techniques known by name|4.131<br>[1.626]|0.706<br>[0.546]|347|0.098|
|Number of techniques adopted on own plot|1.786<br>[1.309]|0.673**<br>[0.225]|347|0.224|
|Number of techniques adopted on any plot|3.738<br>[1.889]|0.733**<br>[0.250]|347|0.241|
|Panel B: CFs' Knowledge and Adoption, wei|ghted||||
|Knowledge score|0.646<br>[0.189]|0.059<br>[0.056]|347|0.138|
|Number of techniques known by name|0.687<br>[0.236]|0.079<br>[0.076]|347|0.071|
|Number of techniques adopted on own plot|0.314<br>[0.220]|0.113**<br>[0.031]|347|0.248|
|Number of techniques adopted on any plot|0.601<br>[0.265]|0.106**<br>[0.034]|347|0.226|

Sources: Contact farmer survey, 2010, 2012, 2013; Household survey 2012, 2013.

Notes: Regressions include the following variables: a constant, age, a completed primary school dummy, a single dummy, number of children, total landholdings, the number of rooms in the dwelling, baseline CF's number of years since formal training, a dummy for missing the baseline CF variable, district indicators, an incentive treatment dummy, and an endline dummy. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. CF=contact farmer; ITT=intent-to-treat eect.

35
Table 5: Eect of a direct SLM training on contact farmers' adoption of individual SLM techniques

|Adoption on<br>any plot|Ctrl. Mean|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|
|Mulching|0.929|0.026<br>[0.046]|347|0.040|
|Strip-tillage|0.548|0.159*<br>[0.072]|347|0.125|
|Pit planting|0.560|0.159***<br>[0.023]|347|0.093|
|Contour farming|0.226|0.147*<br>[0.067]|347|0.241|
|Crop rotation|0.726|0.066<br>[0.051]|347|0.166|
|Row planting|0.440|0.096<br>[0.058]|347|0.081|
|Improved fallowing|0.310|0.080<br>[0.070]|347|0.169|

Sources: Contact farmer survey, 2010, 2012, 2013; Household survey 2012,2013. Notes: Regressions include the same explanatory variables as models in Table 4. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. ITT=intent-to-treat; SLM=sustainable land management.

36
Table 6: Eect of a direct SLM training on contact farmers' knowledge of individual SLM techniques

|Knowledge<br>score|Ctrl. Mean|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|
|Mulching|0.893|0.043*<br>[0.017]|347|0.135|
|Strip-tillage|0.512|0.089<br>[0.057]|347|0.095|
|Pit planting|0.798|0.050<br>[0.088]|347|0.075|
|Contour farming|0.520|0.127<br>[0.116]|347|0.098|
|Crop rotation|0.567|0.008<br>[0.046]|347|0.072|
|Row planting|0.310|-0.026<br>[0.113]|347|0.149|
|Improved fallowing|0.690|0.007<br>[0.040]|347|0.046|

Sources: Contact farmer survey, 2010, 2012, 2013; Household survey 2012, 2013. Notes: Regressions include the same explanatory variables as models in Table 4. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. ITT=intent-to-treat; SLM=sustainable land management.

37
Table 7: Eect of a direct SLM training on contact farmers' maize production

|(7)<br>T+TÖ<br>Dry Year<br>.<br>.<br>908.249<br>[431.334]<br>537.443*<br>[237.038]<br>1930.829<br>[977.493]<br>1635.694*<br>[664.713]<br>ation data.<br>le of the 1981-2013<br> maize yield and<br> by a Wald test.|
|---|
|(6)<br>R2<br>0.089<br>0.147<br>0.121<br>0.152<br>0.097<br>0.157<br>0.124<br>0.156<br> Precipit<br>st quarti<br> Year on <br>termined<br>re.|
|(5)<br>N<br>347<br>347<br>347<br>347<br>347<br>347<br>347<br>347<br>1/2013<br> the r<br>4.<br>h Dry <br>t is de<br>=hecta|
|(4)<br>TÖ<br>Dry Year<br>866.412*<br>[354.536]<br>528.280<br>[257.942]<br>1185.839**<br>[359.641]<br>1370.718**<br>[453.943]<br> 1/2x 1/2198<br>y season is in <br>dels in Table <br>nteraction wit<br> additive eec<br>Metacais; Ha|
|(3)<br>Dry Year<br>178.182<br>[451.867]<br>133.582<br>[440.298]<br>19.641<br>[1014.573]<br>-213.874<br>[996.793]<br>2013; NASA <br>n in the rain<br>iables as mo<br>t T and its i<br>cance on the <br>Mozambican <br>ritical level.|
|(2)<br>T<br>326.399<br>[319.157]<br>182.322<br>[121.077]<br>1137.593<br>[721.392]<br>722.089<br>[438.057]<br>41.837<br>[399.865]<br>9.163<br>[152.561]<br>744.990<br>[632.045]<br>264.976<br>[310.816]<br>rvey 2012, <br>precipitatio<br>anatory var<br>he treatmen<br>ets. Signi<br>nt; MZN=<br>10 percent c|
|(1)<br>Control Mean<br>[SD]<br> controlling for precipitation<br>Original data<br>1441.540<br>[1417.638]<br>Winsorize at 1%<br>1441.540<br>[1417.638]<br>Original data<br>4408.667<br>[4687.363]<br>Winsorize at 1%<br>4408.667<br>[4687.363]<br>ling for precipitation<br>Original data<br>1441.540<br>[1417.638]<br>Winsorize at 1%<br>1441.540<br>[1417.638]<br>Original data<br>4408.667<br>[4687.363]<br>Winsorize at 1%<br>4408.667<br>[4687.363]<br> farmer survey, 2010; Household su<br>is a dummy indicating cumulative <br>. All models include the same expl<br>col 7) presents the total eect of t<br>ociated standard errors are in brack<br> SLM=sustainable land manageme<br>dicate signicance at the 1, 5, and|
|Panel A: Without<br>Revenue per Ha<br>[MZN/Ha]<br>Total revenue<br>[MZN]<br>Panel B: Control<br>Revenue per Ha<br>[MZN/Ha]<br>Total revenue<br>[MZN]<br>Sources: Contact <br>Notes: Dry Year <br>historical average<br>T+TÖDry Year (<br>revenue. The ass<br>Ö=multiplied by;<br>***, **, and * in|

38
Table 8: Eect of direct SLM training intervention on contact farmers' input use

||Ctrl.Mean<br>[SD]|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|
|Burnt farm plot|0.167|-0.015<br>[0.060]|347|0.037|
|Used natural fertilizer|0.524|0.129<br>[0.067]|343|0.166|
|Used chemical fertilizer|0.071|0.065<br>[0.042]|343|0.037|
|Amount of chemical fertilizer used (kg)|2.681<br>[21.992]|35.227<br>[19.514]|341|0.027|
|Amount of chemical fertilizer used (l)|3.855<br>[27.973]|1.282<br>[5.836]|341|0.033|
|Use herbicides/pesticides/fungicides|0.250|0.022<br>[0.032]|343|0.082|
|Amount of herbicides/pesticides/fungicides used (kg)|0.060<br>[0.361]|0.374<br>[0.223]|341|0.014|
|Amount of herbicides/pesticides/fungicides used (l)|4.367<br>[11.714]|-0.928<br>[1.582]|341|0.048|

Sources: Contact farmer survey, 2010; Household survey, 2012, 2013.

Notes: All models include the same explanatory variables as models in Table 4.

***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. ITT=intent-to-treat; SLM=sustainable land management.

The main sample has 347 observations. Sample size varies across models due to missing values in the dependent variables.

39
Table 9: Eect of a direct SLM training intervention on contact farmers' labor allocation

||Pooled Sam|ple|||Endline||||
|---|---|---|---|---|---|---|---|---|
||Control<br>mean [SD]|ITT|N|R<sup>2</sup>|Control<br>mean [SD]|ITT|N|R<sup>2</sup>|
|Hours spent on|6.095|-2.822|346|0.032|6.429|-1.272|168|0.073|
|preparation of land|[14.550]|[3.356]|||[15.353]|[2.998]|||
|Hours spent on seeding|8.214|-3.558**|346|0.021|10.357|-6.567*|168|0.065|
||[15.996]|[1.069]|||[17.862]|[2.943]|||
|Hours spent on|2.607|-1.408|346|0.062|1.738|-0.917|168|0.087|
|transplantation|[7.973]|[0.957]|||[6.666]|[0.736]|||
|Hours spent on irrigation|0.000|-0.038|346|0.028|||||
||[0.000]|[0.044]|||||||
|Hours spent on sacha|10.583|0.454|346|0.198|5.833|-0.690|168|0.072|
||[15.576]|[1.098]|||[14.252]|[1.514]|||
|Hours spent on protection|0.000|0.969|346|0.042|0.000|0.513|168|0.122|
||[0.000]|[0.922]|||[0.000]|[0.499]|||
|Hours spent on harvesting|11.012|-2.288|346|0.122|15.810|-2.234|168|0.114|
||[18.260]|[1.417]|||[19.573]|[1.164]|||
|Total weeks spent on|28.262|-2.986|346|0.029|30.381|-7.084**|168|0.069|
|farming in last year|[18.386]|[2.210]|||[19.196]|[2.508]|||

Sources: Contact farmer survey, 2010; Household survey, 2012, 2013. Notes: All models include the same explanatory variables as models in Table 4. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. ITT=intent-to-treat eect; SLM=sustainable land management.

40
Table 10: Eect of a direct SLM training intervention on other farmers' access to contact farmers, adoption, and knowledge

||Ctrl.Mean<br>[SD]|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|
|Access to CF|||||
|Has access to any contact farmer in the last half year|0.170|0.032<br>[0.027]|10,955|0.046|
|Other Farmer Knowledge and Adoption, unweighted|||||
|Knowledge score|0.341<br>[0.200]|-0.004<br>[0.012]|10,955|0.055|
|Number of techniques known by name|1.654<br>[1.538]|0.000<br>[0.120]|10,955|0.022|
|Number of techniques adopted|0.845<br>[0.891]|-0.034<br>[0.071]|10,955|0.060|

Sources: Contact farmer survey, 2010; Household survey, 2012, 2013.

Notes: Regressions include the following variables: a constant, age, a completed primary school dummy, a dummy for male, a single dummy, a widow dummy, number of children, total landholdings, the number of rooms in the dwelling, baseline CF's number of years since formal training, a dummy for missing the baseline CF variable, district indicators, an incentive treatment dummy, and an endline dummy.

***, **, and * indicate signicance at the 1, 5, and 10 percent critical level.

CF=contact farmer; ITT=intent-to-treat eect; SLM=sustainable land management.

41
Table 11: Eect of a direct SLM training intervention on other farmers' adoption of pit planting, by CFs' characteristics

|CF characteristics|Educ > Median|Age _≥_Median|Land _≥_Median|Same Crop|
|---|---|---|---|---|
|T|0.029|0.035*|0.018|0.025|
||[0.021]|[0.020]|[0.019]|[0.019]|
|CF characteristics|0.011|-0.010|-0.014|0.003|
||[0.020]|[0.016]|[0.016]|[0.023]|
|TÖCF characteristics|0.000|-0.011|0.025|0.026|
||[0.025]|[0.023]|[0.022]|[0.027]|
|N|9,836|9,836|9,836|9,968|
|R<sup>2</sup>|0.010|0.011|0.010|0.010|
|Control mean|0.068|0.068|0.068|0.069|
|T+TÖCF characteristics|0.029|0.023|0.044*|0.052*|
||[0.025]|[0.023]|[0.024]|[0.030]|

Sources: Contact farmer survey, 2010; Household Survey, 2012, 2013. Notes: Regressions include the same explanatory variables as models in Table 10. The cuto values for CF characteristics correspond to the median values of education (7 years), age (41 years at ML, 43 years at EL), and landholdings (2.75 ha at ML, 3.5 at EL) in the sample of CFs. T+TÖCF characteristics (bottom row) presents the total eect of the treatment T and its interaction with the CF characteristic. The associated standard errors are in brackets. Signicance on the additive eect is determined by a Wald test.

***, **, and * indicate signicance at 1, 5, and 10 percent critical level. Ö=multiplied by; CF=contact farmer; SLM=sustainable land management. ML=midline; EL=endline.

42
Table 12: Cost-Benet analysis of a direct SLM training intervention

|Yield and Labor<br>Benets|1|22,262|11,131<br>74<br>505<br>19<br>19<br>7<br>131|76|te of 38 Metacais per 1 USD.|USD) and assumes one extension agent services ve communities.|ication in Table 7.|weeks worked in Table 9.|
|---|---|---|---|---|---|---|---|---|
|Labor Benets|1|22,262|11,131<br>74<br>505<br>0<br>19<br>7<br>131|57|ssuming exchange ra|xtension agent (211|the winsorized spec|T on the number of|
|Yield Benets|1|22,262|11,131<br>74<br>505<br>19<br>19<br>0<br>0|-55|erms of 2012 USD, a|onthly salary of the e|timates of the ITT in|estimates of the IT|
||r of Beneciaries per community|Costs per Beneciary<br>st of trainings|cost of training<br> training cost per farmer<br> cost of extension agent per farmer<br> Benets per Beneciary<br> maize revenue<br> agricultural wage rate<br> of weeks in labor savings<br> labor earnings|erage Benets per Beneciary<br>t benets per CF|F=Contact farmer. Figures presented in t|ost per extension agent is based on the m|enets in maize revenue obtained from es|enets in endline labor savings taken from|
||Numbe<br>CFs|Average<br>Total co|Annual <br>Annual <br>Annual <br>Average<br>Annual <br>Weekly <br>Number<br>Annual|Net Av<br>Total ne|Notes: C|Annual c|Annual b|Annual b|

43
# Appendix A: Additional Tables

Table A.1: Pre-intervention SLM training across treatment status (recalled)

|Variables|Treated mean|Control mean|Dierence in mean|
|---|---|---|---|
|Contact Farmers: before 2010||||
|Learned mulching|0.620|0.762|-0.141*|
|Learned strip-tillage|0.321|0.429|-0.107|
|Learned pit planting|0.504|0.524|-0.020|
|Learned contour farming|0.307|0.381|-0.074|
|Learned crop rotation|0.591|0.690|-0.099|
|Learned row planting|0.285|0.238|0.047|
|Learned improved fallowing|0.212|0.262|-0.050|
|Number of observations|137|42|179|
|Other Farmers: before 2010||||
|Learned mulching|0.306|0.337|-0.031|
|Learned strip-tillage|0.182|0.227|-0.045|
|Learned pit planting|0.145|0.113|0.032|
|Learned contour farming|0.039|0.048|-0.009|
|Learned crop rotation|0.360|0.360|0.000|
|Learned row planting|0.104|0.114|-0.010|
|Learned improved fallowing|0.101|0.104|-0.003|
|Number of observations|4,385|1,499|5,884|

Source: Household survey, 2012. Notes: 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. SLM=sustainable land management.

44
Table A.2: Extension agents' characteristics by treatment status

|ierence|n mean|||||||||163|more|for that post.||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|D|i|0.489<br>-0.071|1.033|0.038|-0.021|0.055|-0.064|-0.032|-0.030||there is|sponses||
||SD|4.962<br>0.601|4.329|2.994|5.563|0.460|0.393|0.601|0.162||t. Since|of EA re||
|Control|Mean|34.925<br>7.263|5.355|4.412|9.645|0.289|0.816|1.276|0.974|38|rative pos|n) value||
||SD|4.646<br>0.534|5.919|2.893|5.265|0.477|0.434|0.601|0.231||dminist|um (mea||
|ated|an|415<br>192|388|451|624|344|752|244|944|5|eir a|xim||
|Tre|Me|35.<br>7.|6.|4.|9.|0.|0.|1.|ulture<br>0.|12|servicing th|ect the ma|level.|
|||||an EA|||||on agric||llegedly|comes re|critical|
|||||became|||||servati||EAs a|us) out|percent|
|||||al section, before|past 5 years|f Agriculture|project|he last 12 months|trainings was con||.<br>n each village the|binary (continuo|the 1, 5, and 10|
|||||r||o|'|t|e||12<br>ig|,|t|
|||eted|EA|gricultu|over the|Ministry|lholders|during|ed in th||rvey, 20<br> We ass|tive post|cance a|
|||pl|s|n a|ed|e|mal|ing|ver||su<br>nt.|tra|ni|
|||m|a|i|v|h||n|o||t <br>e|s|ig|
|||g co|rked|rked|recei|om t|om S|trai|ics c||agen<br>n ag|mini|te s|
|||n|o|o||r|r|n|p||<br>io|d|ca|
|||oli|w|w|ing|g f|g f|s i|to|es|on<br>ns|r a|di|
|||of scho|f years|f years|f train|trainin|trainin|f week|e main|f villag|Extensi<br>A=exte|EA pe|nd * in|
|bles||ge<br>ears|ber o|ber o|ber o|ved|ved|ber o|f th|ber o|es: <br>: E|one|*, a|
|Varia||EA a<br>EA y|Num|Num|Num|Recei|Recei|Num|One o|Num|Sourc<br>Notes|than|***, *|

45
Table A.3: Eect of a direct SLM training intervention on contact farmers' use of demonstration plots and access to extension agents

||Ctrl.|Mean|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|---|
|Used demonstration plot during the last year|0.845||-0.034<br>[0.066]|347|0.043|
|EA visited CF at least once/month|0.512||0.001<br>[0.073]|347|0.033|
|EA visited CF at least once/half year|0.631||0.055<br>[0.123]|347|0.047|
|EA visited CF at least once/year|0.667||0.143<br>[0.123]|347|0.048|

Sources: Contact farmer survey, 2010, 2012, 2013; Household survey, 2012, 2013. Notes: Regressions include the following variables: a constant, age, a completed primary school dummy, a single dummy, number of children, total landholdings, the number of rooms in the dwelling, baseline CF's number of years since formal training, a dummy for missing the baseline CF variable, district indicators, an incentive treatment dummy, and an endline dummy. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. CF=contact farmer; EA=extension agent; ITT=intent-to-treat eect.

46
Table A.4: Other farmers' characteristics

|Variables|Mean|SD|
|---|---|---|
|Is the head of household|0.590|0.492|
|Age|38.243|14.430|
|Years of schooling completed|2.054|2.798|
|Single|0.056|0.229|
|Married|0.849|0.359|
|Divorced, separated, or widowed|0.095|0.293|
|Number of children [ages < 15 years]|2.830|2.041|
|Total hectares of owned land|2.171|2.064|
|Number of rooms in the house|1.423|0.724|
|Number of observations|10,960||

Sources: Household survey, 2012, 2013.

47
Table A.5: Pre-intervention SLM adoption by treatment status (recalled)

|Variables|Treated mean|Control mean|Dierence in mean|
|---|---|---|---|
|Contact farmers: before 2010||||
|Adopted mulching|0.489|0.405|0.084|
|Adopted strip-tillage|0.248|0.214|0.034|
|Adopted pit planting|0.190|0.167|0.023|
|Adopted contour farming|0.007|0.000|0.007|
|Adopted crop rotation|0.314|0.262|0.052|
|Adopted row planting|0.124|0.095|0.029|
|Adopted improved fallowing|0.036|0.024|0.013|
|Number of observations|137|42|179|
|Other Farmers: before 2010||||
|Adopted mulching|0.181|0.203|-0.022|
|Adopted strip-tillage|0.087|0.118|-0.031|
|Adopted pit planting|0.059|0.036|0.023|
|Adopted contour farming|0.002|0.000|0.002|
|Adopted crop rotation|0.121|0.132|-0.011|
|Adopted row planting|0.055|0.059|-0.005|
|Adopted improved fallowing|0.005|0.005|0.000|
|Number of observations|4,385|1,499|5,884|

Source: Household survey, 2012. Notes: 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. SLM=sustainable land management.

48
Table A.6: Eect of a direct SLM training intervention on contact farmers' knowledge and adoption, basic specication

|Ctrl.Mean<br>[SD]|ITT|N|R<sup>2</sup>|
|---|---|---|---|
|CFs' Knowledge and Adoption, unweighted||||
|Knowledge score<br>0.633<br>[0.173]|0.046<br>[0.049]|347|0.019|
|Number of techniques known by name<br>4.131<br>[1.626]|0.646<br>[0.571]|347|0.018|
|Number of techniques adopted on own plot<br>1.786<br>[1.309]|0.594*<br>[0.232]|347|0.023|
|Number of techniques adopted on any plot<br>3.738<br>[1.889]|0.752**<br>[0.244]|347|0.020|
|Sources: Contact farmer survey, 2010, 2012, 2013; Hous|ehold surve|y, 20|12, 2013.|
|Notes: Regressions include the following variables: a cons<br>an endline dummy, and district indicators.|tant, trea|tment|variables,|
|***, **, and * indicate signicance at the 1, 5, and 10 pe<br>CF=contact farmer; ITT=intent-to-treat eect; SLM=su|rcent criti<br>stainable l|cal lev<br>and m|el.<br>anagement.|

49
Table A.7: Eect of a direct SLM training intervention on other farmers' knowledge and adoption, basic specication

||Ctrl.Mean<br>[SD]|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|
|Other Farmers' Knowledge and Adoption, unweighted|||||
|Knowledge score|0.341<br>[0.200]|-0.003<br>[0.012]|10,955|0.049|
|Number of techniques known by name|1.654<br>[1.538]|0.013<br>[0.119]|10,955|0.012|
|Number of techniques adopted|0.845<br>[0.891]|-0.020<br>[0.071]|10,955|0.049|

Sources: Contact farmer survey, 2010; Household survey, 2012, 2013.

Notes: Regressions include the following variables: a constant, treatment variables, a male dummy, an endline dummy, and district indicators.

***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. ITT=intent-to-treat eect; SLM=sustainable land management.

50
Table A.8: Eect of a direct SLM training intervention on contact farmers' adoption and knowledge, includes administrative post xed eects

|Ctrl.Mean<br>[SD]|ITT|N|R<sup>2</sup>|
|---|---|---|---|
|CFs' Knowledge and Adoption, unweighted||||
|Knowledge score<br>0.633<br>[0.173]|0.046<br>[0.036]|347|0.103|
|Number of techniques known by name<br>4.131<br>[1.626]|0.859**<br>[0.367]|347|0.105|
|Number of techniques adopted on own plot<br>1.786<br>[1.309]|0.726***<br>[0.216]|347|0.256|
|Number of techniques adopted on any plot<br>3.738<br>[1.889]|0.658**<br>[0.258]|347|0.244|
|Sources: Contact farmer survey, 2010, 2012, 2013; Hous|ehold survey,|2012|, 2013.|
|Notes: Regressions include the same explanatory variabl<br>except replacing district indicators with administrative p<br>***, **, and * indicate signicance at the 1, 5, and 10 p|es as models <br>ost indicator<br>ercent critical|in Ta<br>s.<br> level.|ble 4,<br>|
|CF=contact farmer; ITT=intent-to-treat; SLM=sustaina|ble land man|agem|ent.|

51
Table A.9: Eect of a direct SLM training intervention on other farmers' access to contact farmers, adoption, and knowledge, includes administrative post xed eects

||Ctrl.Mean<br>[SD]|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|
|Other Farmers' Knowledge and Adoption, unweighted|||||
|Knowledge score|0.341<br>[0.200]|-0.007<br>[0.012]|10,955|0.057|
|Number of techniques known by name|1.654<br>[1.538]|-0.001<br>[0.116]|10,955|0.020|
|Number of techniques adopted|0.845<br>[0.891]|0.003<br>[0.066]|10,955|0.053|

Sources: Contact farmer survey, 2010; Household survey, 2012, 2013.

Notes: Regressions include the same explanatory variables as models in Table 10, except replacing district indicators with administrative post indicators.

***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. CF=contact farmer; ITT=intent-to-treat; SLM=sustainable land management.

52
Table A.10: Attrition of contact farmers and other farmers

|Variables|Treate|d|Contro|l|Dierence|
|---|---|---|---|---|---|
||Mean|SD|Mean|SD|in mean|
|CFs attrited from Midline|0.109|0.313|0.048|0.216|0.062|
|Number of Observations|137||42||179|
|Household attrited from Midline|0.090|0.372|0.087|0.374|0.003|
|Number of Observations|2750||935||3685|

Sources: Household survey, 2012, 2013; Contact farmer survey, 2012, 2013. Notes: CF=contact farmer. 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.

53
Table A.11: Determinants of attrition (contact farmers and other farmers)

||CFs||Other farming HH|
|---|---|---|---|
|Treatment 1|0.053|Treatment 1|0.014|
||[0.066]||[0.013]|
|Treatment 3|0.002<br>[0.055]|Treatment 3|-0.013<br>[0.012]|
|Age|-0.006**<br>[0.003]|Age|-0.001**<br>[0.000]|
|Completed at least|0.056|HH head completed at|-0.004|
|primary school|[0.060]|least primary school|[0.012]|
|Single|-0.091<br>[0.190]|HH head Single|0.023<br>[0.020]|
|||HH head divorced,|0.045***|
|||widow, or separated|[0.017]|
|Total number|-0.011|Total number|-0.007**|
|of children|[0.013]|of children|[0.003]|
|Total landholding|0.005|Total landholding|-0.005|
|[hectares]|[0.011]|[hectares]|[0.003]|
|Total number of rooms|-0.037<br>[0.032]|Total number of rooms|-0.003<br>[0.008]|
|Number of years|-0.034*|Number of years|0.004|
|since formal training|[0.020]|since formal training|[0.003]|
|Missing above|-0.145*|Missing above|0.001|
|variable|[0.076]|variable|[0.015]|
|Household head|-0.035|Household head|-0.002|
|was female|[0.088]|was female|[0.013]|
|% of household|-0.436|% of household|0.171***|
|members was away|[0.350]|members was away|[0.065]|
|HH has non-own|0.023|HH has non-own|-0.013|
|farming work|[0.078]|farming work|[0.012]|
|HH has outside|-0.015|HH has outside|0.003|
|employment|[0.073]|employment|[0.017]|
|2012 precipitation|-0.001|2012 precipitation|-0.001*|
|shock|[0.002]|shock|[0.000]|
|Constant|0.352|Constant|0.043|
||[0.308]||[0.058]|
|N|178|N|3662|
|R2|0.099|R2|0.014|

Sources: Contact farmer survey, 2010, 2012, 2013; Household survey, 2012, 2013. Notes: Regressions include district xed eect. CF=contact farmer; HH=household. Household attrition measured by whether a household surveyed in 2012 could not be interviewed in 2013. The CF attrition outcome reects whether the village had at least one CF interviewed in 2012 but not in 2013.

***, **, and * indicate signicance at the 1, 5, and 10 percent critical level.

54
Table A.12: Eects of contact farmers' adoption of SLM practices on maize revenue

||Total Maize Revenue|
|---|---|
|Adopted mulching on own plot|1458.547<br>[1032.677]|
|Adopted strip tillage on own plot|723.044<br>[572.914]|
|Adopted pit planting on own plot|436.835<br>[1502.123]|
|Adopted contour farming on own plot|1078.387<br>[3145.182]|
|Adopted crop rotation on own plot|957.972<br>[960.541]|
|Adopted row planting on own plot|-1206.388<br>[1472.065]|
|Adopted improved fallowing on own plot|-1871.335<br>[968.155]|
|N<br>|342|
|R<sup>2</sup>|0.187|

Sources: Contact farmer survey, 2010, 2012, 2013; Household survey, 2012, 2013. Notes: Regressions include the same explanatory variables as models in Table 4. Additional controls include dummies for usage of all inputs displayed in Table 8, as well as labor allocated to maize production as displayed in Table 9. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level.

55
Table A.13: Eect of a direct SLM training intervention on contact farmers' adoption of individual SLM techniques, controlling for lagged rainfall

|Adoption|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|any plot|Control<br>Mean|T|Dry Year|TÖ<br>Dry Year|N|R<sup>2</sup>|T+TÖ<br>Dry Year|
|Mulching|0.929|0.045<br>[0.059]|0.034<br>[0.077]|-0.058<br>[0.060]|347|0.042|-0.013<br>[0.035]|
|Strip-tillage|0.548|0.174<br>[0.099]|0.101<br>[0.129]|-0.050<br>[0.119]|347|0.127|0.124<br>[0.072]|
|Pit planting|0.560|0.161***<br>[0.026]|0.125<br>[0.104]|-0.017<br>[0.059]|347|0.097|0.144**<br>[0.046]|
|Contour farming|0.226|0.146<br>[0.097]|0.052<br>[0.086]|-0.002<br>[0.099]|347|0.242|0.145**<br>[0.043]|
|Crop rotation|0.726|0.059|-0.054|0.025|347|0.166|0.084|
|||[0.047]|[0.164]|[0.098]|||[0.100]|
|Row planting|0.440|0.040|0.198|0.137|347|0.117|0.178**|
|||[0.089]|[0.111]|[0.132]|||[0.050]|
|Improved fallowing|0.310|0.101<br>[0.065]|0.166***<br>[0.035]|-0.074<br>[0.038]|347|0.175|0.027<br>[0.078]|

Sources: Contact farmer survey, 2010, 2012, 2013; Household survey, 2012, 2013. Notes: Dry year indicates that the precipitation amount falls in the rst quartile of the historical distribution of cumulative rainfall during the rainy season (1981-2013). Regression model includes the same controls as in Table 4. T+TÖDry Year (col 7) presents the total eect of the treatment T and its interaction with Dry Year on adoption. The associated standard errors are in brackets. Signicance on the additive eect is determined by a Wald test.

***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. ITT=intent-to-treat; SLM=sustainable land management.

56
Table A.14: Eect of a direct SLM training intervention on other farmers' SLM adoption

|Adoption|Ctrl. Mean|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|
|Mulching|0.336|-0.022<br>[0.033]|10,955|0.056|
|Strip-tillage|0.181|-0.035<br>[0.028]|10,955|0.031|
|Pit planting|0.071|0.028<br>[0.018]|10,955|0.016|
|Contour farming|0.005|-0.003<br>[0.003]|10,955|0.006|
|Crop rotation|0.153|0.017<br>[0.018]|10,955|0.005|
|Row planting|0.079|-0.012<br>[0.015]|10,955|0.018|
|Improved fallowing|0.020|-0.007<br>[0.006]|10,955|0.003|

Sources: Contact farmer survey, 2010; Household survey, 2012, 2013. Notes: Regressions include the same explanatory variables as models in Table 10. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. ITT=intent-to-treat; SLM=sustainable land management.

57