metadata: authors: 0: author: Corral,Carolina
1: author: Gine,Xavier
2: author: Mahajan,Aprajit
3: author: Seira,Enrique
count: Mexico
docna: 0: docna: Autonomy and Specificity in Agricultural
            Technology Adoption : Evidence from Mexico
docty: Policy Research Working Paper
subtopic: Fertilizers,Climate Change and Agriculture,Crops and Crop Management Systems,Agricultural Extension,Economic Assistance,Services & Transfers to Poor,Disability,Access of Poor to Social Services,Nonmetallic Mineral Product Manufacturing
teratopic: Agriculture,Macroeconomics and Economic Growth,Social Protections and Labor,Poverty Reduction,Industry
abstracts: cdata!: This paper explores heterogeneity in
            soil quality, lack of knowledge, and autonomy as
            explanations for the low adoption of improved agricultural
            practices. The paper uses data from a randomized field
            experiment that combined localized soil analyses, tailored
            input recommendations, extension services, and an in-kind
            grant. The analysis finds that while neither the degree of
            recommendation specificity (plot versus cluster level) nor
            the extent of autonomy (defined as the freedom of choice in
            spending the in-kind grant) had any effect on adoption
            during the intervention, farmers with autonomy had
            substantially higher adoption of improved practices two
            years after the intervention ended.
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repnme: repnme: Autonomy and Specificity in Agricultural
            Technology Adoption : Evidence from Mexico
display_title: Autonomy and Specificity in
            Agricultural Technology Adoption : Evidence from Mexico
listing_relative_url: /research/2020/08/32352029/autonomy-specificity-agricultural-technology-adoption-evidence-mexico
topicv3: Agriculture Extension m1325967 1009,Agriculture Technology m1326423 1950,Nonmetallic Mineral Mining and Quarrying
            m1326877 1700,Fertilizers m1327354 175,Cash Transfers m1327437 1608
publishtoextweb_dt: 2020-08-25T00:00:00Z
historic_topic: Agriculture,Macroeconomics and Economic Growth,Social Protections and Labor,Poverty Reduction,Industry
origu: DECRG: Finance & Priv Sec Devt (DECFP)
no_of_pages: 73
author_keys: 0: author_key: 1234470
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model_extractions: Policy Research Working Paper 9367 Autonomy and Specificity in Agricultural Technology Adoption

# Evidence from Mexico

_Carolina Corral Xavier Giné Aprajit Mahajan Enrique Seira_ Development Economics Development Research Group August 2020
Policy Research Working Paper 9367

# **Abstract**

This paper explores heterogeneity in soil quality, lack of knowledge, and autonomy as explanations for the low adoption of improved agricultural practices. The paper uses data from a randomized field experiment that combined localized soil analyses, tailored input recommendations, extension services, and an in-kind grant. The analysis finds that while neither the degree of recommendation specificity (plot versus cluster level) nor the extent of autonomy (defined as the freedom of choice in spending the in-kind grant) had any effect on adoption during the intervention, farmers with autonomy had substantially higher adoption of improved practices two years after the intervention ended.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/prwp. The authors may be contacted at xgine@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
# Autonomy and Specificity in Agricultural Technology Adoption: Evidence from Mexico<sup>_∗_</sup>

Carolina Corral<sup>1</sup> , Xavier Giné<sup>2</sup> , Aprajit Mahajan<sup>3</sup> , and Enrique Seira<sup>4</sup>

> 1Feed the Future

> 2World Bank

> 3UC Berkeley 4ITAM

> _∗_ We are grateful to Qué Funciona para el Desarrollo (QFD) for managing the entire project, to Fertilab for the soil analyses, to Agropecuaria Amozoc for providing and mixing fertilizers and to Ipampa S.C. for the provision of agricultural extension services. We would also like to thank Pranab Bardhan, Chico Ferreira, Andy Foster, and Berk Özler for very helpful comments and suggestions. We also thank seminar participants at various workshops for their comments and suggestions. Michelle Infanzón, Victor Manuel Pérez, Bernardo Ribeiro, Mónica Vargas and Victor Vergara provided outstanding research assistance. We also gratefully acknowledge funding from 3ie, BASIS (UC Davis), the World Bank and ISPC-SPIA (under the grant “Strengthening Impact Assessment in the CGIAR System (SIAC)"). Views expressed in this paper are those of the authors, and do not necessarily reflect the opinions of the World Bank, its executive directors, or the countries they represent.This project was reviewed and approved by the ITAM IRB and UCLA IRB # 12–001756. This study is registered in the AEA RCT Registry with the unique identifying number AEARCTR-0006264.
# **1 Introduction**

Policies that increase agricultural productivity arguably have a disproportionate effect on poverty reduction (World Bank, 2008). The Green Revolution introduced high-yielding crop varieties, chemical fertilizer, pesticides and other modern agricultural practices to developing countries. However, even decades after their introduction, the take up of these improved inputs and practices has been uneven (Foster and Rosenzweig, 2010). Traditional farming practices remain widespread and yields for smallholder farmers in the developing world remain low.<sup>1</sup> A variety of explanations have been proposed for the observed low rates of technology adoption in the developing world, including limited information on best practices, credit constraints, risk, poor input quality, and behavioral biases.<sup>2</sup> An alternative view is that heterogeneity in returns reflects heterogeneity in a fixed factor (or infrastructure) thus explaining the observed variation in adoption rates (e.g. Suri, 2011).

In this we test several on the role of information while paper hypotheses explicitly recognizing the importance of heterogeneity in soil quality.<sup>3</sup> We test our hypotheses in an accretive manner: first, using our sample of small rainfed farms in Tlaxcala, Mexico, we confirm that heterogeneity in soil quality leads to heterogeneity in the recommended mix of fertilizers. Next, we test whether farmers adopt these tailored recommendations (based on the soil analyses) and whether productivity improves as a result. We then test whether the level of information specificity, that is, whether recommendations are based on the farmer’s own plot or on a larger geographical cluster, matters for adoption and persistence of improved agricultural practices. This is important because evidence suggests that while individually tailored information may be more effective, it is also more expensive than recommendations based on aggregated information, thus introducing a trade-off.<sup>4</sup> We also offer farmers one of two types of in-kind grant to purchase inputs: inflexible (i.e. subsidizing only the recommended inputs) or flexible (i.e. giving farmers the choice of which inputs to purchase). The difference between the two grants allows us to examine the effect of autonomy, defined here as the ability to choose the inputs purchased with the grant. While the broader notion of autonomy and its intrinsic value has received some attention in the literature,

> 1In Mexico for instance, maize yields for rainfed farmers are only about 2–3 metric tons per hectare (mt/ha). By comparison, rainfed maize yields in the United States are approximately 8 mt/ha (Sweeney et al. 2013; Fernández et al. 2012).

> 2On information, see Conley and Udry (2010); Ashraf et al. (2009); on liquidity and credit see e.g. Karlan et al. (2014); on risk see Karlan et al. (2014); Emerick et al. (2016); Giné et al. (2017); on behavioral biases see Duflo et al. (2011); on input quality see Bold et al. (2017). For a recent overview of the evidence see e.g. Magruder (2018) and Macours (2019).

> 3 See Jayne and Rashid, 2013; Goyal and Nash, 2017. Munshi (2004) and Otsuka and Larson (2013), for example, argue that unobserved farm characteristics such as soil quality are important for technology adoption.

> 4See e.g. Madajewicz et al. (2007) on the efficacy of providing localized information in the context of arsenic poisoning in Bangladesh.

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we focus here on its potential for increasing adherence to the recommendations both during and after the intervention.<sup>5</sup> The experiment consists of a control group and four treatment arms that combine soil analysis and recommendations (either at the plot or cluster level) with flexible or inflexible in-kind grants.<sup>6</sup> Due to budgetary constraints, we did not implement a full factorial design. Rather, farmers in all treatment arms were offered a soil analysis report, a set of input recommendations and a package of agricultural extension services designed to help them implement the recommendations. In addition, three of the four treatment arms were offered an in-kind grant of 2,000 pesos (roughly $150 at the time of the intervention).

In particular, arm _T_ 1 received _individualized or plot level_ soil analyses with input recommendations and an _inflexible_ in-kind grant. Arm _T_ 2 was the same as _T_ 1 except that the soil analysis and recommendations were _averaged_ (so that recommendations were identical for all farmers in the cluster). Comparing _T_ 1 with _T_ 2 allows us to estimate the effect of varying the level of the specificity of the soil analysis and recommendations (conditional on receipt of an inflexible grant). Arm _T_ 3 differed from _T_ 2 in that the in-kind grant was _flexible_ and so farmers in _T_ 3 could use the in-kind grant to purchase any inputs of their choosing sold by a local agro-dealer rather than just the recommended ones. A comparison between _T_ 2 and _T_ 3 thus measures the effect of autonomy as defined above. The conditions for arm _T_ 4 are the same as those for _T_ 2 (or _T_ 3) except that no grant was provided. Comparisons between _T_ 4 and _T_ 2 (or _T_ 3) measure the effect of providing the in-kind grant (conditional on receipt of localized information and recommendations). Finally, a control group of farmers _C_ did not receive any interventions during the experiment, but instead received soil analyses and recommendations – but no extension services – the year after the intervention ended. Comparisons between _C_ and _T_ 4 estimate the effect of providing localized soil analyses, recommendations and extension services. In later years, after the intervention had ended and control farmers had also received soil analyses and recommendations, comparing _C_ and _T_ 4 provides an estimate of the effect of agricultural extension services paired with tailored analyses and recommendations (but with that qualification that the control group received the soil analysis and recommendations one year after _T_ 4).

We first document substantial heterogeneity in soil quality, mostly within (rather than between) clusters.<sup>7</sup> This heterogeneity implies a corresponding variation in the optimal mix of fertilizers. While most farmers used chemical fertilizer prior to the intervention, their input mix was substantially different from the recommended mix: farmers used on average 77%

> 5On the intrinsic importance of autonomy see e.g. Sen (1999); Bartling et al. (2014); Ferreira et al. (2020). We discuss the instrumental value of autonomy further below.

> 6A cluster corresponds to a neighboring INEGI _localidad_ . See footnote 16 in Section 2 for more details. In Mexico there are almost 2,500 municipalities and close to 200,000 INEGI localidades.

> 7Under standard ANOVA assumptions, the “between” cluster component of the total variation is 41% for sand, 31% for Clay, 38% for Silt, 11% for Nitrogen, 21% for Phosphorus, 20% for Potassium and 36% for the soil pH level. See Appendix Table A1 for details.

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more urea than the recommended amount and about 61% more diammonium phosphate (DAP) while using only about 28% of the recommended amount of Potassium Chloride (KCl).

We next examine adoption, our main outcome of interest, using a standardized index of “new” agricultural practices introduced by the intervention.<sup>8</sup> By this metric, farmers who only received the recommendations and extension services ( _T_ 4) adopted 0.33 more practices (measured in standard deviations or _s_ . _d_ .) relative to control farmers (who have an adoption index of zero, by construction). Farmers that received the in-kind grant adopted considerably more practices (ranging from 1.68 to 1.96 _s_ . _d_ . depending on the arm) underscoring the importance of the in-kind grant. Since its value was designed to be roughly equal to the amount spent on fertilizer by control farmers, we view the grant as primarily encouraging experimentation rather than relaxing liquidity constraints.<sup>9</sup> Surprisingly, _T_ 3 farmers, who had the option to entirely ignore the recommendations at no cost, adopted these new practices at the same rate as _T_ 2 farmers who were forced to either adopt the recommendations or forego the grant. Finally, contrary to the work cited above, we find no evidence in favor of specificity since _T_ 1 farmers did not increase adoption of new practices relative to farmers in _T_ 2.<sup>10</sup> We also examine the impact of the treatments on productivity and perhaps unsurprisingly results similar to those on and for farmers that re- find adoption. Average yields profits only ceived the soil analysis, recommendations and extension services (in _T_ 4) are not statistically different from those in the control group. In contrast, and similar to the impact on adoption, we find substantial effects of providing the in-kind grant. Despite a drought, yields for farmers that received a grant were 0.2–0.4 mt/ha higher relative to those for control farmers, corresponding to an increase of approximately 12%–17%. The increase in profits is more muted and not statistically significant at conventional levels. We also find that neither specificity nor autonomy affected yields or profits. In the short run then (i.e. during the intervention) there appears to be no significant downside to providing farmers with autonomy.

Finally, we examine the persistence of the recommended practices in the 2017 growing season (the second growing season after the intervention ended). Farmers who had only received

> 8We classify six practices as “new” since they were uncommon at baseline and recommended by the extension workers. Details of the index construction are in Table 6 and Appendix Table A3. See Bloom et al. (2013) for a similar classification of practices in the context of a management intervention.

> 9 The grant could also be viewed as an implicit endorsement of the recommendations or as a means for the research team to differentiate itself from cheap talk. We do not attempt to distinguish between these alternative explanations.

> 10As we discuss in greater detail below, _T_ 1 farmers had somewhat lower adoption rates than _T_ 2 farmers. Confounding the comparison, however, is the fact that plot-level recommendations were on average more expensive and thus required a larger out-of-pocket expense than cluster level ones. To see why this might be the case, consider a cluster with 2 farmers (A and B) with a plot of 1 ha each. The plot of Farmer A has a deficit of nutrient P of 20 Kg while the plot of Farmer B has an excess of nutrient P of 20 Kg. The average of plot-level recommendations for nutrient P will be 10 Kg. However the average of the cluster level recommendation for P will be 0 Kg, because the excess of the nutrient in the plot of Farmer B compensates the deficit in the plot of Farmer A and thus there is

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the recommendations and extension services in 2015 adopted 0.39 _s_ . _d_ . more practices in 2017 relative to control farmers. Since control farmers received recommendations at the start of the 2016 growing season, this difference reflects the effect of the one-time provision of extension services paired with recommendations (in 2015) relative to receiving just the recommendations (in 2016). We view this as strong suggestive evidence of the complementarities between extension services and recommendations, consistent with work in other contexts.<sup>11</sup> This result contributes to the relatively recent literature attempting to credibly identify the effect of agricultural extension services.<sup>12</sup> More interestingly, farmers who had received the flexible in-kind grant, and thus had autonomy to choose which recommendations to follow in 2015, were substantially more likely to persist with the new practices in 2017 relative to farmers with the inflexible in-kind grant (an increase of 0.55 _s_ . _d_ .) and to farmers in the control group (an increase of 1.08 _s_ . _d_ .). The fact that some farmers continued to use the new practices two years after the intervention suggests that they were perceived as valuable (despite the lack of impacts on our measure of profits). This result suggests that it may be desirable to provide beneficiaries of a program with a measure of autonomy, particularly if the program is top-down and involves expert advice. We next explore various mechanisms behind the result. Consistent with the hypothesis that autonomy induced farmers to pay more attention to the recommendations, we find that farmers with autonomy in _T_ 3 were more likely to remember the recommendations, to have the most trust in project partners and to have a more positive attitude towards experimentation.

Our results contribute most directly to the recent literature on the impact of tailored input recommendations on farmer behavior. Fishman et al. (2016) evaluate a large-scale program that provided plot-level soil analyses and fertilizer recommendations to farmers in Bihar (India). While they find substantial heterogeneity in soil quality, there was no effect of the recommendations on actual fertilizer use. The authors speculate that this could be because farmers did not understand the information, lacked confidence in its reliability or that the recommended fertilizer mixes were too expensive. In our study, we spent a substantial amount of time and resources facilitating farmer comprehension of the soil analyses and recommendations and used a well-known and trusted agricultural extension services firm to convey the information.<sup>13</sup> Murphy et al. (2019) find that providing plot-level soil information to farmers in Western

> 11For example, in an experiment providing management consulting to large firms in India, Bloom et al. (2013) find that merely providing recommendations (as they did to the control firms) had relatively limited effects compared to pairing the recommendations with consultants that helped firms in implementing the recommendations.

> 12See Beaman et al. (2018); Kondylis et al. (2017) on learning in the contact farmer extension model and Cole and Fernando (2020) on the effectiveness of mobile-phone based extension services in India. See Magruder (2018) for a summary of the past decade of work on the effectiveness of extension services in developing country contexts (see e.g Anderson and Feder, 2007, for a review of older work).

> 13Tjernström (2017) also documents soil quality heterogeneity in a sample of Kenyan farms and examines the difficulties it creates for social learning about new technologies.

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Kenya (using SoilDoc, a low-cost soil testing tool) has a positive effect on the willingness to pay for inputs (DAP and manure, among others) but they do not measure longer-term fertilizer use or productivity. Our work is perhaps closest to Harou et al. (2018) who offer soil analyses, fertilizer recommendations (using SoilDoc) and an in-kind grant that covers the cost of fertilizer for approximately 0.2 ha.<sup>14</sup> We complement their work by measuring the impact of recommendation specificity, assessing the effect of autonomy and examining both shorter-run outcomes as well as longer-term persistence.

We also contribute to the literature on mis-allocation (see e.g. Restuccia and Rogerson, 2017, for a survey) by providing micro-evidence on the inefficient use of fertilizer in agricultural production. In our context, since farmers were already familiar with inorganic fertilizer, our intervention recommended changes in the intensive margin (i.e. input mix), rather than the extensive margin, at no extra cost because the value of the recommendations was similar to the average amount that farmers spent on fertilizer. We believe that documenting such misallocation of fertilizer is important both from the perspective of the individual farmer who could improve productivity by reallocating inputs, as well as that of the social planner since the over-use of urea and the consequent nitrogen leaching and surface run-off could have significant negative environmental consequences (see e.g. Wang and Li, 2019, for a review of the evidence).

Our results also contribute to the debate about the appropriate level of specificity when testing soil quality and providing input recommendations. Harou et al. (2018); Duflo et al. (2011); Marenya and Barrett (2009); Sheahan et al. (2013) all find that official recommendations are too generic (typically at a state or country level) to account for local soil heterogeneity. Smallholder farmers in the developing world typically fertilize using average recommendations, with no information on which elements are most needed, and primarily use urea and DAP. Ours appears to be the first paper to compare plot to cluster level recommendations using field experimental evidence.

Finally, we contribute to the literature on autonomy, which has emphasized both its intrinsic as well as its instrumental value on participatory decision-making (broadly construed), compliance (Malesky and Taussig, 2019; Dal Bó et al., 2010), effort (Sjöström et al., 2018) and productivity (e.g. Black and Lynch, 2001; Bonin et al., 1993; Spector, 1986). Using observational data from an agricultural context, Bardhan (2000) finds that Indian farmers are less likely to violate irrigation rules when they themselves have crafted those rules. The reactance literature in psychology (Wicklund, 1974; Fitzsimons and Lehmann, 2004) argues that recommendations by experts may backfire as agents purposefully try to contradict them. We show that providing autonomy in the decision to follow expert advice may in fact lead to higher adherence in the

> 14They study Tanzania, where fertilizer usage is low (less than 1 percent of study farmers had used fertilizer at baseline) and thus the recommendations suggest substantial increases in input use.

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longer-term. From a policy perspective, understanding whether and how autonomy encourages the use of agricultural best practices is important as policy makers re-design agricultural input subsidy and extension programs, which are ubiquitous in developing countries. From an academic perspective, we contribute to the debate by documenting the importance of autonomy for technology adoption and providing some evidence on the possible mechanisms at play, although more research is needed.<sup>15</sup> The rest of the paper is organized as follows. Section 2 describes the context and data used while section 3 provides the details of the design and rationale for the various experimental arms. Section 4 describes the empirical strategy and sections 5 and 6 present the short and long term results, respectively. Section 7 concludes.

# **2 Context**

This project was a collaborative effort between three organizations: our partner NGO “Qué Funciona para el Desarrollo” or QFD; Ipampa S.C., a long-standing local private extension service company, and Agropecuaria Amozoc, a commercial fertilizer dealer. The project was implemented in 13 municipalities of the Mexican state of Tlaxcala, chosen for having substantial rainfed smallholder farmer populations (see Figure 1) with relatively low maize yields (2.7 tons per hectare on average).

In January 2015, QFD advertised the program widely by displaying posters prominently in public locations and handing out informational leaflets. QFD also organized a total of 34 promotional meetings in the principal towns in each municipality. The promotional meetings lasted between 60–90 minutes and typically took place in a large public space (e.g. a municipal auditorium). During the meetings, the research team introduced and explained the intervention, described the eligibility requirements, and the lottery design. A total of 1,299 farmers that attended the promotional meetings and potentially interested farmers were asked to complete a short form.

Between February and March 2015, interested farmers were visited by the research team. During the visit, the team collected a detailed baseline survey on a range of farmer characteristics and agricultural practices during the previous growing season (2014). After the survey, farmers were asked to register a subplot of one hectare for the program where they planned to grow maize. QFD cordoned off this subplot, GPS coordinates for the subplot were recorded,

> 15The literature on the impacts of cash transfer programs is also related (see Benhassine et al., 2015; Baird et al., 2019, and see Molina-Millán et al., 2019 for a review). This literature has examined the difference between unconditional and conditional transfers both in the short- and longer-terms focusing particularly on human capital, consumption, and well-being. In a conditional cash transfer program beneficiaries can _use_ the transfer amount as they please, but the cash is _received_ only if the beneficiary complies with the conditions (Fiszbein and Schady, 2009). In contrast, we provide in-kind grants that directly subsidize (or not) the desired activity.

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and soil samples were collected.

Table 1 presents the timeline of the intervention. In March 2015, the team collected yield expectations and field activities to date. Farmers were divided into 26 strata based on their location and agro-climatic conditions.<sup>16</sup> Individual randomization was done at the stratum level and announced at the end of the March survey.<sup>17</sup> A first mid-line survey was carried out in August 2015 focusing on labor inputs and agricultural practices to date during the growing season of the intervention. We also collected administrative data on fertilizer purchases from our partner agro-dealer. A second mid-line survey was fielded in October 2015, just before the harvest, to measure yield expectations and to record agricultural activities since the first mid-line. In January 2016, we collected yield data for the 2015 growing season. Finally, we collected further information on grain sales from the 2015 harvest in June 2016. In May 2017, 2 years after the experiment, we went back for a final end-line survey to collect information on practices for the 2017 growing season, which allow us to measure persistence. As a result, we have a panel on agricultural practices and input use for the years 2014, 2015 and 2017 and yield information for 2014 and 2015.

To be considered for the farmers had to cultivate maize in at least one eligible program, hectare of owned or rented land (and in no more than 15 hectares), had to be between 18 and 70 years old and had to sow maize in the 2015 growing season. We have consistent panel data on agricultural practices in 2014, 2015 and 2017 for 678 farmers and they comprise the core sample for the study.<sup>18</sup> Table 2 provides summary statistics for the study sample of 678 farmers and their registered plots. Panel A reports farmer characteristics. Incomes were low by Mexican standards with an average self-reported annual income of 29,414 pesos (US$ 2,200). Panel B of Table 2 for all farmers and column 1 of Appendix Table A3 for farmers in the control group, report the agricultural practices at baseline. Farmers cultivate about 2 plots and an average total area of 5.7 hectares. In 2014 (the year before the intervention) fertilizer use in their registered plot was near universal (97%) with farmers carrying out 1.6 fertilizations on average though only 6% fertilized at sowing. Average self-reported yields were about 2 mt/ha and about half sold maize in the market. Only 6% had used agricultural extension services in the past and only 15% had paid for a soil analysis.

> 16 Specifically, study farmers came from 54 localities ( _localidades_ ) of which 29 were quite small and thus merged with the closest _localidad_ using distance between centroids from INEGI’s geographic databases. If there were ties, we chose to merge _localidades_ with the closest altitude. Two of the small _localidades_ were merged into one to give us a total of 26 clusters. The median cluster had 21 farmers, the 25th percentile had 15, and the 75th had 32.

> 17We note that with individual level randomization, there could be spillovers across farmers in different treatments. We study the potential spillovers in Section 5.2.

> 18We lack data for some farmers because they did not sow maize in a particular year (less than 5%), or because they could not be located for the interview. Appendix Table A2 column 1 shows that attrition was not differential across experimental arms.

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Appendix Table A4 compares our study sample to respondents from the nationally representative INEGI survey, both in Mexico and in Tlaxcala. In Panel A we include all farmers, while Panel B restricts the sample to rainfed farmers. Study farmers have lower yields than both the national and the Tlaxcala sample. In terms of agricultural practices, study farmers are less likely to use hybrid seeds relative to the national average (but comparable to the Tlaxcala sample) and are more likely to have used fertilizer and herbicide than either of the INEGI samples. They are also more likely to have used extension services in the past. Panel B shows similar patterns, and both panels suggests that farmers with greater experience and perhaps those interested in improved inputs were more likely to select into the study.

# **3 Experimental Design**

The five experimental arms are a combination of three components: (a) soil analysis and recommendations (individual or averaged), (b) extension services and (c) and in-kind grants (conditional or unconditional) as outlined in the introduction. To summarize:

- **T1** : _Individualized or plot-level_ soil analysis, input recommendations, extension services; inflexible in-kind grant.

- **T2** : _Averaged or cluster-level_ soil analysis, input recommendations, extension services; inflex-

- ible in-kind grant. (i.e. same as **T1** but with averaged instead of individual soil analyses and recommendations).

- **T3** : Averaged soil analysis, input recommendations, extension services; _flexible_ in-kind grant. (i.e. same as **T2** but with a flexible instead of inflexible in-kind grant).

- **T4** : Averaged soil analysis, input recommendations; extension services. (i.e. same as **T3** or **T2** but with _no in-kind grant_ ).

- **C** : No intervention. Control Arm received averaged soil analysis and recommendations the year after the intervention ended (in early 2016).

Budgetary constraints prevented us from using a full factorial design with 18 experimental arms.<sup>19</sup> As mentioned before, we chose to include agricultural extension services in all treatment arms because a pilot in the same study area with a comparable sample had suggested limited value of the soil analysis and recommendations without the extension services, as farmers appeared to greatly value the ability question and discuss the recommendations with the extension agents. Grants were provided in-kind (rather than in cash) for three reasons. First, because agro-dealers did not typically stock fertilizers in the blends required by the recommendations,

> 19These follow from the three possible choices of soil analysis and recommendations, two choices for extension services, and three choices for the grant intervention.

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we partnered with Agropecuaria Amozoc, who agreed to offer the tailored high-quality fertilizer packages to farmers as long as we guaranteed a minimum volume of sales.<sup>20</sup> Second, the in-kind grant was intended as a “push” to farmers to experiment with higher quality inputs (fertilizers manufactured by a reputable high-quality firm). Finally and perhaps most importantly, cash grants were simply not possible because organizations such as QFD were not allowed by law to disburse cash grants. We next discuss the three sub-interventions in greater detail.

# **3.1 Soil Analysis and Specificity of Recommendations**

**Soil analysis:** The research team collected soil samples from the registered sub-plot of every study farmer (treatments and control). The samples were analyzed by Fertilab, a very well known and respected soil testing laboratory in Mexico.<sup>21</sup> Online Appendix A provides details on the soil analysis protocol. The soil analysis recorded (for each plot) the soil texture (percentage of sand, silt and clay), its ability to retain and transfer nutrients (pH levels, sand and lime concentrations, saturation points and cationic exchange capacity or CEC) as well as the levels of the primary macronutrients (nitrogen N, phosphorous P and potassium K), secondary macronutrients (calcium, magnesium, and sulfur), selected micronutrients and the level of organic matter in the soil. Figure OA1 in the Online Appendix provides an example of the soil analysis produced by Fertilab.

**Recommendations:** In addition to the soil analysis, Fertilab also provided the nutrient levels and corresponding fertilizer dosages required to produce maize yields of 4.5 metric tonnes per hectare (mt/ha) under normal rain and temperature conditions. Recommendations are based on a heuristic model that assumes that a certain quantity of N, P, K and micronutrients are needed to reach a target yield per hectare.<sup>22</sup> In theory, the target yield should be chosen to maximize farmer profits. The model, however, assumes that yields are roughly linear in inputs and we therefore chose the target of 4.5 mt/ha because it equated the average cost of fertilizers according to the model to the average expenditure in fertilizers by farmers at baseline.

> 20The dealer was able to blend fertilizers on-site. Packages were available for pick-up from the dealer store which was on average 17.2 km (s.d. 6.7) away from the average farmer. All study farmers had access to the dealer.

> 21Fertilab ( `https://www.fertilab.com.mx/` ) was recommended to us by CIMMYT staff.

> 22This assumption is grounded in the Law of the Minimum formulated by J.V. Liebig in the 1850s which suggests that to reach a target yield, a certain quantity of each nutrient is needed (similar to a Leontief production function). The Fertilab model uses this as well as a cost minimization approach given the price of available fertilizers. For example, for N one can use urea, DAP or ammonium sulfate. Taking into account the cost of the different fertilizers and the soil absorption capacity, the model selects the cheapest fertilizer package to meet the nutrient requirements. If the soil is pH negative (alkaline), then it is better to use ammonium sulfate rather than urea, but if the soil is pH positive (acid) then urea is preferred. There are other maize yield models such as CERES and NLEAP but many of the variables and parameters required by these models are unknown for Tlaxcala (and Mexico in general).

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The fertilizer dosages recommended by Fertilab were divided into two packages according to the timing of application: the first package to be applied at sowing, and the second 30 to 35 days after sowing depending on plant growth. Fertilab also recommended the use of a precision sowing drill at planting to ensure optimal fertilizer use at sowing as well as optimal plant spacing. Finally, the recommendations included the use of pre-emergent herbicides 2 to 40 days after sowing to reduce weeds that could compete for nutrients with the maize plants.

Based on focus group discussions, the research team used the information provided by Fertilab to design a report for farmers that was intuitive and easy to read. The report contained information on (a) plot physical characteristics and nutrient levels, (b) the recommended dosages of nutrients required to achieve a maize yield of 4.5 mt/ha under normal weather conditions, (c) recommended fertilizer amounts and their costs at our partner agro-dealer and (d) a comparison between the farmer’s own 2014 fertilizer costs the costs of the recommendations. The research team was careful to explain the assumptions underlying the recommendations. More details are available in Online Appendix B.

**Specificity of recommendations:** Given the potential heterogeneity in fertilizer recommendations within and across clusters, farmers in _T_ 1 received individualized recommendations, based on the soil analysis from their registered subplot. The rest of the treated farmers in _T_ 2 _− T_ 4 received recommendations based on averages of the soil analyses in their cluster. This averaging was expressly conveyed to them in the report as well as verbally when describing the recommendations. Control farmers received the average recommendations in February 2016, after the intervention had ended.

We assess the heterogeneity in input recommendations by running a standard analysis of variance (ANOVA) in Appendix Table A1. In particular, for registered subplot _i_ in cluster _c_ : _Yic_ = _μ_ + _αc_ + _εic_ and we report estimates of variation within ( _σε_ ) and across clusters ( _σα_ ) and the share of the total variation arising from between cluster variation _σα_<sup>2</sup> _σ_ + _<u>α</u>_<sup>2</sup> _σε_<sup>2.We find substantial</sup> variation in the soil characteristics (panel A) and nutrient recommendations (panel B) both within and across clusters although most of the variation appears to be within clusters.

These results are consistent with Figure 2 which displays the level of N, P and K in the registered plots for the four different agro-climatic areas of the study along with the target level of nutrients required to achieve 4.5 mt/ha according to the Fertilab model. The figure shows that even across different agro-climatic zones, the variation in nutrient content within each zone is much higher than across zones. In addition, Panel (a) of Figure 2 shows a deficit of N relative to the target level of N (red line) which is unsurprising because N is either absorbed by the plant, is lost to the atmosphere or lost via leaching or de-nitrification. Since soil analyses were taken right before sowing for the 2015 season, the N content in the soil should be relatively low. Panel (b) shows a soil level of P that is close to the target level and even higher in some plots, and therefore the recommendations call for relatively low amounts of the P rich fertilizer DAP.

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Finally, Panel (c) shows a significant deficit of K in the soil and thus the recommendations call for an increase of K fertilizer KCl. In Appendix OA A.2 we discuss the stability over time of the soil analyses.

Column 1 of Table 3 presents the average amount of the three main fertilizers (urea, DAP and KCl) and the total cost of application for farmers in the control group before the intervention in 2014. Column 2 reports the joint balance test that the 2014 amounts applied and costs by farmers in _T_ 1– _T_ 4 are not different than those in the control group. Column 3 reports the average amounts and cost that farmers in _T_ 1 should apply in 2015 according to their plot-level individual recommendations. Column 4 in turn checks that the amounts and costs of applying the plot-level recommendations for farmers in _T_ 1 are not different for those in the control group had the latter received the plot-level recommendations (note that the research team had the soil analysis and the recommendations for controls). Columns 5 and 6 are analogous to columns 3 and 4 for the average-level recommendations instead of plot-level ones. These results suggest that farmers in all five groups were quite similar in terms of fertilizer use in 2014 and in terms of recommended fertilizer packages (based on the soil analyses).

It is clear also clear from Table 3 that the mix of fertilizers traditionally used by farmers is quite different from the recommended mix when using either the plot-level or the clusterlevel recommendations. In particular, farmers in 2014 used 77% (respectively, 92%) more urea than required by the plot-level (cluster-level) recommendations. The corresponding overuse of DAP is equally striking – 61% (164% for the averaged recommendations). In contrast to urea and DAP, farmers used only 28% (31%) of the recommended dose of KCl.<sup>23</sup> These differences between status quo fertilization and individually tailored recommendations are statistically significant as reported in column 8 of Table 3.

The total cost of fertilizer application for the individual recommendations is similar to the investment made by farmers in 2014 (p-value 0.55). This is unsurprising because, as mentioned earlier, the target yield of 4.5 mt/ha was chosen to equate the cost of the recommended fertilizer package with average farmers fertilizer costs. Plot-level recommendations use more fertilizer and are therefore more expensive than the cluster averaged recommendations.<sup>24</sup> That individual recommendations are more expensive than average recommendations is not surprising because recommendations using soil analyses from multiple plots will have, on average, lower nutrient deficits and will thus require lower amounts of fertilizer (see Footnote 10).

Panel B of Table 2 reports that farmers in 2014 waited 36.3 days on average between sowing

> 23On average farmers used 267 Kg/ha of Urea and the individualized (averaged) recommendations were 151.2 kg/ha (138.9 kg/ha). Farmers used 49.4 kg/ha of DAP while the individualized (averaged) recommendations were 30.6 kg/ha (18.7 kg/ha). The corresponding figures for KCl are 9.45 kg/ha and 33.3 kg/ha (individualized) and 30.3 kg/ha (averaged). Both urea and DAP are subsidized by the Mexican government which may explain why farmers use them in such large quantities.

> 24Column 7 reports the p-value of the t-test that individual recommendations in column 3 are equal to the average recommendations in column 5. All p-values are statistically significant at conventional levels.

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and the first fertilizer application. The recommendations, however, suggest applying fertilizer at sowing with a precision drill. As a result, not only is the recommended input mix very different from what farmers use at baseline, but the timing of fertilizer application is also quite different, despite the fact that the cost of fertilizers is roughly the same. In our context, precision drills (which are tractor attachments) are the only implement that can sow and fertilize at the same time as they have separate chambers for seeds and fertilizer. However, only 11% of study farmers had used a precision drill prior to 2015. The majority used either draft animal labor . (40%) or a semi-precision drill (40%) The semi-precision drill (also attached to a tractor) or the seed drill used with draft animals can only sow and thus, when used, farmers need to fertilize manually at sowing.

**Fertilizer quality:** In addition to differences in the fertilizer mix and timing, farmers were unfamiliar with the fertilizer brand YARA (a reputed, high-quality manufacturer) stocked by the agro-dealer. In order to assess fertilizer quality, the research team tested samples in a laboratory for each of the three main fertilizers (urea, DAP and KCl) manufactured by YARA and by the most popular manufacturer (providing government subsidized fertilizer) from five different locations.<sup>25</sup> We found that the urea and KCl content was comparable across the two types of manufacturer and generally matched the labelled concentrations. However, DAP concentrations were lower than advertised in the commonly available brand but this was not true for our partner agro-dealer. In fact after accounting for differences in concentrations, the cost per kilogram of nutrient was actually lower for YARA. See Online Appendix C for more details.

# **3.2 Agricultural Extension Services**

Farmers were offered extension services at no cost from Ipampa S.C., a local, well-known and highly respected firm. The extension services package comprised three group training sessions and three plot visits by Ipampa agricultural extension workers (AEWs). The first group meeting introduced farmers to the precision sowing drill and covered the sowing recommendations. The second group meeting covered the application of fertilizer post-sowing and provided strategies to correct nutrient deficiencies. The final meeting was held just before the harvest and emphasized field preparation. In addition to these group meetings, AEWs also visited the registered subplots of interested farmers thrice, just before and after sowing and just before harvesting. AEWs used these individual visits to monitor nutrient deficiencies and other problems with the maize crop.

> 25The locations were Altlzayanca, Apizaco, Calpulalpan, Cuapiaxtla and Muñoz. The lab that analyzed the fertilizer content was Laboratorios A-L de México, in Guadalajara, México.

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# **3.3 Autonomy in In-Kind Grants**

Farmers were provided in-kind grants worth 2,000 pesos (approximately US$150 at the time) to cover approximately half of the average per-hectare total input costs, using prices from the last growing season. Farmers in _T_ 1 and _T_ 2 received an _inflexible_ grant in the sense that it could only be used to purchase items on their shopping list. The inflexible grant was applied sequentially to first cover the sowing costs (i.e. the rental cost of the precision sowing drill worth 800 pesos and the initial fertilizer package) while the remaining amount went towards the second fertilizer package. The farmer was responsible for paying the difference between the total cost of inputs and the grant. For the typical farmer in _T_ 1 ( _T_ 2 respectively), the grant covered the cost of the sowing machinery, the first fertilizer package and approximately one–fifth (one–half) of the second fertilizer package. A farmer who chose not to rent the precision drill or to forego the first or second package would forfeit the subsidy for that input, that is, the farmers could not temporally reallocate the grant. Finally, farmers in _T_ 1 and _T_ 2 could not purchase fractions of the input recommendations (e.g. a half a bag).<sup>26</sup> Farmers in _T_ 3 were offered a _flexible_ grant, that is, they were not required to purchase items on the shopping list nor to follow the recommended timing of application. Instead, they had autonomy to use the 2,000 pesos to purchase any inputs of their own choice from our partner agro-dealer. They could, of course, use the grant to purchase items on the shopping list but were under no obligation to do so and this was made explicit during the intervention. Farmers in _T_ 4, along with the rest in _T_ 1– _T_ 3, were informed about the high quality fertilizer agro-dealer, and given its address and a map. Farmers in _T_ 4 had to pay for the rental as well as any recommended fertilizer and other inputs using their own funds.<sup>27</sup> The grant amounts were directly given to the partner agro-dealer who deducted them from the costs of each farmer’s shopping list. All farmers were informed about the in-kind grant both by the research team (in writing) as well as by the agro-dealer (and it was also reflected in the paper-work filled out by farmers at the dealer). The research team coordinated the logistics of renting the precision sowing drill for all interested farmers in _T_ 1– _T_ 4.

# **3.4 Rationale for the Interventions and Hypotheses**

With the four treatment arms _T_ 1– _T_ 4 and the control group, we can directly test (1) the effect of pairing soil-analysis based tailored recommendations and extension services ( _T_ 4 vs Control), (2) the effect of recommendation specificity ( _T_ 1 vs _T_ 2), (3) the effect of a in-kind grant ( _T_ 1– _T_ 3 vs

> 26The inflexible grant is in effect the targeted input subsidy of many African large scale input subsidy programs. See e.g. Giné et al. (2019); Carter et al. (2019) for randomized evaluations in Tanzania and Mozambique, respectively, and Jayne and Rashid (2013) for a critical review of such programs.

> 27Precision and semi-precision drills are typically owned by local large landowners that use them on their plots but may also rent them on the side.

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_T_ 4), and (4) the effect of autonomy in the use of the grant ( _T_ 3 vs _T_ 2). Comparisons (1), (2) and (4) are, to the best of our knowledge, new in the literature and we briefly review their rationale below.

# **Hypothesis 1 (H1): Extension services paired with tailored recommendations increases practice adoption relative to control farmers (** _T_ 4 **vs** _C_ **)**

The extant research on providing farmers with tailored soil-analysis recommendations (e.g Harou et al., 2018) suggests limited adoption. However, such recommendations appear to be difficult to interpret and implement in isolation. We provide detailed soil analyses and recommendations (more detailed for instance than those provided by SoilDoc) and pair them with high quality extension services. The hypothesis is that the joint provision increases adoption.

Since control farmers received the recommendations at the start of the 2016 growing season, by the 2017 end-line survey, _T_ 4 and _C_ differ only in that _T_ 4 received extension services (in 2015) and had tailored recommendations for a year longer than control farmers. We hypothesize that in 2017, extension services paired with tailored recommendations increase adoption more than tailored recommendations alone.

# **Hypothesis 2 (H2): Plot-level recommendations increases practice adoption relative to clusterlevel recommendations (** _T_ 1 **vs** _T_ 2 **)**

It seems reasonable that farmers are more likely to trust and therefore act on recommendations that are based on the soil quality of their own plots, especially if heterogeneity in soil quality is significant. In addition, previous research, albeit in a different setting (e.g. Madajewicz et al., 2007), suggests that individualized information is more effective. Cluster-level recommendations, however, are cheaper to produce and deliver, so if outcome differences between the two are small, then cluster averaged recommendations may be more cost-effective.

Given the substantial soil heterogeneity we document, our hypothesis is that individualized recommendations will induce more take up and adoption of practices.

There is an important caveat to this test. As we discussed above (see e.g. see p.3) the average cost of the plot-level recommendations was higher than that of the cluster-level recommendations (recall both recommendations calibrated for a yield of 4.5 mt/ha). Holding costs constant would have required reducing target yields for the plot-level calibrations. Instead, we chose to keep target yields fixed for the calibrations (and hence had differing average costs).

# **Hypothesis 3 (H3): The in-kind grant increases practice adoption (** _T_ 1 **–** _T_ 3 **vs** _T_ 4 **)**

The effect of the in-kind grant could work through multiple channels: (a) a direct income effect, (b) an endorsement effect (i.e. signalling that the team’s specific recommendations were not just cheap talk) and (c) directly incentivizing experimentation. Since the tailored recommendations differed dramatically from business-as-usual, focus group discussions with farmers and 14
talks with Ipampa S.C. suggested that some form of subsidy would be helpful for adoption. we are While we cannot separately identify the relative role of these (and other) mechanisms, somewhat skeptical that the in-kind grant operates mostly through an income effect since farmers were already spending an amount about equal to the grant amount on agricultural inputs before the intervention.

**Hypothesis 4 (H4a): Adoption is higher in 2015 among farmers with the inflexible grant (** _T_ 2 **) relative to farmers with the flexible grant (** _T_ 3 **). (H4b): Recommended practices are more likely to persist for farmers with the flexible grant relative to farmers with the inflexible grant.**

Farmers with autonomy are encouraged to make an active decision with the flexible grant and thus may adopt fewer recommended practices than farmers offered the inflexible grant who risk forfeiting the grant amount if they reject the recommendations. However, the lack of choice may prove to be detrimental in the longer run. By virtue of being an active decision, and consistent with other studies showing that autonomy increases performance and positive affect, farmers offered the flexible grant may be more likely to persist with the practices that they do adopt.

# **4 Empirical Framework**

We study the effects of our experimental interventions on the following outcomes for the 2015 intervention season: take up of subsidized inputs and extension services, agricultural practices, changes in the input mix, yields and profits. We also study longer term effects on agricultural practices, knowledge and attitudes towards innovation two years after the end of the program, in 2017.

We present the experimental results in two formats. First, we use a standard regression specification with indicator variables for each experimental arm (and omitting the control group when appropriate). Appendix Tables B1–B7 present the analysis using this specification where the coefficients on each indicator variable are interpretable as the effect of the corresponding treatment. The second format directly tests the hypotheses laid out in Section 3.4. Because each treatment arm is the combination of multiple interventions, we present the effects of their components rather than the treatment arms themselves. In this approach, we map the five arms into a set of four indicator variables: (a) _“Extension”_ which is equal to one if the unit received input recommendations and extension services ( _T_ 1– _T_ 4); (b) _“IndRecomm”_ , which is equal to one if the input recommendations were individualized at the plot level ( _T_ 1); (c) _“Grant”_ , which is equal to one if the in-kind grant was provided ( _T_ 1– _T_ 3) and (d) _“Flexible”_ , which is equal to one if the grant was flexible ( _T_ 3). Table 4 summarizes the mapping between these dummies and those 15
for each treatment arm. We then run the following specification: where _i_ denotes a farmer, _Y_ is the outcome of interest and _t_ is the time period. We include randomization strata fixed effects _αs_ and compute robust standard errors.

Given the presence of the other indicators in specification (1), the coefficient _βE_ on Extension compares outcomes for farmers in _T_ 4 relative to those in the control group (H1). It therefore measures the combined impact of the recommendations and agricultural extension services. _β I_ measures the impact of recommendation specificity by comparing farmers offered plot-level recommendations ( _T_ 1) with farmers in _T_ 2 who were offered cluster-level recommendations (H2); _βG_ measures the impact of the in-kind grant on farmers in _T_ 2, compared to farmers in _T_ 4 (a subset of H3). Finally, _βF_ measures the impact of autonomy by comparing farmers in _T_ 3 with those in _T_ 2 (H4). For completeness, in all results we also provide the p-value associated with the test that _T_ 1, _T_ 2 and _T_ 3 are different from zero.<sup>28</sup> Recall that the effect of flexibility/autonomy, and the effect of individual recommendations/specificity are measured in addition on the effect of the grant. Unless otherwise stated, we use the study sample of 678 farmers and we focus on intent-to-treat (ITT) estimates and examine spillovers (and hence possible SUTVA violations) in Section 5.2. In cases where the dependent variable is sometimes missing we report whether there is differential attrition, and if so, provide Lee bounds (Lee, 2009).

# **5 Short-Term Results**

## **5.1 Take Up**

Table 5 uses the sample of farmers that received soil analyses and recommendations during the intervention (arms _T_ 1 _− T_ 4) and examines the take up of the precision drill during sowing (column 1), the two fertilizer packages (columns 2 and 3), attendance at AEW group meetings and the total number of AEW plot visits (columns 5 and 6). Column 7 uses as the dependent variable the sum of the dependent variables in columns 1-3, 5 and 6 while column 8 uses a standardized index of the outcome in column 7. The take up of these items was verified both from farmer reports as well as administrative data and as a result, mis-classification is not an important concern. The penultimate row of Table 5 also reports the mean of the dependent variable among farmers in _T_ 4 (with the corresponding standard error). The control group is not included in these regressions as no intervention was offered to them.

> 28Note that we can recover the overall impact of being in any treatment arm by combining the _β_ -coefficients. Thus, the test that _T_ 1 = 0 is equivalent to _βE_ + _β I_ + _βG_ = 0, _T_ 2 = 0 is equivalent to _βE_ + _βG_ = 0 and _T_ 3 = 0 is equivalent to _βE_ + _βG_ + _βF_ = 0. Finally, _T_ 4 = 0 is equivalent to _βE_ = 0, which is already reported.

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The take up of the precision drill among farmers without the in-kind grant ( _T_ 4) was 8%. Receiving the grant in addition increased the probability of take up (among farmers in _T_ 2) by 76 percentage points ( _pp_ ), a near ten-fold increase. Farmers that received the plot level recommendations ( _T_ 1) are 67 _pp_ more likely to adopt the precision drill compared to farmers in _T_ 4. Recall that farmers with the inflexible grant ( _T_ 1 and _T_ 2) forfeited the rental amount for the precision drill if they did not use it. This was not the case for farmers with the flexible grant ( _T_ 3) as they could use the grant to purchase additional fertilizer or any other inputs available at the dealer. This is precisely what we observe: farmers with the flexible grant were 12 _pp_ less likely than farmers with the inflexible grant (in _T_ 2) to rent the precision drill, but as we shall see, they were more likely to adopt the first fertilizer package.<sup>29</sup> The take up of the first fertilizer package (column 2) among farmers with just the recommendations and extension services ( _T_ 4) is 7%, but increases by 83 _pp_ for farmers who in addition had an inflexible grant (in _T_ 2) – a more than ten-fold increase. The increases was comparably large, at 90 _pp_ , for farmers with the flexible grant. By contrast, the increase for farmers with the plot level recommendations (and the inflexible grant) was substantively smaller (74 _pp_ ). Thus, the provision of plot level recommendations did not increase take-up of the package (relative to cluster level recommendations). This pattern is also reflected in other outcomes as we see below. Take-up rates for the second package (column 3) are somewhat lower than those for the first package: 4% for farmers who received just the recommendations and extension services ( _T_ 4) and an increase of 74 _pp_ for farmers who received the inflexible grant. Farmers with the flexible grant took up the second package at very similar rates (the treatment effect is 72 _pp_ ) – further evidence that grant flexibility did not decrease take up. As mentioned earlier, take-up rates for the second package among grant recipients are lower than those for the precision drill and the because the did not cover the full cost of the sec- first package likely grant typically ond fertilizer package (while it typically fully covered the costs of the sowing machinery rental and the first package). In fact, farmers in _T_ 2 ( _T_ 1 respectively) needed to pay 400 (700) pesos out-of-pocket to purchase the second package. Farmers in _T_ 2 had to cover lower out-of-pocket amounts than those in _T_ 1 because their recommendations were were on average cheaper. Column 4 shows these out-of-pocket expenses that farmers (with the grant) made to cover the cost of the second package. As expected, farmers with autonomy ( _T_ 3) spend less than farmers in _T_ 2 since they were less likely to use the precision sowing drill and so could use that subsidy amount towards the second package.

Turning next to extension services, columns 5 and 6 record the number of AEW led group sessions attended by the farmer and the number of visits by AEWs at farmer plots where the farmer was present, respectively. These sessions and plot visits were described in section 3 and

> 29While the first package should be applied at sowing with the precision drill to guarantee an optimal spread of fertilizer, farmers who did not use the precision drill were instead advised to use the first package 30-60 days after sowing depending upon plant growth.

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functioned as tutorials and question and answer sessions on best practices for maize cultivation. Farmers in _T_ 4 attended an average of 0.76 sessions (column 5) and had 1.4 plot visits (column 6). Farmers with the inflexible in-kind grant in _T_ 2 attended 1.27 additional sessions and had 1.23 additional plot visits. Strikingly, farmers with the flexible grant attended 1.47 more sessions (relative to _T_ 4), about a fifth of a session more than farmers with the inflexible grant (the number of AEW plot visits were the same for both groups). Autonomy, therefore, seems to generate more engagement.<sup>30</sup> Column 7 summarizes the previous columns by simply recording the total number of adopted program items (nine).<sup>31</sup> Farmers with just the recommendations and extension services adopted 2.34 items while farmers who also received the inflexible grant adopted an additional 4.83 items, confirming the importance of the in-kind grant. Interestingly, farmers with the flexible grant (i.e. who had no obligation to choose any of the nine items) adopted the same number of items on average as those who did not have autonomy – the point-estimate is in fact slightly higher (4.95). Thus, there was no trade-off between autonomy and compliance. In contrast, farmers with plot level recommendations adopted 0.76 _fewer_ items compared to farmers in _T_ 2. In particular, not only were _T_ 1 farmers less likely to purchase the fertilizer packages than farmers in _T_ 2 but they were also less likely to be present during AEW plot visits, which cannot be explained by budgetary reasons. Finally, column 8 reports results using a standardized index with analogous results (here measured in standard deviations).<sup>32</sup> To summarize, farmers that received the in-kind grant were substantially more likely to takeup the recommended packages and to avail services relative to farmers who received only recommendations and extension services. Second, there appears to be little difference in take-up rates between farmers who had the flexible grant – and hence could have turned down all prooffers without the – and those who did not have this gram forfeiting grant flexibility. Finally, we find no evidence that providing plot level recommendations increased take-up relative to providing cluster level recommendations. If anything, take-up was lower in _T_ 1 than _T_ 2 (by 0.62 _s_ . _d_ .).

# **5.2 Practices in 2015**

We now turn to the adoption of agricultural practices recommended by the program. We partition them into those that were prevalent before the intervention (i.e. _“Existing Practices”_ ) and those that were uncommon and that the intervention tried to promote (labelled _“New Prac-_

> 30The overall take up of the group training sessions declines over time with the most attendance around sowing and the least attendance before the harvest (results available upon request).

> 31These were the use of precision drill, 1st and 2nd fertilizer package, 3 group sessions and being present in the 3 plot visits by AEWs.

> 32Following the convention of standardizing each variable, summing the standardized variables, and restandardizing again so that the sum has mean zero and variance 1 in _T_ 4.

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# _tices”_ ).

Appendix Table A3 lists all of the recommended practices and reports their prevalence among farmers in the control group before (column 1) and during our intervention (in column 2).<sup>33</sup> Column 3 reports the p-value of the test that adoption rates among controls are the same in 2014 and 2015. There are no substantial differences (the lowest p-value is 0.17), suggesting that spillovers between treated and control farmers during the intervention were limited.<sup>34</sup> We aggregate practices for each of the two sets of practices into two corresponding indices to mitigate the need for multiple hypothesis testing. “Total Practices Applied” simply counts the number of adopted practices, while “Standardized Index” subtracts the control mean from the total number of practices applied and divides by the control standard deviation (for each element as well as the sum). Table 6 reports the results for the indices while Appendix Table A6 reports the result for the individual practices. Columns 1 and 2 of Table 6 show that our individual interventions had no effect on existing practices, which is unsurprising as the control mean for existing practices is quite high (2.4) as most farmers was already using all three practices.

Turning to new practices, farmers who received only the recommendations and extension services adopt an additional 0.35 practices compared to 0.32 practices in the control group, consistent with H1. Farmers who, in addition, were offered an inflexible grant ( _T_ 2) adopt 2.56 more practices, an almost seven-fold increase and approximately the same increase as the 2.49 additional practices by farmers with a flexible grant (the numbers are not statistically distinguishable). Farmers offered plot level recommendations and the grant ( _T_ 1) adopted about a third of a practice less than farmers in _T_ 2 so receiving plot level recommendations did not increase, and if anything _lowered_ , compliance. These results reinforce the three patterns documented with take in the section: the of the in-kind program up previous importance grant (consistent with H3); the small negative role of plot level specificity (contra H2), and the fact that autonomy did not decrease adoption (contra H4a). The results for the standardized index of new practices in column 4 are consistent with those in column 3.<sup>35</sup>

> 33Existing practices comprise ploughing (56% in 2015), the use of inorganic fertilizers (97%) and covering the applied fertilizer (85%). New practices included deep tillage or ripping (5% in 2015), using hybrid seeds (5%), sowing with a precision drill (10%), fertilizing at sowing (9%), application of pre-emergent herbicide (2%) and using highquality fertilizers (manufactured by YARA) (0%). We did not ask about covering the fertilizer, using high-quality fertilizers or using pre-emergent herbicide after sowing at baseline, so they are only available in 2015.

> 34Using the GPS coordinates, we also assessed whether control farmers with plots located close to those of treated farmers (controlling for the total number of nearby study plots defined with reference to a 500m or 1000m radius) are more likely to adopt the new practices. The intuition is that while the density of study farmers nearby is endogenous, the share of those farmers that is treated is exogenous by virtue of randomization, and so if spillovers were significant, one should detect larger changes in the adoption of recommended practices among control farmers near treated farmers. We find no evidence of any such spillovers.

> 35We test whether the lower adoption of _T_ 1 (relative to _T_ 2) is due to proximity to other study farmers as disagreement across recommendations results in lower willingness to follow their own recommendations. We examine this hypothesis using again plot-level GPS locations. In particular, we examine whether farmers in _T_ 1 with plots close to those of other treated farmers (controlling for the total number of plots from nearby study farmers) were

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In Table 7 we examine fertilizer usage. The primary outcome is the absolute difference between the amount of fertilizer applied and the amount recommended for each of the three main fertilizers (urea, DAP and KCl). We expect treatments to reduce the gap between actual and recommended use.<sup>36</sup> Columns 1–3 report application at sowing while columns 4–6 report total fertilizer application. For brevity we only focus on the results for the total fertilizer applied (the results for fertilizer at sowing are similar).<sup>37</sup> Fertilizer usage did not change relative to controls for farmers who received only the recommendations and extension services ( _T_ 4). By contrast and perhaps unsurprisingly, farmers who were, in addition, offered an inflexible in-kind grant ( _T_ 2) – the bulk of which was earmarked for fertilizer – show a dramatic change in fertilizer application. The overall overall gap for urea reduced by 80.5 Kg/ha (a 71% reduction relative to the mean control value of 113 Kg/ha), the gap in DAP by 29.5 Kg/ha (a 79% reduction) and the gap in KCl by 28.1 Kg/ha (87%). More interestingly, these dramatic reductions were also achieved by farmers with the flexible in-kind grant – who had autonomy and thus were not required to adjust their fertilizer usage as a pre-condition for the grant. In fact, we cannot reject the null that the reductions are the same for these two arms for Urea and DAP. For KCl, the gap was reduced by 22.5 kg/ha for _T_ 3, slightly less then for those offered the inflexible in-kind grant. Finally, farmers who received plot level recommendations had substantively and statistically similar reductions as the other two arms (again, except for KCl).

In B and Table B8 we whether the in fertilizer use in Appendix Appendix explore changes 2015 were related to the gap between fertilizer used and recommended at baseline and whether there was variation in the effectiveness of the various treatments for a given baseline gap. We find that farmers with larger gaps were indeed more responsive to the treatments but that this responsiveness did not vary across treatment arms.

To summarize, we see a doubling of new agricultural practice adoption by farmers who re-

> less likely to adopt intervention practices. Similarly, we assess whether farmers in _T_ 2 _− T_ 4 close to other farmers in _T_ 2 _− T_ 4 were differentially likely to adopt intervention practices (in this case all received the same average recommendations). We find no impact of spillovers defining nearby farmers as before, i.e. those within a 500m or a 1,000m radius.

> 36We also measure this gap for the control arm. As noted above, we performed soil analyses and recommendations for control plots but did not share these with control farmers in 2015.

> 37Recall that the recommendations encouraged farmers to change both the fertilizer mix and the timing of application. Since control farmers delayed the first fertilization by 36 days from the time of sowing on average (see panel B Table 2), columns 1 to 3 show a deficit of fertilizer application among controls of 38.7 Kg/ha of urea, 19.4 Kg/ha of DAP and 16.4 Kg/ha of KCl at the time of sowing. Most farmers offered the in-kind grant (in _T_ 1– _T_ 3) used the precision drill and fertilized at sowing, reducing the absolute gap between recommendation and application at sowing. Among farmers with the flexible grant, these reductions were 28.5 Kg/ha of urea (a 74% reduction), 13.5 Kg/ha of DAP (a 70% reduction) and by 11.4 Kg/ha of KCl (a 70% reduction). The reductions are broadly similar for farmers in _T_ 1 and _T_ 2 as well. Farmers with the flexible grant did apply 3.5 Kg/ha less of urea compared to farmers in _T_ 2, thus increasing the gap between the amount of fertilizer recommended and applied (recall that urea was underused at sowing according to the recommendations). In column 3, farmers in _T_ 3 also apply 3.4 Kg/ha less of KCl than farmers in _T_ 2, while farmers in _T_ 1 with individual recommendations apply 1.8 Kg/ha of KCl less than farmers in _T_ 2 as well. Thus, farmers in _T_ 2 followed the fertilizer recommendations at sowing most closely, followed by farmers in _T_ 1, then farmers in _T_ 3.

20
ceived recommendations and extension services only (relative to control), consistent with H1, but no change in their fertilizer input _mix or timing_ of application. By contrast, farmers who also received the in-kind grant increased new practice adoption rates almost eight-fold and also changed both their fertilizer input mix and timing of application consistent with H3. In contradiction with H2, we find no evidence that specificity of the recommendations led to greater adoption, and finally, we find substantively similar patterns for farmers with and without autonomy, thus rejecting H4a as autonomy did not lead to lower adoption in 2015.

# **5.3 Other Outcomes in 2015**

We now turn to measuring the impact of the program on yields and profits. Self-reported yields are plagued by measurement error both in the numerator (the quantity harvested) as well as in the denominator (area sown).<sup>38</sup> We took two steps to minimize this problem: first, the research team demarcated the registered subplot (which was one hectare in most cases) using GPS devices.<sup>39</sup> Second, we attempted to verify self-reported yields by transporting the harvested grain to a nearby weighing station. We were able to do so for 498 of the 678 study sub-plots.<sup>40</sup> Column 2 of Appendix Table A2 shows that the sample of 498 farmers with yield measurements is not random. To assess the magnitude of the selection problem, we use our measure of selfreported yields but restrict it to the sample of 498 plots with yield measurements. Interestingly, Appendix Table A7 finds very similar results between measured yields in column 1 and selfreported yields in column 2. We thus have confidence in our measure of self-reported yields.

After July, rainfall was below normal in all of the study municipalities, affecting the critical period for plant development.<sup>41</sup> Appendix Table A8 shows that while the rainfall during the initial growing stages (before August) was similar in 2014, 2015 and 2016, total precipitation was significantly lower in 2015 compared to either 2014 or 2016. In both 2015 and 2016, we asked farmers whether they had suffered a drought and while in 2015, 73% reported that they did, only 32% of the same respondents reported a drought in 2016. Finally, we collected yield expectations data in July and November of 2015. In Appendix Table A9 we regress the change in expected yields from July to November against an indicator for whether the respondent reported a drought. The results show that reporting a drought is associated with a decline of 170 Kg/ha in expected yields. Given that the sample is smaller than the core sample, we also

> 38See e.g. Desiere and Jolliffe (2017) for a discussion.

> 39For farmers with a plot area of less than 1 ha, the research team measured how much they had and adjusted the denominator appropriately. Results are robust to excluding these plots.

> 40For 75% of farmers, the crop was still standing at the time of the QFD visit, and the maize was harvested by QFD using a mechanical harvester. After harvesting, the team threshed the maize and transported it to be weighted. The remaining farmers had already harvested at the time of the visit but the production from the registered plot could be identified. In this case, after drying the maize the team visited the farmer with a mechanical sheller to collect the grain and transport it to be weighted. See Online Appendix D for details.

> 41See e.g. Sinclair and Rawlins (1993).

21
report Lee bounds which indicate a decline in yields in the range 240 Kg/ha to 130 Kg/ha. This suggests that the drought was severe enough to change yield expectations.

Column 1 in Table 8 presents the self-reported measure of yields for the full sample of 678 farmers. Yields for control farmers were 2, 360 Kg/ha. The provision of recommendations and extension services increased yields by 210 Kg/ha (a 9% increase relative to the control mean) but the estimate is not statistically significant at conventional levels. The in-kind grant, however, increased yields significantly (both in an economic and statistical sense) relative to those in the control group. For farmers with the inflexible in-kind grant and cluster level recommendations ( _T_ 2), yields rose by 280 Kg/ha (a 12% increase relative to the control mean). The corresponding treatment effect for farmers who received the flexible grant was 360 Kg/ha (a 15% increase). The two numbers are not statistically distinguishable, and our conclusion is that providing farmers with autonomy, did not decrease yield or profits. Yields for farmers with plot level recommendations ( _T_ 1) rose by 400 Kg/ha (a 17% increase), but given the standard errors, this is not statistically different from the yields for arms _T_ 2 or _T_ 3.<sup>42</sup> While the results on yields are encouraging, it is perhaps more important to evaluate impacts on profits. Measuring and valuing both inputs and output, however, is extremely challenging for smallholder farmers (see e.g. Foster and Rosenzweig, 2010). We measured revenues and expenditures on a comprehensive set of agricultural inputs using frequent, detailed surveys throughout the growing season.<sup>43</sup> To calculate revenues we multiplied the price received in the sale of maize by the self-reported quantity harvested.<sup>44</sup> Revenues are reported in column 2 and not surprisingly, show a similar pattern to that for yields in column 1. Expenditures are reported in column 3. For each stage of the growing season – soil preparation, sowing, plant maintenance, and harvesting – we measured labor days in the one hectare subplot, whether it was provided by a family member or hired-in labor, and the wage paid for hired labor. We also measured other inputs, including seeds, fertilizers, sowing machinery, pesticides, herbicides, and harvest machinery and whether the cost was paid by the farmer or by the research team (i.e. we include the up to 2, 000 peso subsidy in the costs and impute harvesting costs if they were paid by the research team).<sup>45</sup> Column 3 shows that grant recipients in _T_ 2 invested 623 more pesos/ha than farmers in _T_ 4 or the control group, who spent on average 5, 280 pesos/ha. Column 4 reports profits as the difference between revenues and costs. Although all the point estimates are positive – and suggest profit increases in the range of 10% (T3), 12% (T2), 20%

> 42In fact, we cannot reject the joint null hypothesis that the treatment effects are the same for all three arms ( _T_ 1, _T_ 2 and _T_ 3).

> 43Enumerators from each of the seven teams lived less than a thirty minutes drive from the study plots assigned to them. Plots were visited several times by the team during the year. Unpaid labor is however not taken into account in our calculations because of the difficulty in imputing a shadow wage.

> 44Only about 70% of farmers sold maize, and we imputed the price of maize for the remaining farmers using the median price in their cluster.

> 45Appendix Table A10 disaggregates expenditures into different categories. Note however, that we do not value unpaid (typically family) labor in labor costs.

22
(T1) and 23% (T4) – they are imprecisely estimated and none of the estimates are significant at conventional levels. In column 5, we remove the amount of subsidy and harvesting costs paid by the program from the costs to consider only the out-of-pocket investment made by each farmer. We find, unsurprisingly, an increase in profits in the range of 2, 300 pesos/ha among farmers with the grant ( _T_ 1, _T_ 2 and _T_ 3) relative to control farmers as well as farmers in _T_ 4.

# **6 Long-Term Practice Persistence**

In February 2016, after the conclusion of the harvest, farmers in the control group received cluster averaged soil analyses and recommendations. We subsequently returned in the summer of 2016 for a short survey where we asked farmers if they still had a copy of their recommendations. Even though the control group had only received their recommendations a few months earlier, they were substantially less likely to have it on hand (58%) than farmers who had also received extension services in 2015 (87%) – the figures were comparably high for the other arms as well. We returned in May 2017 to examine whether any of the practices introduced in 2015 had persisted into the second growing season after the intervention. The survey took place after sowing (but prior to harvest) and we asked about a range of outcomes, including practices in the 2017 season and agricultural knowledge.

Table 9 shows the results on adoption of practices in 2017. Turning to new practices in Column 3, control group farmers reported using 0.42 new practices on average, statistically indistinguishable from the 0.32 new practices used by the same group in the year of the intervention, suggesting that the provision of only the soil analyses and recommendations a year later (without extension services) did little to change practices, and that spillovers from treatment farmers were likely minimal.<sup>46</sup> Farmers in _T_ 4 who had also received extension services in 2015 adopted 0.24 additional practices – an increase of 0.39 _s_ . _d_ . (Columm 4). Thus, the provision of extension services had persistent effects into the second growing season relative to controls consistent with hypothesis H1 for 2017.<sup>47</sup> We view this as encouraging evidence of the longer-term effectiveness of extension services when paired with soil analysis based recommendations. Farmers who, in addition to the recommendations and services, were offered an inflexible grant ( _T_ 2) adopt 0.33 more practices (statistically indistinguishable from the treatment effect for _T_ 4) and farmers who received plot-level recommendations adopted 0.46 more practices. However, we cannot reject the null hypothesis that the treatment effect is the same for all three arms (the p-value is 0.59, see Table B5).

> 46Note also that control farmers were much less likely to have a copy of the recommendations on hand when asked by surveyors.

> 47Since farmers in _T_ 4 received their analyses an recommendations a year before the control group, the differences in outcomes between them and the controls reflect both the effect of the extension services as well as the effect of having had the recommendations for an extra year.

23
More interestingly, farmers who had received the flexible grant adopted 0.78 more practices (a 1.08 standard deviation increase), substantively more than farmers with the inflexible grant in _T_ 2. Thus farmers with autonomy were more likely to persist with new practices introduced by the project two years after the intervention ended, consistent with H4b. In order to examine this A11. In result in greater detail we turn to the individual practices reported in Appendix Table 2017, farmers in _T_ 3 are more likely (than those in _T_ 2) to use hybrid seeds, sow with a precision drill and use YARA fertilizers. Intriguingly, while the rental of the precision drill and YARA fertilizers were subsidized by the in-kind grant, hybrid seeds, while recommended, were not subsidized. It thus appears that farmers with the flexible grant during the intervention perhaps spent more time evaluating different options and made decisions accordingly – adopting in 2017 some practices that were mandatory for farmers in _T_ 1 and _T_ 2 in 2015 and others that were not.

# **6.1 Understanding the Role of Autonomy**

In this subsection we seek to better understand the reasons for adoption by farmers with autonomy, both during as well as after the intervention. As noted above, although farmers with autonomy were not required to follow any of the intervention recommendations, most did so and at rates comparable to those of farmers without autonomy. This was true both for the purchase of inputs (precision drill, fertilizer) as well as extension services (AEW training sessions and plot visits) which were free of charge. It appears that during the intervention farmers with autonomy trusted the recommendations enough to follow them without being required to do so and that this effect persisted into subsequent growing seasons.

We provide two pieces of evidence in support of hypothesis H4a and H4b in Tables 10 and 11. First and most directly, we find that during the intervention farmers with autonomy (i.e. the flexible grant) were 0.35 standard deviations more likely to report trusting recommendations from the implementing partners (Ipampa and the local input supplier) relative to farmers with the inflexible grant in _T_ 2.<sup>48</sup> Second, we find that farmers with autonomy were more likely to report remembering the recommendations and state that they would follow them in the next growing season: farmers in _T_ 3 were 5 _pp_ more likely to report that they remembered the sowing recommendations (relative to farmers in _T_ 2) and 14 _pp_ more likely (than farmers in _T_ 2) in 2016 to report intending to follow the program recommendations in the next growing season. Finally in 2017, farmers with autonomy expressed the highest willingness to pay for (high-quality) KCl, the least known and used fertilizer at baseline, and hence the one whose increased usage was most recommended by the program (Table 11). Consistent with laboratory studies showing that greater autonomy increases effort, we find a 6% increase in hired labor when the grant is

> 48Trust was measured on a five-point Likert scale that ranged from “Always trust” to “Never trust.” See Appendix Table A12

24
flexible compared to when it is inflexible (Column 5 of Table 10).

Farmers with autonomy also evinced a more open attitude towards change and experimentation.<sup>49</sup> In 2017, farmers in _T_ 3 are 0.27 _s_ . _d_ . more likely to exhibit a positive attitude towards change compared to farmers in _T_ 2. This last finding is interesting since it is consistent with the flexible grant leading to a willingness to experiment (perhaps by giving farmers a greater sense of agency). Such inferences, although speculative, do suggest fruitful areas for further research.

To summarize, farmers with the flexible grant in 2015 were more likely to exhibit increased knowledge of and trust in the program after it ended and display a more positive attitude towards experimentation. These changes could help explain why we see a greater persistence in program practices among this group two years after the program ended.

# **7 Conclusion**

Encouraging technology adoption in agriculture among smallholder farmers is an active area of research and a first-order policy issue. In this paper we test several hypotheses on the role of information in technology adoption that explicitly recognize the importance of heterogeneity in soil quality for best practices. We conduct detailed soil analyses and document substantial heterogeneity in soil quality, even within relatively small clusters, and correspondingly heterogeneous optimal recommendations for fertilizer usage. We show that farmers’ current fertilizer deviate from the localized ones both in mix as well as application practices markedly input timing (with a substantial overuse of urea and DAP).

We conducted a randomized experiment with a control and four treatment arms that provided a combination of localized soil analyses, recommendations and extension services, and an in-kind grant. We vary the level of recommendation specificity (plot level versus cluster level) and the degree of autonomy in the usage of the grant (flexible versus inflexible). The experimental design allows us to credibly test the effects of (a) pairing localized recommendations with extension services, (b) recommendation specificity, (c) an in-kind grant and (d) autonomy, with (a), (b) and (d) being, to the best of our knowledge, novel to the literature.

We document that varying the level of recommendation specificity had little effect on outcomes both in the short and longer-term so that there is no downside in choosing to provide cheaper recommendations based on cluster (localidad) level soil analyses relative to more expensive plot-level recommendations, even in a context with substantial heterogeneity. We find that pairing localized recommendations with extension services had modest but persistent ef-

> 49We measure this using a standardized index of five questions described in Appendix C (non-cognitive measures) of Laajaj and Macours (2017). Openness is measured by affirmative answers to agriculture specific issues (e.g. to statements such as “When you are presented with a new agricultural technology, you are willing to try it first”, “In your plots, you prefer to try something new”) as well as more general views about experimentation (e.g. “Generally speaking, you prefer to try new things”).

25
fects on agricultural practices. Adding the in-kind grant had important short-term effects with improved practice adoption and productivity but only during the intervention, with adoption returning to pre-intervention levels afterwards.

However, we find that farmers with greater autonomy (defined as the ability to choose how the in-kind grant was spent) showed substantially greater persistence relative to farmers without such autonomy. We provide some evidence that farmers with greater autonomy had greater trust in the project partners, remembered the recommendations better and also exhibited a more positive attitude towards experimentation after the intervention ended. The results suggest an important instrumental role for autonomy in increasing adherence to externally provided recommendations. We consider examining the interaction between the exercise of autonomy and the adherence to expert advice a fruitful avenue for research.

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# **Tables and Figures**

**Figure 1:** Map of Tlaxcala

29
<!-- Start of picture text --> fo}<br>+<br>fo}<br>oO<br>€<br>a<br>a<br>Zz<br>~—o<br>coN<br>o<br>D<br>2<br>=<br>oO=<br>Oo<br>1 2 3 4<br>Agricultural zone<br><!-- End of picture text --> <!-- Start of picture text --> fo}<br>ice)<br>fo}<br>o<br>=<br>Qa<br>a<br>x<br>ao2¢<br>°<br><=<br>a<br>ao<br>fe}<br>x=<br>a<br>fo}<br>N<br>fo}<br>1 2 3 4<br>Agricultural zone<br><!-- End of picture text --> <!-- Start of picture text --> fo}<br>lo}<br>wo<br>So<br>oO<br>+<br>5<br>£8<br><°<br>£<br>2<br>&&Bono<br>fo}<br>ao<br>fo}<br>oO=<br>Oo<br>1 2 3 4<br>Agricultural zone<br><!-- End of picture text -->
**Table 1:** Timeline of Activities

|**Season/Date**|**Activity**|
|---|---|
|_Pre-planting 2015_||
|January 2015<br>February 2015|Farmer Registration<br>Soil sampling<br>Baseline survey (farmer characteristics<br>and 2014 practices)|
|_Planting 2015_||
|March 2015<br>April-July 2015|Delivery of soil analysis<br>Orders of fertilizers<br>Intervention|
|August 2015|Follow-up survey (2015 practices)|
|_Harvest 2015_||
|October-December 2015<br>February 2016|Yield estimation<br>2015 Self-reported yields survey|
|_Commercialization 2015_||
|June 2016|2015 Commercialization survey<br>(prices, sales and costs)|
|_Planting 2017_||
|May 2017|Follow-up survey (2017 practices)|

31
|p-value|0.29<br>0.37<br>0.81<br>0.31|0.66|0.80|0.15|0.74|0.91|0.07<br>0.43<br>0.19<br>0.31|ue of a F-test that|
|---|---|---|---|---|---|---|---|---|
|Mean<br>(SD)|29.41<br>(30.88)<br>0.52<br>(0.50)<br>0.20<br>(0.40)<br>0.13<br>(0.34)|0.50<br>|(0.50)<br>0.28<br>034|(.)<br>0.06<br>|(0.24)<br>0.15<br>|(0.35)<br>0.87<br>(0.33)|14.14<br>(7.00)<br>28.18<br>(25.54)<br>198.98<br>(137.43)<br>9.60<br>(8.54)|d report the p-val|
||Annual income in 2014 (000s pesos)<br>Reported liquidity constraints (1=Yes)<br>Ever applied for a loan (1=Yes)<br>Never takes risks (1=Yes)|Farmer sold maize in 2014 (1=Yes)|Share of maize production sold in 2014|Ever had extension services (1=Yes)|Ever done soil analysis (1=Yes)|Supported by a government program in 2014 (1=Yes)|Nitrogen (N) (ppm)<br>Phosphorus (P) (ppm)<br>Potassium (K) (ppm)<br>CEC (cmolc/kg)|iations. We also regress the baseline values of each variable on a set of treatment dummies an<br>t of the intervention. See Appendix Table|
|p-value|0.82<br>0.37<br>0.16<br>0.06|0.35|0.93|0.98|0.95|0.97|0.96<br>0.98<br>0.09<br>0.03|nd standard dev<br>5, before the star|
|Mean<br>(SD)|_Panel A: Farmer Characteristics_<br>Male (1=Yes)<br>0.83<br>(0.38)<br>Age (years)<br>56.95<br>(13.52)<br>Years as farmer<br>34.90<br>(16.15)<br>Completed primary school (1=Yes)<br>0.60<br>(0.49)|_Panel B: 2014 Practices & Yields_<br>Number of plots cultivated in 2014<br>2.09<br>|(1.27)<br>Total area cultivated (ha) in 2014<br>5.77<br>445|(.)<br>Number of fertilizations in 2014<br>1.58<br>|(0.54)<br>Number of days between sowing and 1st fertilization in 2014<br>36.32<br>|(21.48)<br>Self-reported yield (ton/ha) in 2014<br>2.06<br>(1.16)|_Panel C: Plot Characteristics_<br>Sand (%)<br>70.93<br>(9.96)<br>Clay (%)<br>14.71<br>(5.43)<br>Slope (degrees)<br>3.24<br>(3.43)<br>pH (1:2 water)<br>6.07<br>(0.71)|Note: This table reports summary statistics for the study sample of 678 farmers in 2014. For each variable, we report the mean a<br>all treatment coeffcients are jointly equal to zero. The data were collected using our Baseline survey conducted in February 201<br>reftab:defvars for the defnition of the variables.|

32
|(5)<br>(6)<br>(7)<br>(8)<br>Average<br>p-value<br>p-value<br>recomm. in 2015<br>Individual<br>Control vs.<br>Mean<br>p-value<br>vs. Average<br>Individual<br>T2-T4<br>T2-T4 vs. C<br>recomm.<br>recomm.|138.90<br>0.94<br>0.00<br>0.00<br>(12.38)<br>18.67<br>0.91<br>0.00<br>0.00<br>(16.73)<br>30.34<br>0.98<br>0.07<br>0.00<br>(17.45)<br>1,672.20<br>0.94<br>0.00<br>0.55<br>(215.33)<br>404<br>sample. Column 1 reports self-reported average amounts applied and total cost of fertil-<br>nd report in column 2 the p-value of an F-test that the dummies are jointly equal to zero.<br>mn 4 we compare the individual recommendations to those that the control group would<br>s to columns 3 and 4 for the average input recommendations and costs for farmers in _T_2,<br>nd individual recommendations given to farmers in _T_1 by regressing each variable on a<br>amounts of each fertilizer and costs of individual and average recommendations by using<br>eports the p-value of a t-test that_T_1=0. We use administrative data on recommendations<br>4.|
|---|---|
|(4)<br>idual<br> in 2015<br>p-value<br>T1 vs. C|0.86<br>0.24<br>0.77<br>0.33<br>ers in our study<br>ment dummies a<br>rs in _T_1. In colu<br>d 6 are analogou<br>l group in 2014 a<br>8 compares the<br>armers in_T_1. It r<br>in February 201|
|(1)<br>(2)<br>(3)<br>Fertilizer<br>Indiv<br>application<br>recomm.<br>Control mean<br>p-value<br>Mean<br>in 2014<br>T1-T4 vs. C<br>T1|Urea(kg/ha)<br>267.81<br>0.72<br>151.23<br>(135.25)<br>(32.64)<br>DAP(kg/ha)<br>49.41<br>0.87<br>30.65<br>(65.27)<br>(26.32)<br>KCL(kg/ha)<br>9.45<br>0.78<br>33.27<br>(26.15)<br>(28.16)<br>Cost(pesos/ha)<br>1,895.49<br>0.55<br>1,882.97<br>(1,181.91)<br>(398.83)<br>Observations<br>130<br>133<br>Note: This table reports average amounts of fertilizers and costs and balance checks for the 678 farm<br>izers in 2014 by the control group (SDs in parentheses). We regress each variable on our set of treat<br>Column 3 reports the average input recommendations and the corresponding input costs for farme<br>have received by reporting the p-values of an F-test analogous to those in column 2. Columns 5 an<br>_T_3 and _T_4. Column 7 compares the amounts of each fertilizer and costs self-reported by the contro<br>dummy that takes value of 1 for farmers in_T_1. It reports the p-value of a t-test that_T_1=0. Column<br>the sample of farmers in_T_1_−T_4 and regressing each variable on a dummy that takes value of 1 for f<br>given to farmers (and hypothetical recommendations to controls) and Baseline survey data collected|

33
**Table 4:**

||Extension|Individual<br>recommendation|Grant|Flexible|
|---|---|---|---|---|
|_T_1|Yes|Yes|Yes|No|
|_T_2|Yes|No|Yes|No|
|_T_3|Yes|No|Yes|Yes|
|_T_4|Yes|No|No|No|
|_C_|No|No|No|No|
|Hypothesis|H1|H2|H3|H4a, H4b|
|Test|_T_4=_C_|_T_1= _T_2|_T_2= _T_4|_T_2= _T_3|

Note: The main specification is _Yit_ = _β_ 0 + _βE_ Extension _i_ + _β I_ IndRecomm _i_ + _βG_ Grant _i_ + _βF_ Flexible _i_ + _αc_ + _εit_ . This table shows how the dummies used are defined from the original treatment arms, the test associated to each _β_ - coefficient and the Hypothesis tested in subsection 3.4.

34
|(8)<br>Total<br>(Std. Index)<br>-0.62**<br>(0.20)<br>3.69***<br>(0.16)<br>-0.03<br>(0.16)<br>548<br>0.56<br>0.00<br>1.00<br>_αc_+_εit_ , where_i_corre-<br>of the outcome variable<br>m to fertilize at sowing.<br>achinery were advised<br>d 45 days after sowing.<br>g sessions each farmer<br>following season. The<br>was visited by the our<br>the outcome in column<br>1, ** p_<_0.05, * p_<_0.1.|
|---|
|(7)<br>Total<br>(0-9)<br>-0.76**<br>(0.31)<br>4.83***<br>(0.26)<br>0.12<br>(0.24)<br>548<br>0.48<br>2.34<br>2.11<br>_i_+_βFFlexiblei_+ <br>andard deviation<br>ovided by our tea<br>use the sowing m<br>t should be applie<br>number of trainin<br>reparation for the<br>y times the farmer<br>dardized index of<br>ntheses. *** p_<_0.0|
|(6)<br># AEW<br> <br>visits (0-3)<br>-0.29**<br>(0.12)<br>1.23***<br>(0.13)<br>0.01<br>(0.09)<br>548<br>0.30<br>1.40<br>1.28<br>_ndRecommi_+_βGGrant_<br>ort the mean and the st<br>precision machinery pr<br>y. Farmers who did not<br>of YARA fertilizers, tha<br>ent variable counts the<br>ement, as well as soil p<br>iable counts how man<br>ndent variable is a stan<br>Standard errors in pare|
|(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>Precision<br>1_st_ Package<br>2_nd_ Package<br>Out-of-pocket<br># training<br>drill (1=Yes)<br>(1=Yes)<br>(1=Yes)<br>(Mex$/ha)<br>sessions (0-3)<br>Individual recommendation (1=Yes)<br>-0.09*<br>-0.09**<br>-0.21***<br>2.90<br>-0.08<br>(0.05)<br>(0.04)<br>(0.05)<br>(39.34)<br>(0.11)<br>Grant (1=Yes)<br>0.76***<br>0.83***<br>0.74***<br>315.68***<br>1.27***<br>(0.04)<br>(0.03)<br>(0.04)<br>(22.42)<br>(0.11)<br>Flexible (1=Yes)<br>-0.12**<br>0.07**<br>-0.02<br>-103.96***<br>0.20**<br>(0.05)<br>(0.03)<br>(0.05)<br>(28.51)<br>(0.09)<br>Observations<br>548<br>548<br>548<br>548<br>548<br>R-squared<br>0.43<br>0.63<br>0.42<br>0.27<br>0.35<br>Mean dep. var. T4<br>0.08<br>0.07<br>0.04<br>0.00<br>0.76<br>SD dep. var. T4<br>0.27<br>0.25<br>0.19<br>0.00<br>0.95<br>Note: this table reports results on the take-up of our proposed treatment by farmers in our sample. Using only the set of 548 treated farmers, we run the following regression: _Yit_ = _β_0+_βI I_<br>sponds to a farmer,_Y_is the outcome of interest and_t_is the time period. We include randomization strata fxed effects and compute robust standard errors. At the bottom of the table, we rep<br>for the group of farmers who did not receive the grant (T4), the omitted category in our regression. In column 1, the dependent variable is a dummy that takes value 1 if the farmer used the<br>In column 2, the dependent variable is a dummy that takes value 1 if the farmer took up the frst package of YARA fertilizers, that should be applied at sowing using the precision machiner<br>to use this package 30-60 days after sowing depending on how their crop grew. In column 3, the dependent variable is a dummy that takes value 1 if the farmer took up the second package<br>In column 4, the dependent variable is the amount that each farmer had to pay for the packages of fertilizers, on top of the QFD subsidies, if they received them. In column 5, the depend<br>attended, out of 3 our team organized. The frst training session introduced farmers on how to fertilize at sowing. The second one aimed at on harvesting and preparations for yield measur<br>third training session covered the importance of using quality fertilizers and herbicides, as well as on the right timing to fertilize during plant development. In column 6, the dependent var<br>team to be provided with technical assistance (out of 3 scheduled visits). In column 7, the dependent variable is the sum of columns 1, 2, 3, 5 and 6, varying from 0 to 9. In column 8, the depe<br>8, computed by standardizing each variable individually, adding them all and standardizing the sum. We use the mean and standard deviation of T4 as reference for the standardized index.|

35
**Table 6:** Practices 2015

||(1)<br>Existing<br>Total practices<br>applied|(2)<br>practices<br>Standardized<br>Index|(3)<br>All new<br>Total practices<br>applied|(4)<br>practices<br>Standardized<br>Index|
|---|---|---|---|---|
|Extension (1=Yes)|0.04|0.06|0.35**|0.33**|
||(0.08)|(0.13)|(0.11)|(0.14)|
|Individual recommendation (1=Yes)|0.03|0.05|-0.33**|-0.28*|
||(0.07)|(0.09)|(0.14)|(0.16)|
|Grant (1=Yes)|0.04|0.12|2.21***|1.63***|
||(0.08)|(0.11)|(0.14)|(0.16)|
|Flexible (1=Yes)|-0.01|-0.01|-0.07|-0.05|
||(0.07)|(0.08)|(0.14)|(0.18)|
|Observations|678|678|678|678|
|R-squared|0.07|0.07|0.55|0.34|
|Mean dep. var. control|2.38|0.00|0.32|0.00|
|SD dep. var. control|0.61|1.00|0.69|1.00|
|T1: _βE_+_βI_+_βG_ =0|0.11|0.03|0.00|0.00|
|T2: _βE_+_βG_ =0|0.25|0.09|0.00|0.00|
|T3: _βE_ + _βG_ + _βF_ =0|0.32|0.09|0.00|0.00|

Note: this table reports results on the agricultural practices performed by the farmers in our study in the 2017 season. Using the full sample of 678 farmers, we run the following regression: _Yit_ = _β_ 0 + _βE Extensioni_ + _β I IndRecommi_ + _βG Granti_ + _βF Flexible_ + _αc_ + _εit_ , where _i_ corresponds to a farmer, _Y_ is the outcome of interest and _t_ is the time period. We include randomization strata fixed effects and compute robust standard errors. At the bottom of the table, we report the mean and the standard deviation of the outcome for the control group, the omitted category in our regression. We also report p-values of linear combinations of the estimates coefficients that map into the original study design. We use data from the Follow-up survey conducted in August 2015. The dependent variable in column 1 is a sum of individual dummies. Each dummy takes value of 1 if the farmer performed one of the so-called existing agricultural practices. In column 2, the dependent variable is the standardized index of the outcome in column 1, computed by standardizing each dummy individually, adding them all and standardizing the sum. We use the mean and standard deviation of the control group as reference for the standardized index. In columns 3 and 4, the dependent variables are analogous to the outcomes in columns 1 and 2, computed for the so-called new practices. The existing practices are: 1.Ploughing, 2. Using inorganic fertilizer and 3. Covering the fertilizer. The new practices are: 1.Ripping, 2.Using hybrid seeds, 3.Fertilizing at sowing, 4.Sowing with precision machinery, 5.Using YARA’s fertilizers, and 6. Using preemergent herbicide Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

36
|(4)<br>(5)<br>(6)<br>plied vs. recommended<br>Total<br>Urea (kg/ha)<br>DAP (kg/ha)<br>KCl (kg/ha)<br>-4.96<br>1.89<br>-0.82<br>(10.80)<br>(5.69)<br>(3.79)<br>12.86<br>3.96<br>8.07***<br>(8.25)<br>(3.84)<br>(1.92)<br>-76.70***<br>-31.81***<br>-27.54***<br>(9.33)<br>(4.92)<br>(2.65)<br>2.55<br>-1.74<br>5.07**<br>(7.57)<br>(3.37)<br>(2.25)<br>665<br>665<br>665<br>0.22<br>0.20<br>0.27<br>114.90<br>37.83<br>32.66<br>82.62<br>47.92<br>33.98<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00<br>es. Using the sample for the full sample of 678 farmers, except for 13 farmers<br> +_βFFlexiblei_ +_αc_+_εit_, where_i_corresponds to a farmer,_Y_ is the outcome of<br>we report the mean and the standard deviation of the outcome for the control<br>inal study design. We use data from the Commercialization survey conducted<br>commended dosages of Urea, DAP and KCl, respectively. In columns 4-6, we<br>p_<_0.1.|
|---|
|(1)<br>(2)<br>(3)<br>Absolute difference, ap<br>At sowing<br>Urea (kg/ha)<br>DAP (kg/ha)<br>KCl (kg/ha)<br>Extension (1=Yes)<br>-1.71<br>0.35<br>-1.43<br>(1.98)<br>(2.43)<br>(1.32)<br>Individual recommendation (1=Yes)<br>3.04<br>3.23<br>1.82**<br>(2.08)<br>(2.02)<br>(0.90)<br>Grant (1=Yes)<br>-30.34***<br>-16.26***<br>-13.41***<br>(2.00)<br>(2.25)<br>(0.85)<br>Flexible (1=Yes)<br>3.54*<br>2.37<br>3.41***<br>(1.89)<br>(1.57)<br>(1.02)<br>Observations<br>665<br>665<br>665<br>R-squared<br>0.47<br>0.32<br>0.41<br>Mean dep. var. control<br>38.69<br>19.40<br>16.44<br>SD dep. var. control<br>14.64<br>21.36<br>14.42<br>T1: _βE_+_βI_+_βG_ =0<br>0.00<br>0.00<br>0.00<br>T2: _βE_+_βG_ =0<br>0.00<br>0.00<br>0.00<br>T3: _βE_+_βG_+_βF_ =0<br>0.00<br>0.00<br>0.00<br>Note: This table reports results on the usage of fertilizers by the farmers in our study in the 2015 season, compared to the recommended dosag<br>for which we do not have data on usage of fertilizers, we run the following regression: _Yit_ = _β_0+_βEExtensioni_ +_βI IndRecommi_ +_βGGranti_ <br>interest and_t_is the time period. We include randomization strata fxed effects and compute robust standard errors. At the bottom of the table,<br>group, the omitted category in our regression. We also report p-values of linear combinations of the estimates coeffcients that map into the orig<br>in June 2016. In columns 1-3, the dependent variables are the absolute differences between the amount applied by farmers at sowing and the re<br>report analogous outcomes for the total amount of fertilizers applied in the full season. Standard errors in parentheses. *** p_<_0.01, ** p_<_0.05, *|

37
|(4)<br>(5)<br>Profts<br>Profts (no subsidy)<br>(Mex$/ha)<br>(Mex$/ha)<br>609.48<br>763.60<br>(529.58)<br>(531.50)<br>223.80<br>36.00<br>(611.89)<br>(612.56)<br>-291.38<br>1539.96**<br>(567.34)<br>(568.21)<br>-57.59<br>-43.55<br>(536.10)<br>(542.22)<br>678<br>678<br>0.21<br>0.23<br>2639.20<br>2718.87<br>4024.33<br>4024.42<br>0.36<br>0.00<br>0.54<br>0.00<br>0.61<br>0.00<br>e run the following regression: _Yit_ = _β_0 +_βEExtensioni_ +<br>riod. We include randomization strata fxed effects and com-<br>group, the omitted category in our regression. We also report<br>zation survey conducted in June 2016. In column 1, we use as<br>ntains the value of farmers’ maize production (per hectare) in<br>mer in the 2015 season by the price the maize could be sold in<br>farmers when computing the value of the maize production.<br>stment in soil preparation activities, fertilizers (chemical and<br>at was the case), as well as the subsidy for fertilizer packages,<br>5, the dependent variable is the cost of production, not includ-<br>farmer sold maize, we use median prices at the municipality<br>re measured at the municipality level on the RHS of columns|
|---|
|(3)<br>Costs<br>(Mex$/ha)<br>46.55<br>(273.37)<br>194.98<br>(229.50)<br>623.25**<br>(263.63)<br>353.14<br>(250.81)<br>678<br>0.22<br>5280.02<br>2351.52<br>0.00<br>0.01<br>0.00<br>ple of 678 farmers, w<br>st and_t_is the time pe<br>tcome for the control<br>from the Commerciali<br>ependent variable co<br>harvested by the far<br>ket as the price for all<br>include the total inve<br>aid by QFD (when th<br>ns 2 and 3. In column<br>. In localities where no<br>lue of one if prices we|
|(2)<br>Revenue<br>(Mex$/ha)<br>656.03<br>(543.49)<br>418.78<br>(599.52)<br>331.87<br>(564.96)<br>295.54<br>(543.03)<br>678<br>0.28<br>7919.22<br>4397.72<br>0.02<br>0.06<br>0.02<br>n. Using the full sam<br>the outcome of intere<br>rd deviation of the ou<br>design. We use data<br>on. In column 2, the d<br>total amount of maize<br>production in the mar<br>ch farmer. Total costs<br>d harvest machines p<br>ent variable in colum<br>e revenues and profts<br>dummy that takes va|
|(1)<br>Self-reported<br>yields (t/ha)<br>Extension (1=Yes)<br>0.21<br>(0.16)<br>Individual recommendation (1=Yes)<br>0.12<br>(0.18)<br>Grant (1=Yes)<br>0.07<br>(0.16)<br>Flexible (1=Yes)<br>0.08<br>(0.16)<br>Observations<br>678<br>R-squared<br>0.25<br>Mean dep. var. control<br>2.36<br>SD dep. var. control<br>1.33<br>T1: _βE_+_βI_+_βG_ =0<br>0.02<br>T2: _βE_+_βG_ =0<br>0.07<br>T3: _βE_+_βG_+_βF_ =0<br>0.02<br>Note: this table reports results on yields and profts earned by farmers in the 2015 seaso<br>_βI IndRecommi_ +_βGGranti_ +_βFFlexiblei_ +_αc_+_εit_ , where_i_corresponds to a farmer,_Y_ is<br>pute robust standard errors. At the bottom of the table, we report the mean and the standa<br>p-values of linear combinations of the estimates coeffcients that map into the original study<br>the dependent variable the maize yields (tons/ha) self-reported by farmers in the 2015 seas<br>the 2015 season. The value of the production (per hectare) is computed by multiplying the<br>the market. We take the median price faced by farmers who sold at least a fraction of their<br>In column 3, the dependent variable is the total cost of production cost self-reported by ea<br>organic), herbicides, pesticides, and labor. We also include the cost of renting the sowing an<br>also paid by QFD. In column 4, the dependent variable is the difference between the depend<br>ing the subsidies paid by QFD. We use median market prices at the locality level to calculat<br>level. To account for this imputation of prices (for 20 farmers in our sample), we include an<br>2-5. Standard errors in parentheses. *** p_<_0.01, ** p_<_0.05, * p_<_0.1.|

38
**Table 9:** Practices 2017

||(1)<br>Existing pra<br>Total practices<br>applied|(2)<br>ctices 2017<br>Standardized<br>Index|(3)<br>All new pra<br>Total practices<br>applied|(4)<br>ctices 2017<br>Standardized<br>Index|
|---|---|---|---|---|
|Extension (1=Yes)|0.10|0.11|0.24**|0.39**|
||(0.10)|(0.12)|(0.11)|(0.16)|
|Individual recommendation (1=Yes)|0.15|0.13|0.13|0.09|
||(0.11)|(0.12)|(0.13)|(0.19)|
|Grant (1=Yes)|-0.21**|-0.20*|0.09|0.14|
||(0.10)|(0.11)|(0.13)|(0.19)|
|Flexible (1=Yes)|0.05|0.05|0.45**|0.55**|
||(0.10)|(0.12)|(0.14)|(0.19)|
|Observations|678|678|678|678|
|R-squared|0.07|0.05|0.20|0.17|
|Mean dep. var. control|2.31|0.00|0.42|0.00|
|SD dep. var. control|0.89|1.00|0.79|1.00|
|T1: _βE_+_βI_+_βG_ =0|0.69|0.69|0.00|0.00|
|T2: _βE_+_βG_ =0|0.34|0.50|0.00|0.00|
|T3: _βE_ + _βG_ + _βF_ =0|0.59|0.76|0.00|0.00|

Note: this table reports results on the agricultural practices performed by the farmers in our study in the 2017 season. Using the full sample of 678 farmers, we run the following regression: _Yit_ = _β_ 0 + _βE Extensioni_ + _β I IndRecommi_ + _βG Granti_ + _βF Flexiblei_ + _αc_ + _εit_ , where _i_ corresponds to a farmer, _Y_ is the outcome of interest and _t_ is the time period. We include randomization strata fixed effects and compute robust standard errors. At the bottom of the table, we report the mean and the standard deviation of the outcome for the control group, the omitted category in our regression. We also report p-values of linear combinations of the estimates coefficients that map into the original study design. We use data from the Final survey conducted in May 2017. The dependent variable in column 1 is a sum of individual dummies. Each dummy takes value of 1 if the farmer performed one of the so-called existing agricultural practices. In column 2, the dependent variable is the standardized index of the outcome in column 1, computed by standardizing each dummy individually, adding them all and standardizing the sum. We use the mean and standard deviation of the control group as reference for the standardized index. In columns 3 and 4, the dependent variables are analogous to the outcomes in columns 1 and 2, computed for the so-called new practices. The existing practices are: (a) ploughing, (b) using inorganic fertilizer and (c) covering the fertilizer. The new practices are: (a) deep tilling (ripping), (b) using hybrid seeds, (c) fertilizing at sowing, (d) sowing with precision machinery, (e) using pre-emergent herbicide and (f) using high-quality fertilizers. Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

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|(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>(6)<br>Knowledge of recommendations<br>Trust in the<br>Would follow<br>recomm.<br>Attitudes<br>Remembers<br>the recomm.<br>from input<br>Number of<br>towards<br>sowing<br>Famers kept<br>in the<br>supplying<br>workers<br>change:<br>recomm.<br>soil analysis<br>following<br>institutions:<br>in 2015<br>standardized<br>(1=Yes)<br>(1=Yes)<br>year (1=Yes)<br>standardized index<br>(exc. harvest)<br>index<br>_Year of data collection_<br>_2015_<br>_2016_<br>_2016_<br>_2015_<br>_2015_<br>_2017_|Extension (1=Yes)<br>0.29***<br>-0.04<br>0.61***<br>-1.44**<br>-0.09<br>(0.06)<br>(0.06)<br>(0.14)<br>(0.51)<br>(0.13)<br>Individual recommendation (1=Yes)<br>0.00<br>-0.06<br>0.38***<br>0.04<br>0.41<br>0.03<br>(0.02)<br>(0.04)<br>(0.06)<br>(0.13)<br>(0.40)<br>(0.11)<br>Grant (1=Yes)<br>-0.01<br>0.07*<br>-0.06<br>0.36**<br>0.18<br>0.14<br>(0.02)<br>(0.04)<br>(0.06)<br>(0.13)<br>(0.43)<br>(0.12)<br>Flexible (1=Yes)<br>0.05*<br>-0.02<br>0.14**<br>0.35**<br>0.76*<br>0.27**<br>(0.03)<br>(0.03)<br>(0.06)<br>(0.12)<br>(0.42)<br>(0.11)<br>Observations<br>522<br>645<br>678<br>638<br>678<br>678<br>R-squared<br>0.07<br>0.15<br>0.13<br>0.24<br>0.15<br>0.13<br>Mean dep. var. control (or T4)<br>0.03<br>0.58<br>0.39<br>-0.00<br>11.55<br>-0.03<br>SD dep. var. control (or T4)<br>0.18<br>0.50<br>0.49<br>1.00<br>4.74<br>1.12<br>T1: _βE_+_βI_+_βG_ =0<br>0.89<br>0.00<br>0.00<br>0.00<br>0.08<br>0.52<br>T2: _βE_+_βG_ =0<br>0.00<br>0.09<br>0.00<br>0.01<br>0.68<br>T3: _βE_+_βG_+_βF_ =0<br>0.13<br>0.00<br>0.54<br>0.00<br>0.32<br>0.01<br>Note: this table reports results on the effect of the grant fexibility on a variety of outcomes.<br>Using a subset of 522 among the 598 treated farmers, in Column 1 report the estimates from the following regression: _Yit_ =<br>_β_0 +_βI IndRecommi_ +_βGGranti_ +_βFFlexiblei_ +_αc_ +_εit_ , where _i_ corresponds to a farmer, _Y_ is the outcome of interest and _t_ is the time period. In Columns 2-6, we report the point estimates of the following specifcation:<br>_Yit_ = _β_0+_βEExtensioni_ +_βI IndRecommi_ +_βGGranti_ +_βFFlexiblei_ +_αc_ +_εit_, We include randomization strata fxed effects and compute robust standard errors. At the bottom of the table, we report the mean and the standard<br>deviation of the outcome for the omitted group of farmers, which in Column 1 corresponds to those who did not the grant (T4). In Columns 2-7, the omitted category is the control group of farmers. We also report p-values of linear<br>combinations of the estimates coeffcients that map into the original study design. In Column 1, the dependent variable is a dummy that takes value 1 if the farmer reported remembering the sowing recommendations were given to<br>him or her as part of our intervention. This question was asked in August 2015 during our Follow-up survey. In Column 2, the dependent variable is a dummy that takes value 1 if the farmer kept record of the soil analysis given to<br>him or her as part of our intervention. 33 farmers did not answer this question. In Column 3, the dependent variable is a dummy that takes value 1 if the farmer reported that he or she would probably follow the recommendations<br>in the following season. Data for Columns 2 and 3 were collected as part of the Commercialization survey, conducted in June 2016. In Column 4, the dependent variable is a standardized index of two individual dummies that take<br>value 1 if the farmers reported trusting the recommendations given by their input suppliers and IPAMPA, a local AES company. To compute the index, we standardize each dummy individually, add them all and standardize the<br>sum. We use the mean and standard deviation of the control group as reference for the standardized index. The questions in the index in Column 4 were asked as part of our Follow-up survey conducted in August 2015. 40 farmers<br>did not answer these questions. The dependent variable in Column 5 is the number of people each farmer reported to have worked in the plot registered to be part of our study, excluding harvesting activities. The data for Column 5<br>were collected as part of our Follow-up survey conducted in August 2015. In Column 6, the dependent variable is a standardized index of 5 questions from the Final Follow-up survey conducted in May 2017 that measured farmers’<br>attitudes towards change. See Appendix TableA12for the defnition of variables. Standard errors in parentheses. *** p_<_0.01, ** p_<_0.05, * p_<_0.1.|
|---|---|

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# **Table 11:** WTP for fertilizers

||(1)<br>Reported WTP for<br>YARA fertilizers|(2)<br>WTP f<br>fertiliz|(3)<br>or a bag of<br>er in 2017(|(4)<br>YARA<br>Mex$)|
|---|---|---|---|---|
||(1=Yes)|Urea|DAP|KCl|
|Extension (1=Yes)|0.20***|71.40***|64.91**|53.11**|
||(0.06)|(19.58)|(24.52)|(20.58)|
|Individual recommendation (1=Yes)|-0.05|-15.16|-11.49|-10.82|
||(0.04)|(14.92)|(19.60)|(18.62)|
|Grant (1=Yes)|0.36***|111.20***|139.15***|110.29***|
||(0.05)|(17.68)|(22.54)|(20.23)|
|Flexible (1=Yes)|0.04|13.75|24.86|46.21**|
||(0.03)|(13.71)|(18.49)|(17.47)|
|Observations|678|678|678|678|
|R-squared|0.33|0.28|0.24|0.25|
|Mean dep. var. control (or T4)|0.33|100.38|121.73|98.46|
|SD dep. var. control (or T4)|0.47|151.12|185.92|157.18|
|T1: _βE_+_βI_+_βG_ =0|0.00|0.00|0.00|0.00|
|T2: _βE_+_βG_ =0|0.00|0.00|0.00|0.00|
|T3: _βE_ + _βG_ + _βF_ =0|0.00|0.00|0.00|0.00|

Note: this table reports results willingness to pay for YARA fertilizers. Using the full sample of 678 farmers in our study, we report the point estimates of the following specification: _Yit_ = _β_ 0 + _βE Extensioni_ + _β I IndRecommi_ + _βG Granti_ + _βF Flexiblei_ + _αc_ + _εit_ , where _i_ corresponds to a farmer, _Y_ is the outcome of interest and _t_ is the time period. We include randomization strata fixed effects and compute robust standard errors. At the bottom of the table, we report the mean and the standard deviation of the outcome for the control group, the omitted category in our regression. We also report p-values of linear combinations of the estimates coefficients that map into the original study design. We use data from the Final survey conducted in May 2017. In Column 1, the dependent variable is a dummy that takes value 1 if the farmer reported his or her willingnes to pay for any of the 3 YARA fertilizers (Urea, DAP and KCl). In Columns 2-4, the dependent variables are the self-reported willingness to pay for a bag of each of the 3 YARA fertilizers. We imput WTP equal to zero for those who did not report their WTPs. Standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

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