# _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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