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).

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