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

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

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

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

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

17