an incentive to misreport attendance, we performed independent audits. First, we veried that the attendance list reected the (randomly assigned) eligibility, and found no contamination of the control group. Second, we showed up unannounced at the trainings in all ve districts and veried attendance verbally. Finally, attendance lists were back-checked: a random set of listed participants were visited in November and December of 2010 and asked whether they attended the SLM training. Our results from these audits indicate that attendance was genuine.

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

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

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

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

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

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