(kilograms per hectare, using GPS-based plot areas) based on self-reported maize production, crop cutting and remote sensing are presented in Table 2 for each round.<sup>24</sup> To quell any concerns of bias in self-reported estimates stemming from exposure to full-plot harvest, the computation of self-reported MAPS II averages excludes plots on which full-plot harvest was conducted. Certain patterns emerge that are consistent with expectations. First, maize production and yield are, on average, higher on pure stand plots than their intercropped counterparts, irrespective of the approach to measurement of maize production. This is part due to still using the entire cultivated plot area, without further adjustments for planting density, while computing the intercropped maize yields. Second, there is a marked decline in productivity from MAPS I to MAPS II. This is in line with the increased incidence of crop damage observed by both plot managers (on the full plot) and enumerators (on the crop cutting sub-plots), primarily attributable to a reported increase in drought.<sup>25</sup> Third, in each round, the overall self-reported maize yield is, on average, at least 85 percent higher than its crop cutting and remote-sensing based counterparts. The comparable degree of discrepancy is at least 25 percent in terms of plot-level maize production – although the self-reported maize production is, on average, 84 percent higher than the comparable figure based on full-plot crop cutting. While the means presented in Table 2 illustrate the over-estimation of maize production and yields on the part of our farmers, they do not address whether a systematic bias is at play. The correlates of over-estimation of yields with respect to sub-plot crop cutting based yield measurement are explored in Section 5.

Fourth, the comparison of the averages for production and yield based on alternative measurement methods, including sub-plot crop cutting, full-plot crop cutting and remote sensing, reveals a greater similarity among the estimated yields. On pure stand plots, in MAPS II, the differences in the average plot-level maize yield based on sub-plot crop cutting versus full-plot crop cutting are not statistically significant, lending confidence to sub-plot crop cutting estimates in this domain. Similarly, in MAPS I, the difference in the average plot-level maize yield on pure stand plots based on sub-plot crop cutting versus remote sensing is also not statistically significant, raising hopes regarding the application of high-resolution satellite imagery based remote sensing for the measurement of crop yields in smallholder production systems.

R<sup>2</sup> for the imputation model was 0.59. Within a multiple imputation framework, we subsequently obtained a single imputation of GPS-based plot area for the maize plots that were not measured, using predictive mean matching. Specifically, we use the linear GPS-based plot area prediction as a distance measure to form a set of 5 nearest neighbors out of the plot sample measured with GPS, and randomly pick one of these neighbors whose observed GPS-based area value replaces the missing value for the incomplete case at hand. The completed data set with the imputed GPS-based plot areas was then collapsed at the household-round-level to induce a better understanding of the scale of the maize farms. The average GPS-based household-level total area cultivated with maize was 0.29 hectares in MAPS I (with a maximum value of 4.49 and a standard deviation of 0.36) and 0.26 hectares in MAPS II (with a maximum value of 2.56 and a standard deviation of 0.26).

> 24 The sample is restricted to those in which remote sensing, crop-cutting, and self-reported estimates are available, and to those households which had crop-cutting and farmer-estimated production estimates in MAPS I and MAPS II. 25 Please see Table A2 for round-specific breakdowns of the reported reasons for production loss.

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