maize production estimates are computed by multiplying the crop cut sub-plot production across the 64m<sup>2</sup> area covered by the 8x8m subplot by the ratio of the entire GPS-based plot area in m<sup>2</sup> to 64m<sup>2</sup> .<sup>16</sup> In addition, prior to the start of the MAPS II fieldwork, half of the target household population was chosen at random, within each of the pure stand and intercropped domain in each EA, to be subject to a full-plot crop cut, as part of which the entire area of the selected plot was harvested, shelled and weighed by the enumerator, with help from the crop cut assistants recruited from the household members and the EA-specific crop cut monitor. On the plots selected for full-plot harvest, the harvest of the designated 8x8m subplot was weighed separately from the full-plot harvest to allow for comparative yield analysis. The full-plot harvests were only weighed in the EAs as their transport to and additional drying and reweighing at a central location was deemed logistically infeasible.

# **4.4.2.2. Remote Sensing**

There is a longstanding and active literature on using remote sensing for agricultural monitoring and assessment. Among more recent advances, the GEOGLAM effort to provide in-season forecasting for major cropping regions (Becker-Reshef et al. 2010, Franch et al. 2015) represents an important step forward, as it has operationalized yield forecasting at the national scale. However, because it is based primarily on high temporal, coarse spatial resolution (~5km) data, it is of little value for field-level assessment. At the same time, many studies have evaluated correlations between different reflectance indices and field or sub-field scale crop biomass or yield in specific site/years (e.g., Shanahan et al. 2001, Lobell et al. 2003, Sibley et al. 2014). Although these studies have demonstrated the potential for accurate satellite-based yield estimates, they generally (i) are for large-scale commercial systems and (ii) rely on calibrations that are specific to sites and/or image timings and thus do not generalize well across broad regions. A recent effort by Lobell et al. (2015) has developed a more general approach to combining crop simulation models and remote sensing data to map yields at the field scale, but this approach so far has been tested at the field scale only in large, homogeneous fields of the U.S.

sub-plot pre-harvested, as reported by the enumerator, based on his/her observation, on the crop-cutting questionnaire. In establishing the pre-harvest percentage, the enumerators were trained extensively to distinguish pre-harvest from crop damage. In cases where the full sub-plot was pre-harvested by the farmer, the observation was dropped from analysis. Of the remaining 440 observations in each wave, 8.4 percent and 20 percent of households pre-harvested a portion of the sub-plot in MAPS I and II, respectively. Of those plots on which pre-harvesting took place, the mean share of the sub-plot pre-harvested was 28.5 percent in MAPS I and 26.2 percent in MAPS II. The crop cut yields were not adjusted for any crop damage that may have materialized between the post-planting and the crop cutting visits.

> 16 While not reported here, the MAPS II average sub-plot crop cutting plot-level maize yield was not sensitive whether one used the 64m<sup>2</sup> crop cut sub-plot area or the 16m<sup>2</sup> crop cut sub-plot area covered by a randomly selected 4x4m quadrant within the 8x8m sub-plot. These results are available upon request.

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