villages located in four strata across Dio ̈ıla. ERIVaS was conducted in three visits by semi-resident<sup>4</sup> enumerators.

During the first visit fieldwork, which was conducted in the post-planting period (SeptemberNovember 2017), each household received a light household questionnaire that elicited basic socioeconomic information, and this was augmented with an agriculture questionnaire that collected detailed information on farm organization, land tenure, crop cultivation and seed use, at either parcel- or parcelplot-level, depending on the topic. Following the random selection of the pure stand sorghum plot, the enumerator visited the plot location with the farmer, delineated the plot boundaries with the farmer and stored them on a handheld GPS unit, and set-up an 8x8m sub-plot for crop cutting, following the same protocol that was published by Gourlay et al. (2019) and that ensured the random placement of the sub-plot on each sampled plot. Each sub-plot was further divided into four 4x4m quadrants for which harvest was ultimately processed, weighed, and recorded separately.

During the second visit fieldwork, which was conducted immediately after the harvest (December 2017), the enumerators harvested the sub-plots with the help of the farmers and recorded the weight of the crop production immediately after the harvest and after additional drying at the enumerator’s residence. During the third visit fieldwork, which was implemented during the post-harvest period (February 2018), each household received an agriculture questionnaire that collected self-reported, parcel-plotlevel information on sorghum production and use of agricultural inputs, including fertilizer, household labor, and hired labor. Our analysis is based on 577<sup>5</sup> pure stand sorghum plots, with matching crop cutting and self-reported yield measures, augmented with plot- and household-level survey data and third-party geospatial data that are linked with georeferenced plot locations.

Intensive data quality controls and supervision measures were put in place to ensure that each crop cut sub-plot was placed in accordance with the desired protocol; was not managed by the farmer differently vis-`a-vis the rest of the plot; was not harvested by the farmer without the presence and supervision of the enumerator; and was harvested to ensure that production could be weighed and recorded separately for each quadrant, without including plants outside each quadrant.<sup>6</sup> These supervision measures contribute to the substantial additional costs associated with crop cutting and provide further impetus for our efforts to get more out of crop cutting, self-reported survey data and cutting-edge imputation techniques to improve agricultural survey data even when crop cutting may not be implemented at full scale. On the other hand, intensive supervision measures are unlikely to eliminate biases in SR yields<sup>7</sup> and these biases may in turn be correlated with self-reported information on the very agricultural inputs,

> 4One field team covered each stratum and resided in the most amenable village of that stratum throughout the

> 5Of the 600 plots selected, we drop 23 from this analysis for the following reasons: 18 crop cut subplots were harvested by farmers in the absence of the enumerators and as such had missing crop cut data, 3 plots had missing self-reported data due to a programming mistake, 1 plot had 100% self-reported loss but 0% enumerator assessed damage, and 1 plot was in the opposite situation: 100% enumerator assessed damage but 0% farmer reported loss.

> 6Appendix C.1 provides details of the fieldwork protocol followed for the crop-cut and the questionnaires fielded are available upon request.

> 7 When we compare household production reported by farmers on their selected plots with the non-selected pure sorghum plots, there is no statistically significant difference, indicating that they do not try to be more accurate when reporting the production on the selected plots.

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