imputation solutions to fill the gap for this measure. We group these situations into two broad categories. In the first category, where the data is ”partially missing”, the household survey collects plot level information and agriculture practices, and crop-cuts on a subsample usually selected randomly. A within-survey imputation framework is applied in this case. In the second category, the household survey has plot level information on agriculture practices and self-reported crop production and no harvest data based on crop-cutting. In this case an across survey imputation framework is applied. We provide more details for each of these cases in the reminder of this section.

# **2.1 Partially missing: Within-survey imputation**

The situation of partially missing crop-cut data is a common scenario in most farm surveys.<sup>2</sup> In such cases, crop yield measurements are obtained in two ways: through relatively inexpensive, but less accurate, self-reported harvest data, and through more fieldwork-intensive, expensive, but more objective measures acquired via crop-cutting on a subsample. This survey design is adopted for cost-efficiency and has the advantage of providing multiple measures of crop yields for a small proportion of plots in a single survey.

In the case of LSMS-ISA surveys, several country programs<sup>3</sup> have integrated crop-cutting into their survey operations. The proportion of the sample with crop-cuts (CC) varies across programs. Generally, a list of plot-crop combinations is made at the Enumeration Area (EA) level. If the number of these combinations exceeds a minimum threshold, a random sample of plots is selected for crop-cut implementation.<sup>4</sup> This survey design is conducive to within-survey (within agricultural season) imputation. A key reason for this is that plot-level information and agricultural practices are collected for all plots in the survey, providing a common basis for the covariates used in statistical modeling. In this context, an important question arises: What proportion of crop-cuts is necessary to obtain valid crop yield statistics? This paper addresses this question by varying the proportion of the sample used to train the model.

# **2.2 Completely missing: Across surveys imputation**

In general, the scenarios we consider for completely missing crop-cut data involve living standard surveys with integrated agricultural modules where crop-cut data were not collected, yet there exists a farm survey within the statistical system that does collect plot-level agricultural practice data. To illustrate, consider Mali or Ethiopia, which conduct repeated cross-sectional crop cutting annually. One might question whether it is feasible to conduct crop cutting less frequently—perhaps every other year or every three years—and rely on data imputation to bridge the gaps while reducing costs. This approach is particularly pertinent for these countries, given

> 2Among countries in West Africa conducting annual farm surveys, Burkina Faso with the Enquˆete Permanente Agricole (EPA) conducts crop-cuts on all plots of the households sampled.

> 3Mali, Ethiopia, Uganda, and Malawi are among the countries with a survey design that includes crop-cut collection on a subsample.

> 4Details of the crop-cut sampling protocol can be found in the survey documentation manual on The World Bank Microdata Catalog: https://microdata.worldbank.org/

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