their recent experiences with conflict. Imputation techniques could be invaluable in fragile and conflict-affected regions, offering a way to circumvent the challenges and practical difficulties of extensive crop-cutting surveys. These techniques provide a more efficient and cost-effective method for estimating crop yields, enabling timely and reliable data analysis. Moreover, they can address data collection disruptions caused by conflict or political instability. By imputing missing values, we can maintain the continuity of data series, leading to more complete and consistent datasets for analysis. This is crucial for assessing agricultural trends and evaluating interventions in situations where direct data collection is challenging.

Another practical application of survey-to-survey imputation of crop yields is seen in the WAEMU harmonized survey program. Here, national statistics offices in West Africa collect plot-level agricultural information while conducting parallel farm surveys with crop-cut operations either in the same or the previous year. An across-survey imputation model could effectively fill these crop-cut data gaps.

In this study, we focus on evaluating the validity of the imputation procedure by forecasting (and backcasting) mean crop-cut yields in the 2018 (2017) dataset using the 2017 (2018) cropcut sample as the training sample.

# **3 Data**

We have access to data from two nationally representative surveys of rural areas in Mali, conducted during two successive agricultural campaigns. The Enquˆete Agricole de Conjoncture Int ́egr ́ee aux Conditions de Vie des M ́enages 2017 (EACI-2017), administered in the 2017-2018 season, covered 8,398 households, while the Enquˆete Agricole de Conjoncture 2018 (EAC2018), carried out in the 2018/2019 season, involved 8,225 households. These datasets were collected by the Statistics Unit of the Ministry of Agriculture, in collaboration with the LSMSISA team of the World Bank. This collaboration resulted in repeated cross-sectional surveys of households (in the same Enumeration Areas, or EAs) that gathered comprehensive data on household characteristics and living conditions. The surveys, with a focus on agriculture, provided detailed and precise plot-level information. For each edition of the survey, households were visited twice: the same households interviewed on the first visit were re-interviewed on the second visit, and the visits were planned to match the timing of the post-planting (between August and October) and post-harvest (November and February) periods of the 2017/18 and 2018/19 agricultural rainy seasons. Semi-resident enumerators conducted these visits, with an additional visit between the two for crop-cut harvesting. In both survey waves, crop cuts were carried out for all crops grown during the main agricultural season. A third of the plots per crop were randomly selected for crop cutting from a list of all plots, which was stratified by the crops cultivated by the sampled households within an enumeration area.

## **3.1 Summary statistics and sample counts**

In this analysis, we concentrate on six crops that appear in 80% of all observations and for which non-standard unit conversion factors are used: millet, sorghum, rice, maize, cowpea, and groundnut. Table 2 presents the sample details for these crops across each survey wave.

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