# **1 Introduction**

Microdata from agricultural surveys is an essential ingredient for empirical analysis of the economic life of smallholder farmers. Particularly in Africa, farm surveys remain the backbone of agricultural statistics (Carletto et al., 2015). However, collecting accurate data in lowincome countries through agricultural surveys is challenging due to various factors, such as the complexity and seasonality of agricultural operations, illiteracy among respondents, and unfamiliarity with standard units of measurement. Overcoming these challenges requires the implementation of complex and costly survey operations. Therefore, the cost-effectiveness and design efficiency of farm surveys, as well as the use of statistical methods to overcome the analysis challenges of their data, remain active areas of methodological research to which this paper intends to contribute.

The analysis focuses on crop yields at the plot level. Generally, plot-level crop yield statistics obtained from agricultural surveys are calculated using either farmers’ reported harvest weight or enumerators’ measured crop-cut harvest weight. Research has shown that self-reported crop yields are prone to non-classical measurement errors and tend to be higher than objectively measured yields obtained through crop cutting (Abay et al., 2019, Desiere and Jolliffe, 2018, Gourlay et al., 2019, Yacoubou Djima and Kilic, 2024). Although enumerator-measured crop-cutting is widely considered the most precise method for quantifying agricultural output (Fermont and Benson, 2011), its implementation is more costly and time-intensive. As a result, this information is often available only for a subset of the survey, leading analysts to rely on statistical methods and imputation techniques to fill these data gaps.

With the recent advances and their off-the-shelves availability, machine learning (ML) algorithms have become go-to tools of analysts to perform prediction exercises so including them as part of an imputation exercise is a natural next step. Economics research has seen the increasing use of ML techniques in recent years, with proven effectiveness. According to Athey (2018), evaluation of ML’s early contributions to economics, ML techniques have been particularly successful when applied to prediction-based problems, such as the one addressed in our study. In addition, utilizing ML in agricultural economics research is a logical approach to model critical variables, such as crop yield, which involves a complex combination of factors, including soil quality, weather, input timing, and management choices, which have non-linearities and interactions (Storm et al., 2019). Previous research attempting to estimate crop-yields in the context of smallholder farmers using ML techniques often attempt this exercise combining manually-labeled optical imagery, and ground data collection, including as part of household and farm surveys. Azzari et al. (2021) provide examples of these studies and recommendations on how large-scale household surveys should be conducted to generate the data needed to train models for satellite-based crop type mapping in smallholder farming systems. Our study relies essentially on survey data integrated with geospatial variables to make crop yield predictions at a granular level. Yacoubou Djima and Kilic (2024) attempt to apply ML techniques for the prediction of yields, leveraging survey data from Mali, to validate an alternative approach to estimate the relationship between crop yields and inputs. The authors conduct a within-survey imputation exercise that derives predicted, otherwise unobserved, objective crop yields that stem from a machine learning model that is estimated with a random sub-sample of plots for 2