which crop cutting and self-reported yields are both available. This approach allows the authors to replicate the relationship between yields and input obtained with crop-cut yields using the predicted yield showcasing the validity of this imputation framework. However, their exercise is conducted with one crop (sorghum) and only considers one agricultural season limiting the external validity of the framework they develop.

Against this background, our analysis attempts to address these limitations. We take advantage of the availability of two consecutive rounds of the nationally representative agriculture survey of Mali collected as part of the Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA)<sup>1</sup> project to validate their approach when used in a surveyto-survey imputation framework. Moreover we extend the imputation exercise to more crops: in addition to sorghum, we work with millet, maize, rice, groundnut and cowpea allowing us to generalize the conditions on which the approach is valid. For reasons of computational ease and tractability, we focus on the estimation of the mean value of yields. We go beyond within-survey imputations exercises to look at survey-to-survey imputations. We also explore which set of covariates are more potent for the imputations exercises. Finally we also explore the effect of training sample sizes and strategies. By varying the size of the training sample, we investigate whether reducing the sample size can still yield reliable imputation results, with the aim of balancing cost efficiency and the accuracy of imputation methods.

Our main findings are threefold: (i) farmers’ reported yields are good predictors of crop-cut yields but the results point to a greater predictive power of integrated geo-spatial variables; (ii) on average the imputation exercises work better for crops that have low intercropping rate, and are more commercialized, which can be related to the accuracy in standard units of the farmerreported yields; (iii) in most of the cases, the imputation approach provides accurate results in the within-survey imputation framework and collecting crop-cut measurements on a sub-sample of plots can offer a cost-effective approach while still achieving reliable ML predictions of CC yields, but less so in the survey-to-survey framework especially when statistics are computed at a desegregated level. These results point to important cost savings in survey operations, but also highlight the stringent survey data requirements in terms of quality.

The paper is organized as follows. Section 2 describes data situations in which crop-cut yields are missing and how the data at our disposal allows us to investigate imputation frameworks that are suited to handle the situations. Section 3 describes the survey data. Section 4 presents the empirical approach. Section 5 discusses the results and section 6 concludes.

# **2 Data situations**

Contexts where yields data based on crop-cuts are unavailable at large scale are very common and it has led researchers to develop statistical methods to overcome such scarcity. Table 1 provides an overview of data situations as it pertains to crop-cut yield and the potential

> 1The Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) is a unique system of longitudinal surveys designed to improve the understanding of household and individual welfare, livelihoods and smallholder agriculture in Africa. Partners countries include: Burkina Faso, Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania, and Uganda. For more information see: https://www.worldbank.org/en/programs/lsms/initiatives/lsms-ISA.

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