## **5.2.2 survey-to-survey imputation**

The procedure is also tested in a survey-to-survey imputations. CC yields from the 2018 survey sample are used as a test set, with the model trained with the 2017 survey sample. Table 9 summarizes the results of the survey-to-survey imputation exercise, with 100 percent of the 2017 sample used as training sample. We find that except for millet, rice, and groundnut the difference between the mean obtained with the ML model is statistically different from that of the CC means even at the national level. However, even when the difference between the mean of the ML yields and the mean of the CC yields is statistically different, it remains smaller than the difference of the SR in most cases. The statistical difference that we detect may be due to the fact that despite computing the standard errors following the MI procedure, we are getting standard errors that do not account enough for the variability across imputations. This is also visible in Figure 10 which shows the means computed for 100 simulations. Looking at the regional level estimates, we find that in general these differences are even more pronounced.

The relatively low accuracy of the survey-to-survey imputation may be somehow surprising in this framework, given that the 2017 and 2018 surveys are very similar in terms of geographical coverage, representativeness and survey instruments. Nevertheless, there are some important differences in the questionnaires between the two surveys that can explain the relatively low accuracy of the 2017 sample for imputing the 2018 yields (for example, the seeding date, which interacted with rainfall is a top predictor of yields, was not collected in 2018). (Gourlay et al., 2019) find more promising year-to-year yield predictions using CC yields in Uganda. However, their results rely on a panel of households interviewed in two consecutive years, and predictions are even more accurate when plots from the same parcel are sampled. Interestingly, in this framework predicting the logs results in lower accuracy than predicting the levels, and the difference in _R_<sup>2</sup> level and log yields is positive and significant for all crops. Overall, it seems that the survey-to-survey imputation provides a very low accuracy level for food staple crops, while it is higher for market-oriented crops, in particular for rice and maize (although lower than the within-survey imputation).

# **6 Conclusion**

We assess the validity of imputation of an objective measure of crop yields in farm surveys in situations in which they are partially missing for efficiency design and when they are completely missing in a survey round. Partial and complete missingness of crop-cut yields might be due to resource constraints and logistical limitations in case certain areas are inaccessible or the presence of enumerators must be reduced in terms of time because of security issues.

The data requirements for the validation process reveal several important findings. First, self-reported (SR) yields are considered significant predictors of crop-cut yields. However, the results indicate that integrated geo-spatial variables make a greater contribution to the accuracy of machine learning (ML) predictions. This suggests that factors such as geographical information, climate data, or other spatial variables play a crucial role in accurately predicting crop yields. Furthermore, the accuracy of ML predictions varies across different crops, indicating heterogeneity. This points to the need for reliable non-standard units when dealing with 15