this phenomenon. They show the difference in yields (SR-CC) across quintiles of plot area (measured via GPS) for the entire sample. These figures highlight a consistent correlation between the SR-CC difference and the quantity of cultivated area, as noted in the literature: farmers’ overestimation is more pronounced on smaller plots and decreases, or even reverses, on larger plots.

The average overestimation of SR yields in comparison to CC yields in Mali is likely influenced by the fact that the average plot area in Mali is larger than in the studies previously mentioned. This larger average plot size means that the underestimation trend observed in other studies begins earlier in the distribution for Mali. Generally, SR yields are overestimated compared to CC yields in the first plot area quintile. However, in each of the subsequent quintiles, SR yields tend to be underestimated. The distinct trend observed for cowpea may be attributed to the fact that cowpeas are typically planted on smaller plots. Despite this, the general trend of decreasing overestimation across land quintiles is still evident, though it is not a linear decrease.

Additionally, the high confidence interval reported for cowpea yields may be attributed to the prevalence of intercropping in its cultivation.<sup>7</sup> Accurately self-reporting harvest quantities in intercropped plots can be particularly challenging. As for groundnut, we observe consistent underestimation across all plots. This finding could be linked to the findings by (Bardasi et al., 2011), which suggests that women’s labor tends to be underestimated by men. A plausible explanation for the underestimation observed in our study is that groundnuts are predominantly cultivated by women, while the majority of respondents for the 2017 and 2018 surveys were men, who usually tend to underreport the product harvested by women. According to EACI-17 and EAC-18 data, groundnut plots, which are managed by women at a higher rate than average (18% compared to the overall average of 7%), also have a high rate of proxy respondents. Specifically, 42% of the information about these plots is not provided directly by the manager.

# **4 Empirical approach**

## **4.1 Validation approach**

We have structured our empirical framework to identify the limitations of imputation accuracy and to offer recommendations for capturing the essential data set. This includes the necessary variables, the most effective machine learning (ML) modeling approach, and the minimum sample size for robust statistical analyses of crop yields when crop-cut data is either completely or partially missing.

To determine the required set of variables and the most effective ML modeling approach, we train our model on 75% of the plots with crop-cut data, then make predictions on the remaining 25%. This process is carried out separately in the EACI 2017 and EAC 2018 datasets.

To explore the limitations imposed by survey sampling when using crop-cut data to train an imputation model, we experiment with the ML approach, and with varying sample sizes of

> 7Among the six crops analyzed, cowpea has the highest rate of intercropping, with 27% of the plots cultivated with cowpea being intercropped.

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