Rubin, 1987) to account for within and between imputation uncertainty. By adhering to these statistical practices, we aim to provide a robust and accurate evaluation of the imputed cropyield data, ensuring that our findings are reflective of the underlying survey data and the complexities of the imputation process.

# **5 Results**

## **5.1 Modeling lessons**

### **5.1.1 Top predictors**

In this section, we look at the most relevant variables as identified in the random forest algorithm prediction<sup>14</sup> for 50 different simulations. Table 5 shows the top 10 most important variables in the prediction for each crop’s yield levels.<sup>15</sup> SR yield variable is an important predictor for crop yield in levels (with the exception of millet), but not as important as for logs predictions (it is the most important predictor of yield in logs only for cowpea). Geospatial variables are consistently among the top predictors for both levels and logs yields. It is reassuring for the machine learning approach to observe that the combinations of farmer practices and different rainfall variables are among the most important predictors for most of the modeled crops (both for logs and levels yield predictions). Indeed, in a context of smallholder farmers like the one in Mali where most of the crop production relies on rainfall, one would expect that yield would be significantly impacted by the complex interaction of timing of the seeding timing and the amount of rain that fall. This underlies the usefulness of collecting detailed agricultural practices information, as well as being able to link the georeferenced data collected with geo-spatial, when conducting a crop yield prediction exercise.

The relevance of SR yield for improving the quality of yield predictions highlights the importance of collecting farmer-reported information on crop production. Therefore, particular attention should be devoted to enhancing the quality of SR yields, for example by reducing the length of the recall period as noted in (Wollburg et al., 2021), and improving the completeness and quality of conversion factors for non-standard measurement units.

To further assess the degree to which the most important variable matter compared to the second one for example, we look at the average value of the importance metric produced by the random forest algorithm for the 50 different simulations we conducted. Figure 3 and Figure 4 exhibit these values for rice yield predictions in levels. Household weights and SR yields appear as one of the top three key variables in the predictions: household weights represent the most important variable for rice yield predictions in levels while SR yields variable is scored as the third most important for rice yield predictions in levels. The plot slope and average annual total rainfall are the second most important variables for levels and logs, respectively. One

> 14For our predictions for log or levels, we rely on the ensemble prediction. But to take a deeper look at the set of covariates, we rely on the covariate importance measure of the Random Forest algorithm, which is the most efficient algorithm for yield predictions for most of the crops when imputing levels, as shown in Table A.1 and it has the lowest error outside the ensemble for the log predictions.

> 15We show the equivalent table for predicting logs in Table B.3.

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