Nevertheless, we replicate the ML approach, as outlined in section 4.2.1, with the nationallyrepresentative EACI sample. Figure 7 summarizes the results for the first sample split out of the 100 replications. The right top-panel shows that ML yields 40 percent of the variation in CC yields, and the level of explained variation is in fact higher than the comparable result based on the ERIVaS sample. Even though we continue to see (in the left-top panel) a higher concentration of values around the mean for ML yields, there is a better tracking of the large values of CC yields. The coefficients for the key inputs in the regressions of ML yields are also comparable to those obtained from the regressions of CC yields, as shown in the bottom two plots of Figure 7. While the size and heterogeneity of the EACI sample are among the factors that improve the predictive accuracy of the ML model vis-`a-vis the results achieved with the ERIVaS sample, the EACI rounding errors in CC yields may also be creating a scenario that ML tools are better equipped to accommodate.

To verify whether we can expect the same results from the ML approach done with ERIVaS (selecting randomly a third of the plots) using the EACI 2017 and make inferences to the population of sorghum plots in Mali, we compare in Table A5, plots and households characteristics of plots selected for crop cutting with those that were not. The statistically significant differences appear to be seldom providing confidence that the selection of plot for CC is analogous to the simulation conducted with the data from the experience.

Finally, Table 12 reports the results from the yield regressions estimated with ML, and separately SR, yields using the entire EACI sample, and the results from the yield regressions estimated with CC yields using the sub-sample of EACI plots that were subject to crop cutting. The coefficient for plot area is negative but not statistically significant. This may be because the mean difference in plot area between the crop cut plots and the rest of the sample was statistically significant. The plots that were not subject to crop cutting were, on average, larger, and in view of the evidence regarding the underestimation in SR yields on larger plots, the IR is weakened when the regression is estimated using the entire sample. And while the coefficients for key inputs are consistently positive and statistically significant in the regressions of SR yields, the comparable coefficients in the regressions of ML yields are not statistically significant and are consistent with those estimated in the regressions of CC yields. These results are fully consistent with the findings based on the ERIVaS data set and raise flags concerning the analyses conducted with SR yields.

# **5 Implications for crop yield measurement in household and farm surveys**

Having demonstrated the promise of the use of ML to derive imputed measures of CC yields, this section provides a few recommendations on what data to collect and how to collect it in an efficient way.

First, given the importance of using SR yields to improve the quality of the ML-based imputed CC yield predictions, survey practitioners should continue to collect farmer-reported information on crop production, and reduce NCME in the resulting data, including by reducing length of farmer recall and improving the completeness and quality of conversion factors for non-standard measurement units, 32