whose relationships with crop yields we are trying to assess.

# **2.3 Descriptive statistics**

Table 1 presents summary statistics of the main plot and household level variables used in the analysis. The first part of the table displays plot characteristics. The variables extracted from the GPS coordinates show that most of the plots are located on similarly flat terrain within a 5 km radius of the household’s dwelling. In terms of land tenure, 95% of the plots are reportedly owned by the households cultivating them. The plot managers of these plots are mostly middle-aged men with low literacy levels. The average size of the associated households was 19,<sup>8</sup> and households were headed overwhelmingly by middle-aged men most of whom were illiterate. The average number of cultivated plots is further indication that agriculture is the main activity for the households. Furthermore, the average GPS-based plot area is considerably larger than the available estimates observed in smallholder farming systems elsewhere in Africa. For example, Desiere and Jolliffe (2018), Gourlay et al. (2019), Abay et al. (2019) report average plot sizes ranging from 0.12 to 0.37 hectares in their sample. The incidence of agricultural input use was, on average, 33% for hired labor and 24% for fertilizer.

Table 2 reports estimates for plot-level for sorghum production (kilograms) and yields (kilograms per hectare).<sup>9</sup> The competing figures are based on plot-level self-reported sorghum production versus estimated sorghum production that is tied to the crop harvest for the 8m x 8m crop cut sub-plot and separately, a randomly chosen 4m x 4m quadrant within the sub-plot.<sup>10</sup> All yield measures use the GPS-based plot areas. As we do not find statistically significant differences between crop cut yields as a function of the size of the crop cut, in the rest of the analysis, CC yields are tied to the 8m x 8m sub-plot.

We see that the average difference between SR yields and each of CC yield is statistically significant at the 1% level, and on average, SR yields are 21% higher than their CC counterparts. The distributional differences between SR and CC yields are, however, not statistically significant. On the other hand, although there are statistically significant distributional differences between SR and CC sorghum production estimates, but we find no statistical difference in the means. The fact that we find significant differences between means but not distributions for yield measures and vice versa for production measures can in part be explained by the standard errors, which are relatively larger at the mean in the case of the production measures, making it difficult to detect differences for the point estimates. These differences are picked-up when we conduct the tests of distributional differences.

> 8The household size may appear to be large but it is typical of rural households in the region: based on 153 observations in the Mali LSMS-ISA 2017 data, the mean number of individuals in households with at least one sorghum plot in the district of Dio ̈ıla is 17, with a 95% confidence interval of [14.9, 19.6].

> 9These results are robust to the treatment of outliers in the SR variable via winsorizing. See Table A1 in Appendix A.

> 10Total CC-based sorghum production is computed by multiplying the 8x8m CC yield with the GPS-based plot area. The harvest measure for one of the four 4m x 4m quadrants is randomly chosen in each of the 8m x 8m subplots to compute the yield and harvest for the CC 4m x 4m method.

6