significant coefficient on plot area indicates a tendency on the part of farmers to over-estimate their yields on smaller plots. The extent of over-estimation in SR yields is greater on plots with higher intensity of agricultural input use.<sup>13</sup> It is difficult to assert formally the mechanisms behind the correlates of the overestimation but knowing the context, it is possible to speculate on the potential explanations. For example, the number of cotton plots cultivated by the household is negatively associated with the farmers’ tendency to overestimate yields. With the ERIVaS study area being in the Malian cotton belt, farmers with cotton plots have received advice and assistance from agriculture extension agents. They are, therefore, likely to have better grasp of the production potential of their plots and to not overestimate their yields. There are additional findings that may be useful to keep in mind for future survey design. For instance, the extent of overestimation is reduced when the harvest is reported in large non-standard units. This is in part explained by the fact that plots with large harvests are of large size and are subject to underestimation of SR yields. This finding also suggests that allowing for non-standard measurement units in data collection and using conversion factors to express them in kilogram-equivalent terms reduce the discrepancies between SR and CC yields. Another correlate of yield overestimation is the enumerator assessed damage on the crop cut, indicating that farmers underestimate the effect on yields of the various damages that their plots may have been subject to during the growing season. More attention should be paid to the further piloting and potential inclusion of related questions in future surveys. Finally, plot managers (also more likely to be the respondents) who are literate have less tendency to overestimate their production, even with a higher number of plots cultivated, underscoring the need to work with literate respondents.

The analysis of the correlates of [log SR _−_ log CC] provides evidence that there is NCME in SR yields and NCME is negatively correlated with plot size and positively correlated with seed and labor inputs per hectare. From a policy standpoint, this implies that the returns to input intensification will be overestimated if SR yields are used instead of their CC counterparts. In other words, the systematic measurement errors in SR yields may be driving up the relationships between yields and inputs, and these relationships may in fact be artifacts of the data. We investigate these assertions analytically in the next section.

# **3 Assessing the relationship between yields and inputs**

## **3.1 Analytical framework**

This section attempts to assess how the errors in SR yields, in comparison to CC yields, affect the characterization of the relationships between land productivity and agricultural inputs, namely land area, seeds and household labor. We start out with the analytical framework proposed by Abay et al. (2019). The estimated yield input relationship is:

> 13This assumes that measurement errors in inputs are not correlated with SR too. We come back to this assumption later in the paper.

10