our conclusions. Beyond the methodological findings, our analysis has significant policy implications as it suggests that using self-reported yields, the returns of additional inputs per area of land are overstated and as such, it would indicate that agricultural intensification could have limited benefit in the context of the study.

The paper is organized as follows. Section 2 discusses the data and provides the descriptive statistics. Section 3 discusses how the choice of the yield measure affects the characterization of the relationship between land productivity and the agricultural inputs of interest, namely land area, seed quantity and household labor. Section 4 presents the machine learning framework and the results. Section 5 discusses the implications for survey data collection protocols. Section 6 concludes.

# **2 Data**

## **2.1 Context**

Our work is primarily informed by data collected during the 2017/2018 agricultural season as part of a methodological survey: the Enquˆete sur les Rendements et l’Identification des Vari ́et ́es du Sorgho (ERIVaS), focused on sorghum yield measurement and variety identification.<sup>1</sup> The fieldwork was conducted in Dio ̈ıla district, which is located in the region of Koulikoro, in south central Mali. Sorghum is a major crop in Mali, planted on 26% of plots cultivated by agricultural households, which account for 90% of rural households representing 80% of the population of Mali.<sup>2</sup> Koulikoro is one of the main growing regions of this staple.

ERIVaS was set up to assess the relative accuracy of self-reporting on land areas, sorghum varieties, and sorghum yields vis-`a-vis objective measurement methods, namely GPS for land area measurement, DNA fingerprinting for varietal identification, and crop cutting and satellite-based methods for crop yield estimation.<sup>3</sup>

## **2.2 Fieldwork details**

Four 10x10 km areas (henceforth referred to as strata) were geospatially delineated in Dio ̈ıla to ensure heterogeneity in terms of relief and terrain type. In each stratum, 150 households were to be selected and the number of households selected in each of the 17 villages located across the four strata was proportional to the total number of households in the villages found during the listing exercise. Given the low incidence of inter-cropping in our study area, only households with pure stand sorghum plots were considered for data collection. In each sampled household, one pure stand sorghum plot was randomly selected among the pure stand sorghum plots cultivated by the household. The result of this sampling exercise was 600 sorghum pure stand plots (one in each) of 600 households distributed in 17

> 1Sorghum yield and varieties identification methodological survey. The survey was designed as part of the World Bank LSMS methodological research agenda on measurement of crop yields in household surveys.

> 2Author’s calculation based on the Mali LSMS-ISA 2017 data.

> 3The analysis related to remote sensing measurement of yields and how the method compares to other methods is pursued in (Lobell et al., 2019). We compare part of the results obtained in both studies later in this analysis.

4