# et al., 2019 for Uganda).

Against this background, the first contribution of our paper is to provide empirical evidence from an alternative smallholder farming system in Mali on non-classical measurement errors (NCME) in self-reported (SR) crop yields vis-`a-vis their crop cutting (CC) counterparts on the same plots and demonstrate the consequences of NCME in SR yields for the estimated relationships between crop yields and agricultural inputs besides land, specifically seeds and labor. The literature’s heavy focus on land is understandable given that it is a primary factor of production. And our analysis fully corroborates the findings of Desiere and Jolliffe (2018), Gourlay et al. (2019), and Abay et al. (2019). However, with rising population densities and low levels of agricultural productivity, especially in Sub-Saharan Africa, more attention is being paid to the effects of measurement errors on the understanding of agricultural intensification (Abay et al., 2021) and of the extent of misallocation of inputs across farms (Gollin and Udry, 2021). In this context, understanding how crop yields relate to the use of other agricultural inputs is of great policy relevance.

To do so, however, it is necessary to ensure that the relationships are correctly assessed and are anchored in high quality survey data. Recognizing the technical and resource constraints that are faced by survey implementing agencies in contexts where data and yield improvements are needed the most, we present and validate an innovative approach to agricultural survey data collection and analysis in a way that requires the implementation of crop cutting only in a sub-sample of households/plots and that leverages machine learning techniques to derive predicted objective measures of crop yields for the sub-sample that is not subject to crop cutting. This constitutes the second contribution of our paper to the literature. In recent years, machine learning (ML) techniques have made their way into economics research and have proved their utility. Athey (2018), as part of an assessment of the early contributions of ML to economics, indicates that ML techniques have been most successful when applied to problems that require predictions as in our case. Furthermore, applying ML in agricultural economics research makes sense to model key variables such as crop yield, which is determined by a complex combination of soil quality, weather, input timing and other management choices, replete with non-linearities and interactions (Storm et al., 2020).

Our analysis showcases (a) the utility of ML techniques for predicting CC yields as a function of SR yields and complementary survey and geospatial data, and (b) the reliability of the resulting predicted CC yields in the analysis of the relationships between crop yields and agricultural inputs. We show analytically and empirically that predicted CC yields, although informed in part by SR yields, have attenuated NCME, which is uncorrelated with the intensity of agricultural input use. Only one-third of the sample is found to be sufficient for implementing crop cutting alongside self-reported survey data collection, such that an ML model can generate predicted CC yields for the rest of the sample in a way that leads to reliable parameter estimates of the relationships between yields and inputs. The findings reveal the importance of using SR yields as a predictor variable, and hence, underscores the need to continue to collect and improve the quality of self-reported survey data on crop production. Our analysis is first conducted with the data from a small-scale methodological survey experiment and is then replicated with the data from a nationally-representative survey to verify the external validity of 3