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Agricultural productivity is hindered in smallholder farming systems due to several factors, including farmers' inability to meet crop-specific soil requirements. This paper focuses on soil suitability for maize production and creates multidimensional soil suitability profiles of smallholder maize plots in Uganda, while quantifying forgone production due to cultivation on less-than-suitable land and identifying groups of farmers that are disproportionately impacted. The analysis leverages the unique socioeconomic data from a subnational survey conducted in Eastern Uganda, inclusive of plot-level, objective measures of maize yields and soil attributes. Stochastic frontier models of maize yields are estimated within each soil suitability class to understand differences in returns to inputs, technical efficiency, and potential yield. Only 13 percent of farmers are cultivating soil that is highly suitable for maize production, while the vast majority are cultivating only moderately suitable plots. Farmers cultivating highly suitable soil have the potential to increase their observed yields by as much as 86 percent, while those at the opposite end of the suitability distribution (with marginally suitable land) operate closer to the production frontier and can only increase yields by up to 59 percent, given the current technology set. There is heterogeneity in potential gains across the wealth distribution, with poorer households facing more heavily constrained potential. Assuming no change in technologies and management practices used by Ugandan farmers, there are limited economic gains tied to closing suitability class-specific productivity gaps, or even at the extreme reaching the average potential productivity levels observed in the high suitability class.

Agriculture.

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Based on a two-round household panel survey conducted in Eastern Uganda, this study shows that the analysis of the inverse scale-productivity relationship is highly sensitive to how plot-level maize production, hence yield (production divided by GPS-based plot area), is measured. Although farmer-reported production-based plot-level maize yield regressions consistently lend support to the inverse scale-productivity relationship, the comparable regressions estimated with maize yields based on sub-plot crop cutting, full-plot crop cutting, and remote sensing point toward constant returns to scale, at the mean as well as throughout the distributions of objective measures of maize yield. In deriving the much-debated coefficient for GPS-based plot area, the maize yield regressions control for objective measures of soil fertility, maize genetic heterogeneity, and edge effects at the plot level; a rich set of plot, household, and plot manager attributes; as well as time-invariant household- and parcel-level unobserved heterogeneity in select specifications that exploit the panel nature of the data. The core finding is driven by persistent overestimation of farmer-reported maize production and yield vis-a-vis their crop cutting-based counterparts, particularly in the lower half of the plot area distribution. Although the results contribute to a larger, and renewed, body of literature questioning the inverse scale-productivity relationship based on omitted explanatory variables or alternative formulations of the agricultural productivity measure, the paper is among the first documenting how the inverse relationship could be a statistical artifact, driven by errors in farmer-reported survey data on crop production.

Crop Cutting --- Household Surveys --- Inverse Scale-Productivity Relationship --- Maize --- Plot Area Measurement --- Remote Sensing --- Yield Measurement

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This paper investigates how land size measurements vary across three common land measurement methods (farmer estimated, Global Positioning System (GPS), and compass and rope), and the effect of land size measurement error on the inverse farm size relationship and input demand functions. The analysis utilizes plot-level ata from the second wave of the Nigeria General Household Survey Panel, as well as a supplementary land validation survey covering a subsample of General Household Survey Panel plots. Using this data, both GPS and self-reported farmer estimates can be compared with the gold standard compass and rope measurements on the same plots. The findings indicate that GPS measurements are more reliable than farmer estimates, where self-reported measurement bias leads to over-reporting land sizes of small plots and under-reporting of large plots. The error observed across land measurement methods is nonlinear and results in biased estimates of the inverse land size relationship. Input emand functions that rely on self-reported land measures significantly underestimate the effect of land on input utilization, including fertilizer and household labor.

Agriculture. --- E-Business. --- Education. --- Land Measurement. --- Private Sector Development. --- Rural Development Knowledge and Information Systems. --- Rural Development. --- Science and Technology Development. --- Science Education. --- Scientific Research and Science Parks. --- Standards and Technical Regulations. --- Survey Methods.

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Crop yields in smallholder systems are traditionally assessed using farmer-reported information in surveys, occasionally by crop cuts for a sub-section of a farmer's plot, and rarely using full-plot harvests. Accuracy and cost vary dramatically across methods. In parallel, satellite data is improving in terms of spatial, temporal, and spectral resolution needed to discern performance on smallholder plots. This study uses data from a survey experiment in Uganda, and evaluates the accuracy of Sentinel-2 imagery-based, remotely-sensed plot-level maize yields with respect to ground-based measures relying on farmer self-reporting, sub-plot crop cutting (CC), and full-plot crop cutting (FP). Remotely-sensed yields include two versions calibrated to FP and CC yields (calibrated), and an alternative based on crop model simulations, using no ground data (uncalibrated). On the ground, self-reported yields explained less than 1 percent of FP (and CC) yield variability, and while the average difference between CC and FP yields was not significant, CC yields captured one-quarter of FP yield variability. With satellite data, both calibrated and uncalibrated yields captured FP yield variability on pure stand plots similarly well, and both captured half of FP yield variability on pure stand plots above 0.10 hectare. The uncalibrated yields were consistently 1 ton per hectare higher than FP or CC yields, and the satellite-based yields were less well correlated with the ground-based measures on intercropped plots compared with pure stand ones. Importantly, regressions using CC, FP and remotely-sensed yields as dependent variables all produced very similar coefficients for yield response to production factors.

Agriculture --- Climate Change and Agriculture --- Crop Cutting --- Crop Model --- Crop Yield --- Crops & Crop Management Systems --- Education --- Educational Sciences --- Food Security --- Health, Nutrition and Population --- Inequality --- Maize --- Nutrition --- Poverty Reduction --- Remote Sensing --- Smallholder Farmer