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Topographical surveying --- Cartography --- Area measurement

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Weights and measures -- France --- Area measurement

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Acreage --- Area Measurement --- Barrier Beaches --- Coastal Wetlands --- Coastal Zone --- Salt marshes --- Tidal flats --- Wetlands --- Massachusetts

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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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Land area is a fundamental component of agricultural statistics, and of analyses undertaken by agricultural economists. While household surveys in developing countries have traditionally relied on farmers' own, potentially error-prone, land area assessments, the availability of affordable and reliable Global Positioning System (GPS) units has made GPS-based area measurement a practical alternative. Nonetheless, in an attempt to reduce costs, keep interview durations within reasonable limits, and avoid the difficulty of asking respondents to accompany interviewers to distant plots, survey implementing agencies typically require interviewers to record GPS-based area measurements only for plots within a given radius of dwelling locations. It is, therefore, common for as much as a third of the sample plots not to be measured, and research has not shed light on the possible selection bias in analyses relying on partial data due to gaps in GPS-based area measures. This paper explores the patterns of missingness in GPS-based plot areas, and investigates their implications for land productivity estimates and the inverse scale-land productivity relationship. Using Multiple Imputation (MI) to predict missing GPS-based plot areas in nationally-representative survey data from Uganda and Tanzania, the paper highlights the potential of MI in reliably simulating the missing data, and confirms the existence of an inverse scale-land productivity relationship, which is strengthened by using the complete, multiply-imputed dataset. The study demonstrates the usefulness of judiciously reconstructed GPS-based areas in alleviating concerns over potential measurement error in farmer-reported areas, and with regards to systematic bias in plot selection for GPS-based area measurement.

Global Positioning System --- Land Area Measurement --- Land Productivity --- Macroeconomics and Economic Growth --- Missing Data --- Multiple Imputation --- Poverty Reduction --- Sub-Saharan Africa