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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

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Climate models generally indicate that climate volatility may rise in the future, severely affecting agricultural productivity through greater frequency of yield-diminishing climate extremes, such as droughts. For Tanzania, where agricultural production is sensitive to climate, changes in climate volatility could have significant implications for poverty. This study assesses the vulnerability of Tanzania's population to poverty to changes in climate variability between the late 20th century and early this century. Future climate scenarios with the largest increases in climate volatility are projected to make Tanzanians increasingly vulnerable to poverty through its impacts on the production of staple grains, with as many as 90,000 additional people, representing 0.26 percent of the population, entering poverty in the median case. Extreme poverty-increasing outcomes are also found to be greater in the future under certain climate scenarios. In the 20th century, the greatest predicted increase in poverty was equal to 880,000 people, while in the 21st century, the highest possible poverty increase was equal to 1.17 million people (approximately 3.4 percent of the population). The results suggest that the potential impacts of changes in climate volatility and climate extremes can be significant for poverty in Sub-Saharan African countries like Tanzania.

Agricultural output --- Agricultural production --- Agricultural productivity --- Agricultural yields --- Climate change --- Climate Change Economics --- Climate Change Mitigation and Green House Gases --- Crop production --- Environment --- Extreme poverty --- Food insecurity --- Food price --- Food prices --- Food Security --- Food supply --- Income --- Income on food --- Irrigation --- Macroeconomics and Economic Growth --- Poor --- Poverty increase --- Poverty Reduction --- Regional Economic Development --- Rural --- Rural areas --- Rural Development --- Rural Poverty Reduction --- Science and Technology Development --- Science of Climate Change