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Crop yield estimation from satellite data requires field observations to fit and evaluate predictive models. However, it is not clear how much field data collection methods matter for predictive performance. To evaluate this, we used maize yield estimates obtained with seven field methods (two farmer estimates, two point transects, and three crop cut methods) and the "true yield" measured from a full-field harvest for 196 fields in three districts in Ethiopia in 2019. We used a combination of nine vegetation indices and five temporal aggregation methods for the growing season from Sentinel-2 SR data as yield predictors in the linear regression and Random Forest models. Crop-cut-based models had the highest model fit and accuracy, similar to that of full-field-harvest-based models. When the farmer estimates were used as the training data, the prediction gain was negligible, indicating very little advantage to using remote sensing to predict yield when the training data quality is low. Our results suggest that remote sensing models to estimate crop yield should be fit with data from crop cuts or comparable high-quality measurements, which give better prediction results than low-quality training data sets, even when much larger numbers of such observations are available. [ABSTRACT FROM AUTHOR]

Maize yields can be improved through many individual technologies and management practices, but the full realization of potential benefits is generally understood to require integrated use of complementary practices. We employed two years of survey data and alternative econometric models to better understand the use of individual and bundled packages of technologies and management practices in Ethiopian maize production, i.e., fertilizers, improved varieties, herbicides, pesticides, manure, intercropping, erosion control and crop rotation (the last three labeled integrated management). Although fertilizer and improved varieties were used on 85% of maize fields, with average yields of 3.4 ton/ha, large yield gaps remain. Complementary management practices improved these yields by as much as 22%, although in variable ways. Integrated management contributed to maize yield only when combined with crop protection (herbicides and/or pesticides). Combining manure with fertilizer and improved variety decreased maize yields, possibly due to manure quality and less inorganic fertilizer used on fields that received manure. Packages including crop protection increased labor productivity by 16–70%, while using integrated management decreased labor productivity by almost half. In summary, the combination of management practices did not automatically lead to increased yields, partly related to the conditions under which practices were applied, indicating the need for site-specific research and recommendations for sustainable intensification. [ABSTRACT FROM AUTHOR]