Search Results (4)

Remote sensing offers a low-cost method for estimating yields at large spatio-temporal scales. Here, we examined the ability of Sentinel-2 satellite imagery to map field-level maize yields across smallholder farms in two regions in Oromia district, Ethiopia. We evaluated how effectively different indices, the MTCI, GCVI, and NDVI, and different models, linear regression and random forest regression, can be used to map field-level yields. We also examined if models improved by adding weather and soil data and how generalizable our models were if trained in one region and applied to another region, where no data were used for model calibration. We found that random forest regression models that used monthly MTCI composites led to the highest yield prediction accuracies (R² up to 0.63), particularly when using only localized data for training the model. These models were not very generalizable, especially when applied to regions that had significant haze remaining in the imagery. We also found that adding soil and weather data did little to improve model fit. Our results highlight the ability of Sentinel-2 imagery to map field-level yields in smallholder systems, though accuracies are limited in regions with high cloud cover and haze. Full article

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. Full article

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. Full article

Lost in the debates about the appropriate scale of production to promote agricultural growth in Africa is the rapid expansion of medium-scale farmers. Using Zambia as a case study, this article explores the causes and consequences of this middle-tier transformation on the future of small-scale agriculture. Combining political economic analysis with household survey data, this article examines the relationships between the growth in medium-scale farmers and changing conditions of land access, inequality, and alienation for small-scale farmers. Growth of medium-scale farmers is associated with high land inequality and rapid land alienation in high potential agricultural areas. This growth is shown to be partially driven by wage earner investment in land acquisition and is leading to substantial under-utilization of agricultural land. These processes are both limiting agricultural growth potential and foreclosing future options for an inclusive agricultural development strategy. Full article