Solar lanterns are promoted across rural Sub-Saharan Africa to improve both lighting in homes and educational outcomes. We undertake a randomized controlled trial in Zimba District, Zambia, to evaluate whether solar lanterns help children study more effectively and improve academic performance. Our research design accounts for potential income effects arising from the giveaways of lanterns and also “blinds” participants to the study’s purpose. We find no evidence that receipt of a lantern improved performance on important national examinations (even though an ex poststatistical power analysis demonstrates that the research should detect economically significant impacts, if present). We also do not observe impacts on self-reported study habits. Several features of Zimba District that are likely to exist in other developing regions appear to drive our results. First, flashlights are the dominant lighting source in rural Zambia rather than traditional options like kerosene lamps or candles. In such environments, solar lights may hold only limited appeal for prospective users. Second, our survey data suggests that other major barriers to educational attainment likely render improved energy access (whether through solar lanterns or otherwise) a relatively unimportant educational input. 

Satellite-derived land cover maps play an important role in many applications, including monitoring of smallholder-dominated agricultural landscapes. New cloud-based computing platforms and satellite sensors offer opportunities for generating land cover maps designed to meet the spatial and temporal requirements of specific applications. Such maps can be a significant improvement compared to existing products, which tend to be coarser than 300 m, are often not representative of areas with fast-paced land use change, and have a fixed set of cover classes. Here, we present two approaches for land cover classification using the Landsat archive within Google Earth Engine. Random forest classification was performed with (1) season-based composites, where median values of individual bands and vegetation indices were generated from four years for each of four seasons, and (2) metric-based composites, where different quantiles were computed for the entire four-year period. These approaches were tested for six land cover types spanning over 18,000 locations in Zambia, with ground “truth” determined by visual inspection of high-resolution imagery from Google Earth. The methods were trained on 30% of these points and tested on the remaining 70%, and results were also compared with existing land cover products. Overall accuracies of about 89% were achieved for the season- and metric-based approaches for individual classes, with 93%and 94% accuracy for distinguishing cropland from non-cropland. For the latter task, the existing Globeland30 dataset based on Landsat had much lower accuracies (around 77% on average), as did existing cover maps at coarser resolutions. Overall, the results support the use of either season or metric-based classification approaches. Both produce better results than those obtained from previous classifiers, which supports a general paradigm shift away from dependence on standard static products and towards custom generation of on-demand cover maps designed to fulfill the needs of each specific application.

One of the greatest challenges in monitoring food security is to provide reliable crop yield information that is temporally consistent and spatially scalable. An ideal yield dataset would not only extend globally and across multiple years, but would also have enough spatial granularity to characterize productivity at the field and subfield level. Rapid increases in satellite data acquisition and platforms such as Google Earth Engine that can efficiently access and process vast archives of new and historical data offer an opportunity to map yields globally, but require efficient and robust algorithms to combine various data streams into yield estimates. We recently introduced a Scalable satellite-based Crop Yield Mapper (SCYM) that combines crop models simulations with imagery and weather data to generate 30 m resolution yield estimates without the need for ground calibration. In this study, we tested new large-scale implementations of SCYM, focusing on three regions with varying crops, field sizes and landscape heterogeneity: maize in the U.S. corn belt (390,000 km²), maize in Southern Zambia (86,000 km²), and wheat in northern India (450,000 km²). As a benchmark, we also tested a simpler empirical approach (PEAKVI) that relates yield to the peak value of a time series of spatially aggregated vegetation indices, similar to methods used in current operational monitoring. Both SCYM and PEAKVI were applied to data from all Landsat's sensors and MODIS for more than a decade in each region, and evaluated against ground-based estimates at the finest available administrative level (e.g., counties in the U.S.). We found consistently high correlations (R² ≥ 0.5) between the spatial pattern of ground- and satellite-based estimates in both U.S. maize and India wheat, with small differences between methods and source of satellite data. In the U.S., SCYM outperformed PEAKVI in tracking temporal yield variations, likely owing to its explicit consideration of weather. In India, both methods failed to track temporal yield changes, with various possible explanations discussed. In Zambia, the PEAKVI approach applied to MODIS tracked yield variations much better (R² > 0.5) than any other yield estimate, likely because the frequent cloud cover in this region confounds the other approaches. Overall, this study demonstrates successful approaches to yield estimation in each region, and illustrates the importance of distinguishing between accuracy for spatial and temporal variation. The 30 m resolution of Landsat-based SCYM does not appear to offer large benefits for tracking aggregate yields, but enables finer scale analyses than possible with the other approaches.

It is widely recognized that an “African green revolution” will require greater use of inorganic fertilizers. Often-made comparisons note that fertilizer use rates in Africa are just 10–20% of those in Asia, Europe and the Americas. Most attempts to explain relatively low-adoption of fertilizer assume yield responses to inorganic fertilization warrant higher application rates and hypothesize that observed use rates are limited by market-based factors. Another explanation may be that application rates are low because African yields are less responsive to inorganic fertilizer than yields in other regions, and less responsive than analysts perceive. Examining the case of Zambia, we evaluate whether yield response to fertilizers could explain adoption and application rates. A model of yield response is constructed and a combination of estimators is employed to mitigate potential biases related to correlation between fertilizer use and unobserved heterogeneity as well as stochastic shocks. Results indicate higher fertilization rates would be marginally profitable or unprofitable in many cases given commercial fertilizer and maize prices. Phosphoric fertilizer is particularly unprofitable on acidic soils, which are common in Zambia and other areas of sub-Saharan Africa. We propose feasible recommendations for diversifying the current government strategy to enhance crop productivity.

Abstract: Many governments in developing countries distribute fertilizer at subsidized prices in an effort to stimulate small farmers' agricultural productivity and food security. Prior fertilizer demand studies have largely failed to account for the effects of government programs on farmers' commercial purchases. Using a double hurdle model and nationally representative rural household panel data in Zambia, we distinguish between these sources and measure the contemporaneous “crowding in” and “crowding out” effects of government input programs on commercial fertilizer sales. Where the private sector is relatively active and average wealth is higher (areas seemingly more likely to be targeted by government programs), results indicate that subsidies have substantially crowded out the private sector, in some cases to the point that such programs could actually lower overall fertilizer use. On the other hand, in poorer areas where the private sector is relatively inactive, subsidies help to generate demand and crowd in private sector retailers. Empirical studies explicitly modeling farmers' fertilizer purchase behavior within a dual marketing framework can provide important insights for agricultural policy discussions in developing countries.