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Acid soils are a major constraint to agricultural productivity in many parts of sub-Saharan Africa, including Ethiopia. Restoring soil pH to optimal ranges for agriculture can have a significant impact on yields, particularly for acid intolerant crops like wheat and barley. The application of agricultural lime is the standard corrective, although the large application requirements, lack of farmer awareness, and weak or non-existent lime supply chains make this a complex problem to address at scale. To date, no large-scale farmer trials of lime application have been undertaken in Ethiopia. This leaves open the question to local policy makers as to the economic benefits given the enormous capital and logistics investments required. To help address this we leverage existing spatial edaphic data and longitudinal crop surveys to simulate the productivity impact of varying lime and fertilizer applications. Our estimates find the impact of moving pH from 5.5 to 6.5, modeled as a lime soil remediation strategy, increases yields by 22% and 19% for wheat and barley, respectively. In addition, at lower pH levels our models indicate that commonly used nitrogen-based fertilizers are less cost-effective. For wheat in highly acidic soils, we find that fertilizers cost over two times as much as a single application of lime over a five-year period. The cost savings of the use of lime reaches as high as 121% of average one-year agricultural household income for wheat; with barley these savings are lower but still substantial at 24%. In general, we advocate for an integrated soil fertility management strategy that applies appropriate levels of fertilizer on pH balanced soil. If successful, Ethiopia's acid soil reclamation could become a modest version of Brazil's successful "cerrado miracle" and serve as an example for Africa. [ABSTRACT FROM AUTHOR]

Timely and accurate agricultural impact assessments for droughts are critical for designing appropriate interventions and policy. These assessments are often ad hoc, late, or spatially imprecise, with reporting at the zonal or regional level. This is problematic as we find substantial variability in losses at the village-level, which is missing when reporting at the zonal level. In this paper, we propose a new data fusion method—combining remotely sensed data with agricultural survey data—that might address these limitations. We apply the method to Ethiopia, which is regularly hit by droughts and is a substantial recipient of ad hoc imported food aid. We then utilize remotely sensed data obtained near mid-season to predict substantial crop losses of greater than or equal to 25% due to drought at the village level for five primary cereal crops. We train machine learning models to predict the likelihood of losses and explore the most influential variables. On independent samples, the models identify substantial drought loss cases with up to 81% accuracy by mid- to late-September. We believe the proposed models could be used to help monitor and predict yields for disaster response teams and policy makers, particularly with further development of the models and integration of soon-to-be available high-resolution, remotely sensed data such as the Harmonized Landsat Sentinel (HLS) data set. [ABSTRACT FROM AUTHOR]

Despite the routine collection of annual agricultural surveys and significant advances in GIS and remote sensing products, little econometric research has integrated these data sources in estimating developing nations’ agricultural yields. In this paper, we explore the determinants of wheat output per hectare in Ethiopia during the 2011–2013 principal Meher crop seasons at the kebele administrative area. Using a panel data approach, combining national agricultural field surveys with relevant GIS and remote sensing products, the model explains nearly 40% of the total variation in wheat output per hectare across the country. Reflecting on the high interannual variability in output per hectare, we explore whether these changes can be explained by weather, shocks to, and management of rain-fed agricultural systems. The model identifies specific contributors to wheat yields that include farm management techniques (e.g. area planted, improved seed, fertilizer, and irrigation), weather (e.g. rainfall), water availability (e.g. vegetation and moisture deficit indexes) and policy intervention. Our findings suggest that woredas produce between 9.8 and 86.5% of their locally attainable wheat yields given their altitude, weather conditions, terrain, and plant health. In conclusion, we believe the combination of field surveys with spatial data can be used to identify management priorities for improving production at a variety of administrative levels. [ABSTRACT FROM AUTHOR]