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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]

This paper builds on existing theory and proposes a framework to identify vulnerability to climate change in food systems by examining historic cases where common environmental problems caused famine. Cases presented are (1) Ireland's Potato Famine, (2) El Niño induced famines during the Colonial period, and (3) Ethiopia between 1965 and 1997. Three factors stand out as common in each. Prior to each famine: (1) there were very few ways that people could obtain a living in the worst affected regions; (2) livelihoods in famine stricken communities came to depend on highly specialized agro-ecosystems that were sensitive to environmental change; (3) institutions failed to provide adequate safety nets to protect livelihoods from failure. This analysis suggests that vulnerability to climate change in food systems can be assessed by looking at agro-ecosystems, livelihoods and institutions. Local conditions, however, mean that ways of measuring these three factors will vary from place to place. As a result, direct comparisons are difficult. By conceptualizing these three variables as the axes of a three dimensional "vulnerability" space, it is possible to compare regions and look at trends over time by studying the paths through this "space" as traced by changes at the agroecosystem, livelihood, and institutional scale. [ABSTRACT FROM AUTHOR]