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With recent growth in funding and research on "resilience building", interest in climate services has risen dramatically. Included in this trend is an increased emphasis on the use of climate and weather information for a range of purposes across multiple scales. Non-governmental organisations (NGOs) and other non-state actors across Africa have responded accordingly, and are increasingly acting as brokers, and sometimes producers, of climate services as part of their activities. Drawing on research from Burkina Faso and Ethiopia as part of the Building Resilience and Adaptation to Climate Extremes and Disasters (BRACED) programme, this paper critically examines the evolving climate services landscape and raises questions about what the future holds for climate services in sub-Saharan Africa. We ask two questions: How have national climate services in these countries evolved since the early 2000s when they first came to prominence? And how have NGO contributions to these services evolved over time? Our findings highlight a considerable evolution in the aims and capacities of climate service systems over this period. NGOs have contributed to this progress on multiple fronts, but we note that important opportunities for innovation remain. We also raise concerns about how the current financing and governance models may influence priority setting and the sustainability of "projectised" services. Accordingly, we call for a better understanding how power and politics shape the development and deployment of climate services. This paper provides insight on the evolving landscape of climate services, actors involved in its provision and implications for the future. [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]

Ethiopia's agriculture is predominantly rainfed and hence any irregularity in weather conditions has adverse welfare implications. Using panel data, this paper analyzes the effect of rainfall shocks on Ethiopian rural households' food security and vulnerability over time while controlling for a range of other factors. To this end, we generate a time-variant household food security index which is developed by principal components analysis. Based on this index, households are classified into relative food security groups and their socioeconomic differences are assessed. The exploratory results show that compared to the less secured households, the more secured ones have male and literate household heads, tend to have a greater number of economically active household members, own more livestock, experience better rainfall outcome, and participate in local savings groups. Using the food security index as the dependent variable, we use a fixed effects instrumental variable regression model to identify determinants of households' food security over time and find that rainfall variability is an important factor. Moreover, household size, participation in local savings groups, and livestock ownership positively affect food security. Results from multinomial logistic regression model complement the fixed effects instrumental variable regression results by showing that the level and variability of rainfall are important determinants of persistent food insecurity and vulnerability. The results highlight the need for efficient risk reduction and mitigation programs to improve risk exposure and coping ability of rural households. Careful promotion of investment in infrastructure to support irrigation and water resources development is one aspect worth considering. [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]