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Biffis E, Chavez E, 2017, Satellite data and machine learning for weather risk management and food security, Risk Analysis, Vol: 37, Pages: 1508-1521, ISSN: 1539-6924
The increase in frequency and severity of extreme weather events poses challenges for the agricultural sector in developing economies and for food security globally. In this article, we demonstrate how machine learning can be used to mine satellite data and identify pixel-level optimal weather indices that can be used to inform the design of risk transfers and the quantification of the benefits of resilient production technology adoption. We implement the model to study maize production in Mozambique, and show how the approach can be used to produce countrywide risk profiles resulting from the aggregation of local, heterogeneous exposures to rainfall precipitation and excess temperature. We then develop a framework to quantify the economic gains from technology adoption by using insurance costs as the relevant metric, where insurance is broadly understood as the transfer of weather-driven crop losses to a dedicated facility. We consider the case of irrigation in detail, estimating a reduction in insurance costs of at least 30%, which is robust to different configurations of the model. The approach offers a robust framework to understand the costs versus benefits of investment in irrigation infrastructure, but could clearly be used to explore in detail the benefits of more advanced input packages, allowing, for example, for different crop varieties, sowing dates, or fertilizers.
Chavez E, Conway G, Ghil M, et al., 2015, An end-to-end assessment of extreme weather impacts on food security, Nature Climate Change, Vol: 5, Pages: 997-1001, ISSN: 1758-678X
Both governments and the private sector urgently require better estimates of the likely incidence of extreme weather events1, their impacts on food crop production and the potential consequent social and economic losses2. Current assessments of climate change impacts on agriculture mostly focus on average crop yield vulnerability3 to climate and adaptation scenarios4,5. Also, although new-generation climate models have improved and there has been an exponential increase in available data6, the uncertainties in their projections over years and decades, and at regional and local scale, have not decreased7,8. We need to understand and quantify the non-stationary, annual and decadal climate impacts using simple and communicable risk metrics9 that will help public and private stakeholders manage the hazards to food security. Here we present an ‘end-to-end’ methodological construct based on weather indices and machine learning that integrates current understanding of the various interacting systems of climate, crops and the economy to determine short- to long-term risk estimates of crop production loss, in different climate and adaptation scenarios. For provinces north and south of the Yangtze River in China, we have found that risk profiles for crop yields that translate climate into economic variability follow marked regional patterns, shaped by drivers of continental-scale climate. We conclude that to be cost-effective, region-specific policies have to be tailored to optimally combine different categories of risk management instruments.
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