Abstract
A reliable forecast of food production in a given region, under the effects of climate change and increased occurrence of extreme events, is a prerequisite to developing resilience in the future food supply. As the climate is changing, an increasing occurrence of extreme events combined with shift in seasonal weather pattern is rendering traditional agricultural practices a high level of risk. Currently, strategies to plan for an upcoming season are based on data from the recent seasons. Current methods to forecasting food production levels and derisk an upcoming season in any given region, are rudimentary and at best, not scalable. The advent of big data and new data sources such as weather forecasts, remote sensing, scalable machine-learning methods and cloud computing has created new opportunities for understanding the impact of an upcoming growing season. In order to demonstrate the usefulness of the current data sources and methods, this chapter presents a methodology that combines seasonal weather forecasts, geo-spatial information derived from remote-sensing, risks posed by extreme events and crop growth models to estimate production risk at a regional scale. The method was validated for multiple growing seasons in some counties in Iowa.
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Twarakavi, N.K.C. et al. (2022). Big Data Analytics for Climate-Resilient Food Supply Chains: Opportunities and Way Forward. In: Reddy, G.P.O., Raval, M.S., Adinarayana, J., Chaudhary, S. (eds) Data Science in Agriculture and Natural Resource Management. Studies in Big Data, vol 96. Springer, Singapore. https://doi.org/10.1007/978-981-16-5847-1_9
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