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Satellite data and machine learning for weather risk management and food security

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Title: Satellite data and machine learning for weather risk management and food security
Authors: Biffis, E
Chavez, E
Item Type: Journal Article
Abstract: 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.
Issue Date: 11-Aug-2017
Date of Acceptance: 28-Apr-2017
URI: http://hdl.handle.net/10044/1/48374
DOI: 10.1111/risa.12847
ISSN: 1539-6924
Publisher: Wiley
Start Page: 1508
End Page: 1521
Journal / Book Title: Risk Analysis
Volume: 37
Issue: 8
Copyright Statement: © 2017 Society for Risk Analysis. . This is the accepted version of the following article, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/risa.12847/abstract
Sponsor/Funder: European Institute of Innovation and Technology - EIT
Funder's Grant Number: KIC WINNERS
Keywords: Science & Technology
Social Sciences
Life Sciences & Biomedicine
Physical Sciences
Public, Environmental & Occupational Health
Mathematics, Interdisciplinary Applications
Social Sciences, Mathematical Methods
Mathematical Methods In Social Sciences
Machine learning
satellite data
weather risk
Machine learning
satellite data
weather risk
Strategic, Defence & Security Studies
Publication Status: Published
Online Publication Date: 2017-06-27
Appears in Collections:Imperial College Business School
Grantham Institute for Climate Change
Faculty of Natural Sciences