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Satellite data and machine learning for weather risk management and food security
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![]() | Accepted version | 1.9 MB | Adobe PDF | View/Open |
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 Mathematics Mathematical Methods In Social Sciences Machine learning satellite data weather risk INSURANCE CLIMATE SECURITIZATION INVESTMENTS MAIZE 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 |