Imperial College London
Alternate

DrEnricoBiffis

Business School

Associate Professor of Actuarial Finance
 
 
 
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Contact

 

+44 (0)20 7594 9767e.biffis

 
 
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Location

 

4.0453 Prince's GateSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Biffis:2017:10.1111/risa.12847,
author = {Biffis, E and Chavez, E},
doi = {10.1111/risa.12847},
journal = {Risk Analysis},
pages = {1508--1521},
title = {Satellite data and machine learning for weather risk management and food security},
url = {http://dx.doi.org/10.1111/risa.12847},
volume = {37},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Biffis,E
AU  - Chavez,E
DO  - 10.1111/risa.12847
EP  - 1521
PY  - 2017///
SN  - 1539-6924
SP  - 1508
TI  - Satellite data and machine learning for weather risk management and food security
T2  - Risk Analysis
UR  - http://dx.doi.org/10.1111/risa.12847
UR  - http://hdl.handle.net/10044/1/48374
VL  - 37
ER  -
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