Satellite Data and Machine Learning for Weather Risk Management and Food Security

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.

Satellite-Based Crop Monitoring and Yield Estimation—A Review

“…Yield for the counties studied were accurately estimated thereafter. Machine learning's ability to handle simple and complex relations between variables presents itself as a powerful tool for processing big data (Biffis & Chavez, 2017). In related instances, other studies have employed satellite data with convolutional, and or artificial neural networks to monitor crop condition and estimate yields (Fieuzal et al, 2017;Ali et al, 2017;Saeed et al, 2017).…”

A Web Platform for Integrated Vulnerability Assessment and Cyber Risk Management

“…For this reason he is not able to chose correct values of impact and likelihood for every vulnerability of every assets. In the last years there has been an increase of machine learning techniques use not only into the traditional sectors of risk analysis [17,18] but also into the cyber risk management [19,20] with the aim of predicting risk and avoiding cybercrime in future [21]. Our work goes in this direction: trying to predict cyber risk through the use of custom algorithms and Matrix factorization [22][23][24].…”

Advances in Risk Analysis with Big Data

“…In this special issue, Biffis and Chavez (21) demonstrate how the big data mediated machine learning method can be employed to mine satellite data and identify optimal weather indices for agricultural food-related weather risk management. The authors make use of the data to design a proper risk transfer scheme.…”