Version 1
: Received: 17 April 2024 / Approved: 18 April 2024 / Online: 19 April 2024 (09:52:23 CEST)
Version 2
: Received: 22 April 2024 / Approved: 23 April 2024 / Online: 23 April 2024 (17:17:18 CEST)
How to cite:
Abd Rabuh, A.; Teeuw, R.M.; Oakey, D.R.; Argyriou, A.V.; Foxley-Marrable, M.; Wilkins, A. Sustainable Geoinformatic Approaches to Insurance for Small-Scale Farmers in Colombia. Preprints2024, 2024041283. https://doi.org/10.20944/preprints202404.1283.v1
Abd Rabuh, A.; Teeuw, R.M.; Oakey, D.R.; Argyriou, A.V.; Foxley-Marrable, M.; Wilkins, A. Sustainable Geoinformatic Approaches to Insurance for Small-Scale Farmers in Colombia. Preprints 2024, 2024041283. https://doi.org/10.20944/preprints202404.1283.v1
Abd Rabuh, A.; Teeuw, R.M.; Oakey, D.R.; Argyriou, A.V.; Foxley-Marrable, M.; Wilkins, A. Sustainable Geoinformatic Approaches to Insurance for Small-Scale Farmers in Colombia. Preprints2024, 2024041283. https://doi.org/10.20944/preprints202404.1283.v1
APA Style
Abd Rabuh, A., Teeuw, R.M., Oakey, D.R., Argyriou, A.V., Foxley-Marrable, M., & Wilkins, A. (2024). Sustainable Geoinformatic Approaches to Insurance for Small-Scale Farmers in Colombia. Preprints. https://doi.org/10.20944/preprints202404.1283.v1
Chicago/Turabian Style
Abd Rabuh, A., Max Foxley-Marrable and Alan Wilkins. 2024 "Sustainable Geoinformatic Approaches to Insurance for Small-Scale Farmers in Colombia" Preprints. https://doi.org/10.20944/preprints202404.1283.v1
Abstract
This article presents a low-cost insurance system developed for small-hold farms in disaster-prone regions, primarily using free Earth Observation (EO) data and Free Open Source Software (FOSS) – collectively termed sustainable geoinformatics - with a proprietary mobile phone app that enables verification. The study examined 30 farms in Risaralda department, Colombia. A Digital Elevation Model (12.5m pixels) from the ALOS PALSAR satellite sensor was used with a Geographical Information System (GIS) to map the terrain, drainage and geohazards of each farming district. Google Earth Engine (GEE) was used to carry out Time Series Analysis of 15 EO and weather datasets for 1998 to 2020. This analysis enabled the levels of risk from hydrometeorological hazards to be determined for each farm of the study, providing key data for the setting of insurance premiums. A parametric insurance product was piloted using GPS-tagged, time-stamped mobile phone photos to verify crop health. Key features of this insurance system are its low operational cost and rapid damage verification, relative to conventional approaches to farm insurance. It is an affordable form of insurance for small-scale farmers, with the rapid verification and payment of claims. This relatively fast, low-cost and affordable approach to insurance for small-scale farming enhances sustainable development by enabling policy-holder farmers to recover more quickly from disasters.
Keywords
deep learning; digital data poverty; disaster risk management; earth observation; extreme weather; GIS; machine learning; parametric insurance; small farms; sustainable geoinformatics.
Subject
Environmental and Earth Sciences, Sustainable Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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