Sustainable Geoinformatic Approaches to Insurance for Small-Scale Farmers in Colombia

Ahmad Abd Rabuh, Richard M. Teeuw (), Doyle Ray Oakey, Athanasios V. Argyriou, Max Foxley-Marrable and Alan Wilkins
Additional contact information
Ahmad Abd Rabuh: Analytics, IS & Operations Department, Oxford Brookes University, Oxford OX3 0BP, UK
Richard M. Teeuw: Centre for Applied Geoscience, University of Portsmouth, Portsmouth PO1 3QL, UK
Doyle Ray Oakey: Primer Sol Consulting, Monument, CO 80132, USA
Athanasios V. Argyriou: Laboratory of Geophysical—Satellite Remote Sensing & Archaeo-Environment (GeoSat ReSeArch), Institute for Mediterranean Studies (IMS), Foundation for Research & Technology Hellas (FORTH), 74100 Rethymno, Greece
Max Foxley-Marrable: Revolution Data Platforms, Ottawa, ON K2J, Canada
Alan Wilkins: Mercari Risk Management Ltd., Southampton SO45 5TE, UK

Sustainability, 2024, vol. 16, issue 12, 1-22

Abstract: This article presents a low-cost insurance system developed for smallholder farms in disaster-prone regions, primarily using free Earth observation (EO) data and free open source software’s (FOSS), collectively termed “sustainable geoinformatics.” The study examined 30 farms in Risaralda Department, Colombia. A digital elevation model (12.5 m pixels) from the ALOS PALSAR satellite sensor was used with a geographic 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 developed using a proprietary mobile phone app that collected GPS-tagged, time-stamped mobile phone photos to verify crop damage, with further verification of crop health also provided by daily near-real-time satellite imagery (e.g., PlanetScope with 3 m pixels). Machine learning was used for feature identification with the photos and the satellite imagery. Key features of this insurance system are its low operational cost and rapid damage verification relative to conventional approaches to farm insurance. This relatively fast, low-cost, and affordable approach to insurance for small-scale farming enhances sustainable development by enabling policyholder farmers to recover more quickly from disasters.

Keywords: digital data poverty; disaster risk management; earth observation; extreme weather; GIS; machine learning; parametric insurance; small farms; sustainable geoinformatics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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