Abstract
Index insurance has been introduced as a solution to tackle several challenges that prevail in the agricultural insurance sector of developing countries. One of the main implementation challenges in these countries is the lack of reliable weather data for index development and implementation. The increasing availability of satellite data could ease the constraints of data access. Meanwhile, the suitability of various satellite products for yield estimation across world regions has to undergo a thorough assessment. This study contributes to the literature by systematically analyzing the accuracy of some globally available satellite data, namely the Global Satellite Mapping of Precipitation (GSMaP), Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), and the Global Land Data Assimilation System (GLDAS) compared to ground-level weather information for 14 different indicators for the case of Uzbekistan. Our analysis indicates that those sources may provide the necessary data for an accessible and adequate climate service. However, a considerable risk of overestimation and underestimation depending on the source of satellite data may exist, especially for precipitation data in the conditions of Central Asia. Among the tested datasets, GSMaP showed a relatively better performance than CHIRPS in precipitation estimation for drought and flood detection. In order to reduce detection inaccuracy, the application of satellite weather products for index insurance is possible when temporal aggregation (e.g., monthly, seasonal) is considered. Globally available climate data could serve as a good source to establish index insurance products in Central Asia; however, a careful selection of source and index is required.
Highlights
Agricultural insurance is a risk management tool that can assist in coping with climate risks in agricultural areas by protecting assets, opening access to credits, mitigating risk, maintaining the resilience of farmers, and supporting food security
This study aims to analyze the suitability of globally available satellite data, namely the Global Satellite Mapping of Precipitation (GSMaP), Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), as well as the Global Land Data Assimilation System (GLDAS), as a potential source of weather data for devel oping and implementing index insurance in Central Asia
The main aim of the study was to investigate the suitability of freely available satellite temperature and precipitation data for designing and implementing an index insurance in Central Asia by analyzing the sat ellites products’ accuracy and ability to detect droughts or floods using Metrological Drought Indices (MDI)
Summary
Agricultural insurance is a risk management tool that can assist in coping with climate risks in agricultural areas by protecting assets, opening access to credits, mitigating risk, maintaining the resilience of farmers, and supporting food security. Because of high costs, moral hazard and adverse selection, traditional agricultural insurance, known as “loss-indemnifying” insurance has not yet effectively assisted and mitigated all of the risks for farmers in developing countries. For the case of index-based insurance, indemnity payments are determined by an index that is neither affected by farm-individual production decisions nor vulnerable to manipulation by third parties. This approach is aimed at reducing adverse selection and problems of moral hazard, which are frequent issues in traditional agricultural insurances (Fisher et al, 2019; World Bank, 2015)
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