Abstract
Abstract. Vegetation indices based on satellite images, such as the normalized difference vegetation index (NDVI), have been used in countries like the USA, Canada and Spain for damaged pasture and forage insurance over the last few years. This type of agricultural insurance is called satellite-index-based insurance (SIBI). In SIBI, the occurrence of damage is defined as normal distributions. In this work a pasture area at the north of the Community of Madrid (Spain) has been delimited by means of Moderate Resolution Imaging Spectroradiometer (MODIS) images. A statistical analysis of NDVI histograms was applied to seek for alternative distributions using the maximum likelihood method and χ2 test. The results show that the normal distribution is not the optimal representation and the generalized extreme value (GEV) distribution presents a better fit through the year based on a quality estimator. A comparison between normal and GEV is shown with respect to the probability under a NDVI threshold value throughout the year. This suggests that an a priori distribution should not be selected and a percentile methodology should be used to define a NDVI damage threshold rather than the average and standard deviation, typically of normal distributions. Highlights. The GEV distribution provides better fit to the NDVI historical observations than the normal one. Differences between normal and GEV distributions are higher during spring and autumn, which are transition periods in the precipitation regimen. NDVI damage threshold shows evident differences using normal and GEV distributions both covering the same probability (24.20 %). NDVI damage threshold values based on percentile calculation are proposed as an improvement in the index-based insurance in damaged pasture.
Highlights
Agricultural insurance addresses the reduction of the risk associated with crop production and animal husbandry
normalized difference vegetation index (NDVI) series were obtained for each pixel of the study area using frequency bands provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) product named MOD09A1
According to the results obtained in the study area using maximum likelihood method (MLM) and the χ 2 test, it can be concluded that normal distributions are not a good fit to the NDVI observations, and generalized extreme value (GEV) distributions provide a better approximation
Summary
Agricultural insurance addresses the reduction of the risk associated with crop production and animal husbandry. The importance of weather-index-based insurances (WIBIs) for agriculture has been increasing, mainly in developing countries (Gommes and Kayitakire, 2013). This interest can be explained by the potential that IBI constitutes a risk management instrument for small farmers. It can be considered within the context of renewed attention to agricultural development as one of the milestones of poverty reduction and increased food security, as well as the accompanying efforts from various stakeholders to develop agricultural risk management instruments, including agricultural insurance products
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