Application of land remote sensing data in agricultural insurance system

2022

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DOI 10.46666/2022-3.2708-9991.19
Data confidence 70 medium
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Abstract

The goal is to determine the areas of application of land remote sensing data for the purpose of index agricultural insurance. Methods – monographic, cartographic, comparative analysis, abstract-logical. Results – the authors note that agricultural production is quite a risky activity, directly dependent on climatic features of the region. In this regard, agricultural insu rance is the most effective way of financial protection of agricultural producers of the republic. The stage at which this process is currently located does not allow us to speak about the use of this tool as a systemic institution for the development of agricultural sector with the range of opport unities that are widely used in practice o f economically developed countries. The situation in the field of agricultural insurance in Kazakhstan is characterized by a small share of insured areas of crops and animals, despite the support of this type of insurance by the State. The introduction of modern technologies in the form of information on remote land survey will simplify the mech anism of agricultural risk insurance and increase the volume of insurance operations, ensuring r eduction in adverse outcomes in the effective management of agro-industrial complex. The foreign experience of using various variations of index agricultural insurance based on information on the observation of land surface by aviation and space means has been studied. Conclusions – the analysis of the current state of insurance coverage in agro-industrial production, including insurance products, made it possible to calculate the probability of insured case in crop insurance u sing the index methodology and obtain more accurate and representative solutions through the use of a large array of initial statistical data. Proposals have been developed to improve the exis ting mechanisms of insurance procedures to increase competitiveness of products, raw materials and food.