METHODOLOGY FOR SELECTING REGIONS TO STUDY THE ADAPTATION OF AGRICULTURE TO CLIMATE CHANGE
DOI:
https://doi.org/10.17059/ekon.reg.2023-2-14Keywords:
small sample, diversity, natural agricultural zones, technical efficiency, partial equilibrium, linear programming, scenario analysisAbstract
The impact of climate change on the social and institutional conditions of agriculture (as opposed to technological ones) in Russia has hardly been studied. With a limited budget, such research should examine a small sample of regions. To reduce the subjectivity, a formalised methodology for creating and ranking small samples of regions was developed. While occupying the largest possible share in the country’s gross agricultural production, the regions included in the sample should significantly differ in natural and agricultural zones, agricultural production efficiency, contribution of peasant farms to agricultural output. Unlike other methods, the proposed technique uses a linear programming problem, where all corner solutions are integer. Data envelopment analysis (DEA) was utilised to ensure the inclusion of both efficient and inefficient regions in the sample. In accordance with these requirements, Altai, Krasnoyarsk, Krasnodar krais and Moscow oblast were selected for analysis. For the regions included in at least one of the five best samples (such as Volgograd, Saratov and Leningrad oblasts), a model of partial equilibrium on the wholesale markets of agricultural products of the constituent entities of the Russian Federation (VIAPI model) was applied to assess the impact of scenario climate change on the output and wholesale prices of ten types of agricultural products. The research revealed that while the production in the selected regions is resistant to this influence, except for Altai and Krasnoyarsk krais, regional market prices are still rising due to the impact of world prices for milk products and grain.
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