On а Model for Drough Risk Reduction in Agriculture

Introduction. The article analyzes the changes in moisture content of the soil in the south of the European territory of Russia (ETR) associated with climate warming. It is shown that both in the foothill and steppe climatic zones there is a tendency to decrease this parameter, which greatly increases the relevance of developing methods to reduce risks in agriculture associated with droughts. A model based on different vulnerability of agricultural crops to this dangerous weather phenomenon is presented, the results of model calculations for the conditions of the steppe zone of the Kabardino-Balkarian Republic (KBR) are presented.

Materials and research methods. When conducting research, the Selyaninov hydrothermal coefficient was used as an indicator characterizing the moisture content of the soil. The values of this coefficient were calculated using data from 13 weather stations on the amount of precipitation and air temperature for 1961-2018. The development of a risk reduction model in this paper is considered within the framework of decision theory. As a target in the problem, you can use a function that describes the gain or loss of agriculture. Using this method of choosing an action to reduce risks avoids the formation of a set of actions from which it is necessary to choose the most appropriate one.

Research results and their discussion. The results of the calculations carried out in the work showed that the consequence of climate change in the south of the EPR will be a significant deterioration in the conditions for the production of agricultural products. The consequences of such a trend will be extremely negative for agricultural production in the area. As a result of solving the problem, it is possible to determine the optimal structure of crop production from the point of view of the criterion used, taking into account the probability of droughts. As such a criterion, the maximum expected volume of crop production was used, taking into account the impact of droughts.

Conclusions. A method has been proposed to reduce the losses of agriculture from droughts, taking into account their different vulnerability to various crops. The model was written in the framework of linear programming, which makes it possible to determine the optimal structure of agricultural production in terms of the criterion used. The method can be used in regions with different production and economic conditions.

1. Ashabokov B.A., Fedchenko L.M., Tashilova A.A., Kesheva L.A., Teunova N.V. Spatio-temporal climate change in the south of the European territory of Russia, assessment of its consequences, methods and models of adaptation of the agro-industrial complex. Fregat LLC, Nalchik, 2020. 476 p. (In Russ.).

2. Bedritsky A.I., Korshunov A.A., Khandozhko L.A. Basics of optimal adaptation of the Russian economy to dangerous manifestations of weather and climate. Meteorology and Hydrology. 2009. No. 4. P. 5–14 (In Russ.).

3. Volkova V.I., Badakhova G.Kh., Barekova M.V., Kaplan G.L. Features of the atmospheric circulation of the transitional period and fluctuations in the dates of the beginning of spring in the Central Ciscaucasia // Science. Innovations. Technologies. 2021. No.1. P. 125–138 (In Russ.).

4. Report on climate features in the territory of the Russian Federation for 2020. Roshydromet. Moscow, 2021. 104 p. (In Russ.).

5. Zoidze E.K., Khomyakova T.V., Shostak Z.A. et al. On the problem of adequate agro-climatic provision of the economy of the Russian economy in the context of climate change // Meteorology and Hydrology. 2010. No. 8. P. 73–86 (In Russ.).

6. Kattsov V.M. Report on climate risks in the Russian Federation. St. Petersburg. Roshydromet, 2017, 106 p. (In Russ.).

7. Kini R.L., Raifa H.M. Decision Making Under Multiple Criteria: Preferences and Substitutions. Radio and communication. 1981. 560 p. (In Russ.).

8. Meleshko V.P., Kattsov V.M., Govorkov V.A. Anthropogenic climate change in the 21st century in Northern Eurasia // Meteorology and hydrology. 2004. No. 7. P. 5–26 (In Russ.).

9. Selyaninov G.T. Agro-climatic map of the world. L.: Gidrometeoizdat, 1966. 12 p. (In Russ.).

10. Taha H. Introduction to Operations Research. Moscow, Mir, 1985. Vol. 2. 496 p. (In Russ.).

11. Office of the Federal State Statistics Service for the North Caucasian Federal District. Kabardino-Balkarian Republic. URL: https://stavstat.gks.ru/ofstatistics_kbr (Аccessed: 03/01/2023) (In Russ.).

12. Drumond A., Liberato M.L.R., Reboita M.S., Taschetto A.S. Weather and Climate Extremes: Current Developments. Atmosphere. 2020. Vol. 11. P. 24. https://doi.org/10.3390/atmos11010024

13. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 p.

14. Santos, F.D.; Ferreira, P.L.; Pedersen, J.S.T. The Climate Change Challenge: A Review of the Barriers and Solutions to Deliver a Paris Solution. Climate 2022, 10, 75. https://doi.org/10.3390/cli10050075

15. Shen D., Shi W.-F., Tang W., Wang Y., Liao J. The Agricultural Economic Value of Weather Forecasting in China. Sustainability 2022, 14, 17026. https://doi.org/10.3390/su142417026