КОЛИЧЕСТВЕННАЯ ОЦЕНКА ВЛИЯНИЯ ИЗМЕНЕНИЙ КЛИМАТА НА ПРОДУКТИВНОСТЬ ЗЕРНОВОГО ПРОИЗВОДСТВА НА ЮГЕ СИБИРИ

Abstract

The result of the numerical assessment of the agroclimatic potential influence on the productivity indicators of the Siberia’s grain production is presented on the basis of retrospective meteorological data (1985-2020) and the data on grain production (2007-2020). To predict the productivity of grain-producing territories, an artificial intelligence method was used: a linear cellular automaton, which allows constructing a long-term forecast (with a lead time of more than a year) based on the revealed memory depth of the bonitet time series. The results of the forecast models are confirmed by the testing of nonlinear dynamics methods: phase analysis, R/S-analysis. The error in the forecast of the climate bonitet varied within 7-16%. The proposed methodology makes it possible to obtain the grain production productivity forecasts depending on the cycle of changes in meteorological factors. These forecasts can be applied in the development of management decisions in order to increase the productivity of grain production, in particular, for organic farming and to reduce the risks associated with future climate change.