Statistical model for evaluation of the impact of climatic conditions on the crops production: the regional aspects

Abstract

The work examines the statistical model for evaluation of the impact of climatic conditions on the crops production in Ukraine. The conducted content analysis of academic literary sources enables to arrive at conclusion that the majority of Ukrainian scholars consider changes in climatic zones of Ukraine a positive trend for crops production. It must be emphasized, nonetheless, that the increase in natural heat provision for crops production against the backdrop of a significant reduction in average annual precipitation considerably diminishes the sizes of cultivated and harvested areas, gross yield and overall crop yield of basic crops and perennial plantings. To perform calculations on key statistical indicators of crops production the following tools have been employed: methods of analysis of absolute, relative and average values; methods of elaboration and study of groupings; methods of analysis of the structure of statistical populations; methods of cross-impact analysis of indicators; methods of trend studies. The analysis concerned the dynamics of change in statistical indicators of crops production in Kherson oblast over the period of 1990–2019: gross yield of cereal and leguminous crops; total harvesting area of cereal and leguminous crops; wheat yields; cereal and leguminous crops production per capita. Periods of diverse degrees of occurrence of atmospheric precipitation in Kherson oblast according to the level of liquid saturation have been grouped: dry, medium, humid. It has been proved that winter wheat yields are affected by the following factors: size of the cultivation area and average annual precipitation. It is established that the digitalization of the agriculture contributes to the decrease in pressure on land and water resources, provision of conditions for “clean”, sustainable and eco-friendly agricultural products, increase in gross yield of crops, provision of conditions for efficient use of resources, capability of Big Data processing. Prospects for further research lie in elaboration of a multi-factor non-linear modeling of winter wheat yield with account for the factors of humus and soil pH; average annual atmospheric temperature, etc.