Monitoring and evaluation of agricultural development in Krasnodar region

In modern conditions, the development of agricultural industries is a strategically important task affecting the country’s food security [15]. The Krasnodar Territory is a leader in the agricultural sector in Russia – the share of the region’s output was 6,7% by the end of 2024 in the total volume, which makes this region a strategically important object of analysis in the food security system of the Russian Federation. Monitoring and evaluation of the development of crop production and animal husbandry are necessary to identify new trends, threats, or determine the effectiveness of developed and implemented measures to mitigate risk events [16]. In the course of the study, a system of indicators was analyzed, including yield, average milk yield, average annual wool shearing, etc. The dynamics of key performance indicators of economic entities engaged in agriculture has been studied using horizontal analysis. The active development of the agro-industrial complex of the Krasnodar Territory in the period 2020- 2024 was revealed, which is reflected in the growth of gross harvest due to increased yields in the field of crop production. At the same time, animal husbandry is developing at a slow pace: milk yield per cow has increased by only 5% in 15 years. Using statistical methods based on MS Excel, a trend forecast of output for the period 2025-2027 was built [17]. According to the established trend, the output of the Krasnodar Territory is increasing by an average of 33 million rubles per year. By 620 the output volume is projected at 2027 billion rubles, which is 718,27% higher than in 2024, or 19,88 billion rubles. The correlation coefficient shows a close relationship between the output of the Krasnodar Territory and the time factor (119,12), confirming the statistical significance of the constructed regression equation. Deviations from the trend are caused by the influence of other factorial features, which determines the relevance of research aimed at detecting hidden variables.