Russian Federation
Russian Federation
Russian Federation
UDC 332.362
One of the most promising areas in crop yield forecasting is the use of time series (time series) value analysis methods, which make it possible to analyze the historical dynamics of crop production in the Krasnodar Territory and identify stable trends and cyclical fluctuations. Based on the identified irregularities, it is possible to build short- and medium-term forecasts that take into account the inertia of the industry's development. The introduction of economic and mathematical forecasting methods into the practice of managing the crop industry in the Krasnodar Territory will make it possible to make more informed management decisions, optimize the use of resources and increase the economic efficiency of agricultural production. Forecasting, as an effective production management function, is a necessary stage of planning, increases its scientific validity and the quality of business plans. The economy of the agrarian regions of Russia largely depends on the stability and productivity of agriculture, the level of yield of major crops (cereals, oilseeds). Grain yields: winter wheat and cucumbers play a key role in the economic well-being of the region and rural development. Accurate and timely forecasting of grain yields is becoming not just an agrotechnical tool, but a powerful lever for planning, allocating resources and stimulating the sustainable development of villages and agricultural holdings in the Krasnodar Territory. Crop production is one of the key sectors of the Krasnodar Territory's economy, ensuring the region's food security and significant export potential. Effective management of the development of this industry requires accurate forecasting of its main indicators, such as yield, acreage and gross production. Economic and mathematical methods provide powerful tools for solving this problem, allowing us to take into account many factors affecting the dynamics of the crop industry in the Krasnodar Territory.
agriculture, crop production, agro-industrial complex, agricultural land, productivity, forecasting
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