Прогнозирование урожайности яровой пшеницы в лесостепной зоне Среднего Поволжья по методу наименьших квадратов

Abstract

The efficient use of arable land is based on the prediction of crop yields. In extensive farming system the productivity forecasting was carried out by scores of soil fertility, where the level of soil fertility was associated with soil type, and crop yields depended on the leading basic and sustained properties - humus content, cation-exchange capacity, particle size distribution. The system of intensive farming is based on the use of mineral and organic fertilizers. The leading factor in the formation of crop yield is the soil security by mobile soil nutrients on the background of optimal soil parameters. Nowadays, crop yield forecasting is made with the use of the available to plants macro nutrition content. The article presents the results of spring wheat yield prognosis in the Laishevo municipal district with the use of MatLab (matrix operations). The matrix was made according to the data over the last 43 years, it’s moving averages with steps of 11 and 22 years, the content of mobile phosphorus and potassium, determined by the method of Kirsanov. The predictive ability is confirmed by correlation analysis, for the actual number of crop coefficients of correlation with phosphorus and potassium are 0.52 and 0.61, respectively, for the moving average yields are equal to 0.94 and 0.95. A comparison of the actual spring wheat yield (УФ) with the calculated data (the model 1 and the model 2) shows the average deviation of 30%. Similar calculations for the derived series of the moving average of crop yield for the step length of 11 years gives a marked decrease in the deviation of 5-6 %. This convergence of data with the calculated У11 (the model 1 and the model 2, in the left part of the table) indicates for the elimination of weather factor for У11, which influences the overall level of productivity of spring wheat in the forest-steppe zone. The conclusion of the article is the inclusion of agro-climatic conditions (precipitation and temperature) for further calculation of crop yields forecasting.