TO THE QUESTION OF FORECASTING THE PRODUCTIVITY OF WINTER RYE WITHIN THE AGROLANDSCAPE OF A MORAINE HILL

The yield monitoring (1998-2023) of winter rye was conducted within the moraine hill in order to find patterns in the influence of weather and landscape conditions on it. Crops were grown in the Tver region without fertilizers. It was revealed that its productivity varied from 0 to 63 c/ha. The years of research are combined into three clusters that differ in the nature of the spatial variability of the yield within the agrolandscape. The first cluster - years with cold and dry demi-season periods, the second - with warm and damp spring and autumn and dry summer, the third - with standard demi-season periods and wet summer. The maximum yield, as well as its spatial variability, was observed in the second cluster. The probability of occurrence of the climatic conditions of the first cluster is 42%, the second - 33%, the third - 25%. It is shown that changes in the climatic conditions lead to a transformation of a set of landscape factors that reliably affect the yield. For the first cluster, an increase in yield depends on stones (26%) and fine fractions (8%) in the soil, as well as on the altitude (13%). The degree of heating of the territory (3%) and soil acidity (4%) have a negative effect. For the second cluster, the yield positively depends on the sum of dust fractions (11%) and the altitude (10%), and negatively on the content of coarse dust (8%), phosphorus (4%) and the curvature of the surface (6%) in the soil. Under the conditions of the third cluster, an increase in pH (9%) has a positive effect on the yield, and a negative effect - on the altitude (22%) and the content of potassium in the soil (7%). Models have been developed that make it possible to predict the yield of a crop in the space of agrolandscapes of a terminal moraine ridge, as well as genetically close territories. Combining yield maps obtained for conditions of different clusters will allow identifying areas optimal for growing crops, as well as predicting yield variability under changing climatic conditions.