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In the present article, the patterns of the geographic variability in yields of rye within Polesia and the Forest-Steppe zone of Ukraine are presented and the correlation of the factors and dynamics of an agroeconomic and agroecological nature was determined. The dynamics of rye yields in the study area over time were determined as being characterized by three extreme points: two local maxima and one local minimum. Specific terms of the polynomial curve of the fourth order can be meaningfully interpreted and applied to describe the dynamics of productivity. Free members of the polynomial indicate culture productivity in the starting period. Dynamics of the productivity that can be explained by the regression indicate that agrotechnological and agrecological conditions of agricultural production are a pervasive factor that determines the presence of a general trend. The determination coefficient of the regression total trend can be interpreted as an indicator of the role of the agrotechnological and agroeconomic factors in the dynamics of productivity. The residue of the trend regression model can be interpreted so as to include the agroecological component of the rye yields dynamics. Their analysis revealed seven key components that together explained 58.4% of the total variability of the space feature. The principal components of vibrational patterns reflect the specific nature of variation of rye yields over time, which are spatially defined. Vibrational effects are environmental in nature. Geographically weighted principal component analysis showed the transience of environmental spatial modes which determine the oscillating component of rye yield variation over time. Spaces within which the structure of ecological interactions remains unchanged can be considered as the basis of agroecological zoning areas.

biosystems diversity

Agroeconomic and agroecological aspects of spatial variation of rye (Secale cereale) yields within Polesia and the Forest-Steppe zone of Ukraine: The usage of geographically weighted principal components analysis