THE PERSPECTIVES OF THE APPLICATION OF GEOGRAPHICALLY WEIGHTED PRINCIPAL COMPONENTS ANALYSIS FOR ESTIMATION OF MAIZE YIELDS SPATIAL VARIABILITY

World population is projected to increase 35% by 2050, which will require a 70-100% rise in food production given projected trends in diets, consumption, and income. Therefore, the study of the reasons of the stagnation of grain crop yields and the opportunities for its increase in the countries of the world, and in Ukraine, in particular, is a critical issue at the present time. This paper aims to explore spatial heterogeneity present in the maize yields data collected from 206 administrative districts in Polissya and Forest-Steppe zone of Ukraine for 27 years using the GWPCA method. The principal components analysis of the residues of the time trend regression models allowed establishing 2 principal components, which together explain 34.1% of variation in the maize yield. There are two spatial determined processes that influence the yield of maize and have the oscillatory dynamics of different periodicity. The oscillating phenomena are of an ecological nature. Geographically weighted principal component analysis showed spatial non-stationary environmental regimes, which determine the oscillatory component of variation of maize yield over time. Geographically weighted principal component analysis allows us to investigate local patterns of maize yield dynamics. Based on the approximate types of local dynamics, we have conducted a cluster analysis for each principal component. Due to cluster mapping the ecologically homogeneous territories where the dynamics of environmental processes are the same can be established. Consequently, the application of geographically weighted principal components analysis with regard to yield data of any crop enables to conduct agroecological zoning of the territory and to identify dynamic aspects of yield determinants