Ability of the geographically weighted principal components analysis for assessing of the maize (Zea mays L.) spatial nonstationarity morphometrics traits interrelation
DOI:
https://doi.org/10.14255/2308-9628/15.113/13Keywords:
principal components analysis, morphometric traits, spatial variability.Abstract
Spatial patterns of maize morphometrics traits interrelation variability with geographically weighted principal components analysis at large-scale level have been revealed. Spatial variability of covariation structures which describes interrelation between morphometrics indicators and density of standing of maize have been established. The global pattern of interrelation obtained by means of the classical principal component analysis have been shown as not to be identical to local covariation structures. Local covariation structures which found with geographically weighted principal components analysis within an optimum kernel bandwidth are characterised by much higher level of nonrandom variability which is described by first three principal components. The part of a dispersion which specifies in a coordination of morphological structures, is characterised by natural spatial trends of a variation. Local covariation structures form spatially natural patterns of the placing. Feature of these structures is quantitative redistribution of those values or other signs within the limits of enough invariant configurations. The continuity covariation structures in a qualitative sense at various scale levels (global and local) but with local quantitative specificity is shown. This specificity is shown in prevalence of this or that indicator as basic marker main components at local level. Revealing of spatial patterns covariation structures puts a problem of understanding of the nature of this spatial regularity.
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