Multivariate analysis applied to evaluate the spatial variability of a soil subjected to conservation management

Abstract

Monitoring the spatial variability of soil attributes is an important tool in crop management. Multivariate statistical methods and geostatistics were jointly applied to evaluate the influence of different attributes on spatial variability in the soil. The aim of this research was to apply multivariate principal component analysis to map the spatial variability of the soil in an area subjected to different methods of conservation management and identify the most relevant physical and chemical attributes. Disturbed and undisturbed soil samples were collected and georeferenced at 99 different points in the study area at depths of 0.00 - 0.10 m and 0.10 - 0.20 m to measure the physical and chemical attributes of the soil; multivariate principal component analysis (PC) was then applied to the data. Geostatistics was applied to the PCs showing greater explanatory capacity, evaluating spatial dependence, and generating maps of spatial variability. The variance in the soil attributes was explained by the first eight PCs. Soil density, particle density and pH had the greatest influence on the spatial variability of the soil since they showed the highest correlation with the PC with the greatest explanatory power. The PCs that correlated with at least two of the soil attributes showed moderate (PC1 and PC6) and strong (PC2, PC4 and PC5) spatial dependence. The generated maps helped identify the joint influence of the variability of the most relevant attributes, making it possible to characterise regions under different methods of conservation management.