Statistical Models for Soil Attributes

This book explores applied methodologies for assessing soil attributes through diverse multivariate modeling techniques. Specifically, the feasibility of employing different multivariate models to uncover insights from the soil attribute data is examined. The dataset comprises soil attribute measurements collected from various locations and periods, constituting a multivariate dataset. To address this complexity, advanced statistical techniques were applied to reduce dimensionality and extract meaningful patterns. Multivariate statistics aids in understanding the different aims and backgrounds, elucidating how variables are related to each other. The practical implementation of multivariate statistics may involve various univariate and multivariate analyses to comprehend the relationship among variables and their relevance to the studied problems (Johnson and Wicheren, 1996). Various multivariate analysis techniques, including multivariate analysis of variance, Principal Component Analysis, Factor Analysis, Canonical Correlation Analysis, Discriminant Analysis, Cluster Analysis, and multivariate regression analysis, were employed. The multivariate techniques utilized in this research provide valuable tools for understanding complex relationships between soil attributes. By revealing essential latent factors and interdependencies among measured variables, these techniques contribute to a more comprehensive understanding of soil properties and their interactions. This knowledge holds significance for diverse applications such as agriculture, environmental studies, and land management practices. The diverse analyses undertaken provide valuable insights into soil parameter variations, relationships, and their impact on fertility, facilitating targeted soil improvement strategies for enhanced agricultural productivity.