Discriminant Modeling for Soil Attributes: Relationship between Sandy Soil Categories and Macronutrients

In this study, discriminant analysis techniques were employed to investigate the relationship between sandy soil categories and macronutrients. Sandy soil has excellent drainage properties due to its coarse texture. It allows water to infiltrate quickly, preventing water logging and reducing the risk of root rot. The assumptions of linear discriminant function analysis, such as the normality of regressors, multicollinearity, and homoscedasticity were carefully examined. A parametric technique called discriminant analysis (DA) is used to identify the weightings of quantitative variables or predictors that best distinguish between two or more categories of dependent variables. The data were transformed using the Box-Cox method to improve normality, and a multivariate analysis of variance was employed to determine whether there were significant differences in soil macronutrients between the sand groups. The results showed that there were significant differences in soil macronutrients between the sand groups, and the classification accuracy of the discriminant function was 67%. The findings suggested that the discriminant function analysis could be used for classifying soil types based on their macronutrient content, particularly in sandy soil. By examining the discriminant weights assigned to each nutrient, and determining pH, EC and OM nutrients have the most significant impact on the sand categories. This information can be used to prioritize variables for further investigation.