Redundancy Analysis (RDA) is a multivariate statistical technique that extends principal component analysis (PCA) by incorporating explanatory variables. RDA and PCA differ primarily in their focus and application. While PCA is used for dimensionality reduction by identifying principal components that explain the most variance in the data, RDA is designed to explore the relationships between a set of response variables and explanatory variables, providing insights into how environmental or management factors influence soil properties. The main assumptions required for RDA include linear relationships between variables, normality, homogeneity of variances, and the independence of observations. RDA handles high-dimensional soil data by reducing complexity through ordination, which allows for simultaneous analysis of multiple response variables and their relationships with explanatory variables. This makes RDA particularly useful in soil science research as it helps elucidate complex interactions among soil properties and management practices, guiding sustainable soil management strategies. Before applying RDA, researchers should check for assumptions such as linearity, data distribution, and multicollinearity, and ensure proper data scaling and transformation to achieve reliable and interpretable results.