Abstract
International Journal of Agricultural Research and Multidisciplinary Research Trends, 2026;1(2):22-26
A Multivariate Statistical Framework for Agricultural Data: Integrating Principal Component Analysis, Canonical Correlation Analysis and ANOVA
Abstract
Agricultural systems are inherently multivariate, driven by complex interactions among crop performance, soil characteristics, environmental conditions, and management practices. Multivariate statistical methods provide a robust framework for extracting latent structures, identifying homogeneous patterns, and modeling inter-variable relationships. This paper presents a comprehensive analytical framework for multivariate agricultural data, integrating exploratory data analysis, Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA), and Analysis of Variance (ANOVA). Particular emphasis is placed on ANOVA assumption diagnostics that frequently arise in environmental datasets. Utilizing a public agricultural dataset in R, the framework demonstrates an end-to-end analytical workflow from preprocessing to model interpretation, highlighting key soil physicochemical drivers and quantifying their impact on crop disease severity.
Keywords
ANOVA, multivariate, principal component analysis, canonical correlation, multicollinearity
Licence
Licensed under CC BY 4.0. The authors retain copyright. The content is peer-reviewed for academic purposes, and readers should verify information before use. The journal is not liable for any errors or consequences arising from the use of this article. 
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