MULTIVARIATE STATISTICAL ANALYSIS OF SOIL-WATER IRRIGATION SYSTEMS: METHODS, APPLICATIONS AND RESEARCH GAPS

Abstract

Soil-water-irrigation systems are increasingly being affected by the deterioration of water quality, soil conditions and climatic changes. These complex datasets are highly interrelated. Traditional univariate analysis methods have limitations in effectively addressing the complex interrelationship between variables. Therefore, there is a need for the application of robust multivariate statistical analysis. This study critically reviews the application of multivariate statistical analysis in the soil-water-irrigation system, with a focus on irrigation planning and management. The study integrates the most commonly used data structures for the analysis of the variables in the agricultural irrigation system. The variables in the study include soil physicochemical characteristics, irrigation water quality variables and agro-climatic variables. The most commonly used multivariate statistical analysis methods, such as Principal Component Analysis, Factor Analysis, Cluster Analysis, regression models, Principal Component Regression, Partial Least Square Regression and composite indices, are critically discussed in terms of their advantages, limitations and applicability in irrigation planning and management. The study focuses on the most critical methodological aspects of multivariate statistical analysis, which have a significant influence on the outcome of the analysis. In addition, the review also reveals some significant gaps in the research in terms of methodological validation, integration of multivariate models into decision support systems and difficulties in cross-regional applications. Some emerging trends in data analysis and opportunities for combining multivariate analysis with other advanced data-driven techniques are also presented in this review, providing a framework for future research and irrigation management strategies.