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Author(s)
Rajarathinam, A.
Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli, India.
Ramji, M.
Department of Statistics, Manonmaniam Sundaranar University, Tirunelveli, India.
ISBN 978-81-969208-7-6 (Print)
ISBN 978-81-969208-5-2 (eBook)
DOI: 10.9734/bpi/mono/978-81-969208-7-6
This study includes an extensive examination of multivariate statistical models used in analyzing soil properties, providing details on how they are applied across various geographical and temporal contexts. This study, consisting of eight chapters, explores the problematic world of soil science and provides light on the different statistical methods used to understand the complexity of soil composition, nutrient content, and pH levels.
The authors are given a brief overview of the multivariate statistical models employed in investigating soil parameters in Chapter I, which serves as the starting point of our journey. This chapter also provides a clear organizational framework and a road map for the study.
We are given an insightful tour of the body of research on soil characteristics in Chapter II. This chapter lays a strong foundation for the subsequent chapters by emphasizing canonical correlation analysis, cluster analysis, factor analysis, principal component analysis, multinomial logistic regression, and multiple linear discriminant analysis. The following chapters will highlight the various methodologies used in soil research.
As we explore Canonical correlation analysis, Chapter III takes us on an international journey that includes the Tirunelveli district and the United States from 1964 to 2011. This chapter explores the interactions between crop fertilizers and primary nutrient content, illustrating the complexity of agricultural practices and soil management.
Chapter IV takes us to the village of Muthur in 2020–2021, where Canonical Correlation Analysis, Principal Component Analysis, and Factor Analysis combine to reveal the complex web of soil properties. The flexibility of these statistical models in determining soil composition is demonstrated in this chapter.
The crucial problem of dimensionality reduction in soil attribute databases is covered in Chapter V. This chapter presents practical methods for managing and enhancing the insight of complicated data by applying numerous linear discriminant analyses.
Applying Multinomial Logistic regression, Chapter VI takes a predictive turn. Here, we develop regression models to categorize the soil pH, an essential step in understanding the health of the soil and its compatibility with various agricultural practices.
Finally, Chapter VII summarizes the significant discoveries, insightful conclusions, and illuminating directions for future research in soil parameter analysis as the conclusion of our voyage.
Each part of this study advances knowledge of the complex world under our feet, where soil properties are crucial to agriculture, ecology, and environmental research. We believe this work will be a valuable tool for academics, teachers, and professionals in soil science, encouraging additional research and innovation in environmentally responsible soil management.
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