The book "Contemporary Statistical Models for the Plant and Soil Sciences" by Oliver Schabenberger and Francis J. Pierce is a comprehensive guide to statistical methods relevant to plant and soil science research. The authors emphasize the importance of contemporary statistical models in addressing the complex data structures and analyses common in these fields. The book covers a wide range of topics, including linear models, nonlinear models, generalized linear models, mixed models, and spatial data analysis. Each chapter provides both theoretical foundations and practical applications, with a focus on the use of statistical software such as SAS and S-PLUS. The book also includes a CD-ROM containing data sets, SAS programs, and additional mathematical details, making it a valuable resource for researchers and students in the plant and soil sciences. The authors highlight the need for statistical methods that can handle correlated, clustered, and spatial data, as well as non-Gaussian and nonlinear responses, which are increasingly common in modern agricultural and environmental studies.The book "Contemporary Statistical Models for the Plant and Soil Sciences" by Oliver Schabenberger and Francis J. Pierce is a comprehensive guide to statistical methods relevant to plant and soil science research. The authors emphasize the importance of contemporary statistical models in addressing the complex data structures and analyses common in these fields. The book covers a wide range of topics, including linear models, nonlinear models, generalized linear models, mixed models, and spatial data analysis. Each chapter provides both theoretical foundations and practical applications, with a focus on the use of statistical software such as SAS and S-PLUS. The book also includes a CD-ROM containing data sets, SAS programs, and additional mathematical details, making it a valuable resource for researchers and students in the plant and soil sciences. The authors highlight the need for statistical methods that can handle correlated, clustered, and spatial data, as well as non-Gaussian and nonlinear responses, which are increasingly common in modern agricultural and environmental studies.