Contemporary Statistical Models for the Plant and Soil Sciences: 1st Edition (Hardback) book cover

Contemporary Statistical Models for the Plant and Soil Sciences

1st Edition

By Oliver Schabenberger, Francis J. Pierce

CRC Press

760 pages | 134 B/W Illus.

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Hardback: 9781584881117
pub: 2001-11-13
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Despite its many origins in agronomic problems, statistics today is often unrecognizable in this context. Numerous recent methodological approaches and advances originated in other subject-matter areas and agronomists frequently find it difficult to see their immediate relation to questions that their disciplines raise. On the other hand, statisticians often fail to recognize the riches of challenging data analytical problems contemporary plant and soil science provides.

The first book to integrate modern statistics with crop, plant and soil science, Contemporary Statistical Models for the Plant and Soil Sciences bridges this gap. The breadth and depth of topics covered is unusual. Each of the main chapters could be a textbook in its own right on a particular class of data structures or models. The cogent presentation in one text allows research workers to apply modern statistical methods that otherwise are scattered across several specialized texts. The combination of theory and application orientation conveys ìwhyî a particular method works and ìhowî it is put in to practice.

About the CD-ROM

The accompanying CD-ROM is a key component of the book. For each of the main chapters additional sections of text are available that cover mathematical derivations, special topics, and supplementary applications. It supplies the data sets and SAS code for all applications and examples in the text, macros that the author developed, and SAS tutorials ranging from basic data manipulation to advanced programming techniques and publication quality graphics.

Contemporary statistical models can not be appreciated to their full potential without a good understanding of theory. They also can not be applied to their full potential without the aid of statistical software. Contemporary Statistical Models for the Plant and Soil Science provides the essential mix of theory and applications of statistical methods pertinent to research in life sciences.


"This text [presents] many of the newer statistical modeling techniques for data analysis using examples familiar to plant and soil scientists…keeping the mathematical complexity to a minimum. I applaud the authors for their efforts to bring the current state of the area of statistical modeling into the realm of the plant and soil sciences."

--Clarence E. Watson, Experimental Statistics and Plant and Soil Sciences, Mississippi State University, USA

"My overall impression is that it is a superbly crafted text replete with many carefully chosen examples that instructively demonstrate contemporary models and modelling practices. The authors' attention to fine detail in the presentation of materials is evident in every chapter. Researchers, instructors, and students alike doubtlessly will find the snippets of SAS code and specially tailored macros to be of immense value when fitting data to the contemporary models described in this treatise."

--Timothy Gregoire, School of Forestry and Environmental Studies, Yale University, New Haven , USA

Table of Contents

Statistical Models

Mathematical and Statistical Models

Functional Aspects of Models

The Inferential Steps ó Estimation and Testing

t-Tests in Terms of Statistical Models

Embedding Hypotheses

Hypothesis and Significance Testing ó Interpretation of the p-Value

Classes of Statistical Models

Data Structures


Classification by Response Type

Classification by Study Type

Clustered Data

Autocorrelated Data

From Independent to Spatial Data ó A Progression of Clustering

Linear Algebra Tools


Matrices and Vectors

Basic Matrix Operations

Matrix Inversion ó Regular and Generalized Inverse

Mean, Variance, and Covariance of Random Vectors

The Trace and Expectation of Quadratic Forms

The Multivariate Gaussian Distribution

Matrix and Vector Differentiation

Using Matrix Algebra to Specify Models

The Classical Linear Model: Least Squares and Alternatives


Least Squares Estimation and Partitioning of Variation

Factorial Classification

Diagnosing Regression Models

Diagnosing Classification Models

Robust Estimation

Nonparametric Regression

Nonlinear Models


Models as Laws or Tools

Linear Polynomials Approximate Nonlinear Models

Fitting a Nonlinear Model to Data

Hypothesis Tests and Confidence Intervals


Parameterization of Nonlinear Models


Generalized Linear Models


Components of a Generalized Linear Model

Grouped and Ungrouped Data

Parameter Estimation and Inference

Modeling an Ordinal Response



Linear Mixed Models for Clustered Data


The Laird-Ware Model

Choosing the Inference Space

Estimation and Inference

Correlations in Mixed Models


Nonlinear Models for Clustered Data


Nonlinear and Generalized Linear Mixed Models

Towards an Approximate Objective Function


Statistical Models for Spatial Data

Changing the Mindset

Semivariogram Analysis and Estimation

The Spatial Model

Spatial Prediction and the Kriging Paradigm

Spatial Regression and Classification Models

Autoregressive Models for Lattice Data

Analyzing Mapped Spatial Point Patterns



Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
SCIENCE / Life Sciences / Botany
SCIENCE / Life Sciences / General
TECHNOLOGY & ENGINEERING / Agriculture / General