Mathematical Models of Crop Growth and Yield / Edition 1

by Allen R. Overman, Richard V. Scholtz III

Overview

Highlighting effective, analytical functions that have been found useful for the comparison of alternative management techniques to maximize water and nutrient resources, this reference describes the application of viable mathematical models in data analysis to increase crop growth and yields. Featuring solutions to various differential equations, the book covers the characteristics of the functions related to the phenomenological growth model. Including more than 1300 literature citations, display equations, tables, and figures and outlining an approach to mathematical crop modeling, Mathematical Models of Crop Growth and Yield will prove an invaluable resource.

Product Details

ISBN-13:	9780824708252
Publisher:	Taylor & Francis
Publication date:	08/27/2002
Series:	Books in Soils, Plants, and the Environment , #91
Edition description:	New Edition
Pages:	344
Product dimensions:	6.00(w) x 9.00(h) x (d)

About the Author

Overman, Allen R.; Scholtz III, Richard V.

Table of Contents

INTRODUCTION. Historic Background. Yield Response Models. Growth Models. Environmental Input. Summary. Exercises. References. SEASONAL RESPONSE MODELS. Background. Extended Logistic Model. Extended Multiple Logistic Model. Water Availability. Legume/Grass Interaction. Summary. Exercises. References. GROWTH RESPONSE MODELS. Background. Empirical Growth Model. Extended Empirical Growth Model. Phenomenological Growth Model. Expanded Growth Model. Exercises. References. MATHEMATICAL CHARACTERISTICS OF MODELS. Background. Phenomenological Growth Model. Expanded Growth Model. Rational Basis for Logistic Model. Coupling Among Applied Soil and Plant Components. Accumulation of Dry Matter and Plant Nutrients. Exercises. References. PASTURE SYSTEMS. Background. Quadratic Model. Linear Exponential Model. Summary. Exercises. References. NONLINEAR REGRESSION FOR MATHEMATICAL MODELS. Background. Logistic Model. Probability Model. Confidence Contours. Sensitivity Analysis. Dimensionless Plots. Correlation Coefficient. Exercises. References. Index.

Editorial Reviews

Outlining an approach to mathematical crop modeling, Overman and Scholtz (a professor and research fellow, respectively of the Department of Agricultural and Biological Engineering, U. of Florida) analytical functions are presented in a format that progresses from simpler models towards greater levels of complexity and comprehensiveness. Chapters cover seasonal response models, growth response models, mathematical characteristics of models, pasture systems, and nonlinear regression for mathematical models. Annotation c. Book News, Inc., Portland, OR