Allen R Overman, Richard V Scholtz

Mathematical Models of Crop Growth and Yield

• Broschiertes Buch

Produktbeschreibung

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.

---

Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 344
• Erscheinungstermin: 5. September 2019
• Englisch
• Abmessung: 226mm x 152mm x 23mm
• Gewicht: 476g
• ISBN-13: 9780367395896
• ISBN-10: 0367395894
• Artikelnr.: 57732376

Autorenporträt

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

Inhaltsangabe

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.