Modeling crop production systems : principles and application / Phool Singh.

Format:

Book

Author/Creator:

Singh, Phool.

Language:

English

Subjects (All):

Food crops--Mathematical models.

Food crops.

Agricultural systems--Mathematical models.

Agricultural systems.

Physical Description:

1 online resource (534 p.)

Edition:

1st ed.

Place of Publication:

Enfield, (NH) : Science Publishers, c2008.

Language Note:

English

Summary:

The use of simulation models is a necessity and also an aid in the decision-making process in sustainable agricultural systems. Organizing the experimental knowledge of crop production systems without the book keeping and deductive methods of mathematics is very difficult. This book aims to guide readers in the process by which the properties of the systems can be grasped in the framework of mathematical structure with minimal mathematical prerequisites. The objective of this book is to help the undergraduate, graduate and post-graduate students in the disciplines of agronomy, plant breeding, agricultural meteorology, crop physiology, agricultural economics, entomology, plant pathology, soil science and ecology (environmental science). This book may also be useful for administrators in various agricultural universities in order to direct research, extension and teaching activities. Planners at national and state levels may also benefit from this book.

Contents:

Cover

Half Title

Title Page

Copyright Page

Dedication

Preface

Contents

1. PHILOSOPHY, ROLE AND TERMINOLOGY OF SYSTEM SCIENCE

1.1 History of system science

1.1.1 Infancy

1.1.2 Juvenile phase

1.1.3 Adolescence

1.1.4 Maturity

1.2 General topology and terminology of systems

1.2.1 Variable

1.2.2 Parameter

1.2.3 System

1.2.4 Dynamic process/ model /system

1.2.5 Continuous versus discrete state spaces

1.2.6 Stochastic versus deterministic descriptions

1.2.6.1 Stochastic models of exponential growth

1.2.7 Modeling

1.2.8 Model

1.2.9 Steps in modeling

1.2.9.1 First Step: Define the problem

1.2.9.2 Second Step: Component identification

1.2.9.3 Third Step: Specify component behavior

1.2.9.4 Fourth Step: Computer implementation

1.2.9.5 Fifth Step: Validation

1.2.9.6 Sixth Step: Analysis

1.2.9.6.1 Sensitivity analyses

1.2.9.6.2 Stability analyses

1.3 Three problems

1.3.1 System management problem

1.3.2 Pure research problem

1.3.3 System design problem

References

2. DEVELOPMENT OF MODEL STRUCTURE

2.1 Variables and their classification

2.1.1 Individual observations

2.1.2 Sample of observations

2.1.3 Variables

2.1.4 Population

2.1.5 Variables and their classification

2.1.5.1 Measurement variables

2.1.5.2 Discontinuous variables

2.1.6 Ranked variables

2.1.7 Nomina] variables or attributes

2.1.8 Variate

2.1.9 Derived variable

2.1.10 Interval variable

2.1.11 Ratio variable

2.1.12 Rate-quantity variable

2.1.13 Example

2.1.13.1 Components

2.1.13.1.1 Person

2.1.13.1.2 Car

2.1.13.1.3 Highway

2.1.13.1.4 Environment

2.1.14 Exercise

2.2 Relationship between variables

2.2.1 Causal loop diagrams

2.2.1.1 Direct relations

2.2.1.2 Indirect relations.

2.2.1.3 Relationship between rate and quantity variable

2.2.2 Types of relationship between variables

2.2.2.1 Direct (together) relations

2.2.2.2 Inverse relations

2.2.2.3 Indeterminate relations

2.2.2.4 Feedback relationship

2.2.3 Example of public address system

2.2.3.1 Step 1

2.2.3.2 Step 2. Qualitative description of the system

2.2.3.3 Step 3. Definition of relevant components, subsystems, and interactions

2.2.3.4 Step 4. Definition of relevant variables

2.2.3.5 Step 5. Representation of the relations between the variables

2.2.3.6 Step 6. Description of the subsystems

2.2.3.7 Step 7. The model equations

2.2.3.8 Step 8. Studying the behaviour of the mode]

2.2.3.9 Example of feedback relationship: Simple public address system

2.2.3.10 Example: Amplifier circuit with negative feedback

2.2.3.11 Effect of feedback on response to change in input

2.3 Structural (black box) model

2.4 Refinement in structural models

2.4.1 The structure of crop simulation models

3. SPECIFICATION OF COMPONENT BEHAVIOR

3.1 Algebraic form

3.1.1 Matrix algebraic form for studying a specific behavior of components

3.1.1.1 Use of matrix algebra in principal component analysis

3.1.1.2 Use of matrix algebra in linear programming for optimization of the system

3.1.1.2.1 Remark

3.1.1.3 Use of matrix algebra for distance measurements

3.1.1.3.1 Calculation of group distances to make a dendogram

3.2 Integral-differential form

3.2.1 Example for formulating a differential equations

3.2.2 The absorption law of Lambert

3.3 Parameter estimation

3.3.1 Statistical procedure

3.3.1.1 Finding the best parameter values for linear equations

3.3.1.1.1 Useful characteristic of extrema

3.3.1.1.2 Expressions for parameters a and b.

3.3.1.1.2.1 Derivative of a function of a funcrtion: The chain rule

3.3.1.1.2.2 Graphical representation

3.3.1.2 How good is the best fitting curve

3.3.1.3 Random versus systematic deviations

3.3.1.4 Linear approximations for quick estimating a good fitting curve

3.3.1.5 Weighing of data

3.3.1.5.1 Example

3.3.1.6 Error due to data transformation

3.3.1.6.1 Example: Error due to data transformation

3.3.1.6.1.1 Graphical representation

3.3.1.7 Correlation between variables

3.3.1.7.1 Example

3.3.1.8 Forced correlation

3.3.1.8.1 Example

3.3.1.9 Statistical procedure for parameters estimation of normal distribution curve

3.3.1.9.1 Practical uses of normal distribution curve and table of normal distribution (double tail)

3.3.1.9.1.1 Example (Quirin 1978)

3.3.1.9.1.2 Example (Quirin 1978)

3.3.1.9.1.3 Differences between two population mean or proportions

3.3.1.9.1.4 Interval estimation

3.3.1.10 Parameter estimation of samples and the universe of discourse

3.3.1.11 Parameter estimation and hypothesis testing

3.3.1.11.1 Example (1)

3.3.1.11.2 Example (2)

3.3.1.11.3 Example (3)

3.3.1.11.4 Example (4)

3.3.1.11.5 Example (5)

3.3.1.11.6 Example (6)

3.3.1.11.7 Example (7)

3.3.1.12 Crop performance indices

3.4 Non-statistical procedure for estimating the parameters (physical approach)

3.4.1 Non-statistical procedure of parameter estimation

3.4.1.1 Cuestimate of the intrinsic rate of increase

3.4.1.2 Computer language programming and simulation studies on large computer as a non-statistical approach for estimating parameters and for sensitivity analysis

3.4.1.3 Non-statistical approach for parameter estimate in stochastic models

3.4.1.4 Estimation of binomial coefficient wit hnon-statistical method

3.4.1.4.1 Example from Lewis (1971).

3.4.1.4.2 Binomial distribution (theorem)

3.4.1.5 Multinomial distribution

3.4.1.5.1 Example

3.4.1.6 Poisson distribution

3.4.1.7 Optimum seeking designs as a non-statistical approach in design of simulation experiments

3.4.1.8 Fitting model equations to experimental data

3.4.1.8.1 Selecting equations for fitting

3.4.1.8.2 Standard equation types

3.4.1.9 Mathematical formulation for solving the differentia] equation (analytical solution)

3.4.1.10 Mathematical formulation for solving the difference equation (numerical solution)

3.4.1.10.1 The finite difference approach

3.4.1.10.2 The Euler technique

3.4.1.10.3 An iterated second order Runge-Kutta method

4. COMPUTER IMPLEMENTATION

4.1 Model software requirement

4.1.1 General purpose languages

4.1.2 Special-purpose simulation languages

4.1.3 Requirement of general-purpose or special purpose language

4.1.4 Requirement of special-purpose language

4.1.5 Recent softwares developed

4.2 Generalized model

4.2.1 Specialization and generalization

4.2.2 Constraints and characteristics of specialization and generaliza tion

4.3 Software specification

4.3.1 Command language

4.3.1.1 Data manipulating language for the hierarchial model

4.3.1.1.1 The GET command

4.3.1.1.2 THE GET PATH and GET NEXT WITHIN PARENT retrieval commands

4.3.1.1.3 HDML commands for update

4.3.1.1.4 IMS: A hierarchial DBMS

4.3.2 Program

4.3.2.1 Flowcharting

4.3.2.1.1 General flowcharting rules

4.3.2.1.2 Flowchart symbols and their use

4.3.2.1.3 Examples of simple flowcharts

4.3.2.2 Introduction of basic programming

4.3.2.2.1 BASIC program

4.3.2.2.2 Line number

4.3.2.2.3 REM

4.3.2.2.4 READ and DATA

4.3.2.2.5 PRINT

4.3.2.2.6 LET

4.3.2.2.7 Variables

4.3.2.2.8 Constants

4.3.2.2.9 GOTO

4.3.2.2.10 STOP.

4.3.2.2.11 IF. THEN

4.3.2.2.12 FOR and NEXT

4.3.2.2.13 Numeric functions

4.3.2.2.14 PRINT TAB

4.3.2.2.15 PRINT USING (TRS-80 only)

4.3.2.2.16 GOSUB and RETURN

4.3.2.2.17 GRAPH SUBROUTINE

4.3.2.2.18 Arrays and subscripted variables

4.3.2.2.19 Matrix subroutine

4.3.2.2.19.1 Inputting data to a matrix

4.3.2.2.19.2 Printing a matrix

4.3.2.2.19.3 Scalar multiplication by a constant, K

4.3.2.2.19.4 Post-multiplication of a matrix by a vector, X ©

4.3.2.2.20 Important command mode instructions for apple ii and TRS-80

4.3.2.2.20.1 Apple 0 plus

4.3.3 Data structure

4.3.3.1 Object data structure

4.3.3.2 The relational data structure

4.3.3.2.1 Relational model concepts

4.3.3.2.1.1 Domains, attributes, tupels, and relations

4.3.3.3 Network data structure

4.3.3.3.1 Network data modeling concepts

4.3.3.3.1.1 Records, record types, and data items

4.3.3.3.1.2 Set types and their basic properties

4.3.3.3.2 Special type of sets

4.3.3.3.3 Stored representations of set instances

4.3.3.3.4 Using sets to represent M : N relationships

4.3.3.4 Hierarchial data structure

4.3.3.4.1 Hierarchial database structures

4.3.3.4.1.1 Parent-child relationships and hierarchial schemas

4.3.3.4.1.2 Properties of a hierarchial schema

4.3.3.4.1.3 Hierarchial occurrence trees

4.3.3.4.1.4 Linearized form of a hierarchial occurrence tree

4.3.3.4.1.5 Virtual parent-child relationships

4.4 Data systems

4.4.1 Centralized data system

4.4.1.1 Centralized DBMS (Database Management System) Architect

4.4.1.2 Client-server architecture

4.4.1.3 Client-server architectures for DBMSs

4.4.2 Hierarchial data system

4.4.2.1 Integrity constraints in the hierarchial model

4.4.2.2 Data definition in the hierarchial model

4.4.2.3 Data manipulation language for the hierarchial model.

4.4.2.3.1 The get command.

Notes:

Bibliographic Level Mode of Issuance: Monograph

Includes bibliographical references and index.

ISBN:

1-57808-641-8

OCLC:

647703730