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

• Autore: Singh Phool
• Pubblicazione: Enfield, (NH), : Science Publishers, c2008
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (534 p.)
• Disciplina: 631.501/5118
• Soggetto topico: Food crops - Mathematical models; Agricultural systems - Mathematical models
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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 -- References -- 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 -- References -- 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.
• Titolo autorizzato: Modeling crop production systems
• ISBN: 1-57808-641-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910825249903321
• Lo trovi qui: Univ. Federico II