Mathematical programming for agricultural, environmental, and resource economics

Mathematical Programming for Agricultural, Environmental, and Resource Economics educates students on the techniques of mathematical programming and their application to agricultural, resource, and environmental economics problems.This text artfully combines an introductory text in math programming, algebraic and geometric concepts while also presenting differential calculus designedfor upper-level undergraduates and graduate students. This text is designed specifically to help students develop the quantitative skills necessary for cutting edge research and techniques INDICE: Preface PART 1. LINEAR PROGRAMMING Chapter 1: Introductory Concepts and the Graphical Approach to Linear Programming 1.1 Applications of Linear Programming in Agriculture, Environment, and Resources Economics 1.2 Components of the General Form for the Model 1.3 Standard Assumptions of Linear Programming Models 1.4 Formulating Linear Programming Problems 1.5 The Graphical Approach for Solving Linear Programming Maximization Problems 1.6 The Graphical Approach for Solving Linear Programming Minimization Problems 1.7 Sensitivity Analysis with the Graphical Approach 1.8 Summary 1.9 Exercises Chapter 2: The Simplex Method to Solving Linear Programming Problems 2.1 The Simplex Method fora Simple Maximization Problem 2.2 The Simplex Method for Maximization Problems: General Case 2.3 The Simplex Method and Minimization Problems 2.4 Summary 2.5 Exercises Chapter 3: Sensitivity Analysis using the Simplex Method and Duality 1. Simplex-Based Sensitivity Analysis for Maximization Problems 3.2 Simplex-Based Sensitivity Analysis for Minimization Problems 3.3 Duality 3.4 SolvingLP Problems Using Solver 3.5 Summary 3.6 Appendix: Summation and Matrix Notation 3.5 Exercises Chapter 4: Farm-Level Linear Programming Models 4.1 Static Models of a Crop Farm 4.2 Dynamic Models 4.3 Crop-Livestock Enterprises 4.4 Model Validation 4.5 Research Application: Crop Farm Model 4.6 Research Application: Economic Feasibility of an Energy Crop on a South Alabama Cotton-Peanut Farm 4.7 Summary 4.8 Exercises Chapter 5: Transportation and Assignment Models for Food and Agricultural Markets 5.1 General Transportation Model 5.2 Extensions to the Model 5.3 The Transshipment Model 5.4 The Assignment Model 5.5 Research Application: U.S. Dairy Sector Simulator 5.6 Summary 5.7 Exercises Chapter6: Resource and Environmental Economics Applications of Linear Programming 6.1 Linear Programming Applications in Land Use Planning 6.2 Optimal Stocking Problem for a Game Ranch 6.3 Efficient Irrigation and Cropping Patterns: A Linear Programming Example 6.4 Research Application: Optimizing Grizzly Bear Corridor Design 6.5 Summary 6.6 Exercises PART 2. RELAXING THE ASSUMPTION OF LINEAR PROGRAMMING Chapter 7: Integer and Binary Programming 7.1 Background on Integer programming 7.2 The Branch and Bound Solution Procedure 7.3 Mixed-integer Programs 7.4 Solvers Integer and Binary Programming Options 7.5 Capital Budgeting Problems A Case of Water Conservation 7.6 Distribution System Design 7.7 Sensitivity Analysis in Integer Programming 7.8 Research Application: Optimizing Agricultural Land Protection in Delaware 7.9 Comparison of Sequential and Simultaneous Approaches to Binary Linear Programming 7.10 Summary 7.11 Exercises Chapter 8: Optimization of Nonlinear Functions 8.1 Slopes of Functions 8.2 Shortcut Formulas for Derivatives 8.3 Unconstrained Optimization 8.4 Multivariate Functions 8.5 Constrained Optimization with Equality Constraints 8.6 Kuhn-Tucker Conditions and Constrained Optimization with Inequality Constraints 8.7 Solving Constrained Optimization Problems with Solver 8.8 Research Application: Optimal Advertising 8.9 Research Application: Water Pollution Abatement Policies 8.10 Summary 8.11 Exercises Chapter 9: Global Approaches to Nonlinear Optimization 9.1 Development of Nonlinear Problems 9.2 SOCP Barrier Solver 9.3 Evolutionary Solver 9.4 Interval Global Solver 9.5 A Forestry Example Using Nonlinear Excel Functions 9.6 Research Applications: Crop Farming in Northeast Australia 9.7 Research Applications: An Analysis of Energy Market Deregulation 9.8 Summary 9.9 Exercises Chapter 10: Risk Programming Models 10.1 Expected Value, Variance, and Covariance 10.2 Agricultural Decision Analysis under Risk and Uncertainty 10.3 Quadratic Risk Programming 10.4 Linearized Version of QuadraticRisk Programming 10.5 Target MOTAD 10.6 Chance Constrained Programming 10.7 Discrete Stochastic Sequential Programming 10.8 Issues in Measuring Risk in Risk Programming 10.9 Research Application: Quadratic Risk Programming 10.10 Research Application: Discrete Stochastic Sequential Programming 10.11 Research Application: Agriculture and Climate Change 10.12 Summary 10.13 Exercises Chapter 11: Price Endogenous Mathematical Programming Models 11.1 The Market under Perfect Competition 11.2 The Market under Monopoly/Monopsony and Imperfect Competition 11.3 Spatial Equilibrium Models 11.4 Industry Models 11.5 Research Application: A Spatial Equilibrium Model for Imperfectly Competitive Milk Markets11.6 Research Application: Climate Change and U.S. Agriculture 11.7 Summary 11.8 Exercises Chapter 12: Goal Programming 12.1 Goal Programming 12.2 Non-preemptive Goal Problem 12.3 Preemptive Goal Programming 12.4 Deriving Weights forGoal Programming 12.5 Research Application: Optimal Parasite Control Programs12.6 Research Application: Forest Land Protection 12.7 Summary 12.8 ExercisesChapter 13: Dynamic Programming 13.1 A Network Problem 13.2 Characteristics of Dynamic Programming Problems 13.3 A Production Inventory Problem 13.4 A Capital Budgeting Problem 13.5 Comments on DP 13.6 Research Application: Animal Health in Developing Countries 13.7 Research Application: Conversion to Organic Arable Farming 13.8 Summary 13.9 Exercises

• ISBN: 978-0-470-59936-5
• Editorial: John Wiley & Sons
• Encuadernacion: Cartoné
• Páginas: 512
• Fecha Publicación: 31/12/2010
• Nº Volúmenes: 1
• Idioma: Inglés