Mathematical Programming for Agricultural, Environmental, and Resource Economics

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  • Edition: 1
  • Format: Hardcover
  • Copyright: 2012-01-01
  • Publisher: Wiley

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Mathematical Programming Models for Agriculture, Environmental, and Resource Economics provides a comprehensive overview of mathematical programming models and their applications to real world and important problems confronting agricultural, environmental, and resource economists. Unlike most mathematical programming books, the principal focus of this text is on applications of these techniques and models to the fields of agricultural, environmental, and resource economics. The three fundamental goals of the book are to provide the reader with: (1) a level of background sufficient to apply mathematical programming techniques to real world policy and business to conduct solid research and analysis, (2) a variety of applications of mathematical programming to important problems in the areas of agricultural, environmental, and resource economics, and (3) a firm foundation for preparation to more advanced, Ph.D. level books on linear and/or nonlinear programming. Despite its introductory nature, the text places significant emphasis on real world applications of mathematical programming to decision problems. A wide array of examples and case studies are used to convey the various programming techniques available to decision analysts.

Author Biography

Harry M. Kaiser is the Gellert Family Professor of Applied Economics and Management at Cornell University. He teaches and conducts research in the areas of price analysis, marketing, industrial organization, policy, and quantitative methods. He served as President of the Northeastern Agricultural and Resource Economics Association from 2006-2008, was on the executive board of directors of the American Agricultural Economics Association from 2003-2005, and is currently on the executive board of directors of the Council on Food, Agricultural, and Resource Economics. Kent D. Messer is an assistant professor in the Departments of Food and Resource Economics and Economics at the University of Delaware. He has pioneered the application of optimization to conservation projects, such as working land preservation, wetland conservation, and endangered species protection. He was the founding Executive Director of the Bluff Lake wildlife area and nature center in Denver, CO. He received a B.A. from Grinnell College, a M.S. from the University of Michigan's School of Natural Resources and Environment, and Ph.D. from Cornell University.

Table of Contents

Linear Programming
Introductory Concepts and the Graphical Approach to Linear Programming
Applications of Linear Programming in Agriculture, Environment, and Resources Economics
Components of the General Form for the Model
Standard Assumptions of Linear Programming Models
Formulating Linear Programming Problems
The Graphical Approach for Solving Linear Programming Maximization Problems
The Graphical Approach for Solving Linear Programming Minimization Problems
Sensitivity Analysis with the Graphical Approach
The Simplex Method to Solving Linear Programming Problems
The Simplex Method for a Simple Maximization Problem
The Simplex Method for Maximization Problems: General Case
The Simplex Method and Minimization Problems
Sensitivity Analysis using the Simplex Method and Duality
Simplex-Based Sensitivity Analysis for Maximization Problems
Simplex-Based Sensitivity Analysis for Minimization Problems
Solving LP Problems Using Solver
Appendix: Summation and Matrix Notation
Farm-Level Linear Programming Models
Static Models of a Crop Farm
Dynamic Models
Crop-Livestock Enterprises
Model Validation
Research Application: Crop Farm Model
Research Application: Economic Feasibility of an Energy Crop on a South Alabama Cotton-Peanut Farm
Transportation and Assignment Models for Food and Agricultural Markets
General Transportation Model
Extensions to the Model
The Transshipment Model
The Assignment Model
Research Application: U.S. Dairy Sector Simulator
Resource and Environmental Economics Applications of Linear Programming
Linear Programming Applications in Land Use Planning
Optimal Stocking Problem for a Game Ranch
Efficient Irrigation and Cropping Patterns: A Linear Programming Example
Research Application: Optimizing Grizzly Bear Corridor Design
Relaxing the Assumption of Linear Programming
Integer and Binary Programming
Background on Integer programming
The Branch and Bound Solution Procedure
Mixed-integer Programs
Solver's Integer and Binary Programming Options
Capital Budgeting Problems - A Case of Water Conservation
Distribution System Design
Sensitivity Analysis in Integer Programming
Research Application: Optimizing Agricultural Land Protection in Delaware
Comparison of Sequential and Simultaneous Approaches to Binary Linear Programming
Optimization of Nonlinear Functions
Slopes of Functions
Shortcut Formulas for Derivatives
Unconstrained Optimization
Multivariate Functions
Constrained Optimization with Equality Constraints
Kuhn-Tucker Conditions and Constrained Optimization with Inequality Constraints
Solving Constrained Optimization Problems with Solver
Research Application: Optimal Advertising
Research Application: Water Pollution Abatement Policies
Global Approaches to Nonlinear Optimization
Development of Nonlinear Problems
SOCP Barrier Solver
Evolutionary Solver
Interval Global Solver
A Forestry Example Using Nonlinear Excel Functions
Research Applications: Crop Farming in Northeast Australia
Research Applications: An Analysis of Energy Market Deregulation
Risk Programming Models
Expected Value, Variance, and Covariance
Agricultural Decision Analysis under Risk and Uncertainty
Quadratic Risk Programming
Linearized Version of Quadratic Risk Programming
Target MOTAD
Chance Constrained Programming
Discrete Stochastic Sequential Programming
Issues in Measuring Risk in Risk Programming
Research Application: Quadratic Risk Programming
Research Application: Discrete Stochastic Sequential Programming
Research Application: Agriculture and Climate Change
Price Endogenous Mathematical Programming Models
The Market under Perfect Competition
The Market under Monopoly/Monopsony and Imperfect Competition
Spatial Equilibrium Models
Industry Models
Research Application: A Spatial Equilibrium Model for Imperfectly Competitive Milk Markets
Research Application: Climate Change and U.S. Agriculture
Goal Programming
Goal Programming
Non-preemptive Goal Problem
Preemptive Goal Programming
Deriving Weights for Goal Programming
Research Application: Optimal Parasite Control Programs
Research Application: Forest Land Protection
Dynamic Programming
A Network Problem
Characteristics of Dynamic Programming Problems
A Production Inventory Problem
A Capital Budgeting Problem
Comments on DP
Research Application: Animal Health in Developing Countries
Research Application: Conversion to Organic Arable Farming
Table of Contents provided by Publisher. All Rights Reserved.

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