Multiple Criteria Analysis for Agricultural Decisions

Despite the recognition given to the existence of multiple objectives in agricultural decision making, very little seems to have been done by agricultural economists to develop and use methodologies that model the decision situations realistically. This is particularly intriguing when one notices the amount of effort that has been devoted to the development and use of multi-criteria decision making (MCDM) techniques in disciplines such as management science, water resources research and forest planning. There are virtually no (text) books available in agricultural economics which explain the nature and structure of MCDM techniques and demonstrate how they can be used to model decisions involving multiple objectives. There is a need for such a book and this book attempts to fill the gap. It is divided into three parts. The first, comprising two chapters, is philosophical in nature and deals with the rationale behind the use of multiple criteria decision making (MCDM) techniques in decision making, and the fundamental concepts that must be understood to grasp the nature of these methods.

Table of Contents:
PART ONE. MULTIPLE CRITERIA IN AGRICULTURAL DECISIONS. 1. Main Features of the MCDM Paradigm. Criticism of the traditional paradigm for decision making. Economic versus technological decisions. Multiple objectives and goals in agricultural economics. Historical origins of the MCDM paradigm. Plan of the book. 2. Some Basic Concepts. Attributes, objectives and goals. Distinction between goals and constraints. Pareto optimality. Trade-offs amongst decision making criteria. A first approximation of the main MCDM approaches. PART TWO. MULTIPLE CRITERIA DECISION MAKING TECHNIQUES. 3. Goal Programming (GP). Introductory example for handling multiple-criteria in a farm planning model. The role of deviational variables in GP. Lexicographic goal programming (LGP). Sensitivity analysis in LGP. The graphical method for LGP. The sequential linear method (SLM) for LGP. A brief comment on other LGP algorithms. Weighted goal programming (WGP). A critical assessment of GP. Some extensions of GP. 4. Multiobjective Programming (MOP). An approximation of the multiobjective programming problem. The pay-off matrix in MOP. The constraint method. The Weighting Method. The noninferior set estimation method (NISE). Multigoal programming. Some issues related to the use of MOP techniques. 5. Compromise Programming (CP). An intuitive treatment of the concept of distance measures. A discrete approximation of the best-compromise solution. Compromise programming - a continuous setting. The method of the displaced ideal. Pros and cons of goal programming, multiobjective programming and compromise programming. Relationships among different MCDM approaches. 6. The Interactive MCDM Approach. Structure of an interactive MCDM process. The STEM method. The method of Zionts and Wallenius. Interactive multiple goal programming (IMGP). An assessment of the interactive MCDM approaches. 7. Risk and Uncertainty and the MCDM Techniques. Risk programming techniques in agricultural planning within MCDM framework. Compromise-risk programming. Game theory models and the MCDM framework. Games with multiple goals and GP. Compromise games. PART THREE. CASE STUDIES. 8. A Compromise Programming Model for the Agrarian Reform Programme in Andalusia, Spain. The background. Trade-off curves for seasonal labour, employment and gross margin. Compromise sets. An approximation of the efficient set in a three-dimensional space. Concluding comments. 9. Livestock Ration Formulation and MCDM Techniques. A livestock ration formulation example. Ration formulation as a LGP problem. Ration formulation as a MOP problem. 10. Livestock Ration Formulation with Penalty Functions via GP. Penalty functions in diet formulation. Diet formulation as a WGP model with penalty functions. Diet formulation problem as an LGP model with penalty functions. An assessment. 11. Optimum Fertilizer Use via GP with Penalty Functions. 12. An LGP Model for Logistics Planning in an Agribusiness Company. The background. Data.