Operations Research Models and the Management of Agricultural and Forestry Resources: A Review and Comparison <s> Risk and Uncertainty Analysis <s> Current game theoretic models for farnt planning are limited in scope and restrictive in their assumptions. This paper reviews the current status of such models, compares them to the quadratic programming model, and then combines the desirable features of the game theoretic and quadratic Programming approaches by means of parametric linear programming <s> BIB001 </s> Operations Research Models and the Management of Agricultural and Forestry Resources: A Review and Comparison <s> Risk and Uncertainty Analysis <s> Quadratic decision criteria for farm planning are theoretically appealing but difficult to handle computationally. This paper reviews the advantages of the quadratic approach and develops a linear alternative which, while retaining most of the desired features of the quadratic models, can be readily solved on conventional linear programming codes with the parametric option. <s> BIB002 </s> Operations Research Models and the Management of Agricultural and Forestry Resources: A Review and Comparison <s> Risk and Uncertainty Analysis <s> Constrained games arise when the mixed strategies of a finite rectangular game are subject to further constraints in the form of linear inequalities. Such games permit more realistic and convenient formulation of many decision problems. This paper offers a generalization of the constrained game model which widens the scope of game-theoretic approaches in farm planning. Solution procedures are proposed for Savage's minimax regret, Hurwicz's pessimism-optimism index, and the "Wald-maximax" and "Wald-Savage" criteria. It is shown that the proposed approaches can handle uncertainty in the inequality constraints. An application to a farm planning problem concludes the paper. <s> BIB003 </s> Operations Research Models and the Management of Agricultural and Forestry Resources: A Review and Comparison <s> Risk and Uncertainty Analysis <s> The popular first edition of this standard text on risk analysis and management in agriculture has been almost wholly revised. The second edition includes much new material. There is a revised chapter on incorporating decision makers' preferences into the analysis of risky choices that recognizes and gives consideration to some recently published challenges to the legitimacy of the expected utility hypothesis. While it is argued that the theory is still the best way to handle risk aversion, at least in prescriptive analyses, more attention is given to the practical problems of utility function elicitation. It is shown how estimates of (or ranges of) degree of risk aversion might best be obtained and consistently applied. The new edition has a much more thorough treatment of stochastic simulation, including some novel yet simple methods for dealing to stochastic dependency between uncertain quantities. The treatment of policy aspects of risk in agriculture has been revised and extended to account for such important current issues as new approaches to crop insurance, the management of serious animal and crop diseases and food safety issues. As with the first edition, the book contains a blend of theory with simple illustrative examples to enable readers to see how the principles being discussed can be made operational using modern software. <s> BIB004
Managing agricultural activities is characterized by risk and uncertainty. Because farmers' incomes vary with weather conditions, market price changes, and crop and animal diseases, decision-support models in agriculture should include risk and uncertainty. Game theory models of games against nature are conventional means of analyzing agricultural decisions under uncertainty. Their purpose is to find a pure or mixed strategy that optimizes the decision maker's aspirations according to a certain behavioral criterion (for example, maximizing the minimum outcome, minimizing the largest regret, or maximizing the minimum benefit).  introduced the use of game theory in agriculture, whereas BIB001 and BIB003 introduced the idea of parametric games, optimizing one criterion (for example, maximizing the minimum outcome) while considering another criterion as a parametric constraint (for example, minimizing the largest regret). These models establish a trade-off frontier between the criteria considered. The gametheory approach can be generalized with the help of goal programming, leading to compromise games (Romero and Rehman 2003, Chapter 7) . The oldest approach to risk programming in agriculture is a direct application of the  approach to portfolio theory, as was initially suggested by  . In his approach, the model defines the risk of an agricultural enterprise through the variability of its returns, measured by variance. Then the model establishes an efficient frontier by minimizing the variance of the cropping pattern, treating the expected return as a parametric constraint. To avoid the use of parametric quadratic programming, BIB002 suggested minimizing the mean absolute deviation instead of the variance. The second phase of Markowitz's approach consists of maximizing the expected utility of the decision maker over the efficient frontier. However, this maximization can be rigorous only when returns follow a normal distribution of probability or when the decision maker's utility function is quadratic. However, the normal distribution of returns is a hypothesis, which has not been widely corroborated empirically, and quadratic utility functions have many logical flaws (for example, absolute risk aversion increases with wealth). Some analysts (for example,  ) have proposed mitigating this problem by approximating the maximum expected utility over the efficient frontier. Anderson et al. (1977, p. 204) criticized the application of game-theory rules on the grounds that the decision criteria used are incompatible with the axioms of rational choice. However, analysts are reviving the games-based approach and criticizing the axioms of rational choice underlying Markowitzean Downloaded from informs.org by [200.89.69 .180] on 20 July 2015, at 12:33 . For personal use only, all rights reserved. 449 approaches (for example, Zeleny 1982, pp. 437-438) . Despite the preponderance of these two approaches, analysts have proposed and applied other methods to deal with risk and uncertainty in agriculture. They include safety-first models, chance-constraint programming, and stochastic programming. With safetyfirst models, the decision makers lexicographically satisfy their preference for safety and then might take profit-driven approaches. In chance-constraint programming, the analyst deals with the uncertainty of the available resources by assuming that their distribution probability is known and by fixing a lower limit on the probability of the respective constraint being satisfied. Finally, with stochastic programming, some or all coefficients of the constraint set are random variables. BIB004 reviews the technical aspects, potentials, and limitations of these approaches.