Risk and uncertainty are inescapable factors in agriculture which require careful management. Farmers face production risks from the weather, crop and livestock performance, and pests and diseases, as well as institutional, personal and business risks. This revised third edition of the popular textbook includes updated chapters on theory and methods and contains a new chapter discussing the state-contingent approach to the analysis of production and the use of copulas to better model stochastic dependency. Aiming to introduce agricultural decision making, probability and risk preference, this book is an indispensable guide for students and researchers of agriculture and agribusiness management.

Farming as it is practised in market industrialized countries is predominantly a family business. This book argues that the nature of the farm business cannot be properly understood without reference to the family that operates it. Examples are taken from the UK, USA, Europe and Australasia.

Decision making to achieve a balance between the economic goals of producers and environmental quality benefits is complex. Most of the time we consider that the balance is skewed to one side or the other and that there are winners and losers. From the production perspective, the loser is the economic return in exchange for environmental quality while from the environmental perspective, the loser is the environment at the expense of agricultural production and increased inputs. In reality, there are opportunities within agriculture for a win-win situation; however, to explore the endless possibilities that constitute acceptable solutions is extremely difficult. Over the past few years there has been an increasing development of decision support tools that provide a framework that could be applied to agriculture. As I have worked with producers extensively over the past few years, it has become apparent that we could provide more information to the American producer that would help them evaluate different scenarios in their farming systems and evaluate potential alternatives through a combination of simulation tools and decision support systems. These are easy concepts to suggest, but more difficult to implement. As an effort to expand our understanding of the potential of decision support tools for economic and environmental balance, a proposal was made to the Organisation for Economic Co-operation and Development (OECD) in two theme areas. The outcome yielded an international workshop that assembled an international group of experts in decision support systems, simulation models, and agricultural production through industry, consultants, and producers. This group also addressed enhancing environmental quality in agricultural systems. ---Jerry L. Hatfield from Preface.

1. Introduction. 1.1. Formulating the risk problem. 1.2. Decision criteria. 1.3. Decision making under risk : fact and fiction -- 2. Probability theory - a mathematical basis for making decisions under risk and uncertainty. 2.1. Set theory and probability. 2.2. Random variables. 2.3. Conditional probability and independence. 2.4. Some useful distribution functions. 2.5. Expected value, moments, and the moment generating function. 2.6. Estimating probability functions. 2.7. Martingales and random walks. 2.8. Summary -- 3. Expected utility - the economic basis of decision making under risk. 3.1. Consumption and utility. 3.2. Expected utility. 3.3. Expected value - variance and expected utility models. 3.4. Problems with expected utility. 3.5. Summary -- 4. Risk aversion in the large and small. 4.1. Arrow-Pratt risk aversion coefficient. 4.2. Eliciting risk aversion coefficients. 4.3 Summary -- 5. Portfolio theory and decision making under risk. 5.1. The expected value - variance frontier. 5.2. A simple portfolio. 5.3. A graphical depiction of the expected value-variance frontier. 5.4. Mean-variance versus direct utility maximization. 5.5. Derivation of the expected value-variance frontier. 5.6. Summary -- 6. Whole farm-planning models. 6.1. Farm portfolio models. 6.2. Minimize total absolute deviation. 6.3. Focus-loss. 6.4. Target MOTAD. 6.5. Direct utility maximization. 6.6. Discrete sequential stochastic programming. 6.7. Chance-constrained programming. 6.8. Interpreting shadow values from risk programming models. 6.9. Summary -- 7. Risk efficiency approaches - stochastic dominance. 7.1. Stochastic dominance. 7.2. Applications of stochastic dominance. 7.3. Summary -- 8. Dynamic decision rules and the value of information. 8.1. Decision making and Bayesian probabilities. 8.2. Concepts of information. 8.3. A model of information. 8.4. Summary -- 9. Market models of decision making under risk. 9.1. Risk equilibrium from the consumer's point of view. 9.2. The role of the riskless asset. 9.3. Risk equilibrium from the firm's perspective. 9.4. Arbitrage pricing theorem. 9.5. Empirical applications of capital market models. 9.6. Summary -- 10. Option pricing approaches to risk. 10.1. Introductions to options and futures. 10.2. Real option valuation. 10.3. Crop insurance. 10.4. Summary -- 11. State contingent production model : the stochastic production set. 11.1. Depicting risk and input decisions in the production function. 11.2. State Production set and input requirement set. 11.3. Distance functions and risk aversion. 11.4. Summary -- 12. Risk, uncertainty, and the agricultural firm - a summary and outlook