The book "Mathematical Programming for Economic Analysis in Agriculture" by Peter B. R. Hazell and Roger D. Norton is a comprehensive guide to the theory and application of mathematical programming models in agricultural economics. It highlights the advancements in computing technology and methods that have facilitated the use of these models, making them more adaptable and realistic for various agricultural scenarios. The authors emphasize the unique advantages of mathematical programming models in addressing the complex and interconnected nature of the agricultural sector, incorporating detailed micro-level data to analyze policy issues such as pricing, employment, investment decisions, comparative advantage, and risk analysis. The book is structured into three main parts: the farm model, the sector model, and applications and extensions for policy analysis. It covers the theoretical foundations, practical techniques, and real-world applications of these models, making it suitable for both students and practitioners. Key topics include linear programming, risk management, market equilibrium, and the integration of farm-level decisions with regional aggregates. The authors provide detailed guidelines for building and implementing models, emphasizing the importance of model validation and the role of technology in enhancing policy analysis. The book also includes a foreword by Richard A. King, who underscores the significance of quantitative methods in agricultural economics and the potential of mathematical programming models to improve policy analysis in developing countries. The authors acknowledge numerous contributors and sponsors who have supported the development and application of the models discussed in the book.The book "Mathematical Programming for Economic Analysis in Agriculture" by Peter B. R. Hazell and Roger D. Norton is a comprehensive guide to the theory and application of mathematical programming models in agricultural economics. It highlights the advancements in computing technology and methods that have facilitated the use of these models, making them more adaptable and realistic for various agricultural scenarios. The authors emphasize the unique advantages of mathematical programming models in addressing the complex and interconnected nature of the agricultural sector, incorporating detailed micro-level data to analyze policy issues such as pricing, employment, investment decisions, comparative advantage, and risk analysis. The book is structured into three main parts: the farm model, the sector model, and applications and extensions for policy analysis. It covers the theoretical foundations, practical techniques, and real-world applications of these models, making it suitable for both students and practitioners. Key topics include linear programming, risk management, market equilibrium, and the integration of farm-level decisions with regional aggregates. The authors provide detailed guidelines for building and implementing models, emphasizing the importance of model validation and the role of technology in enhancing policy analysis. The book also includes a foreword by Richard A. King, who underscores the significance of quantitative methods in agricultural economics and the potential of mathematical programming models to improve policy analysis in developing countries. The authors acknowledge numerous contributors and sponsors who have supported the development and application of the models discussed in the book.