Author
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Macneil, Michael |
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Submitted to: Encyclopedia of Animal Science
Publication Type: Book / Chapter Publication Acceptance Date: 3/1/2004 Publication Date: 1/31/2005 Citation: Macneil, M.D. 2005. p. 612-614. In: Mathematical models: production systems. Encyclopedia of Animal Science. Marcel Kekker, Inc. New York, New York. Interpretive Summary: The processes involved in producing palatable and nutritious food for people from livestock and poultry are numerous, complex, and interactive. Traditional experimentation has resulted in a tremendous quantity of detailed, but relatively fragmented, information about these processes. Integrating this information to develop ?best? practices for application in managing agricultural production systems is a daunting task. When approached in an ad hoc manner, this integration will very possibly be flawed and the resulting management practices far from optimal. Experimental evaluation of the proposed practices is further hampered by the fact that sufficient replication for testing hypotheses is difficult to obtain. In attempting to overcome these problems, mathematical models have been developed and used to guide management of agricultural production systems. This paper provides a brief review of how mathematical models may be used in planning agricultural production systems. The intended audience for this work is students and others having limited familiarity with mathematical modeling and/or agricultural production systems. Technical Abstract: The goal of this paper is to provide a brief review of how mathematical models may be used in planning agricultural production systems. The processes involved in producing palatable and nutritious food for people from livestock and poultry are numerous, complex, and interactive. Traditional experimentation has resulted in a tremendous quantity of detailed, but relatively fragmented, information about these processes. Integrating this information to develop ?best? practices for application in managing agricultural production systems is a daunting task. When approached in an ad hoc manner, this integration will very possibly be flawed and the resulting management practices far from optimal. Experimental evaluation of the proposed practices is further hampered by the fact that sufficient replication for testing hypotheses is difficult to obtain. To illustrate the scope of applications for mathematical models in studying agricultural production systems, examples of 1) planning livestock production in developing countries, 2) disease resistance, 3) nutrition and ration formulation, 4) grazing, 5) using genetic resources, and 6) decision support systems are provided. I conclude mathematical models are valuable tools to increase understanding of production systems. Using models, investigation of factors influencing the behavior of systems can be conducted in a more timely and comprehensive manner and at less cost than if the systems were manipulated directly. |