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Brief history and future of animal simulation models for science and application

J. Black

Published 2014 in Animal Production Science

ABSTRACT

Mathematical equations have been used to add quantitative rigour to the description of animal systems for the last 100 years. Initially, simple equations were used to describe the growth of animals or their parts and to predict nutrient requirements for different livestock species. The advent of computers led to development of complex multi-equation, dynamic models of animal metabolism and of the interaction between animals and their environment. An understanding was developed about how animal systems could be integrated in models to obtain the most realistic prediction of observations and allow accurate predictions of as yet unobserved events. Animal models have been used to illustrate how well animal systems are understood and to identify areas requiring further research. Many animal models have been developed with the aim of evaluating alternative management strategies within animal enterprises. Several important gaps in current animal models requiring further development are identified: including a more mechanistic representation of the control of feed intake; inclusion of methyl-donor requirements and simulation of the methionine cycle; plus a more mechanistic representation of disease and the impact of microbial loads under production environments. Reasons are identified why few animal models have been used for day-to-day decision making on farm. In the future, animal simulation models are envisaged to function as real-time control of systems within animal enterprises to optimise animal productivity, carcass quality, health, welfare and to maximise profit. Further development will be required for the integration of models that run real time in enterprise management systems adopting precision livestock farming technologies.

PUBLICATION RECORD

  • Publication year

    2014

  • Venue

    Animal Production Science

  • Publication date

    2014-11-19

  • Fields of study

    Agricultural and Food Sciences, Computer Science, Environmental Science

  • Identifiers
    DOI 10.1071/AN14650
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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