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Agent-Based Modeling to Improve Beef Production from Dairy Cattle: Model Description and Evaluation

Author

Listed:
  • Addisu H. Addis

    (Animal Science, School of Agriculture and Environment, Massey University, Palmerston North 4442, New Zealand
    Applied Biology, College of Natural and Computational Sciences, University of Gondar, Gondar P.O. Box 196, Ethiopia)

  • Hugh T. Blair

    (Animal Science, School of Agriculture and Environment, Massey University, Palmerston North 4442, New Zealand)

  • Paul R. Kenyon

    (Animal Science, School of Agriculture and Environment, Massey University, Palmerston North 4442, New Zealand)

  • Stephen T. Morris

    (Animal Science, School of Agriculture and Environment, Massey University, Palmerston North 4442, New Zealand)

  • Nicola M. Schreurs

    (Animal Science, School of Agriculture and Environment, Massey University, Palmerston North 4442, New Zealand)

  • Dorian J. Garrick

    (AL Rae Research Centre for Genetics and Breeding, Massey University, Hamilton 3214, New Zealand)

Abstract

Agent-based modeling (ABM) enables an in silico representation of complex systems and captures agent behavior resulting from interaction with other agents and their environment. This study developed an ABM to represent a pasture-based beef cattle finishing systems in New Zealand (NZ) using attributes of the rearer, finisher, and processor, as well as specific attributes of dairy-origin beef cattle. The model was parameterized using values representing 1% of NZ dairy-origin cattle, and 10% of rearers and finishers in NZ. The cattle agent consisted of 32% Holstein-Friesian, 50% Holstein-Friesian–Jersey crossbred, and 8% Jersey, with the remainder being other breeds. Rearers and finishers repetitively and simultaneously interacted to determine the type and number of cattle populating the finishing system. Rearers brought in four-day-old spring-born calves and reared them until 60 calves (representing a full truck load) on average had a live weight of 100 kg before selling them on to finishers. Finishers mainly attained weaners from rearers, or directly from dairy farmers when weaner demand was higher than the supply from rearers. Fast-growing cattle were sent for slaughter before the second winter, and the remainder were sent before their third winter. The model finished a higher number of bulls than heifers and steers, although it was 4% lower than the industry reported value. Holstein-Friesian and Holstein-Friesian–Jersey-crossbred cattle dominated the dairy-origin beef finishing system. Jersey cattle account for less than 5% of total processed beef cattle. Further studies to include retailer and consumer perspectives and other decision alternatives for finishing farms would improve the applicability of the model for decision-making processes.

Suggested Citation

  • Addisu H. Addis & Hugh T. Blair & Paul R. Kenyon & Stephen T. Morris & Nicola M. Schreurs & Dorian J. Garrick, 2022. "Agent-Based Modeling to Improve Beef Production from Dairy Cattle: Model Description and Evaluation," Agriculture, MDPI, vol. 12(10), pages 1-10, October.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:10:p:1615-:d:933907
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    References listed on IDEAS

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    1. Andrea Scalco & Jennie I. Macdiarmid & Tony Craig & Stephen Whybrow & Graham. W. Horgan, 2019. "An Agent-Based Model to Simulate Meat Consumption Behaviour of Consumers in Britain," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(4), pages 1-8.
    2. Nijdam, Durk & Rood, Trudy & Westhoek, Henk, 2012. "The price of protein: Review of land use and carbon footprints from life cycle assessments of animal food products and their substitutes," Food Policy, Elsevier, vol. 37(6), pages 760-770.
    3. Addisu H. Addis & Hugh T. Blair & Paul R. Kenyon & Stephen T. Morris & Nicola M. Schreurs, 2021. "Optimization of Profit for Pasture-Based Beef Cattle and Sheep Farming Using Linear Programming: Young Beef Cattle Production in New Zealand," Agriculture, MDPI, vol. 11(9), pages 1-14, September.
    4. Addisu H. Addis & Hugh T. Blair & Paul R. Kenyon & Stephen T. Morris & Nicola M. Schreurs, 2021. "Optimization of Profit for Pasture-Based Beef Cattle and Sheep Farming Using Linear Programming: Model Development and Evaluation," Agriculture, MDPI, vol. 11(6), pages 1-16, June.
    5. Yang, Qihui & Gruenbacher, Don & Heier Stamm, Jessica L. & Brase, Gary L. & DeLoach, Scott A. & Amrine, David E. & Scoglio, Caterina, 2019. "Developing an agent-based model to simulate the beef cattle production and transportation in southwest Kansas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 526(C).
    6. Lee, Ju-Sung & Filatova, Tatiana & Ligmann-Zielinska, Arika & Hassani-Mahmooei, Behrooz & Stonedahl, Forrest & Lorscheid, Iris & Voinov, Alexey & Polhill, J. Gareth & Sun, Zhanli & Parker, Dawn C., 2015. "The complexities of agent-based modeling output analysis," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 18(4).
    7. van Selm, Benjamin & de Boer, Imke J.M. & Ledgard, Stewart F. & van Middelaar, Corina E., 2021. "Reducing greenhouse gas emissions of New Zealand beef through better integration of dairy and beef production," Agricultural Systems, Elsevier, vol. 186(C).
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    1. Addisu H. Addis & Hugh T. Blair & Paul R. Kenyon & Stephen T. Morris & Nicola M. Schreurs & Dorian J. Garrick, 2023. "Agent-Based Modelling to Improve Beef Production from Dairy Cattle: Young Beef Production," Agriculture, MDPI, vol. 13(4), pages 1-10, April.

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