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Using multiple objective programming in a dairy cow breeding program.

P R Tozer1, J R Stokes

  • 1Department of Dairy and Animal Science, The Pennsylvania State University, University Park 16802, USA. ptozer@das.psu.edu

Journal of Dairy Science
|January 30, 2002
PubMed
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Dairy producers can optimize sire selection by balancing breeding goals. Multiple-objective programming reveals tradeoffs between maximizing Net Merit, minimizing inbreeding, and reducing semen costs, showing that prioritizing one goal impacts others.

Area of Science:

  • Animal Breeding and Genetics
  • Operations Research
  • Dairy Science

Background:

  • Sire selection in dairy herds is complex, involving multiple, often conflicting, objectives.
  • Key breeding goals include maximizing genetic merit, minimizing inbreeding, and controlling costs.

Purpose of the Study:

  • To investigate the impact of different objective weights on optimal sire portfolio selection in Jersey cows.
  • To quantify the tradeoffs between maximizing Net Merit, minimizing inbreeding, and minimizing semen expenditure.

Main Methods:

  • Utilized multiple-objective programming, specifically integer programming models.
  • Estimated ideal and anti-ideal values for single objectives (Net Merit, inbreeding, semen cost).
  • Developed models to minimize maximum deviations from single-objective optima, analyzing tradeoffs.

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Main Results:

  • A model with equal objective weights decreased average inbreeding by 3% but reduced Net Merit by $170.
  • A model prioritizing Net Merit (2x weight) decreased Net Merit by $100 and inbreeding by 2%.
  • Reducing the inbreeding coefficient negatively impacts Net Merit, with significant influence from producer-assigned objective weights.

Conclusions:

  • There are inherent tradeoffs between maximizing Net Merit, minimizing inbreeding, and controlling semen costs in sire selection.
  • The weighting assigned to each objective by the dairy producer significantly influences the resulting optimal sire portfolio and breeding outcomes.