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A multi-level hierarchic Markov process with Bayesian updating for herd optimization and simulation in dairy cattle.
R M Demeter1, A R Kristensen, J Dijkstra
1Animal Breeding and Genomics Centre, Wageningen University, 6700 AH, Wageningen, the Netherlands. robert.demeter@wur.nl
Journal of Dairy Science
|November 29, 2011
Summary
This study developed a dairy cattle herd optimization model for making economically sound insemination and replacement decisions. The model simulates herd performance, aiding research and farm management for improved dairy farming practices.
Area of Science:
- Agricultural Science
- Animal Science
- Mathematical Modeling
Background:
- Herd optimization models are crucial for understanding dairy farming systems.
- Economically optimal insemination and replacement decisions are key to efficient dairy production.
Purpose of the Study:
- To develop a novel herd optimization and simulation model for dairy cattle.
- To determine economically optimal insemination and replacement decisions for individual cows and simulate whole-herd outcomes.
Main Methods:
- Formulated the optimization problem as a multi-level hierarchic Markov process.
- Applied a state space model with Bayesian updating to simulate milk yield variation.
- Incorporated a new cattle feed intake model and an additional model hierarchy level for efficiency.
Main Results:
- The model determines optimal insemination, replacement, and culling decisions.
- Optimal culling decisions were sensitive to milk yield variation.
- Model outcomes aligned with actual Dutch herd performance.
Conclusions:
- The developed model provides a valuable tool for dairy herd management and research.
- The model's sensitivity analysis highlights the importance of milk yield in culling decisions.
- Anticipated application in both scientific research and agricultural extension services.
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