A multi-level hierarchic Markov process with Bayesian updating for herd optimization and simulation in dairy cattle

Abstract

Herd optimization models that determine economically optimal insemination and replacement decisions are valuable research tools to study various aspects of farming systems. The aim of this study was to develop a herd optimization and simulation model for dairy cattle. The model determines economically optimal insemination and replacement decisions for individual cows and simulates whole-herd results that follow from optimal decisions. The optimization problem was formulated as a multi-level hierarchic Markov process, and a state space model with Bayesian updating was applied to model variation in milk yield. Methodological developments were incorporated in 2 main aspects. First, we introduced an additional level to the model hierarchy to obtain a more tractable and efficient structure. Second, we included a recently developed cattle feed intake model. In addition to methodological developments, new parameters were used in the state space model and other biological functions. Results were generated for Dutch farming conditions, and outcomes were in line with actual herd performance in the Netherlands. Optimal culling decisions were sensitive to variation in milk yield but insensitive to energy requirements for maintenance and feed intake capacity. We anticipate that the model will be applied in research and extension.

The utility of sensor technology to support reproductive management on dairy farms

“…These culling rules may therefore provide more realistic results than the assumption of optimal culling decisions used in other models (e.g. Demeter, et al, 2011) because in practice culling decisions are not always economically optimal.…”

“…However, as increased estrus detection rates cause shorter calving intervals, the random culling process will lead to an increase in the annual number of culled cows for the situation with the activity meter. Culling is often not a random process, fertility problems in general, and more specifically failure to conceive, are known reasons for culling (Brickell and Wathes, 2011, Dechow and Goodling, 2008, Demeter, et al, 2011. Some studies have suggested that approximately 20% of the cows were culled because of fertility problems (Brickell andWathes, 2011, Dechow andGoodling, 2008).…”

“…This positive effect on culling was simulated using culling rules for fertility problems. However, these rules did not capture the complexity inherent in optimizing culling decisions for individual cows in practice (Demeter, et al, 2011).…”

“…Dynamic programming models are typically used to estimate an optimal approach (de Vries, 2004, Demeter, et al, 2011, Gröhn, et al, 2003, Heikkila, et al, 2012, Houben, et al, 1994, Kennedy and Stott, 1993. In the economics of culling decisions, many factors are involved, such as milk production, feed consumption, and animal health (Demeter, et al, 2011). Farmers consider these factors in their decision making but tend to make suboptimal decisions in practice (Rajala-Schultz and Gröhn, 2001).…”

“…These fixed costs are often obtained from studies on the economics of culling decisions, which focus on estimating culling costs or optimizing culling approaches. Dynamic programming models are typically used to estimate an optimal approach (de Vries, 2004, Demeter, et al, 2011, Gröhn, et al, 2003, Heikkila, et al, 2012, Houben, et al, 1994, Kennedy and Stott, 1993. In the economics of culling decisions, many factors are involved, such as milk production, feed consumption, and animal health (Demeter, et al, 2011).…”