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Erik Jørgensen

Optimal Replacement Policies for Dairy Cows Based on Daily Yield Measurements

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Markov decision processes (MDP) with finite state and action space have often been used to model sequential decision making over time in dairy herds. However, the length of each stage has been at least 1 mo, resulting in models that do not support decisions on a daily basis. The present paper describes the first step of developing an MDP model that can be integrated into a modern herd management system. A hierarchical MDP was formulated for the dairy cow replacement problem with stage lengths of 1 d. It can be used to assist the farmer in replacement decisions on a daily basis and is based on daily milk yield measurements that are available in modern milking systems. Bayesian updating was used to predict the performance of each cow in the herd and economic decisions were based on the prediction. Moreover, parameters in the model were estimated using data records of the specific herd under consideration. This includes herd-specific lactation curves.
Original languageEnglish
JournalJournal of Dairy Science
Volume93
Issue1
Pages (from-to)75-92
Number of pages17
ISSN0022-0302
DOIs
Publication statusPublished - 2010

    Research areas

  • hierarchical Markov decision process, stochastic dynamic programming, state space model, herd management

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