Abstract

Milking cows once daily is a management tool that has been implemented to improve physical and financial results of seasonal pasture-based dairy farms. The Molly cow model integrates physiology and metabolism of dairy cattle; however, milk production during short-term changes in milking frequency (e.g., 1× milking) is not well represented. The model includes a representation of variable rates of cell quiescence and death. However, the rate constants governing cell death and the return of quiescent to active cells are not affected by milking frequency. An empirical assessment of the problem was conducted, and it was hypothesized that changing the current representation of the rate of cell death in response to short-term 1× milking would more accurately represent active and quiescent cells and improve predictions of milk production. An extra senescent cell flux was added to account for cell loss during periods of 1× milking. Additional changes included a gradual decline in the rate of 1× stimulated senescence during 1× milking, and a structural change in cell cycling between active and quiescent cells during and after short-term 1× milking. Data used for parameter estimation were obtained from 5 studies where 1× milking or different feeding strategies were tested. Parameter estimates of cell loss indicated that 1× milking would affect a small proportion of quiescent cells to cause extra cell death. This added cell senescence was influenced by the length of 1× milking such that cell senescence peaked on d 1 of 1× milking and decayed from that point. The new structure in the model includes a variable rate of cell death in response to 1× milking and a gradual rate of return of quiescent cells back to the active pool in response to switching to 2× milking after short-term 1× milking. Root mean square errors, mean bias, and slope bias declined by at least 50% for predictions of energy-corrected milk yield and fat percent. The model showed quantitative agreement with production data from short-term 1× milking. The accuracy of predictions was improved and the error was reduced by implementing modifications in the model in response to changes in milking frequency.

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