Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 504651
Title A prognostic model to predict the success of artificial insemination in dairy cows based on readily available data
Author(s) Rutten, C.J.; Steeneveld, W.; Vernooij, J.C.M.; Huijps, K.; Nielen, M.; Hogeveen, H.
Source Journal of Dairy Science 99 (2016)8. - ISSN 0022-0302 - p. 6764 - 6779.
DOI https://doi.org/10.3168/jds.2016-10935
Department(s) Business Economics
WASS
Publication type Refereed Article in a scientific journal
Publication year 2016
Keyword(s) Dairy - Insemination success - Prognostic model - Reproduction
Abstract

A prognosis of the likelihood of insemination success is valuable information for the decision to start inseminating a cow. This decision is important for the reproduction management of dairy farms. The aim of this study was to develop a prognostic model for the likelihood of successful first insemination. The parameters considered for the model are readily available on farm at the time a farmer makes breeding decisions. In the first step, variables are selected for the prognostic model that have prognostic value for the likelihood of a successful first insemination. In the second step, farm effects on the likelihood of a successful insemination are quantified and the prognostic model is cross-validated. Logistic regression with a random effect for farm was used to develop the prognostic model. Insemination and test-day milk production data from 2,000 commercial Dutch dairy farms were obtained, and 190,541 first inseminations from this data set were used for model selection. The following variables were used in the selection process: parity, days in milk, days to peak production, production level relative to herd mates, milk yield, breed of the cow, insemination season and calving season, log of the ratio of fat to protein content, and body condition score at insemination. Variables were selected in a forward selection and backward elimination, based on the Akaike information criterion. The variables that contributed most to the model were random farm effect, relative production factor, and milk yield at insemination. The parameters were estimated in a bootstrap analysis and a cross-validation was conducted within this bootstrap analysis. The parameter estimates for body condition score at insemination varied most, indicating that this effect varied most among Dutch dairy farms. The cross-validation showed that the prognosis of insemination success closely resembled the mean insemination success observed in the data set. Insemination success depends on physiological conditions of the cow, which are approximated indirectly by production and reproduction data that are routinely recorded on the farm. The model cannot be used as a detection model to distinguish cows that conceive from cows that do not. The model validation indicates, however, that routinely collected farm data and test-day milk yield records have value for the prognosis of insemination success in dairy cows.

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