Predicting fertility of dairy cows a game changer

Predicting fertility of dairy cows a game changer

Herd Management
CHANGE MAKERS: Agriculture Victoria Research scientists Professor Jennie Pryce and Dr Phuong Ho.

CHANGE MAKERS: Agriculture Victoria Research scientists Professor Jennie Pryce and Dr Phuong Ho.

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Agriculture Victoria research scientists have developed a model that can predict how likely a dairy cow is to conceive to first insemination with up to 77 per cent accuracy.

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Agriculture Victoria research scientists have developed a model that can predict how likely a dairy cow is to conceive to first insemination with up to 77 per cent accuracy.

The world-first research combines mid-infrared spectroscopy (MIR), which shines an infrared light through cows' milk, with other on-farm data for 3000 dairy cows from 19 herds across Australia.

Cow fertility is a key driver of profitability for Australia's dairy industry but, until now, there has been little research towards enabling farmers to predict the outcome of insemination.

Agriculture Victoria research scientist and leader of this DairyBio initiative Professor Jennie Pryce said dairy farmers could use this research to optimise their breeding decisions, increasing farm productivity and profitability.

"The expected outcome of this research is a valuable prediction tool for farmers who choose to herd-test in early lactation, before the joining season starts," she said.

"We are now collaborating with DataGene and the herd test centres, working towards implementing the research and providing the best advice for farmers."

Agriculture Victoria research scientist Dr Phuong Ho said farmers could optimise breeding decisions using prior knowledge of how likely an individual cow is to become pregnant after insemination.

"Sexed or premium bull semen could be used for cows predicted to have a high likelihood of conception, whereas cows with predicted poor fertility could be mated using semen from beef bulls, multiple doses, or semen from bulls of known high genetic merit for fertility," Dr Ho said.

"Additionally, farmers might adjust feeding or management strategies to help predicted poor cows improve their physiological condition and probability of conception."

The model combines information from milk MIR samples, which farmers routinely collect, with information on fertility genomic breeding values, cow genotype, milk yield, age of cow at lactation and days in milk when the sample was taken and at insemination.

The model is currently undergoing extensive validation using data from New South Wales dairy farms before being made available to dairy farmers.

This research is part of the DairyBio initiative between Agriculture Victoria, Dairy Australia and the Gardiner Foundation, in collaboration with DataGene.

The paper, Classifying the fertility of dairy cows using milk mid-infrared spectroscopy, is published in the Journal of Dairy Science, CSIRO.


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