Diagnosing the pregnancy status of dairy cows: How useful is milk mid-infrared spectroscopy?
journal contributionposted on 22.01.2021, 00:38 by P Delhez, PN Ho, N Gengler, H Soyeurt, Jennie Pryce
© 2020 American Dairy Science Association Pregnancy diagnosis is an essential part of successful breeding programs on dairy farms. Milk composition alters with pregnancy, and this is well documented. Fourier-transform mid-infrared (MIR) spectroscopy is a rapid and cost-effective method for providing milk spectra that reflect the detailed composition of milk samples. Therefore, the aim of this study was to assess the ability of MIR spectroscopy to predict the pregnancy status of dairy cows. The MIR spectra and insemination records were available from 8,064 Holstein cows of 19 commercial dairy farms in Australia. Three strategies were studied to classify cows as open or pregnant using partial least squares discriminant analysis models with random cow-independent 10-fold cross-validation and external validation on a cow-independent test set. The first strategy considered 6,754 MIR spectra after insemination used as independent variables in the model. The results showed little ability to detect the pregnancy status as the area under the receiver operating characteristic curve was 0.63 and 0.65 for cross-validation and testing, respectively. The second strategy, involving 1,664 records, aimed to reduce noise in the MIR spectra used as predictors by subtracting a spectrum before insemination (i.e., open spectrum) from the spectrum after insemination. The accuracy was comparable with the first approach, showing no superiority of the method. Given the limited results for these models when using combined data from all stages after insemination, the third strategy explored separate models at 7 stages after insemination comprising 348 to 1,566 records each (i.e., progressively greater gestation) with single MIR spectra after insemination as predictors. The models developed using data recorded after 150 d of pregnancy showed promising prediction accuracy with the average value of area under the receiver operating characteristic curve of 0.78 and 0.76 obtained through cross-validation and testing, respectively. If this can be confirmed on a larger data set and extended to somewhat earlier stages after insemination, the model could be used as a complementary tool to detect fetal abortion.
The milk mid-infrared spectral data were obtained as part of the MIRforProfit project "Integrating very large genomic and milk mid-infrared data to improve profitability of dairy cows," funded by the Australian Government Department of Agriculture (Canberra, Australia) as part of the Rural R&D for Profit program. The authors thank DairyBio project, funded by Dairy Australia (Melbourne, Australia); The Gardiner Foundation (Melbourne, Australia); and Agriculture Victoria (Melbourne, Australia) for supporting this research. The staff of DataGene (Melbourne, Australia) are gratefully acknowledged for providing the data used in this study. Special thanks are given to Peter Nish from TasHerd Pty. Ltd. (Hadspen, Tasmania, Australia) for providing MIR spectral data, Hico Pty. Ltd. (Maffra, Victoria, Australia) for collecting milk samples, and the farmers whose data were used. The first author, Pauline Delhez, who is Research Fellow of the National Fund for Scientific Research (FNRS, Brussels, Belgium), completed this research while on a sabbatical at Agriculture Victoria, supported by the FNRS and the University of Liege through funding for research stays abroad. The authors have not stated any conflicts of interest.
JournalJournal of Dairy Science
Pagination11p. (p. 3264-3274)
PublisherAmerican Dairy Science Association
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Science & TechnologyLife Sciences & BiomedicineAgriculture, Dairy & Animal ScienceFood Science & TechnologyAgriculturegestationprediction accuracymilk compositiondiscriminant analysisMETHANE EMISSIONSBOVINE-MILKPREDICTIONGESTATIONSTAGEYIELDMilkAnimalsCattlePregnancy TestsSpectrophotometry, InfraredLeast-Squares AnalysisROC CurvePregnancyFourier AnalysisAustraliaFemaleDairy & Animal Science