Pregnancy status predicted using milk mid-infrared spectra from dairy cattle

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dc.citation.issue4
dc.citation.volume105
dc.contributor.authorTiplady KM
dc.contributor.authorTrinh M-H
dc.contributor.authorDavis SR
dc.contributor.authorSherlock RG
dc.contributor.authorSpelman RJ
dc.contributor.authorGarrick DJ
dc.contributor.authorHarris BL
dc.coverage.spatialUnited States
dc.date.accessioned2024-04-16T01:50:57Z
dc.date.accessioned2024-07-25T06:52:26Z
dc.date.available2022-02-16
dc.date.available2024-04-16T01:50:57Z
dc.date.available2024-07-25T06:52:26Z
dc.date.issued2022-04
dc.description.abstractAccurate and timely pregnancy diagnosis is an important component of effective herd management in dairy cattle. Predicting pregnancy from Fourier-transform mid-infrared (FT-MIR) spectroscopy data is of particular interest because the data are often already available from routine milk testing. The purpose of this study was to evaluate how well pregnancy status could be predicted in a large data set of 1,161,436 FT-MIR milk spectra records from 863,982 mixed-breed pasture-based New Zealand dairy cattle managed within seasonal calving systems. Three strategies were assessed for defining the nonpregnant cows when partitioning the records according to pregnancy status in the training population. Two of these used records for cows with a subsequent calving only, whereas the third also included records for cows without a subsequent calving. For each partitioning strategy, partial least squares discriminant analysis models were developed, whereby spectra from all the cows in 80% of herds were used to train the models, and predictions on cows in the remaining herds were used for validation. A separate data set was also used as a secondary validation, whereby pregnancy diagnosis had been assigned according to the presence of pregnancy-associated glycoproteins (PAG) in the milk samples. We examined different ways of accounting for stage of lactation in the prediction models, either by including it as an effect in the prediction model, or by pre-adjusting spectra before fitting the model. For a subset of strategies, we also assessed prediction accuracies from deep learning approaches, utilizing either the raw spectra or images of spectra. Across all strategies, prediction accuracies were highest for models using the unadjusted spectra as model predictors. Strategies for cows with a subsequent calving performed well in herd-independent validation with sensitivities above 0.79, specificities above 0.91 and area under the receiver operating characteristic curve (AUC) values over 0.91. However, for these strategies, the specificity to predict nonpregnant cows in the external PAG data set was poor (0.002-0.04). The best performing models were those that included records for cows without a subsequent calving, and used unadjusted spectra and days in milk as predictors, with consistent results observed across the training, herd-independent validation and PAG data sets. For the partial least squares discriminant analysis model, sensitivity was 0.71, specificity was 0.54 and AUC values were 0.68 in the PAG data set; and for an image-based deep learning model, the sensitivity was 0.74, specificity was 0.52 and the AUC value was 0.69. Our results demonstrate that in pasture-based seasonal calving herds, confounding between pregnancy status and spectral changes associated with stage of lactation can inflate prediction accuracies. When the effect of this confounding was reduced, prediction accuracies were not sufficiently high enough to use as a sole indicator of pregnancy status.
dc.description.confidentialfalse
dc.edition.editionApril 2022
dc.format.pagination3615-3632
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/35181140
dc.identifier.citationTiplady KM, Trinh M-H, Davis SR, Sherlock RG, Spelman RJ, Garrick DJ, Harris BL. (2022). Pregnancy status predicted using milk mid-infrared spectra from dairy cattle.. J Dairy Sci. 105. 4. (pp. 3615-3632).
dc.identifier.doi10.3168/jds.2021-21516
dc.identifier.eissn1525-3198
dc.identifier.elements-typejournal-article
dc.identifier.issn0022-0302
dc.identifier.piiS0022-0302(22)00102-3
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71080
dc.languageeng
dc.publisherElsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S0022030222001023?via%3Dihub
dc.relation.isPartOfJ Dairy Sci
dc.rights(c) 2022 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectFourier-transform mid-infrared spectra
dc.subjectdairy cattle
dc.subjectmachine learning
dc.subjectmilk composition
dc.subjectpregnancy prediction
dc.subjectAnimals
dc.subjectCattle
dc.subjectFemale
dc.subjectLactation
dc.subjectLeast-Squares Analysis
dc.subjectMilk
dc.subjectNew Zealand
dc.subjectPregnancy
dc.subjectSpectrophotometry, Infrared
dc.titlePregnancy status predicted using milk mid-infrared spectra from dairy cattle
dc.typeJournal article
pubs.elements-id451479
pubs.organisational-groupOther
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