Machine learning classification of breeding protocol descriptions from Canadian Holsteins.

Alcantara, L M; Schenkel, F S; Lynch, C; Oliveira Junior, G A; Baes, C F; Tulpan, D (2022). Machine learning classification of breeding protocol descriptions from Canadian Holsteins. Journal of dairy science, 105(10), pp. 8177-8188. American Dairy Science Association 10.3168/jds.2021-21663

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Dairy farmers are motivated to ensure cows become pregnant in an optimal and timely manner. Although timed artificial insemination (TAI) is a successful management tool in dairy cattle, it masks an animal's innate fertility performance, likely reducing the accuracy of genetic evaluations for fertility traits. Therefore, separating fertility traits based on the recorded management technique involved in the breeding process or adding the breeding protocol as an effect to the model can be viable approaches to address the potential bias caused by such management decisions. Nevertheless, there is a lack of specificity and uniformity in the recording of breeding protocol descriptions by dairy farmers. Therefore, this study investigated the use of 8 supervised machine learning algorithms to classify 1,835 unique breeding protocol descriptions from 981 herds into the following 2 classes: TAI or other than TAI. Our results showed that models that used a stacking classifier algorithm had the highest Matthews correlation coefficient (0.94 ± 0.04, mean ± SD) and maximized precision and recall (F1-score = 0.96 ± 0.03) on test data. Nonetheless, their F1-scores on test data were not different from 5 out of the other 7 algorithms considered. Altogether, results presented herein suggest machine learning algorithms can be used to produce robust models that correctly identify TAI protocols from dairy cattle breeding records, thus opening the opportunity for unbiased genetic evaluation of animals based on their natural fertility.

Item Type:

Journal Article (Original Article)

Division/Institute:

05 Veterinary Medicine > Department of Clinical Research and Veterinary Public Health (DCR-VPH) > Institute of Genetics
05 Veterinary Medicine > Department of Clinical Research and Veterinary Public Health (DCR-VPH)

UniBE Contributor:

Baes, Christine Francoise

Subjects:

500 Science > 590 Animals (Zoology)
600 Technology > 630 Agriculture

ISSN:

0022-0302

Publisher:

American Dairy Science Association

Language:

English

Submitter:

Pubmed Import

Date Deposited:

05 Sep 2022 13:32

Last Modified:

05 Dec 2022 16:23

Publisher DOI:

10.3168/jds.2021-21663

PubMed ID:

36055841

Uncontrolled Keywords:

Canadian Holstein breeding protocol description machine learning classifier timed artificial insemination

BORIS DOI:

10.48350/172666

URI:

https://boris.unibe.ch/id/eprint/172666

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