Machine learning classification of breeding protocol descriptions from Canadian Holsteins
ABSTRACT: 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 evaluati...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-10-01
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| Series: | Journal of Dairy Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0022030222004866 |
| _version_ | 1818023348629667840 |
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| author | L.M. Alcantara F.S. Schenkel C. Lynch G.A. Oliveira Junior C.F. Baes D. Tulpan |
| author_facet | L.M. Alcantara F.S. Schenkel C. Lynch G.A. Oliveira Junior C.F. Baes D. Tulpan |
| author_sort | L.M. Alcantara |
| collection | DOAJ |
| description | ABSTRACT: 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. |
| first_indexed | 2024-12-10T03:42:54Z |
| format | Article |
| id | doaj.art-2f46c1babb9f42a1a85ef712516f332f |
| institution | Directory Open Access Journal |
| issn | 0022-0302 |
| language | English |
| last_indexed | 2024-12-10T03:42:54Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Dairy Science |
| spelling | doaj.art-2f46c1babb9f42a1a85ef712516f332f2022-12-22T02:03:30ZengElsevierJournal of Dairy Science0022-03022022-10-011051081778188Machine learning classification of breeding protocol descriptions from Canadian HolsteinsL.M. Alcantara0F.S. Schenkel1C. Lynch2G.A. Oliveira Junior3C.F. Baes4D. Tulpan5Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario N1G 2W1, CanadaCentre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario N1G 2W1, Canada; Corresponding authorCentre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario N1G 2W1, CanadaCentre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario N1G 2W1, CanadaCentre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario N1G 2W1, Canada; Institute of Genetics, Department of Clinical Research and Veterinary Public Health, University of Bern, Bern, 3001, SwitzerlandCentre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario N1G 2W1, CanadaABSTRACT: 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.http://www.sciencedirect.com/science/article/pii/S0022030222004866breeding protocol descriptionCanadian Holsteinmachine learning classifiertimed artificial insemination |
| spellingShingle | L.M. Alcantara F.S. Schenkel C. Lynch G.A. Oliveira Junior C.F. Baes D. Tulpan Machine learning classification of breeding protocol descriptions from Canadian Holsteins Journal of Dairy Science breeding protocol description Canadian Holstein machine learning classifier timed artificial insemination |
| title | Machine learning classification of breeding protocol descriptions from Canadian Holsteins |
| title_full | Machine learning classification of breeding protocol descriptions from Canadian Holsteins |
| title_fullStr | Machine learning classification of breeding protocol descriptions from Canadian Holsteins |
| title_full_unstemmed | Machine learning classification of breeding protocol descriptions from Canadian Holsteins |
| title_short | Machine learning classification of breeding protocol descriptions from Canadian Holsteins |
| title_sort | machine learning classification of breeding protocol descriptions from canadian holsteins |
| topic | breeding protocol description Canadian Holstein machine learning classifier timed artificial insemination |
| url | http://www.sciencedirect.com/science/article/pii/S0022030222004866 |
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