Symposium review: Use of multiple biological, management, and performance data for the design of targeted reproductive management strategies for dairy cows*
ABSTRACT: As the reproductive efficiency of dairy cattle continues to improve in response to better management and use of technology, novel reproductive management approaches will be required to improve herd performance, profitability, and sustainability. A potential approach currently being explore...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-05-01
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| Series: | Journal of Dairy Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0022030222001746 |
| _version_ | 1811330881842839552 |
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| author | J.O. Giordano E.M. Sitko C. Rial M.M. Pérez G.E. Granados |
| author_facet | J.O. Giordano E.M. Sitko C. Rial M.M. Pérez G.E. Granados |
| author_sort | J.O. Giordano |
| collection | DOAJ |
| description | ABSTRACT: As the reproductive efficiency of dairy cattle continues to improve in response to better management and use of technology, novel reproductive management approaches will be required to improve herd performance, profitability, and sustainability. A potential approach currently being explored is targeted reproductive management. This approach consists of identifying cows with different reproductive and performance potential using multiple traditional and novel sources of biological, management, and performance data. Once subgroups of cows that share biological and performance features are identified, reproductive management strategies specifically designed to optimize cow performance, herd profitability, or alternative outcomes of interest are implemented on different subgroups of cows. Tailoring reproductive management to subgroups of cows is expected to generate greater gains in outcomes of interest than if the whole herd is under similar management. Major steps in the development and implementation of targeted reproductive management programs for dairy cattle include identification and validation of robust predictors of reproductive outcomes and cow performance, and the development and on-farm evaluation of reproductive management strategies for optimizing outcomes of interest for subgroups of cows. Predictors of cow performance currently explored for use in targeted management include genomic predictions; behavioral, physiological, and performance parameters monitored by sensor technologies; and individual cow and herd performance records. Once the most valuable predictive sources of variation are identified and their effects quantified, novel analytic methods (e.g., machine learning) for prediction will likely be required. These tools must identify groups of cows for targeted management in real time and with no human input. Despite some encouraging research evidence supporting the development of targeted reproductive management strategies, extensive work is required before widespread implementation by commercial farms. |
| first_indexed | 2024-04-13T16:10:33Z |
| format | Article |
| id | doaj.art-1a734b31f7ef4e338b2e8695e3b6f35f |
| institution | Directory Open Access Journal |
| issn | 0022-0302 |
| language | English |
| last_indexed | 2024-04-13T16:10:33Z |
| publishDate | 2022-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Dairy Science |
| spelling | doaj.art-1a734b31f7ef4e338b2e8695e3b6f35f2022-12-22T02:40:15ZengElsevierJournal of Dairy Science0022-03022022-05-01105546694678Symposium review: Use of multiple biological, management, and performance data for the design of targeted reproductive management strategies for dairy cows*J.O. Giordano0E.M. Sitko1C. Rial2M.M. Pérez3G.E. Granados4Corresponding author; Department of Animal Science, Cornell University, Ithaca, NY 14853Department of Animal Science, Cornell University, Ithaca, NY 14853Department of Animal Science, Cornell University, Ithaca, NY 14853Department of Animal Science, Cornell University, Ithaca, NY 14853Department of Animal Science, Cornell University, Ithaca, NY 14853ABSTRACT: As the reproductive efficiency of dairy cattle continues to improve in response to better management and use of technology, novel reproductive management approaches will be required to improve herd performance, profitability, and sustainability. A potential approach currently being explored is targeted reproductive management. This approach consists of identifying cows with different reproductive and performance potential using multiple traditional and novel sources of biological, management, and performance data. Once subgroups of cows that share biological and performance features are identified, reproductive management strategies specifically designed to optimize cow performance, herd profitability, or alternative outcomes of interest are implemented on different subgroups of cows. Tailoring reproductive management to subgroups of cows is expected to generate greater gains in outcomes of interest than if the whole herd is under similar management. Major steps in the development and implementation of targeted reproductive management programs for dairy cattle include identification and validation of robust predictors of reproductive outcomes and cow performance, and the development and on-farm evaluation of reproductive management strategies for optimizing outcomes of interest for subgroups of cows. Predictors of cow performance currently explored for use in targeted management include genomic predictions; behavioral, physiological, and performance parameters monitored by sensor technologies; and individual cow and herd performance records. Once the most valuable predictive sources of variation are identified and their effects quantified, novel analytic methods (e.g., machine learning) for prediction will likely be required. These tools must identify groups of cows for targeted management in real time and with no human input. Despite some encouraging research evidence supporting the development of targeted reproductive management strategies, extensive work is required before widespread implementation by commercial farms.http://www.sciencedirect.com/science/article/pii/S0022030222001746targeted managementpredictionfertilitydairy cow |
| spellingShingle | J.O. Giordano E.M. Sitko C. Rial M.M. Pérez G.E. Granados Symposium review: Use of multiple biological, management, and performance data for the design of targeted reproductive management strategies for dairy cows* Journal of Dairy Science targeted management prediction fertility dairy cow |
| title | Symposium review: Use of multiple biological, management, and performance data for the design of targeted reproductive management strategies for dairy cows* |
| title_full | Symposium review: Use of multiple biological, management, and performance data for the design of targeted reproductive management strategies for dairy cows* |
| title_fullStr | Symposium review: Use of multiple biological, management, and performance data for the design of targeted reproductive management strategies for dairy cows* |
| title_full_unstemmed | Symposium review: Use of multiple biological, management, and performance data for the design of targeted reproductive management strategies for dairy cows* |
| title_short | Symposium review: Use of multiple biological, management, and performance data for the design of targeted reproductive management strategies for dairy cows* |
| title_sort | symposium review use of multiple biological management and performance data for the design of targeted reproductive management strategies for dairy cows |
| topic | targeted management prediction fertility dairy cow |
| url | http://www.sciencedirect.com/science/article/pii/S0022030222001746 |
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