4.7 Review

Symposium review: Use of multiple biological, management, and performance data for the design of targeted reproductive management strategies for dairy cows

Journal

JOURNAL OF DAIRY SCIENCE
Volume 105, Issue 5, Pages 4669-4678

Publisher

ELSEVIER SCIENCE INC
DOI: 10.3168/jds.2021-21476

Keywords

implementation by targeted management; prediction; fertility; dairy cow

Funding

  1. USDA National Institute of Food and Agriculture (NIFA)
  2. Animal Health Program [2017-67015-26772, NYC-2020-21-255, 1021189]

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As dairy cattle reproductive efficiency improves, targeted reproductive management approaches are being explored to enhance herd performance and profitability. These approaches involve identifying subgroups of cows based on various biological and performance data, and implementing tailored reproductive management strategies. Despite the need for further work before widespread implementation, there is promising research evidence supporting the development of these strategies.
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.

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