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研讨会综述:利用多种生物、管理和性能数据为奶牛制定有针对性的繁殖管理策略。

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

机构信息

Department of Animal Science, Cornell University, Ithaca, NY 14853.

Department of Animal Science, Cornell University, Ithaca, NY 14853.

出版信息

J Dairy Sci. 2022 May;105(5):4669-4678. doi: 10.3168/jds.2021-21476. Epub 2022 Mar 17.

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.

摘要

随着奶牛繁殖效率的不断提高,应对更好的管理和技术应用,需要新的繁殖管理方法来提高牛群的性能、盈利能力和可持续性。目前正在探索的一种潜在方法是靶向繁殖管理。这种方法包括使用多种传统和新型的生物、管理和性能数据来识别具有不同繁殖和表现潜力的奶牛。一旦确定了具有相似生物和性能特征的奶牛亚组,就可以针对不同的奶牛亚组实施专门设计的繁殖管理策略,以优化奶牛的表现、牛群的盈利能力或其他感兴趣的结果。将繁殖管理定制到奶牛亚组有望比整个牛群采用类似管理获得更大的利益。奶牛靶向繁殖管理计划的开发和实施的主要步骤包括识别和验证繁殖结果和奶牛表现的稳健预测因子,以及开发和在农场评估优化奶牛亚组感兴趣结果的繁殖管理策略。目前用于靶向管理的奶牛表现预测因子包括基因组预测;通过传感器技术监测的行为、生理和性能参数;以及个体奶牛和牛群表现记录。一旦确定了最有价值的变异预测源并量化了它们的影响,就可能需要新型的分析方法(例如机器学习)进行预测。这些工具必须实时识别出需要进行靶向管理的奶牛群体,而且无需人工输入。尽管有一些令人鼓舞的研究证据支持靶向繁殖管理策略的开发,但在商业农场广泛实施之前,还需要做大量的工作。

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