Data-driven technologies and management practices for improving the sustainability of reproductive management
Julio O. Giordano A *A
Abstract
Dairy farms must constantly evolve to achieve sustainability goals, including profitability, minimal environmental impacts, and improving the well-being of cows and people. Data-driven management practices and automated technologies are a growing opportunity for improving the sustainability of reproductive management. Precision livestock farming technologies coupled with other herd management data, genomic information, and environmental monitoring tools are enabling the development of data-driven targeted reproductive management and automation of management tasks. An improved understanding of associations between putative data predictors of reproductive outcomes enables targeted reproductive management for cows that share similar expected performance or biological features. Tailored management interventions can be applied on subgroups of cows based on automated estrus alerts, genomic predictions, and ovarian status at the time of non-pregnancy diagnosis. Targeted interventions can lead to shorter interbreeding intervals, increased fertility, and fewer unnecessary interventions on cows. Major advances in engineering, advanced data analytics, and a better understanding of dairy cattle biology through data have also enabled progress with automation of management tasks such as detection of estrus, synchronization of ovulation, and pregnancy testing. Although concerted efforts in research and application are still needed to fully realize the full sustainability benefits of data-driven management and automation, collectively these innovations are reshaping reproductive management of dairy cattle.
Keywords: automation, dairy farm, dairy sustainability, data-driven, detection of estrus, fertility, predictors, reproductive management, synchronization of ovulation, targeted management.
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