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Dairy producers have a variety of Precision Dairy technologies available to them, which creates the need for evaluation of new information streams generated by these technologies. At this point, a number of dairies are just collecting information, but may not have the technical skills or understanding to evaluate the data, let alone implement changes to their decision-making process. This issue has created the demand for research that integrates new decision criteria into daily herd management. Academics need experience with these new data sets and potential methodologies to contribute to producer-targeted recommendations. This case study serves as investigative research intended to gain familiarity with the complexities and availability of these types of data sets. This initial work has provided results that show significant relationships between newly available variables and milk production. While evidence suggests that increased efficiency is made possible by these precision technologies, the research addressing the significant hurdles to adoption is still in its infancy. This quantile regression analyzes a herd over one year to estimate a production function that uses cow-level input factors such as resting bouts, steps taken, eating time and body weight. Results demonstrate the ability of these technologies to create value to herd management strategies.
Keywords: Precision Dairy (PD), Dairy, Quantile Regression


Author(s): Mark T. (1), Richard J. ( 1)

Organization(s): University of Kentucky (1)

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