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ARS Home » Midwest Area » West Lafayette, Indiana » Livestock Behavior Research » Research » Publications at this Location » Publication #223504
Title: Assessing the potential value of automated body condition scoring through stochastic simulation

Author
item BEWLEY, J - PURDUE UNIVERSITY
item BOEHLJE, M - PURDUE UNIVERSITY
item GRAY, A - PURDUE UNIVERSITY
item HOGEVEEN, H - UTRECHT UNIVERSITY
item Eicher, Susan
item SCHUTZ, M - PURDUE UNIVERSITY

Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: 3/24/2008
Publication Date: 7/7/2008
Citation: Bewley, J.M., Boehlje, M.D., Gray, A.W., Hogeveen, H., Eicher, S.D., Schutz, M.M. 2008. Assessing the potential value of automated body condition scoring through stochastic simulation [abstract]. Journal of Dairy Science. 91, E-Suppl. 1:404.

Interpretive Summary:

Technical Abstract: Automated body condition scoring (BCS) using digital images has been shown to be feasible. The primary objective of this research was to identify factors that influence the profitability of investment in an automated BCS system. An expert opinion survey was conducted to provide estimates for potential improvements from technology adoption. Experts indicated that the most important benefits would be disease reduction followed by nutritional cohort management, reproduction, animal well-being, energy efficiency, and genetics. A stochastic simulation model of a dairy was utilized to perform a Net Present Value (NPV) analysis. Benefits of technology adoption were estimated through assessment of the impact of BCS on the incidence of ketosis, milk fever, and metritis, conception rate at first service, and energy efficiency. Improvements in reproductive performance had the largest influence on revenues followed by energy efficiency and then disease reduction. Stochastic variables with the most influence on NPV were: variable cost increases; odds ratios for ketosis and milk fever incidence, and conception rates at first service; uncertainty of the impact of ketosis, milk fever, and metritis on days open, unrealized milk, veterinary costs, labor, and discarded milk; and the change in the percentage of cows with BCS at calving = 3.25 before and after technology adoption. The deterministic inputs impacting NPV were herd size, management level, and milk production. Investment in this technology may be profitable, but results were herd-specific. A simulation modeling a deterministic 25% decrease in the percentage of cows with BCS at calving = 3.25 showed a positive NPV in 87.8% of 1000 iterations. Investment profitability was highly dependent on the current BCS distribution, the magnitude of negative impacts of extreme BCS, and management capacity to make changes necessary to achieve optimal BCS. Development of this technology may benefit dairy producers by a more efficient method of assessing BCS.