Assessing the potential value for an automated dairy cattle body condition scoring system through stochastic simulation

J.M. Bewley (Department of Animal Sciences, Purdue University, West Lafayette, Indiana, USA)
M.D. Boehlje (Department of Agricultural Economics, Purdue University, West Lafayette, Indiana, USA)
A.W. Gray (Department of Agricultural Economics, Purdue University, West Lafayette, Indiana, USA)
H. Hogeveen (Faculty Veterinary Medicine, Utrecht University, Utrecht, The Netherlands)
S.J. Kenyon (Department of Veterinary Clinical Sciences, Purdue University, West Lafayette, Indiana, USA)
S.D. Eicher (Livestock Behavior Research Unit, Agricultural Research Service, USDA, West Lafayette, Indiana, USA)
M.M. Schutz (Department of Animal Sciences, Purdue University, West Lafayette, Indiana, USA)

Agricultural Finance Review

ISSN: 0002-1466

Publication date: 11 May 2010

Abstract

Purpose

Automated body condition scoring (BCS) through extraction of information from digital images has been demonstrated to be feasible; and commercial technologies are being developed. The primary objective of this research was to identify the factors that influence the potential profitability of investing in an automated BCS system.

Design/methodology/approach

An expert opinion survey was conducted to provide estimates for potential improvements associated with technology adoption. A stochastic simulation model of a dairy system, designed to assist dairy producers with investment decisions for precision dairy farming technologies 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.

Findings

Improvements in reproductive performance had the largest influence on revenues followed by energy efficiency and then by disease reduction. The impact of disease reduction was less than anticipated because the ideal BCS indicated by experts resulted in a simulated increase in the proportion of cows with BCS at calving 3.50. The estimates for disease risks and conception rates, obtained from literature, however, suggested that this increase would result in increased disease incidence. Stochastic variables that had the most influence on NPV were: variable cost increases after technology adoption; the odds ratios for ketosis and milk fever incidence and conception rates at first service associated with varying BCS ranges; 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 level of milk production. Investment in this technology may be profitable but results were very herd‐specific. A simulation modeling a deterministic 25 percent decrease in the percentage of cows with BCS at calving ≤3.25 demonstrated a positive NPV in 86.6 percent of 1,000 iterations.

Originality/value

This investment decision can be analyzed with input of herd‐specific values using this model.

Keywords

Citation

Bewley, J.M., Boehlje, , Gray, A.W., Hogeveen, H., Kenyon, S.J., Eicher, S.D. and Schutz, M.M. (2010), "Assessing the potential value for an automated dairy cattle body condition scoring system through stochastic simulation", Agricultural Finance Review, Vol. 70 No. 1, pp. 126-150. https://doi.org/10.1108/00021461011042675

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Publisher

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Emerald Group Publishing Limited

Copyright © 2010, Emerald Group Publishing Limited

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