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. A stochastic simulation model for assessment of investments in Precision Dairy Farming technologies: model enhancements and utility demonstration [abstract]. Journal of Dairy Science. 91, E-Suppl 1:557.
Technical Abstract: A previously described stochastic simulation model of a dairy enterprise was modified for improved robustness. This model was developed to evaluate investments in Precision Dairy Farming technologies and was constructed to embody the biological and economic complexities of a dairy farm system within a partial budgeting framework. The @Risk add-in (Palisade Corp., Ithaca, NY) for Excel was employed to account for the stochastic nature of key variables by Monte Carlo simulation. The model comprised a series of modules, which synergistically provide the necessary inputs for profitability analysis. Model enhancements included addition of a retention pay-off (RPO) module for cost of culling calculations, an average cow simulation module, a body condition score module, a herd size control algorithm, a best management practice adherence factor, a technology stage adjustment factor, and updated, literature-based estimates for disease impact. Technology benefits are appraised from changes in disease incidence, disease impact, and reproductive performance. The influence of stochastic input and output prices on RPO, days open (DO), and disease was examined with 5000 iterations of a simulation of an average 1000-cow US dairy herd. For example, during the 1st month of the first lactation, increasing replacement price, slaughter price, milk price, or feed cost by 1 SD changed RPO by +$196.39, -$80.08, -$9.02, and -$2.16, respectively. As slaughter price, feed cost, milk price, and replacement price increased by 1 SD, the cost of a DO changed by -$0.24, -$0.23, +$0.20, and +$0.20, respectively. Sensitivity for costs of displaced abomasum, dystocia, ketosis, mastitis, metritis, retained placenta, and milk fever were also investigated. The RPO, DO costs, and disease costs were highly sensitive to stochastic prices and deterministic inputs. This model will be useful for producers, consultants, extension agents and others wanting to assess the economic impact of investing in new technology.