Join us this Thursday, May 24, 2012, for a free live webcast entitled, "A Stochastic Simulation Model for Dairy Business Investment Decisions " to be presented by Dr. Jeffrey Bewley.
A dynamic, stochastic, mechanistic simulation model of a dairy enterprise was developed to evaluate the cost and benefit streams coinciding with investments in Precision Dairy Farming technologies. The model was constructed to embody the biological and economical complexities of a dairy farm system within a partial budgeting framework. A primary objective was to establish a flexible, user-friendly, farm-specific, decision-making tool for dairy producers or their advisers and technology manufacturers.
The basic deterministic model was created in Microsoft Excel. @RISK was employed to account for the stochastic nature of key variables within a Monte Carlo simulation. Net present value was the primary metric used to assess the economic profitability of investments. The model comprised a series of modules, which synergistically provided the necessary inputs for profitability analysis. Estimates of biological relationships within the model were obtained from the literature in an attempt to represent an average or typical U.S. dairy. Technology benefits were appraised from the resulting impact on disease incidence, disease impact, and reproductive performance. The economic feasibility of investment in an automated BCS system was explored to demonstrate the utility of this model.
An expert opinion survey was conducted to obtain estimates of potential improvements from adoption of this technology. Benefits were estimated through assessment of the impact of BCS on the incidences of ketosis, milk fever, and metritis; conception rate at first service; and energy efficiency. Improvements in reproductive performance had the greatest influence on revenues followed by energy efficiency and disease reduction, in order. 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.
Investment decisions for Precision Dairy Farming technologies can be analyzed with input of herd-specific values using this model. This free live webcast will go into detail about the model, as well.
Dr. Jeffrey Bewley, is Assistant Professor and Extension Dairy Specialist, specializing in Dairy Systems Management, Dairy Decision Making, and Precision Dairy Farming, at the University of Kentucky. He received his BS from the University of Kentucky, MS from the University of Wisconsin-Madison, and PhD from Purdue University. Jeffrey has a keen appreciation for the opportunities automation provides for more precise management. His own work is in the technical and economic assessment of Precision Dairy Farming technologies, body condition scoring, and temperature monitoring. His outreach program is focused on developing tools and strategies for improved decision-making on dairy farms.