Good business decision-making in dairy farming depends on the availability of high quality and current data about the profitability of the business production unit: the cow.

Every day producers are faced with important decisions related to culling or keeping, breeding, grouping, treating, and feeding cows, among many others. Unfortunately, despite the many technologies and data available today, dairy producers continue to lack tools that provide an accurate and detailed account of individual cow profitability, with minimal effort, and in real time. Thus, most of the individual cow decision-making continues to be done based on averages for the herd or groups, parameters that do not truly reflect cow profitability, and in some cases perception or "gut feeling".

To help dairy managers overcome this barrier to good business decision-making, our research group developed the "MyCow$" tool.

This software tool leverages dairy data to automatically calculate the profitability of individual cows in real time. Under ideal conditions of data availability, a series of interconnected algorithms use detailed individual cow input and output data generated by precision technologies and events recorded in dairy herd management software to estimate the costs and revenues of key drivers of cow profitability. This data is integrated with prices of key inputs and outputs to calculate cash flow of individual cows, automatically, and in real time.

Briefly, the daily revenue of a cow is calculated by adding the value of the milk produced, accounting for volume and components at each milking, if such data is available. Other revenues including calves and beef sales are estimated based on sex and breed of the calf born and body weight of the cow sold. Key production costs such as feed cost are calculated based on estimations of feed intake from the cow production, body weight, and other parameters.

Detailed calculations of health and breeding costs are based on individual events and expenses incurred with every disease and breeding event. Finally, replacement costs for cows that exit the herd due to sale or death are calculated through accounting of heifer replacement costs.

Recognizing that not all dairy farms have access to the same detailed data streams from precision technologies, herd management software, and financials, MyCow$ has some built-in flexibility to work on dairies that have access to different types of data. Although calculations are not as accurate under those scenarios, the values are still representative and allow for fair cow-to-cow comparison as all calculations are the same for all cows.

Although individual cow calculations are at the core of the tool, MyCow$ can also report data for groups of cows formed based on cow features or the occurrence of specific events in the lifetime of cows. For example, users can evaluate daily and accumulated revenues, costs, and cash flow for cows grouped based on their pen location, lactation or parity number, reproductive status (e.g., pregnant versus open cows), genetic merit for traits of interest, or could be grouped based on whether they were affected by certain health disorders.

The tool also allows reporting data for specific durations (e.g., one day, one week, last 12 months, last two months) by calendar date or by days in milk, enabling comparisons for daily or accumulated outcomes for different timespans.

Because of the variety of analyses that could be conducted, the potential value and use of MyCow$ in dairy decision-making is large and diverse. Among many others, producers can compare the effects of management practices such as different diets or reproductive management programs, use of technology and products, or the effects of specific events such as health disorders or pregnancy.

As a simple example, a dairy interested in the effect of health disorders on early lactation economic performance could compare cows affected or not by diseases commonly observed after freshening. To this end, we used MyCow$ at one dairy to estimate the difference in cash flow for the first 60 days in milk for cows that had or did not have metritis, clinical mastitis, and clinical ketosis. Using average market input and output prices for 2023 to 2024 for this dairy of interest, the difference in cash flow for cows with and without metritis, mastitis, and ketosis were $98, $125, and $109, respectively (keep in mind these differences are only relevant to this dairy). Using this dairy-specific information herd managers could not only make informed decisions about treatments but also the value of management strategies to prevent these disorders.

TAKEAWAYS

Obviously, MyCow$ can be a powerful tool but requires high-quality dairy data that not all dairies have available today. We hope that tools like MyCow$ will encourage dairies to consider adopting new or making better use of precision technologies and data management practices that can help them with financial and economic decision making. Although MyCow$ is not yet available for deployment to the dairy industry, we are working on bringing it to commercial application soon.

Funding and support to develop MyCow$ was provided primarily by the New York Farm Viability Institute grant FVI20032 and by USDA National Institute of Food and Agriculture (Washington, D.C.), Animal Health Program Project 2017-67015-26772, Hatch project NYC-2020-21-255, Multistate project 1021189, and Farm of the Future program Project 2023-77038-38865.

Julio Giordano, Martin Perez, Allison Kerwin, Sebastian Aguero, and Angela George collaborated on this research through the Dairy Cattle Biology and Management Laboratory at Cornell University. Visit the laboratory’s website.

This article appeared in PRO-DAIRY's The Manager in November 2024. To learn more about Cornell CALS PRO-DAIRY, visit PRO-DAIRY.