{"id":5461,"date":"2025-03-25T10:58:58","date_gmt":"2025-03-25T09:58:58","guid":{"rendered":"https:\/\/simherd.com\/?p=5461"},"modified":"2025-03-25T14:21:58","modified_gmt":"2025-03-25T13:21:58","slug":"simherd-contributes-to-scientific-study-on-the-economic-value-of-sensor-based-health-monitoring","status":"publish","type":"post","link":"https:\/\/simherd.com\/en\/simherd-contributes-to-scientific-study-on-the-economic-value-of-sensor-based-health-monitoring\/","title":{"rendered":"SimHerd Contributes to Scientific Study on the Economic Value of Sensor-Based Health Monitoring"},"content":{"rendered":"\n
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Content from Pfrombeck et al. (2025), Journal of Dairy Science. Read the full article here.<\/a><\/em><\/p>\n

Digital health tools are changing dairy management\u2014but do they pay off?<\/strong> That was the key question addressed in a recent peer-reviewed study, where SimHerd played a central role<\/strong> in evaluating the return on investment (ROI) of sensor-based disease monitoring in dairy herds.<\/p>\n

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Evaluation og a sensor bolus in a field trail<\/h3>\n

The study, was conducted by German researchers from Institute f\u00fcr Agricultural Engineering and Animal Husbandry, Bavarian State Research Center for Agriculture<\/a> and Faculty of Sustainable Agriculture and Energy Systems, University of Applied Science Weihenstephan-Triesdorf<\/a> in collaboration with SimHerd. It examined a commercial rumen bolus sensor<\/strong> capable of tracking core body temperature and activity levels to detect early signs of common diseases such as: mastitis, hypocalcemia, retained placenta, and claw- and leg problems.<\/p>\n

Data was collected over a two-year period from 65 cows at a research and demonstration farm. The system’s detection rates varied across disease types, showing the highest sensitivity for hypocalcemia and retained placenta (up to 64%)<\/strong>, and the lowest for lameness-related conditions.<\/p>\n

Modeling Economic Outcomes<\/h3>\n

To assess the financial impact of using such a sensor system, researchers used the SimHerd Expert model<\/strong><\/a> to simulate herd performance across three herd health scenarios: poor<\/strong>, average<\/strong>, and good health<\/strong>.<\/p>\n

A panel of nine practicing veterinarians<\/strong> provided expert estimates on how earlier detection might affect key health parameters, including: treatment costs, days of milk withdrawal, somatic cell count (SCC), and risk of mild vs. severe diseases cases.<\/p>\n

These values were used to parameterize SimHerd, which then simulated 10,000 iterations<\/strong> per scenario to estimate changes in gross margin (GM)<\/strong> with and without the sensor system. The analysis also accounted for investment costs, potential labor savings, and time spent following up on false-positive alerts.<\/p>\n

Key Findings: Economic Return Varies with Herd Health<\/h3>\n

The economic outcomes differed significantly depending on the herd’s baseline health:<\/p>\n

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\ud83d\udfe2 Poor-health herds<\/strong>: Net returns of up to \u20ac119 per cow\/year<\/strong> and nearly 100% chance of a positive ROI<\/strong>.<\/p>\n

\ud83d\udfe1 Average-health herds<\/strong>: Variable but often positive returns, depending on assumptions about labor and sensor accuracy.<\/p>\n

\ud83d\udd34 Good-health herds<\/strong>: Lower or even negative returns, due to fewer disease events to detect early.<\/p>\n

The figure<\/strong> from the study illustrates this clearly: the probability of a positive economic return<\/strong> is substantially higher in herds with poorer health. In contrast, up to 75% of simulations<\/strong> showed a negative return<\/strong> in already healthy herds.<\/p>\n\n\t\t<\/div>\n\t\t\t<\/div>\n\n\t\n\n<\/div>\n\n\t\t<\/div>\n\t\t\t<\/div>\n\n\t\n\n<\/div>\n\n\t\t<\/div>\n\t\t\t<\/div>\n\n\t\n\n<\/div>\n

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