Look at "leading" not "lagging" indicators to improve dairy herd performance and health
November 23, 2013
Motivated dairy managers know that herd records are important. Monitoring the key indicators of individual cow or herd performance can shed light on emerging problems and better informs management decisions. But are we monitoring the right things?
Economists for years have used the terminology of leading and lagging indicators to describe factors predicting the direction of the economy. "Leading indicators" are predictive of the future performance whereas "lagging indicators" are historical confirmation of past performance. Proactive dairy managers need to put more focus on monitoring "leading indicators" of performance. A focus on leading indicators positions you to take corrective action before, not after, problems occur. It's not that lagging indicators are bad, it's just that they are too slow.
Currently many dairy performance measures are what would be classified as lagging indicators. However, we are at the dawn of a new high-tech era for the dairy industry. Individual cow milk weights and milk conductivity are already automatically collected at each milking. Emerging sensor technology and today's massive analytical capacity are opening new monitoring opportunities. New robotic milking equipment can already collect up to 157 data points daily on every milking cow. Easily ten times more data collected than the most advanced dairy recording systems today. Examples of the data stream being automatically collected are milk yield, milk components (fat, protein, lactose and progesterone), body condition, body weights, temperature, rumination, rumen pH, activity, lying behavior and location. Dairy researchers here at the University of Minnesota and elsewhere are working to find novel patterns and relationships among different kinds of dairy farm information in order to make the earliest possible predictions of future performance.
The 21-day pregnancy rate is thought to be the best overall measure of herd reproductive success. But since its computation occurs several weeks after the management activity that resulted in pregnancy, it is by definition a "lagging indicator" of herd reproductive performance.
The 21-day estrous detection rate is closer to the management action and tells us more about the success of detecting cows in heat and breeding them but it is still a "lagging indicator". Are there measures further upstream that could be more timely predictors of herd reproductive success? Fresh cows with negative energy balance, high SCC, fat-to-protein ratios >1.4 have poorer reproductive performance. Perhaps a measure of transition cow success like the Wisconsin DHI Transition Cow Index may be a more effective "leading indicator" of herd reproductive performance. What if we could convert data automatically collected daily into a similar measure of transition cow success? Preliminary study here at the University of Minnesota indicates this may be possible. Nearly all sub-par cow health and performance originates as a consequence of transition cow failures making transition cow success the most universal leading indicator of cow health and performance.
Mastitis is assessed by SCC level and counting the number and severity of clinical mastitis cases. However, since both are measured after the mammary gland infection has already occurred, they are also "lagging indicators". Some potential "leading indicators" are not always high-tech. Cow hygiene score and bulk tank milk culture results are certainly "leading indicators" of environmental mastitis and are already being used. Teat end condition as well the completeness and consistency of teat dip application are also important leading indicators of mammary gland health. Unfortunately these data are sporadically collected and not stored in a form for easy and effective analysis. Here again emerging technology is coming to our rescue. Smart phone and tablet on-farm electronic data collection and "cloud based" analysis software are now making possible sophisticated use of these valuable on-farm observations.
Moving closer to the causes of problems, the leading indicators of evolving problems are often more general in nature and not specific to any specific disease or condition. Change in milk production, for example, is the most sensitive signal of biological status. Research here at the University of Minnesota has shown that statistical process control analysis of daily milk yield alone detected the onset of disease up to 8 to 10 days prior to the clinical disease. The difficulty is that without specific symptoms of a disease or condition, what do you treat? Treatment is not the goal nor is it a victory. It is a failure. My suggestion is to shift the focus to "treating" the production system, alleviating pre-disposing causes preventing sub-par health and productivity altogether.
I am not advocating that we abandon many of our traditional measures of cow/herd performance. A balanced approach seems best. However, the scientific literature is full of examples of potential leading indicators of sub-par health and performance that are yet untapped and not routinely tracked at most farms. But as technology improves and more sensitive "leading indicators" of both cow and herd performance evolve, we need to utilize them. In the meantime, a good starting point is to review the reports and metrics you use today. Justify why you're measuring what you're measuring. If you think you're already using leading indicators, challenge them. Do the metrics provide insight into the causes of potential future problems or just a confirmation of past performance? Stay tuned – help is on the way.