In the "Animal Health: Dairy Transition & Reproductive Health" session at the 2016 Joint Annual Meeting conference in Salt Lake City, Utah, researchers presented data demonstrating the newest methods of evaluating and preventing early-lactation diseases and the potential costs of diagnosis.
It is well accepted that metritis can be a costly and detrimental disease that may be difficult to diagnose correctly. Although typically defined as the presence of a flaccid uterus containing fetid fluid within the first 10 days postpartum, producers and farm staff can easily underdiagnose a case of mild metritis.
Researchers with Elanco Animal Health collected Dairy Comp records from one operation that reported no cases, mild cases and severe cases of metritis and analyzed the data to determine how reporting quality records can affect economic loss. By not reporting the incidence of mild metritis, modeled data suggested that producers could lose an additional 110 kg of milk per cow per lactation, resulting in an 18,000 kg loss for a herd. Overall, misclassification can reduce economic profits and severely underestimate the diagnostic and treatment cost of each incidence.
While proper data reporting is crucial to determine economic losses, technology can be used to aid in the understanding of disease incidence effects on cost.
One example of disease monitoring through technology was presented by researchers from Colorado State University. Using AFIMilk (Kibbutz Afikim, Israel) to explore milk fat:protein and fat:lactose ratios in postpartum cows, researchers explored how milk yield and component fluctuation can be used to predict disease.
They found that five days prior to diagnosis, cows with a higher fat:lactose ratio had an increased risk of developing milk fever and metritis. However, cows with a higher fat:protein ratio had increased risk of developing ketosis. Additionally, the sensitivity of AFIMilk in predicting hypocalcemia was 100% when fat:lactose was compared to data from three to five days prior. Although the sensitivity and specificity varied by disease, comparing cows within the same group proved the most effective method for determining how milk component ratios affect cow disease.
Although most current research focuses on disease detection, predicting a disease prior to its incidence may prove to be more useful in preventing sick and cull cows. Researchers from the University of Alberta have been using direct inspection by mass spectrometry to determine metabolites in blood, milk and urine to predict metabolic disease and lameness.
Metabolites are typically small molecules that function as intermediate products of metabolism. Unlike traditional testing, the presence of metabolites does not require cutoff points to indicate the presence and severity of disease. Cows were examined eight weeks and four weeks before calving, at disease diagnosis and four weeks and eight weeks after calving to determine the incidence of disease.
Researchers discovered 180 metabolites mostly associated with ketosis and 154 metabolites associated with lameness. Metabolite correlation to ketosis and lameness ranged from 0.95 to 1.00. While this process is extremely costly, future research could create cow-side and cost-effective monitoring techniques for metabolites.
Amanda Lee is originally from Palatine, Ill. She completed her bachelor's of science degree at Knox College in 2013 and is currently working on her master's degree in animal sciences at the University of Kentucky. Her research focuses on using precision dairy technology to predict metabolic disease and better understand heat stress.