Usefulness of the total and differential somatic cell count based udder health group concept for evaluating herd management practices and udder health in dairy herds.

Subclinical mastitis and associated economic losses are a steady challenge in the dairy industry. The combination of the well-established somatic cell count (SCC) parameter and the new differential SCC (DSCC) opens up the possibility to categorise cows into four different udder health groups (UHG) based on results from a single milk recording/dairy herd improvement (DHI) test: UHG A: healthy/normal, ≤ 200,000 cells/mL and DSCC ≤ 65 %; B: suspicious, ≤ 200,000 cells/mL and DSCC > 65 %; C: (subclinical) mastitis, > 200,000 cells/mL and DSCC > 65 %; D: chronic/persistent mastitis, > 200,000 cells/mL and DSCC ≤ 65 %. The objectives of this study were to investigate 1) herd management practises among herds in different UHG categories and 2) herd performance parameters depending on the proportion of cows in UHG A. A total number of 41 herds in Styria, Austria, and Thuringia, Germany, were visited and interviewed for the first part of the study. The herds were categorised into 3 UHG categories depending on the proportion of cows in UHG A: I = >65 %; II = 55-65 %; and III = <55 %. Those with good udder health and best herd performance (+9 % milk yields, +11 % longevity, -35 % antibiotic treatments) applied distinct preventive measures, in particular excellent cubicle management and early antibiotic treatment (P < 0.05 each). However, preventive measures were applied to a lower extent in other herds. Herds were categorised differently using the UHG concept compared to SCC alone as the UHG-based categorisation allowed to clearer distinguish herds with medium-good from those with good udder health. A total number of 129,812 regular milk recording/DHI test day results of 890 Austrian and 183 German herds was used for the second part of the study. Results revealed a trend of increasing daily production as proportions of cows in UHG A increase. In conclusion, the UHG concept allowed clearer distinction of herds with good, medium-good, and poor udder health and could be used to promote practises leading to better animal health, less antibiotic treatments, and higher milk quality.

Associations between different udder health groups defined based on a combination of total and differential somatic cell count and the future udder health status of dairy cows.

Mastitis, in particular in its subclinical form, which may spread unnoticeable within a herd, continues to be a major challenge in the dairy industry. Somatic cell count (SCC) is a broadly used proxy for subclinical mastitis. The recently introduced Differential SCC (DSCC) representing the combined proportion of polymorphonuclear neutrophils and lymphocytes as a percentage of total SCC, can be used in combination with SCC to categorise cows into four different udder health groups (UHG) depending on actual test day results: UHG A: healthy/normal, ≤200,000 cells/mL and DSCC ≤65 %; B: suspicious, ≤200,000 cells/mL and DSCC >65 %; C: (subclinical) mastitis, >200,000 cells/mL and DSCC >65 %; D: chronic/persistent mastitis, >200,000 cells/mL and DSCC ≤65 %. The objective of our study was to investigate to what extent the UHG aid in determining different statuses of cows: I) leaving herd before next test day, II) having >200,000 cells/mL at the next test day, and III) having ≤200,000 cells/mL at the next 3 test days. Multivariable logistic regression analysis was used to evaluate these statuses based on routinely generated dairy herd improvement (DHI) data from Austria, China, Estonia, Germany, and Spain. Cows in groups C (odds ratio (OR): 2.13, 95 % confidence interval (CI): 1.95-2.34) and, particularly, D (OR: 3.91, 95 % CI: 3.31-4.62) were significantly more likely to leave herds compared to cows in group A. Late-lactating cows indicated the highest likelihood (OR: 16.03, 95 % CI: 14.44-17.81) to leave herds in our analysis. Interestingly, we found that cows in UHG B had significantly higher odds (OR: 2.77, 95 % CI: 2.58-2.98) to have >200,000 cells/mL at the next test day compared to cows in group A. As anticipated, cows in UHG B (OR: 0.40, 95 % CI: 0.38-0.42), C (OR: 0.08, 95 % CI: 0.07-0.09), and D (OR: 0.16, 95 % CI: 0.14-0.19) each were significantly less likely to have ≤200,000 cells/mL at the next 3 test days compared to cows in group A. Above described results are an example from Germany, but the same trends could be seen across all countries considered in our study. In conclusion, our findings illustrate that the UHG concept reveals additional valuable information about udder health and culling based a single test day over working with SCC only. Actual decisions in day-to-day farm management that could be taken were not investigated here and need to be further explored.

Establishment of a Sandwich-ELISA for simultaneous quantification of bovine pregnancy-associated glycoprotein in serum and milk.

Bovine pregnancy-associated glycoproteins (boPAG) are expressed by trophoblast cells in the bovine placenta. The multigene family of boPAG belongs to the group of aspartic proteases. The accumulation and circulation in maternal blood and milk has made boPAG very useful and important for pregnancy diagnosis in cattle. The goal of the present study was to develop and validate a new Sandwich-ELISA which allows the detection of boPAG in maternal serum and whole milk. Therefore, 984 serum and 928 milk samples were collected monthly from 231 Holstein Friesian cows (Bos Taurus) from one week after insemination (p.i.) until six weeks postpartum. The ELISA is able to identify a cow as being pregnant at day 30 p.i. in serum and at day 40 p.i in milk with threshold values of 1.0 ng/ml in serum and 0.0165 ng/ml in milk. The postpartum half-life of boPAG was estimated to be 6.4 days in serum and 7.1 days in milk. The boPAG profile established during pregnancy in serum and milk showed a typical pattern. The amount of boPAG found in milk was 1.5 % of the amount of boPAG present in serum. In conclusion, a Sandwich-ELISA has been developed to quantify boPAG in serum and in whole milk simultaneously with the same test procedure. This is time saving for farmers and more efficient for laboratories.

Investigation of dairy cow performance in different udder health groups defined based on a combination of somatic cell count and differential somatic cell count.

Mastitis is still the costliest disease in milk production. In particular, its subclinical form, which may spread unnoticeably within a herd, is a major challenge. Somatic cell count (SCC) is broadly used as an indicator for mastitis and thus the basis for udder health management programmes, e.g. through dairy herd improvement (DHI) testing. Since recently, differential somatic cell count (DSCC, representing the combined proportion of polymorphonuclear neutrophils and lymphocytes as a percentage of total SCC) is available in addition. Our study was aimed to investigate dairy cow performance in four newly defined udder health groups (UHG) based on SCC and DSCC results from DHI testing. In total, 961,835 test-day results generated in Austria, China, Estonia, Germany, and Spain between January 2019 and March 2020 were available for data analyses. Cows were categorised into four UHG depending on test day SCC and DSCC results (UHG A: healthy/normal, ≤200,000 cells/mL and ≤65 %; B: suspicious, ≤200,000 cells/mL and >65 %; C: mastitis, >200,000 cells/mL and >65 %; D: chronic/persistent mastitis, >200,000 cells/mL and ≤65 %). Linear mixed effect models were used to compare the performance of cows between the UHG based on the parameters milk weight, energy-corrected milk, fat, protein, lactose, and estimated milk value. Highest performance was found for cows in UHG A and ranged between 21.4 (Austria) and 38.3 kg per cow and day (Spain). Interestingly, cows in group B were significantly less productive (0.9-2.4% less daily milk production) compared to those in group A. Cows in groups C (6.0-9.8% less daily production compared to group A) and D were, as expected, even less productive with a particularly significant drop for cows in group D (17.5-38.5% less daily production). These trends could be observed in all countries involved in this study. Proportions of cows in the four different UHG differed between countries, changed slightly within countries depending on season, differed depending on parity and days in milk, and were seen to vary hugely between herds. In conclusion, this study demonstrates changes in performance of dairy cows depending on their udder health status as defined based on the combination of SCC and DSCC. In particular cows in UHG B and D are of interest as they cannot be identified working with SCC only. Nevertheless, the actual udder health management measures that could be taken based on the new UHG still require further investigation.

[Comparison of Discriminant Analysis and Decision Trees for the Detection of Subclinical Keratoconus]. / Vergleich von Diskriminanzanalyse und Entscheidungsbäumen zur Erkennung des frühen Keratokonus.

BACKGROUND: Iatrogenic keratectasia is one of the most dreaded complications of refractive surgery. In most cases, keratectasia develops after refractive surgery of eyes suffering from subclinical stages of keratoconus with few or no signs. Unfortunately, there has been no reliable procedure for the early detection of keratoconus. In this study, we used binary decision trees (recursive partitioning) to assess their suitability for discrimination between normal eyes and eyes with subclinical keratoconus. PATIENTS AND METHODS: The method of decision tree analysis was compared with discriminant analysis which has shown good results in previous studies. Input data were 32 eyes of 32 patients with newly diagnosed keratoconus in the contralateral eye and preoperative data of 10 eyes of 5 patients with keratectasia after laser in-situ keratomileusis (LASIK). The control group was made up of 245 normal eyes after LASIK and 12-month follow-up without any signs of iatrogenic keratectasia. RESULTS: Decision trees gave better accuracy and specificity than did discriminant analysis. The sensitivity of decision trees was lower than the sensitivity of discriminant analysis. CONCLUSION: On the basis of the patient population of this study, decision trees did not prove to be superior to linear discriminant analysis for the detection of subclinical keratoconus.