The objective of this study was to benchmark the prevalence of lameness, hock and knee injuries, and neck and back injuries among high-performance, freestall-housed dairy herds in Wisconsin. A random selection of 66 herds with 200 or more cows was derived from herds that clustered with high performance in year 2011 Dairy Herd Improvement records for milk production, udder health, reproduction, and other health parameters. Herds were surveyed to collect information about management, facilities, and well-being. Well-being measures were obtained through direct observation of the high-producing mature cow group, surveying 9,690 cows in total. Total herd size averaged (mean ± standard deviation) 851±717 cows, ranging 203 to 2,966 cows, with an energy-corrected milk production of 40.1±4.4kg/cow per day. Prevalence of clinical lameness (5-point scale, locomotion score ≥3) and severe lameness (locomotion score ≥4) averaged 13.2±7.3 and 2.5±2.7%, respectively. The prevalence of all hock and knee injuries, including hair loss, swelling, and ulceration, was similar at 50.3±28.3 and 53.0±24.0%, respectively. Severe (swelling and ulceration) hock and knee injury prevalence were 12.2±15.3 and 6.2±5.5%, respectively. The prevalence of all neck injuries (including hair loss, swelling and ulceration) was 8.6±16.3%; whereas the prevalence of swollen or abraded necks was low, averaging 2.0±4.1%. Back injuries (proportion of cows with missing or abraded spinous processes, hooks, or pins) followed a similar trend with a low mean prevalence of 3.6±3.4%. Overall, physical well-being characteristics of this selection of high-producing, freestall-housed dairy herds provide evidence that lameness and injury are not inevitable consequences of the confinement housing of large numbers of dairy cattle. In particular, lameness prevalence rivals that of lower-production grazing systems. However, hock and other injury risk remains a concern that can be addressed through a choice in stall surface type. Use of deep, loose bedding yielded significant advantages over a mat or mattress type surface in terms of lameness, hock and knee injury, and proportion of cows with dirty udders (distinct demarcated to confluent plaques of manure). The performance benchmarks achieved by these herds may be used to set standards by which similarly managed herds may be judged using welfare audit tools.

Principal component analysis (PCA) is a variable reduction method used on over-parameterized data sets with a vast number of variables and a limited number of observations, such as Dairy Herd Improvement (DHI) data, to select subsets of variables that describe the largest amount of variance. Cluster analysis (CA) segregates objects, in this case dairy herds, into groups based upon similarity in multiple characteristics simultaneously. This project aimed to apply PCA to discover the subset of most meaningful DHI variables and to discover groupings of dairy herds with similar performance characteristics. Year 2011 DHI data was obtained for 557 Upper Midwest herds with test-day mean ≥200 cows (assumed mostly freestall housed), that remained on test for the entire year. The PCA reduced an initial list of 22 variables to 16. The average distance method of CA grouped farms based on best goodness of fit determined by the minimum cophenetic distance. Six groupings provided the optimal fitting number of clusters. Descriptive statistics for the 16 variables were computed per group. On observations of means, groups 1, 2, and 6 demonstrated the best performances in most variables, including energy-corrected milk, linear somatic cell score (log of somatic cell count), dry period intramammary infection cure rate, new intramammary infection risk, risk of subclinical intramammary infection at first test, age at first calving, days in milk, and Transition Cow Index. Groups 3, 4, and 5 demonstrated the worst mean performances in most the PCA-selected variables, including DIM, age at first calving, risk of subclinical intramammary infection at first test, and dry period intramammary infection cure rate. Groups 4 and 5 also had the worst mean herd performances in energy-corrected milk, Transition Cow Index, linear somatic cell score, and new intramammary infection risk. Further investigation will be conducted to reveal patterns of management associated with herd categorization. The PCA and CA should be used when describing the multivariate performance of dairy herds and whenever working with over-parameterized data sets, such as DHI databases.

A survey of management practices was conducted to investigate potential associations with groupings of herds formed by cluster analysis (CA) of Dairy Herd Improvement (DHI) data of 557 Upper Midwest herds of 200 cows or greater. Differences in herd management practices were identified between the groups, despite underlying similarities; for example, freestall housing and milking in a parlor. Group 6 comprised larger herds with a high proportion of primiparous cows and most frequently utilized practices promoting increased production [e.g., 84.4% used recombinant bovine somatotropin (rbST)], decreased lameness (e.g., 96.9% used routine hoof trimming for cows), and improved efficiency in reproduction [e.g., 93.8% synchronized the first breeding in cows (SYNCH)] and labor (e.g., mean ± SD, 67 ± 19 cows per 50-h per week full-time equivalent worker). Group 1 had the best mean DHI performances and followed most closely group 6 for the rate of adoption of intensive management practices while tending to outperform group 6 despite a generally smaller mean herd size (e.g., 42.3 ± 3.6 kg vs. 39.9 ± 3.6 kg of energy-corrected milk production; 608 ± 352 cows vs. 1,716 ± 1,405 cows). Group 2 were smaller herds with relatively high levels of performance that used less intensive management (e.g., 100% milked twice daily) and less technology (33.3 vs. 73.0% of group 1 used rbST). Group 4 were smaller but poorer-performing herds with low turnover and least frequently used intensive management practices (e.g., 39.1% SYNCH; 30.4% allowed mature, high-producing cows access to pasture). Group 5 used modern technologies and practices associated with improved production, yet had the least desirable mean DHI performance of all 6 groups. This group had the lowest proportion of deep loose-bedded stalls (only 52.2% used sand bedding) and the highest proportion (34.8%) of herds not using routine hoof trimming. The survey of group 3 herds did not reveal strong trends in management. The differences identified between herd groupings confirm significant variation in management practices linked to variation in overall herd performance measured by DHI variables. This approach provides an opportunity for consultants and outreach educators to better tailor efforts toward a certain type of dairy management philosophy, rather than taking a blanket approach to applying recommendations to farms simply because of their larger herd size.

Principal component analysis (PCA) is used on datasets with vast numbers of numeric variables and limited observations (e.g., dairy herd improvement [DHI] data) for unbiased selection of uncorrelated variables that describe the largest amount of variance. Cluster analysis (CA) divides objects of interest (e.g., dairy herds) into groups on the basis of similarity in multiple characteristics simultaneously. The aims of this project were to develop a novel method for discovering important DHI variables by use of PCA and then grouping herds by those variables via CA, and to survey herds to determine herd management characteristics of each group.