Supplementary materials.

Perinatal mortality (PM) is a common issue on dairy farms, leading to calf losses and increased farming costs. The current knowledge about PM in dairy cattle is, however, limited and previous studies lack comparability. The topic has also primarily been studied in Holstein-Friesian cows and closely related breeds, while other dairy breeds have been largely ignored. Different data collection techniques, definitions of PM, studied variables and statistical approaches further limit the comparability and interpretation of previous studies. This article aims to investigate the factors contributing to PM in two underexplored breeds, Simmental (SIM) and Brown Swiss (BS), while comparing them to German Holstein on German farms, and to employ various modelling techniques to enhance comparability to other studies, and to determine if different statistical methods yield consistent results. A total of 133,942 calving records from 131,657 cows on 721 German farms were analyzed. Amongst these, the proportion of PM (defined as stillbirth or death up to 48 hours of age) was 6.1%. Univariable and multivariable mixed-effects logistic regressions, random forest and multimodel inference via brute-force model selection approaches were used to evaluate risk factors on the individual animal level. Although the balanced random forest did not incorporate the random effect, it yielded results similar to those of the mixed-effect model. The brute-force approach surpassed the widely adopted backwards variable selection method and represented a combination of strengths: it accounted for the random effect similar to mixed-effects regression and generated a variable importance plot similar to random forest. The difficulty of calving, breed and parity of the cow were found to be the most important factors, followed by farm size and season. Additionally, four significant interactions amongst predictors were identified: breed—calving ease, breed—season, parity—season and calving ease—farm size. The combination of factors, such as secondiparous SIM breed on small farms and experiencing easy calving in summer, showed the lowest probability of PM. Conversely, primiparous GH cows on large farms with difficult calving in winter exhibited the highest probability of PM. In order to reduce PM, appropriate management of dystocia, optimal heifer management and a wider use of SIM in dairy production are possible ways forward. It is also important that future studies are conducted to identify farm-specific contributors to higher PM on large farms.</div

Only significant contrasts extracted from the four most influential interactions by mixed-effects logistic regression for potential risk factors for perinatal mortality of 133,942 single calves born between December 2015 and August 2019 on 721 German dairy farms.

Odds ratios (ORs) are represented by the ratio of odds of one category in the numerator (e.g., easy) to the odds of another category in the denominator (e.g., medium). The specific category in the numerator serves as the reference category for that particular OR.</p

Results of univariable and multivariable mixed-effects logistic regressions on the cow-level and pairwise comparison of potential risk factors for perinatal mortality of 133,942 single calves born between December 2015 and August 2019 on 721 German dairy farms.

Odds ratios (ORs) are represented by the ratio of odds of one category in the numerator (e.g., SIM) to the odds of another category in the denominator (e.g., GH). The specific category in the numerator serves as the reference category for this particular OR.</p

Fig 2 -

The importance of all pairwise interactions for the prediction of perinatal mortality measured by: the separate interactions (a) and all interactions in the same model (b) via Analysis of Deviance Table (Type III Wald Chi-Square tests) with global P values, by the random forest algorithm via mean decrease accuracy (c) and by the brute-force variable selection tool (d), which measures the importance value for a particular predictor by the sum of the weights for the models in which the predictor appears during the variable selection procedure. “Vint” stands for the importance of interactions and “Vimp” stands for the importance of variables (predictors). Significance codes: ‘***’ < 0.001, ‘**’ < 0.01, ‘*’ < 0.05, ‘.’ < 0.1‘. ‘Calving ease’ is abbreviated as ‘calving’ to conserve plot space.</p

Description of variables included in the statistical analyses as potential risk factors for perinatal mortality of 133,942 single calves born between December 2015 and August 2019 on 721 German dairy farms.

Description of variables included in the statistical analyses as potential risk factors for perinatal mortality of 133,942 single calves born between December 2015 and August 2019 on 721 German dairy farms.</p

Fig 3 -

Probabilities of perinatal mortality predicted by the four most important interactions which remained after several interaction selection procedures in the final mixed-effects logistic regression: breed—calving ease (a), parity—season (b), breed—season (c) and calving ease—farm size (d). Breed categories: GH–German-Holstein, SIM–Simmental, BS–Brown-Swiss, others–multiple breeds with low counts. Parity: 1 –primiparous, 2 –secondiparous, 3+—multiparous. Calving ease: easy—no assistance, medium—one helper and light use of mechanical tools, or difficult—several helpers, mechanical pulling tools, or surgery. ‘Calving ease’ is abbreviated as ‘calving’ to conserve plot space.</p

Fig 1 -

The importance of variables for the prediction of perinatal mortality measured by: the univariable (a) and multivariable (b) Analysis of Deviance Table (Type III Wald Chi-Square tests) with global P values, by the random forest algorithm via mean decrease accuracy (c) and by the brute-force model selection tool (d) which measures the importance value for a particular predictor by the sum of the weights for the models in which the predictor appears during the variable selection procedure. Significance codes: ‘***’ < 0.001, ‘**’ < 0.01, ‘*’ < 0.05, ‘.’ < 0.1‘. ‘Calving ease’ is abbreviated as ‘calving’ to conserve plot space. All analyses were based on 133,942 parturitions from 721 farms.</p

Only significant contrasts extracted from the four most influential interactions by mixed-effects logistic regression for potential risk factors for perinatal mortality of 133,942 single calves born between December 2015 and August 2019 on 721 German dairy farms.

Odds ratios (ORs) are represented by the ratio of odds of one category in the numerator (e.g., GH) to the odds of another category in the denominator (e.g., others). The specific category in the numerator serves as the reference category for that particular OR.</p

Table_1_Benchmarking calf health: Assessment tools for dairy herd health consultancy based on reference values from 730 German dairies with respect to seasonal, farm type, and herd size effects.pdf

Good calf health is crucial for a successfully operating farm business and animal welfare on dairy farms. To evaluate calf health on farms and to identify potential problem areas, benchmarking tools can be used by farmers, herd managers, veterinarians, and other advisory persons in the field. However, for calves, benchmarking tools are not yet widely established in practice. This study provides hands-on application for on-farm benchmarking of calf health. Reference values were generated from a large dataset of the “PraeRi” study, including 730 dairy farms with a total of 13,658 examined preweaned dairy calves. At herd level, omphalitis (O, median 15.9%) was the most common disorder, followed by diarrhea (D, 15.4%) and respiratory disease (RD, 2.9%). Abnormal weight bearing (AWB) was rarely detected (median, 0.0%). Calves with symptoms of more than one disorder at the same time (multimorbidity, M) were observed with a prevalence of 2.3%. The enrolled farms varied in herd size, farm operating systems, and management practices and thus represented a wide diversity in dairy farming, enabling a comparison with similar managed farms in Germany and beyond. To ensure comparability of the data in practice, the reference values were calculated for the whole data set, clustered according to farm size (1–40 dairy cows (n = 130), 41–60 dairy cows (n = 99), 61–120 dairy cows (n = 180), 121–240 dairy cows (n = 119) and farms with more than 240 dairy cows (n = 138), farm operating systems (conventional (n = 666), organic (n = 64)) and month of the year of the farm visit. There was a slight tendency for smaller farms to have a lower prevalence of disorders. A statistically significant herd-size effect was detected for RD (p = 0.008) and D (p ® (Microsoft®) based calf health calculator were developed as tools for on-farm benchmarking (https://doi.org/10.6084/m9.figshare.c.6172753). In addition, this study provides a detailed description of the colostrum, feeding and housing management of preweaned calves in German dairy farms of different herd sizes and farm type (e.g., conventional and organic).</p