Data used in the present study included 1,095,980 first-lactation test-day records for protein yield of 154,880 Holstein cows housed on 196 large-scale dairy farms in Germany. Data were recorded between 2002 and 2009 and merged with meteorological data from public weather stations. The maximum distance between each farm and its corresponding weather station was 50 km. Hourly temperature-humidity indexes (THI) were calculated using the mean of hourly measurements of dry bulb temperature and relative humidity. On the phenotypic scale, an increase in THI was generally associated with a decrease in daily protein yield. For genetic analyses, a random regression model was applied using time-dependent (d in milk, DIM) and THI-dependent covariates. Additive genetic and permanent environmental effects were fitted with this random regression model and Legendre polynomials of order 3 for DIM and THI. In addition, the fixed curve was modeled with Legendre polynomials of order 3. Heterogeneous residuals were fitted by dividing DIM into 5 classes, and by dividing THI into 4 classes, resulting in 20 different classes. Additive genetic variances for daily protein yield decreased with increasing degrees of heat stress and were lowest at the beginning of lactation and at extreme THI. Due to higher additive genetic variances, slightly higher permanent environment variances, and similar residual variances, heritabilities were highest for low THI in combination with DIM at the end of lactation. Genetic correlations among individual values for THI were generally >0.90. These trends from the complex random regression model were verified by applying relatively simple bivariate animal models for protein yield measured in 2 THI environments; that is, defining a THI value of 60 as a threshold. These high correlations indicate the absence of any substantial genotype × environment interaction for protein yield. However, heritabilities and additive genetic variances from the random regression model tended to be slightly higher in the THI range corresponding to cows' comfort zone. Selecting such superior environments for progeny testing can contribute to an accurate genetic differentiation among selection candidates.

The aim of this study was to assess the impact of heat stress in dairy cows on test-day records for production traits and somatic cell score (SCS) in the state of Lower Saxony, Germany. Three different production systems were defined: A production system characterized by intensive crop production (=indoor housing), a pasture based production system, and a maritime region. Heat stress was assessed by two temperature-humidity indices (THI) modelled as random regression coefficients in an analysis of variance: One (THI_Bo) was defined as an average of hourly THI, calculated from hourly recorded temperatures and humidities, the other (THI_Ra) was based on daily maximal temperature and daily minimal humidity. In all production systems, THI_Bo=60 and THI_Ra=70 were identified as general thresholds denoting a substantial decline in test-day milk yield. For daily fat and protein percentage, no universally valid thresholds were identified. In contrast for SCS, especially in the maritime region, heat stress as well as cold stress thresholds were found. Regression analysis was used to study the change in test day milk yield in response to THI of those THI ranges with an obvious decline in milk yield. Regression coefficients were −0.08 kg/THI_Bo and −0.16 kg/THI_Ra for the crop production system, −0.17 kg/THI_Bo and −0.23 kg/THI_Ra for the pasture based system, and −0.26 kg/THI_Bo and −0.47kg/THI_Ra for the maritime region. Based on statistical information criteria, identified thresholds for THI_Bo should be given preference over THI_Ra when applying genetic studies on heat stress in German Holstein cows.

Rideability, i.e. the ease and comfort with which a horse can be ridden, is considered to be one of the most important traits in riding horses. However, at present rideability is evaluated rather subjectively in breeding horse performance tests. The aim of the present study was to evaluate the role horse behaviour as well as degree and quality of rein tension might play in judges’ evaluation of horses’ rideability. Mares (n = 33) and stallions (n = 13) from two different mare- and one stallion-testing station were observed twice during their performance test dressage training. During these rides, rein tension was measured continuously, and frequency of behaviour patterns such as head-tossing, tail swishing, and snorting was recorded. Rein tension parameters showed reasonable repeatabilities within horse-rider pairs (e.g. mean rein tension: r² = 0.61±0.11; variance of rein tension: r² = 0.52±0.14). Regression analysis revealed that a larger proportion of variance in rideability scores could be explained by maximum (17%), mean (16%) and variance (15%) of rein tension compared to horses’ or riders’ behavioural parameters (tail-swishing: 5% and rider’s use of hands: 5%, respectively). According to mixed model analysis, rideability scores dropped (all P<0.05) with increasing mean, maximum and variability in rein tension (e.g. −0.37±0.14 scores per additional 10 Newton in mean tension). However, mean rein tension differed between testing stations (P<0.0001) ranging between 9.1±1.6 N in one station and 21.7±1.3 N in another station. These results indicate that quantity and consistency of rein tension is either directly or indirectly an important factor for judges to derive rideability scores. Given the importance of rein tension parameters to both rider comfort and horse welfare, potentially, measurements of rein tension along with behaviour observations assessing the quality of rein contact (e.g. distinguishing a light contact from attempts to evade contact) might be used to make the assessment of rideability more impartial.

Test-day records for protein yield, protein percent, fat percent and somatic cell score combined with diagnoses for health traits from 19,870 Holstein cows kept in 9 large-scale contract herds in the region of Thuringia, Germany, were used to infer genetic parameters. From an electronic database system for recording diagnoses, 15 health disorders with highest incidences were extracted and grouped into the following 5 disease categories: claw disorders, mastitis, female fertility, metabolism, and ectoparasites. In a bayesian approach, threshold methodology was applied for binary distributed health disorders and linear models were used for gaussian test-day observations. Variances and variance ratios for health disorders were from univariate and covariance components among health disorders and between health disorders, and test-day production traits were from bivariate repeatability models. Incidences of health disorders increased with increasing parity and were substantially higher at the beginning of lactation. Only incidences for ectoparasites slightly increased with increasing stage of lactation. Heritabilities ranged from 0.00 for ectoparasites to 0.22 for interdigital hyperplasia. Heritabilities of remaining health disorders were in a narrow range between 0.04 (corpus luteum persistent) and 0.09 (dermatitis digitalis). Clustering diseases into categories did not result in higher heritabilities. The variance ratio of the permanent environmental component was higher than the heritability for the same trait, pointing to the conclusion that non-genetic factors influence repeated occurrence of health problems during lactation. Repeatabilities were relatively high with values up to 0.49 for interdigital hyperplasia. Genetic correlations among selected health disorders were low and close to zero, disproving the assumption that a cow being susceptible for a specific disease is also susceptible for other types of health disorders. Antagonistic genetic relationships between test-day protein yield and health disorders were found for ovarian cysts (0.57) and clinical mastitis (0.29). Remaining genetic correlations between diseases and production traits were close to zero. The genetic correlation between clinical mastitis and somatic cell score was 0.69. This study revealed reliable genetic parameters for health disorders and underlined the possibility of precise health data recording by farmers from contract herds that can be used for genetic evaluation of health traits.