Our aims were to (1) determine how interdigital skin temperature (IST), measured using infrared thermography, was associated with different stages of digital dermatitis (DD) lesions and (2) develop and validate models that can use IST measurements to identify cows with an active DD lesion. Between March 2019 and March 2020, infrared thermographic images of hind feet were taken from 2,334 Holstein cows across 4 farms. We recorded the maximum temperature reading from infrared thermographic images of the interdigital skin between the heel bulbs on the hind feet. Pregnant animals were enrolled approximately 1 to 2 mo precalving, reassessed 1 wk after calving, and again at approximately 50 to 100 d postpartum. At these time points, IST and the clinical stage of DD (M-stage scoring system: M1-M4.1) were recorded in addition to other data such as the ambient environmental temperature, height, body condition score, parity, and the presence of other foot lesions. A mixed effect linear regression model with IST as the dependent variable was fitted. Interdigital skin temperature was associated with DD lesions; compared to healthy feet, IST was highest in feet with M2 lesions, followed by M1 and M4.1 lesions. Subsequently, the capacity of IST measurements to detect the presence or absence of an active DD lesion (M1, M2, or M4.1) was explored by fitting logistic regression models, which were tested using 10-fold validation. A mixed effect logistic regression model with the presence of active DD as the dependent variable was fitted first. The average area under the curve for this model was 0.80 when its ability to detect presence of active DD was tested on 10% of the data that were not used for the model's training; an average sensitivity of 0.77 and an average specificity of 0.67 was achieved. This model was then restricted so that only explanatory variables that could be practically recorded in a nonresearch, external setting were included. Validation of this model demonstrated an average area under the curve of 0.78, a sensitivity of 0.88, and a specificity of 0.66 for 1 of the time points (precalving). Lower sensitivity and specificity were achieved for the other 2 time points. Our study adds further evidence to the relationship between DD and foot skin temperature using a large data set with multiple measurements per animal. Additionally, we highlight the potential for infrared thermography to be used for routine on-farm diagnosis of active DD lesions.

The digital cushion is linked to the development of claw horn lesions (CHL) in dairy cattle. The objectives of this study were to (1) estimate genetic parameters for digital cushion thickness (DCT), (2) estimate the genetic correlation between DCT and CHL, and (3) identify candidate genes associated with DCT. A cohort of 2,352 Holstein dairy cows were prospectively enrolled on 4 farms and assessed at 4 time points: before calving, immediately after calving, in early lactation, and in late lactation. At each time point, CHL was recorded by veterinary surgeons, and ultrasonographic images of the digital cushion were stored and retrospectively measured at 2 anatomical locations. Animals were genotyped and pedigree details extracted from the national database. Genetic parameters were estimated following a single-step approach implemented in AIREMLF90. Four traits were analyzed: the 2 DCT measurements, sole lesions (sole hemorrhage and sole ulcers), and white line lesions. All traits were analyzed with univariate linear mixed models; bivariate models were fit to estimate the genetic correlation between traits within and between time points. Single-marker and window-based genome-wide association analyses of DCT traits were conducted at each time point; candidate genes were mapped near (<0.2 Mb) or within the genomic markers or windows with the largest effects. Heritability estimates of DCT ranged from 0.14 to 0.44 depending on the location of DCT measurement and assessment time point. The genetic correlation between DCT and sole lesions was generally negative, notably between DCT immediately after calving and sole lesions in early or late lactation, and between DCT in early or late lactation and sole lesion severity in early or late lactation. Digital cushion thickness was not genetically correlated with white line lesions. A polygenic background to DCT was found; genes associated with inflammation, fat metabolism, and bone development were mapped near or within the top markers and windows. The moderate heritability of DCT provides an opportunity to use selective breeding to change DCT in a population. The negative genetic correlation between DCT and sole lesions at different stages of production lends support to current hypotheses of sole lesion pathogenesis. Highlighted candidate genes provide information regarding the complex genetic background of DCT in Holstein cows, but further studies are needed to explore and corroborate these findings.

Foot characteristics have been linked to the development of sole lesions (sole hemorrhage and sole ulcers) and white line lesions, also known as claw horn disruption lesions (CHDL). The objective of this study was to examine the association of claw anatomy and sole temperature with the development of CHDL. A cohort of 2,352 cows was prospectively enrolled from 4 UK farms and assessed at 3 time points: before calving (T1precalving), immediately after calving (T2-calving), and in early lactation. At each time point body condition score was recorded, a thermography image of each foot was taken for sole temperature measurement, the presence of CHDL was assessed by veterinary surgeons, and an ultrasound image was taken to retrospectively measure the digital cushion and sole horn thickness. Additionally, at the postcalving time point, foot angle and heel depth were recorded. Four multivariable logistic regression models were fit to separately examine the relationship of precalving and postcalving explanatory variables with the development of either white line lesions or sole lesions. Explanatory variables tested included digital cushion thickness, sole horn thickness, sole temperature, foot angle, and heel depth. Farm, parity, body condition score, and presence of lesion at the time of measurement were also included in the models. A thicker digital cushion shortly after calving was associated with decreased odds of cows developing sole lesions during early lactation (odds ratio [OR]: 0.74, 95% confidence interval [CI]: 0.65-0.84). No association was found between digital cushion thickness and development of white line lesions. Sole temperature after calving was associated with increased odds of the development of sole lesions (OR: 1.03, 95% CI: 1.02-1.05), and sole temperature before and after calving was associated with the development of white line lesions (T1-precalving OR: 1.04, 95% CI: 1.01-1.07; T2-calving OR: 0.96, 95% CI: 0.93-0.99). Neither foot angle nor heel depth was associated with the development of either lesion type. However, an increased sole horn thickness after calving reduced the odds of cows developing sole lesions during early lactation (OR: 0.88, 95% CI: 0.83-0.93), highlighting the importance of maintaining adequate sole horn when foot trimming. Before calving, animals with a lesion at the time of measurement and a thicker sole were more likely to develop a sole lesion (OR: 1.23, 95% CI: 1.09-1.40), compared with those without a sole lesion. The results presented here suggest that white line and sole lesions may have differing etiopathogenesis. Results also confirm the association between the thickness of the digital cushion and the development of sole lesions, highlight the association between sole horn thickness and sole lesions, and challenge the potential importance of foot angle and heel depth in the development of CHDL.

One suggested approach to improve the reproductive performance of dairy herds is through the targeted management of subgroups of biologically similar animals, such as those with similar probabilities of becoming pregnant, termed pregnancy risk. We aimed to use readily available farm data to develop predictive models of pregnancy risk in dairy cows. Data from a convenience sample of 108 dairy herds in the UK were collated and each herd was randomly allocated, at a ratio of 80:20, to either training or testing data sets. Following data cleaning, there were a total of 78 herds in the training data set and 20 herds in the testing data set. Data were further split by parity into nulliparous, primiparous, and multiparous subsets. An XGBoost model was trained to predict the insemination outcome in each parity subset, with predictors from farm records of breeding, calving and milk recording. Training data comprised 74,511 inseminations in 45,909 nulliparous animals, 86,420 inseminations in 39,439 primiparous animals, and 158,294 inseminations in 32,520 multiparous animals. The final models were evaluated by predicting with the testing data, comprising 31,740 inseminations in 19,647 nulliparous animals, 38,588 inseminations in 16,215 primiparous animals, and 65,049 inseminations in 12,439 multiparous animals. Model discrimination was assessed by calculating the area under receiver operating characteristic curves (AUC); model calibration was assessed by plotting calibration curves and compared across test herds by calculating the expected calibration error (ECE) in each test herd. The models were unable to discriminate between insemination outcomes with high accuracy, with an AUC of 0.63, 0.59 and 0.62 in the nulliparous, primiparous and multiparous subsets, respectively. The models were generally well-calibrated, meaning the model-predicted pregnancy risks were similar to the observed pregnancy risks. The mean (SD) ECE in the test herds was 0.038 (0.023), 0.028 (0.012) and 0.020 (0.008) in the nulliparous, primiparous and multiparous subsets respectively. The predictive models reported here could theoretically be used to identify subgroups of animals with similar pregnancy risk to facilitate targeted reproductive management; or provide information about cows' relative pregnancy risk compared with the herd average, which may support on-farm decision-making. Further research is needed to evaluate the generalizability of these predictive models and understand the source of variation in ECE between herds; however, this study demonstrates that it is possible to accurately predict pregnancy risk in dairy cows using readily available farm data.

Digital dermatitis (DD) is a polybacterial disease endemic to most UK dairy farms. It poses a major financial and welfare threat and is characterized by high incidence and recurrence rates. We aimed to investigate the association between the UK EBV for resistance to digital dermatitis, the digital dermatitis index (DDI), and the frequency of DD, heel horn erosion (HHE), and interdigital hyperplasia (IH) in a population of Holstein dairy cows. We enrolled and genotyped 2,352 cows from 4 farms in a prospective cohort study. Foot lesion records were recorded by veterinary surgeons for each animal at 4 time points during a production cycle, starting at approximately 2 mo before calving and ending in late lactation. Importantly, these records were not used in the calculation of the DDI. Lesion records were matched to the animal's own DDI (n = 2,101) and their sire's DDI (n = 1,812). Digital dermatitis index values in our study population ranged from -1.41 to +1.2 and were transformed to represent distance from the mean expressed in SD. The relationship between the DDI and the presence of DD was investigated using a logistic regression model, with farm, parity, and a farm-parity interaction fitted as covariates. A multivariable logistic regression model was fitted to evaluate the relationship between HHE and DDI with farm fitted as a covariate. Finally, a univariable logistic regression model with DDI as explanatory variable was used to investigate the relationship between IH and DDI. The odds ratio of an animal being affected by DD was 0.69 for 1 SD increase in the animal's DDI (95% CI = 0.63-0.76). The odds of HHE and IH were 0.69 (95% CI = 0.62-0.76) and 0.58 (95% CI = 0.49-0.68) respectively for 1 SD increase in DDI. The adjusted probability of DD was 32% (95% CI = 27-36%) for cows with mean DDI value of 0, while it was 24% (95% CI = 20-29%) in cows with a DDI value of +1. Sire DDI breeding values were standardized in the same way and then binned into terciles creating an ordinal variable representing bulls of high, medium, and low genetic merit for DD resistance. The daughters of low genetic merit bulls were at 2.05 (95% CI = 1.60-2.64), 1.96 (95% CI = 1.53-2.50), and 2.85 (95% CI = 1.64-5.16) times greater odds of being affected by DD, HHE, and IH, respectively, compared with the daughters of high genetic merit bulls. The results of this study highlight the potential of digital dermatitis genetic indexes to aid herd management of DD, and suggest that breeding for resistance to DD, alongside environmental and management control practices, could reduce the prevalence of the disease.