Lameness represents a major welfare and health problem for the dairy industry across all farming systems. Visual mobility scoring, although very useful, is labour-intensive and physically demanding, especially in large dairies, often leading to inconsistencies and inadequate uptake of the practice. Technological and computational advancements of artificial intelligence (AI) have led to the development of numerous automated solutions for livestock monitoring. The objective of this study was to review the automated systems using AI algorithms for lameness detection developed to-date. These systems rely on gait analysis using accelerometers, weighing platforms, acoustic analysis, radar sensors and computer vision technology. The lameness features of interest, the AI techniques used to process the data as well as the ground truth of lameness selected in each case are described. Measures of accuracy regarding correct classification of cows as lame or non-lame varied with most systems being able to classify cows with adequate reliability. Most studies used visual mobility scoring as the ground truth for comparison with only a few studies using the presence of specific foot pathologies. Given the capabilities of AI, and the benefits of early treatment of lameness, longitudinal studies to identify gait abnormalities using automated scores related to the early developmental stages of different foot pathologies are required. Farm-specific optimal thresholds for early intervention should then be identified to ameliorate cow health and welfare but also minimise unnecessary inspections.

Grape extracts of the Greek species Vitis vinifera possess potent antioxidant properties in vitro. The freeze/thaw process and the preparation of semen during assisted reproductive techniques can adversely affect the functional integrity of spermatozoa. The objective was to assess the effect of three different concentrations (1 μg ml−1, 2 μg ml−1 and 5 μg ml−1) of a polyphenol-rich grape pomace extract on motility, viability, acrosomal and lipid peroxidation status of thawed bovine spermatozoa after 2 and 4 hrs of incubation. The results indicate that the percentage of “Rapid” spermatozoa remained significantly increased (p <0.05) in the presence of 5 μg ml−1 of the extract, compared to the control after 2 hrs of incubation. Additionally, the incubation of spermatozoa with 2 μg ml−1 and 5 μg ml−1 of the extract for 2 hrs resulted in a significantly better maintenance of viable spermatozoa with intact acrosome (p <0.05). The other parameters did not show statistically significant changes. Moreover, the presence of 2 μg ml−1 and 5 μg ml−1 of the extract kept the levels of Malondialdehyde (MDA) production in significantly lower level, compared to the other groups, after 2 hrs and 4 hrs of incubation (p <0.05). Particularly, a dose-dependent effect was noticed after 2 hrs of incubation. Our results suggest that the grape pomace extract exerts a powerful antioxidant role, by suppressing lipid peroxidation, and provides protection in terms of motility and acrosomal integrity, which are correlated with in vivo fertility. The optimal extract concentration is 5 μg ml−1.

Monitoring body condition score (BCS) is a useful management tool to estimate the energy reserves of an individual cow or a group of cows. The aim of this study was to develop and evaluate the performance of a fully-automated 2D imaging system using a machine learning algorithm to generate real-time BCS for dairy cows. Two separate data sets were used for training and testing. The training data set included 34,150 manual BCS (MAN_BCS) assigned by 5 experienced veterinarians during 35 visits in 7 dairy farms. Ordinal regression methods and deep learning architecture were used when developing the algorithm. Subsequently, the testing data set was used to evaluate the developed BCS prediction algorithm on 4 of the participating farms. An experienced human assessor (HA1) visited these farms and performed 8 whole-milking-herd BCS sessions. Each farm was visited twice allowing for 30 d (±2 d) to pass between visits. The MAN_BCS assigned by HA1 were considered the ground truth data. At the end of the validation study, MAN_BCS were merged with the stored automated BCS (AI_BCS) resulting in a testing data set of 9,657 single BCS. A total of 3,817 cows in the testing data set were scored twice 30 d (±2 d) apart and the change in their BCS (ΔBCS) was calculated. A subset of cows in one farm were scored twice on consecutive days to evaluate the within-observer agreement of both the human assessor and the system. The manual BCS of 2 more assessors (HA2 and HA3) were used to assess the inter-observer agreement between humans. Finally, we also collected ultrasound measurements of backfat thickness (BFT) from 111 randomly selected cows with available MAN_BCS and AI_BCS. Using the testing data set, intra- and inter-observer agreement for single BCS and ΔBCS were estimated by calculating the simple percentage agreement (PA) at 3 error levels, and the weighted kappa (κ_w) for the exact agreement. A Bland-Altman plot was constructed to visualize the systematic and proportional bias. The association between MAN_BCS and AI_BCS and the BFT was assessed with Passing-Bablock regressions. The system had an almost perfect repeatability with a κ_w of 0.99. The agreement between MAN_BCS and AI_BCS was substantial, with an overall κ_w = 0.69. The overall PA at the exact, ± 0.25 and ± 0.50 -unit of BCS error range between MAN_BCS and AI_BCS was 44.4, 84.6 and 94.8%, respectively, greater than the PA obtained between HA1 vs. HA3. The Bland-Altman plot revealed a minimal systematic bias of -0.09 with a proportional bias at the extreme scores. Furthermore, despite the low κ_w of 0.20, the overall PA at the exact and ± 0.25 -unit of BCS error range between MAN_BCS and AI_BCS regarding the ΔBCS was 45.7 and 88.2%, respectively. A strong linear relationship was observed between BFT and AI_BCS (ρ = 0.75), although weaker than that between BFT and MAN_BCS (ρ = 0.91). The system was able to predict single BCS and ΔBCS with satisfactory accuracy, comparable to that obtained between trained human scorers.

Most cows experience a period of nutrient deficit during the periparturient period. Body condition scoring (BCS) is widely used on farms to assess body nutrient reserves and mobilization. The aims of this study were to: (i) determine the association of BCS with ultrasound measurements of backfat (BFT) and longissimus dorsi muscle thickness (LDT) during the periparturient period of Holstein cows from different herds, accounting for potential sources of variation, such as herd, parity and period relative to calving and (ii) establish reference intervals (RIs) for BFT and LDT per BCS estimate. Two-hundred and fifty-two cows from six commercial farms were used. Body condition scores, BFT and LDT were assessed at seven time-points during the periparturient period. Assessments of BCS estimates as predictors of BFT and LDT and the contribution of BFT and LDT to BCS estimates were performed with the use of linear mixed models. Reference intervals for BFT and LDT per BCS estimate were established with the Reference Value Advisor. One unit of BCS change was associated with 8.2 mm of BFT and 10.9 mm of LDT pre- and postpartum. Range of BFT and LDT in established RIs per BCS was wide with significant overlap. Both subcutaneous fat and, to a lesser degree, skeletal muscle reserves contribute to BCS estimation. Repeated BCS estimations credibly predict energy balance status in periparturient dairy cows. The metabolic state of muscle tissue should be assessed by repeated ultrasound measurements.

The aims of this study were to assess (1) the variation of protein metabolism biomarkers and factors affecting them during the transition period, (2) the association of each biomarker with skeletal muscle reserves and their changes, and (3) the association of these biomarkers with postpartum health, colostrum quality, reproduction, and milk production. For this purpose, 238 multiparous Holstein cows from 6 herds were used in a prospective cohort study. Plasma concentrations of 3-methylhistidine (3-MH) and 1-methylhistidine (1-MH) and serum concentrations of total protein (TP), albumin (ALB), urea nitrogen (BUN), and creatinine (SCR) were determined for each cow at -21, -7, 7, 21, and 28 d relative to calving. Clinical diseases were recorded during the first 28 d postcalving, and presence of subclinical ketosis (scKET) was investigated at 7 and 21 d. Colostrum quality was estimated by Brix refractometry. Reproduction data by 150 d in milk (DIM) and milk production records were also available. Linear mixed models including the fixed effects of time point, herd, parity, body condition score (-21 d), duration of dry period and postparturient diseases were fitted to assess the variation in each biomarker's concentration. The association between the biomarkers' concentration during the prepartum period with the odds for each postparturient disease and for a combined trait (CD_1-28), defined as the presence of at least one clinical condition during the first 28 d after calving, were assessed with separate binary logistic models for time points -21 d and -7 d. The relationship of each biomarker's concentration with longissimus dorsi thickness (LDT) and the changes in LDT (ΔLDT) was assessed with pairwise correlations. Separate general linear models were used to assess the association of each biomarker with colostrum Brix values and milk production traits. Finally, the associated hazard for first artificial insemination (AI) and for pregnancy by 150 DIM (PREG_150DIM) was assessed with Cox proportional hazard models, whereas odds for pregnancy to the first AI (PREG_1stAI) were assessed with binary logistic models. The level of 3-MH was affected mainly by herd, time points, and their interaction. Higher 3-MH was associated with increased odds for metritis and CD_1-28, increased hazard for PREG_150 DIM and with increased milk production. 1-Methylhistidine was affected mainly by herd, scKET and occurrence of displaced abomasum. Higher 1-MH was associated with better colostrum quality, increased odds for scKET, increased hazard for first AI by 150 DIM and with decreased milk production. Both 3-MH and 1-MH were weakly to moderately negatively correlated with LDT and moderately to strongly negatively correlated with ΔLDT at the corresponding time periods. Additionally, higher TP was associated with increased odds for metritis and CD_1-28 and increased milk production, while higher ALB was associated with increased odds for scKET and increased milk production. Moreover, higher BUN was associated with decreased odds for scKET, increased odds for PREG_1stAI and increased milk production. Higher SCR was associated with decreased odds for retained fetal membranes, metritis, and CD_1-28. Periparturient protein metabolism is significantly associated with postpartum health, colostrum quality, reproduction, and milk production; mechanisms involved require further investigation.