Vaccines are the most effective and sustainable intervention to control ticks and tick-borne diseases (TBD). Using a personalized vaccine design based on regional tick genotypes, a Rhipicephalus appendiculatus Subolesin protective antigen was used in a field trial evaluating tick vaccine efficacy, effectiveness, and safety in cattle infested with multiple tick species in different Ugandan agro-ecological zones. Vaccination with SUB was safe with a protective capacity against anemia and infection, and reduced the number of infested cattle, tick fitness (feeding and reproduction) with vaccine effectiveness against multiple tick species between 93.2% at 167-196 days post-vaccination (dpv) and 61.4% at 251–327 dpv. Total integrated vaccine efficacy/effectiveness was estimated as 98.8%. The Subolesin-based vaccine is protective against multiple cattle tick infestations under field conditions in Uganda. These results support registration and commercialization of the vaccine to reduce tick populations and associated risks for human and animal TBD and chemical acaracides in Uganda.

This study determined the nature of the relationship between liveweight and body condition score (BCS) and assessed the influence of the stage of the annual cycle and pregnancy-rank on the relationship between liveweight and BCS in Romney ewes. Data were collected from the same ewes at different ages (8–18, 19–30, 31–42, 43–54, 55–66 and ≥67 months), stages of the annual cycle (pre-breeding, at pregnancy diagnosis, pre-lambing and weaning) and pregnancy-rank (non-pregnant, single or twin). Linear regression was determined as being sufficient to accurately describe the relationship between liveweight and BCS. Across all data, a one-unit change in BCS was associated with 6.2 ± 0.05 kg liveweight, however, this differed by stage of the cycle, pregnancy-rank and ewe age (p <0.05). The average liveweight per unit change in body condition score increased with the age of the ewe and was greatest at weaning and lowest pre-lambing. Among pregnancy-ranks, the average liveweight per unit change was also greater during pregnancy diagnosis than pre-lambing and was greatest among single and lowest in non-pregnant ewes. The results support the hypothesis that the relationship between liveweight and BCS is affected by the interaction between stage of the annual cycle, pregnancy-rank and ewe age.

Body condition score (BCS) in sheep (Ovis aries) is a widely used subjective measure of the degree of soft tissue coverage. Body condition score and liveweight are statistically related in ewes; therefore, it was hypothesized that BCS could be accurately predicted from liveweight using machine learning models. Individual ewe liveweight and body condition score data at each stage of the annual cycle (pre-breeding, pregnancy diagnosis, pre-lambing and weaning) at 43 to 54 months of age were used. Nine machine learning (ML) algorithms (ordinal logistic regression, multinomial regression, linear discriminant analysis, classification and regression tree, random forest, k-nearest neighbors, support vector machine, neural networks and gradient boosting decision trees) were applied to predict BCS from a ewe’s current and previous liveweight record. A three class BCS (1.0–2.0, 2.5–3.5, >3.5) scale was used due to high-class imbalance in the five-scale BCS data. The results showed that using ML to predict ewe BCS at 43 to 54 months of age from current and previous liveweight could be achieved with high accuracy (>85%) across all stages of the annual cycle. The gradient boosting decision tree algorithm (XGB) was the most efficient for BCS prediction regardless of season. All models had balanced specificity and sensitivity. The findings suggest that there is potential for predicting ewe BCS from liveweight using classification machine learning algorithms.

The body condition score (BCS) in sheep (Ovis aries) is a widely used subjective measure of body condition. Body condition score and liveweight have been reported to be statistically and often linearly related in ewes. Therefore, it was hypothesized that current BCS could be accurately and indirectly predicted using a ewe’s lifetime liveweight, liveweight change, and previous BCS record. Ewes born between 2011 and 2012 (n = 11,798) were followed from 8 months to approximately 67 months of age in New Zealand. Individual ewe data was collected on liveweight and body condition scores at each stage of the annual cycle (pre-breeding, pregnancy diagnosis, pre-lambing, and weaning). Linear regression models were fitted to predict BCS at a given ewe age and stage of the annual cycle using a ewe’s lifetime liveweight records (liveweight alone models). Further, linear models were then fitted using previous BCS and changes in liveweight, in addition to the lifetime liveweight records (combined models). Using the combined models improved (p < 0.01) the R² value by 39.8% (from 0.32 to 0.45) and lowered the average prediction error by 10% to 12% (from 0.29 to 0.26 body condition scores). However, a significant portion of the variability in BCS remained unaccounted for (39% to 89%) even in the combined models. The procedures found in this study, therefore, may overestimate or underestimate measures by 0.23 to 0.32 BCS, which could substantially change the status of the ewe, leading to incorrect management decisions. However, the findings do still suggest that there is potential for predicting ewe BCS from liveweight using linear regression if the key variables affecting the relationship between BCS and liveweight are accounted for.

The relationship between ewe body condition score (BCS) and liveweight (LW) has been exploited previously to predict the former from LW, LW-change, and previous BCS records. It was hypothesized that if fleece weight and conceptus-free liveweight and LW-change, and in addition, height at withers were used, the accuracy of current approaches to predicting BCS would be enhanced. Ewes born in 2017 (n = 429) were followed from 8 mo to approximately 42 mo of age in New Zealand. Individual ewe data were collected on LW and BCS at different stages of the annual production cycle (i.e., prebreeding, at pregnancy diagnosis, prelambing, and weaning). Additionally, individual lambing dates, ewe fleece weight, and height at withers data were collected. Linear regression models were fitted to predict current BCS at each ewe age and stage of the annual production cycle using two LW-based models, namely, unadjusted for conceptus weight and fleece weight (LW alone1) and adjusted (LW alone2) models. Furthermore, another two models based on a combination of LW, LW-change, previous BCS, and height at withers (combined models), namely, unadjusted (combined1) and adjusted for conceptus and fleece weight (combined2), were fitted. Combined models gave more accurate (with lower root mean square error: RMSE) BCS predictions than models based on LW records alone. However, applying adjusted models did not improve BCS prediction accuracy (or reduce RMSE) or improve model goodness of fit (R²) (P > 0.05). Furthermore, in all models, both LW-alone and combined models, a great proportion of variability in BCS, could not be accounted for (0.25 ≥ R² ≥ 0.83) and there was substantial prediction error (0.33 BCS ≥ RMSE ≥ 0.49 BCS) across age groups and stages of the annual production cycle and over time (years). Therefore, using additional ewe data which allowed for the correction of LW for fleece and conceptus weight and using height at withers as an additional predictor did not improve model accuracy. In fact, the findings suggest that adjusting LW data for conceptus and fleece weight offer no additional value to the BCS prediction models based on LW. Therefore, additional research to identify alternative methodologies to account for individual animal variability is still needed.

Sheep liveweight and liveweight change are vital tools both for commercial and research farm management. However, they can be unreliable when collection procedures are not standardized or when there are varying time delays between sheep removal from grazing and weighing. This study had two stages with different objectives: (1) A liveweight loss study to determine the effect of herbage availability (Low and High) on the rate of liveweight loss of ewes at different pregnancy stages (approximately 100 days of pregnancy: P100 and 130 days: P130) and ranks (single and twin); (2) A follow-up liveweight loss study to develop and validate correction equations for delayed liveweights by applying them to data sets collected under commercial conditions. Results from each stage showed that the rate of liveweight loss varied by herbage availability and stage of pregnancy (p < 0.05) but not pregnancy-rank (p > 0.05). Further, the rate of liveweight loss differed by farm (p < 0.05). Applying liveweight correction equations increased the accuracy of without delay liveweight estimates in P100 ewes by 56% and 45% for single-bearing and twin-bearing ewes, respectively, when offered the Low-level diet. In ewes offered the High-level diet, accuracies of without delay liveweight estimates were increased by 53% and 67% for single-bearing and twin-bearing ewes, respectively. Among P130 ewes, accuracy was increased by 43% and 37% for single-bearing and twin-bearing ewes, respectively, when offered the Low herbage level and by 60% and 50% for single-bearing and twin-bearing ewes, respectively, when offered the High herbage level. In conclusion, a short-term delay of up to 8 hours prior to weighing, which is commonly associated with practical handling operations, significantly reduced the liveweight recorded for individual sheep. Using delayed liveweights on commercial farms and in research can have consequences for management practices and research results; thus, liveweight data should be collected without delay. However, when this is not feasible, delayed ewe liveweights should be corrected and, in the absence of locally devised correction equations, the ones generated in the current study could be applied on farms with similar management conditions and herbage type.