Comparison of visual assessment and heart girth tape measurement for estimating the weight of cattle in clinical practice

Evaluation of a Forefront Weight Scale from an Automated Calf Milk Feeder for Holstein and Crossbred Dairy and Dairy-Beef Calves

Recording of body weights of dairy calves may assist producers in monitoring the health status of calves and making feed-related management decisions. Traditional methods of weighing calves can be time-consuming and labor-intensive. The objective of this study was to evaluate a forefront weight scale on stalls attached to an automated calf milk feeder system to determine the accuracy for measuring the calf body weights of Holstein and crossbred dairy calves. The study was conducted at the University of Minnesota West Central Research and Outreach Center, Morris, MN, dairy. Eighty-eight Holstein and crossbred calves were fed either 8 L/d or ad libitum milk from September 2019 to February 2020 and March 2020 to July 2020. Crossbred calves were Grazecross crossbreds composted of Jersey, Viking Red, and Normande, ProCross crossbreds composed of Holstein, Montbéliarde, and Viking Red, Limousin-sired crossbred dairy × beef bull calves, and Limousin-sired crossbred dairy × beef heifer calves. The Limousin-sired calves were from Holstein or crossbred dams. Calves were introduced to the Holm & Laue Calf Expert and Hygiene Station automatic calf feeder (Holm & Laue GmbH & Co. KG, Westerrönfeld, Germany) at 5 days of age and were weaned at 56 d. Forefront weight scales were attached to four hygiene station feeding stalls on the automated calf milk feeder, and calves were required to place both front hooves on the scale to access milk. The calf weights from the automated milk feeder were compared to the gold standard calibrated electronic scale (Avery Weigh-Tronix LLC, Fairmont, MN scale). Calves were weighed once per week using the electronic scale, and those weights were compared to the most recent weight recorded by the forefront scale. The associations of the weights from the automated milk feeder scale and the electronic scale were determined with Pearson correlations (PROC CORR of SAS) and Bland-Altman plots (PROC SGPLOT of SAS). Furthermore, PROC GLM of SAS was used to regress the electronic scale body weight on the forefront weight scale body weight for each calf. A total of 600 weight observations were used for statistical analysis. The Pearson correlation of the electronic scale compared to the forefront weight scale was high (0.991), and the concordance correlation coefficient was high (0.987). Correlations for individual calves ranged from 0.852 to 0.999 and were classified as high. Correlations of the electronic scale and forefront weight scale for breed groups ranged from 0.990 to 0.994. The slope of the regression line was 0.9153, and the 95% confidence interval was between 0.906 and 0.925. A mean bias of 0.529 kg was observed from the Bland-Altman plots. The results suggest that there is potential for the forefront weight scale to be used on automated calf milk feeders to accurately record the body weights of calves and support management decision-making, identify sick calves, and help producers determine the proper dosage of medications for calves based on body weight.

Models to predict live weight from heart girth in crossbred beef heifers

The objective of this study was to develop and evaluate linear, quadratic, and exponential mathematical models to predict live weight (LW) from heart girth (HG) in crossbred heifers raised in tropical humid conditions in Mexico. Live weight (363.32 ± 150.88 kg) and HG (166.83 ± 24.88 cm) were measured in 400 heifers aged between 3 and 24 months. Linear and non-linear regression was used to construct the prediction models. The goodness of fit of the models was evaluated using the Akaike information criterion (AIC), the Bayesian information criterion (BIC), coefficient of determination (R²), mean squared error (MSE), and root MSE (RMSE). In addition, the developed models were evaluated through internal and external cross-validation (k-folds) using independent data. The ability of the fitted models to predict the observed values was evaluated based on the root mean square error of prediction (RMSEP), R², and mean absolute error (MAE). The correlation coefficient between LW and HG was r = 0.98 (P < 0.001). The quadratic model showed the lowest values of MAE (736.57), RMSEP (27.13), AIC (3783.95), and BIC (3799.91). Additionally, this model exhibited better goodness-of-fit values regarding external and internal validation criteria (higher R² and lower RMSEP and MAE), thus having better predictive performance. The RMSE represented about 8% of the observed LW. Heart girth is highly correlated (r = 0.98) with LW. The quadratic model showed a high predictive capacity for crossbred heifers kept in tropical conditions.

Accuracy of heart girth tapes in the estimation of weights of pre-weaned calves

BACKGROUND

Heart girth tapes (HGTs) are often used as an alternative to weight scales for calves. This study investigated the accuracy of HGT in estimating bodyweight and daily liveweight gain (DWLG) of pre-weaned calves, and the impact of inter-observer variation.

METHOD

In Study 1, 119 calves were weighed using HGT and electronic scales on multiple occasions. Mixed-effects models for both bodyweight and DLWG were used to determine the accuracy of HGT compared to the electronic scales. Simulation data were used to further analyse the accuracy of DLWG estimation including for factors such as the effect of group size on group DLWG estimates.In Study 2, 10 observers weighed 20 pre-weaned calves, using HGT and electronic scales. Mixed-effect model was used to investigate the impact of different observers on the accuracy of HGT on measuring bodyweights.

RESULTS

Mixed-effects model results suggest HGT provides a relatively accurate estimation of weight (MAE: 2.66 kg) and relatively inaccurate estimation of DLWG (MAE 0.10 kg/d). Simulated data identified associations between time between weight dates and error in DLWG estimation, with MAE of individual DLWG estimation decreasing from 0.43 kg/d when 14 days apart to 0.08 kg/d when 70 days apart. Increased calf numbers reduced error rates of group DLWG estimation, with <0.05 kg/d error achieved in >90% of simulations when 12 calves were weighed 70 days apart.

CONCLUSIONS

HGTs are relatively accurate at estimating individual bodyweights but are unreliable methods for measuring DLWG in individual calves, particularly weighed within a short-time period. Estimates at group level however are relatively accurate, providing there is a suitable period of time between weigh dates and an appropriate number of calves per group.

A survey of calf management practices and farmer perceptions of calf housing in UK dairy herds

Adoption of optimal management techniques for rearing dairy calves has significant effects on their health, welfare, and productivity. Despite much published literature on best practice, calf morbidity and mortality rates remain high. This survey aimed to establish current calf management practices in the United Kingdom, along with farmer perceptions surrounding different housing types. A survey containing 48 questions was distributed online to UK farmers via social media, online forums, and a convenience sample of veterinary practices and was completed by 216 participants. A descriptive analysis with frequency distributions was calculated, with chi-squared tests, linear regression and multinomial regression performed to assess associations between variables. There was a low level of regular veterinary involvement in day-to-day health decision making for calves (3/216, 1.4%), highlighting the need for appropriate staff training and standard operating procedures to ensure prudent antimicrobial usage. Restricted calf milk feeding remains highly prevalent in the United Kingdom, with most calves fed milk replacer (114/216, 52.8%), twice daily (189/216, 87.5%), initially given milk at 4 L/d (66/216, 30.6%) or 6 L/d (47/216, 21.8%). There was, however, a small number of farmers initially feeding only 2 to 3 L/d (28/216, 13.0%). Euthanasia of bull calves (5/216, 2.3%) and feeding antimicrobial waste milk to calves (8/216, 3.7%) both occurred on some farms. With regard to housing, use of individual calf pens has reduced from around 60% in 2010 to 38.4% in this study (83/216), with this reduction being partly driven by the policy of UK milk buyers. Farmer perceptions indicated that individual housing was thought to help to improve calf health and feed monitoring of calves, suggesting that successful use of group housing requires a higher level of stockmanship. The majority of farmers did not provide fresh bedding to calves on a daily basis (141/216, 65.3%), and relatively few disinfected both the calf housing (38.0%) and ground (47.7%) between calves, suggesting that hygiene practices may require additional attention in farm management protocols.

Technical note: Estimating body weight of dairy calves with a partial-weight scale attached to an automated milk feeder

Monitoring early growth in neonatal dairy calves provides insight into the effectiveness of a producer's nutrition program and helps monitor for sickness. The objective of this study was to validate whether a partial-weight scale attached to an automated milk feeder (Combi; DeLaval, Tumba, Sweden) could precisely and accurately estimate calf weights compared with twice-weekly weights of a calibrated electronic scale (gold standard; Brecknell PS1000; Avery Weigh-Tronix LLC, Fairmont, MN). Holstein heifers (n = 20) were enrolled in this study from birth until 84 d of age. All heifers were eligible to receive 10 L of milk replacer/d for 56 d and were subsequently weaned using a step-down strategy across 14 d. The automated milk feeder had a radio frequency identification panel placed directly above the scale such that calves had to stand with both front hooves on the scale platform to access milk from the nipple. An algorithm was created to summarize and clean the scale measurements of any non-biologically relevant data points. The relationship between the daily weight average from the partial scale and the electronic scale was analyzed using Pearson correlations, linear regressions, and Bland-Altman plots. Data from the partial weight scale were considered precise if the correlation coefficient and coefficient of determination were very high (>0.90) and the mean bias from the Bland-Altman plots included zero with the 95% interval of agreement. The partial-weight scale was considered accurate if the slope from the linear regression did not differ significantly from 1. We found a very high Pearson correlation coefficient (0.99) and coefficient of determination (0.99). Bland-Altman plots were deemed acceptable and nonbiased; the Bland-Altman difference (feeder weight - scale weight) was 0.45 ± 2.33 kg (mean ± standard deviation). The slope of the linear regression was not different from 1 (slope = 1.01; 95% confidence interval = 1.00-1.03), suggesting that the partial-weight scale was accurate. In summary, the partial-weight scale was validated, with a high precision and accurate estimate of weight in calves compared with the gold standard electronic scale. The partial-weight scale may be a useful tool for producers to estimate calf weight if a data cleaning algorithm is used.

Data-driven approach to using individual cattle weights to estimate mean adult dairy cattle weight

BACKGROUND

Knowledge of accurate weights of cattle is crucial for effective dosing of individual animals and for reporting antimicrobial usage. For the first time, we provide an evidence-based estimate of the average weight of UK dairy cattle to better inform farmers, veterinarians and the scientific community.

METHODS

Data were collected for 2747 lactating dairy cattle from 20 farms in the UK. Data were used to calculate a mean weight for lactating dairy cattle by breed and a UK-specific mean weight. Trends in weight by lactation number and production level were also explored.

RESULTS

Mean weight for adult dairy cattle in this study was 617 kg (sd=85.6 kg). Mean weight varied across breeds, with a range of 466 kg (sd=56.0 kg, Jersey) to 636 kg (sd=84.1, Holsteins). When scaled to UK breed proportions, the estimated UK-specific mean weight was 620 kg.

CONCLUSION

This study is the first to calculate a mean weight of adult dairy cattle in the UK based on on-farm data. Overall mean weight was higher than that most often proposed in the literature (600 kg). Evidence-informed weights are crucial as the UK works to better monitor and report metrics to measure antimicrobial use and are useful to farmers and veterinarians to inform dosing decisions.

Estimation of Live Weight by Body Measurements in the Miranda Donkey Breed

The use of the measurement of heart girth (HG), in locations where a scale is not available, with the application of weight estimation formulas or special weight tapes, is well established as a practical and accurate way to estimate the live weight (LW). Although several studies were performed to correlate donkey body measurements and LW, none of these was done in the large frame European donkey breeds. When using smaller frame breeds formulas, the tendency was to underestimate the live weight of larger frame breeds. The sample used in this study consisted of 65 Miranda breed donkeys, with ages ranging from 4 days to 15.4 years (6.6 ± 4.4 years). The studied population mean LW was 280.8 ± 106.1 kg (32.5-475.5 kg); the mean height was 127.4 ± 14.7 cm (69-157.5 cm); the mean body length (BL) was 131.4 ± 25.3 cm (59-184 cm); and the mean HG was 143.8 ± 23.1 cm (71-175 cm). All the correlations between LW and the body measurements taken were statistically significant (P < .001), but the degree of accuracy was higher in the HG (r = 0.937) than in the BL (r = 0.915) or height (r = 0.894). The formula that best estimates the LW was performed by Quadratic model and was based on the HG measurement: LW = 98.138-3.0386 × HG + 0.0293 × HG² (LW in kilogram; HG in centimeter). The formula found can be used to create a weighing tape, adapted to large frame European donkey breeds, to be used to estimate weight and better adapt medication dosages and carried load for each animal.