Relationships between live weight, body condition, dimensional and ultrasound scanning measurements and carcass attributes in adult Angora goats

Predicting carcass tissue composition in Blackbelly sheep using ultrasound measurements and machine learning methods

This study aimed to predict Blackbelly sheep carcass tissue composition using ultrasound measurements and machine learning models. The models evaluated were decision trees, random forests, support vector machines, and multi-layer perceptrons and were used to predict the total carcass bone (TCB), total carcass fat (TCF), and total carcass muscle (TCM). The best model for predicting the three parameters, TCB, TCF, and TCM was random forests, with mean squared error (MSE) of 0.31, 0.33, and 0.53; mean absolute error (MAE) of 0.26, 0.29, and 0.53; and the coefficient of determination (R2) of 0.67, 0.69, and 0.76, respectively. The results showed that machine learning methods from in vivo ultrasound measurements can be used as determinants of carcass tissue composition, resulting in reliable results.

Predictability of carcass traits in live Tan sheep by real-time ultrasound technology with least-squares support vector machines

This study aimed to investigate the performance of least-squares support vector machines to predict carcass characteristics in Tan sheep using noninvasive in vivo measurements. A total of 80 six-month-old Tan sheep (37 rams and 43 ewes) were examined. Back fat thickness and eye muscle area between the 12th and 13th ribs were measured using real-time ultrasound in live Tan sheep. All carcasses were dissected to hind leg, longissimus dorsi muscle, lean meat, fat, and bone to determine carcass composition. Multiple linear regression (MLR), partial least squares regression (PLSR), and least-squares support vector machines (LSSVM) were applied to correlate the live Tan sheep characteristics with carcass composition. The results showed that the LSSVM model had a better efficacy for estimating carcass weight, longissimus dorsi muscle weight, lean meat weight, fat weight, lean meat, and fat percentage in live lambs (R = 0.94, RMSE = 0.62; R = 0.73, RMSE = 0.02; R = 0.86, RMSE = 0.47; R = 0.78, RMSE = 0.63; R = 0.73, RMSE = 0.02; R = 0.65, RMSE = 0.03, respectively). LSSVM algorithm was a potential alternative to the conventional MLR method. The results demonstrated that LSSVM model might have great potential to be applied to the evaluation of sheep with superior carcass traits by combining with real-time ultrasound technology.

Correlations between body measures with live weight in young male goats

Data analysis in goat production, such as those related to body and scrotal measurements, indicate the productive and reproductive animal development. The current study aimed to evaluate the correlations between thoracic perimeter (TP), body length (BL), body compacity (BC), body volume (BV), and scrotal circumference (SC) with body weight (BW) in young male goats of Saanen and Boer breeds. It was used 38 Saanen and 24 Boer male goats, with age average of 7.2 ± 2.0 months. Thoracic perimeter and body length measurements were obtained using a tape measure (cm) and the live weight (kg) a mechanic scale. The variables body compacity (BC) and body volume (BV) were calculated using the equations:  and . Boer breed showed live weight and body compacity higher than Saanen breed (p < 0.05). Regarding correlations between biometric measurements and body weight, we did not find any statistical differences between the breeds (p > 0.05). The scrotal circumference presented the lowest association with body weight (p < 0.05). However, all biometric measurements showed highly significant correlations with live body (p < 0.01). In conclusion, thoracic perimeter was the main measure of body weight predictor, considering efficiency and practical aspects.

Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition

Simple Summary

The welfare of farm animals is a growing concern in the EU and across the world. In milk production, there is a strong need to assess the welfare of dairy cows. One of the most sound assessment initiatives has been practiced using protocols developed by the Welfare Quality project. These protocols mainly support the assessment of cow welfare with animal-based indicators. However, evaluating these indicators is time-consuming and expensive, so using precision livestock farming (PLF) solutions is a way forward and is becoming a reality in the dairy industry. This review presents advances in PLF solutions, particularly in the last five years, and for assessing the animal-based indicators of lameness, mastitis, and body condition in dairy cattle farming.

Abstract

Specific animal-based indicators that can be used to predict animal welfare have been the core of protocols for assessing the welfare of farm animals, such as those produced by the Welfare Quality project. At the same time, the contribution of technological tools for the accurate and real-time assessment of farm animal welfare is also evident. The solutions based on technological tools fit into the precision livestock farming (PLF) concept, which has improved productivity, economic sustainability, and animal welfare in dairy farms. PLF has been adopted recently; nevertheless, the need for technological support on farms is getting more and more attention and has translated into significant scientific contributions in various fields of the dairy industry, but with an emphasis on the health and welfare of the cows. This review aims to present the recent advances of PLF in dairy cow welfare, particularly in the assessment of lameness, mastitis, and body condition, which are among the most relevant animal-based indications for the welfare of cows. Finally, a discussion is presented on the possibility of integrating the information obtained by PLF into a welfare assessment framework.

Pre- and Post-Slaughter Methodologies to Estimate Body Fat Reserves in Lactating Saanen Goats

Simple Summary

In this study, we present the results of a trial on which we compared pre- and post-slaughter methodologies to estimate body fat reserves in dairy goats. Our results evidenced that fat thickness measured with ultrasound in the perirenal region was the best pre-slaughter measurement for estimating fat reserves in lactating Saanen goats, whereas empty body weight and hot carcass weight were the best post-slaughter predictors for estimating fat reserves. Body condition score could be a useful tool, but it seems that it needs to be re-evaluated to predict adequately fat depots in lactating Saanen goats.

Abstract

This work aimed to compare pre- and post-slaughter methodologies to estimate body fat reserves in dairy goats. Twenty-six lactating Saanen goats ranging from 43.6 to 69.4 kg of body weight (BW) and from 1.84 to 2.96 of body condition score (BCS; 0–5 range) were used. Fifteen pre-slaughter and four post-slaughter measurement values were used to estimate the weight of fat in the omental (OM), mesenteric (MES), perirenal (PR), organ (ORG), carcass (CARC), and non-carcass components (NC) and total (TOT, calculated as the sum of CARC and NC) depots in goats. The pre-slaughter measurements were withers height; rump height; rump length; pelvis width; chest depth; shoulder width; heart girth; body length; sternum height; BW; BCS assessed in the lumbar (BCSl) and sternal (BCSs) regions; and fat thickness measured by ultrasound in the lumbar (FTUSl), sternal (FTUSs), and perirenal (FTUSpr) regions. The post-slaughter measurements were hot carcass weight (HCW), empty body weight (EBW), and fat thickness measured by digital caliper in the lumbar (FTDCl) and sternal (FTDCs) regions. Linear and multiple regressions were fit to data collected. BW, BCS (from lumbar and sternal regions), all somatic measurements, and fat thickness measured by ultrasound in the lumbar and sternal regions were not adequate to estimate the weight of total fat in lactating Saanen goats (R² ≤ 0.55). The best pre-slaughter and post-slaughter estimators of OM, MES, PR, ORG, NC, and TOT fat were FTUSpr and EBW, respectively. Among pre- and post-slaughter measurements, BCSl (R² = 0.63) and HCW (R² = 0.82) provided the most accurate predictions of CARC fat, respectively. Multiple regression using the pre-slaughter variables FTUSpr, BW, and BCSl yielded estimates of TOT fat with an R² = 0.92 (RSD = 1.14 kg). On the other hand, TOT fat predicted using the post-slaughter variables HCW and FTDCs had an R² = 0.83 (RSD = 1.41 kg). These results confirm that fat reserves can be predicted in lactating Saanen goats with high precision using multiple regression equations combining in vivo measurements.

Predicting the Digestive Tract Development and Growth Performance of Goat Kids Using Sigmoidal Models

The transition from monogastric to rumination stage is crucial in ruminants' growth to avoid stressors-weaning and neonatal mortalities. Poor growth of the digestive tract could adversely affect the performance of the animal. Modeling informative growth curves is of great importance for a better understanding of the effective development pattern, in order to optimize feeding management system, and to achieve more production efficiency. However, little is known about the digestive tract growth curves. For this reason, one big goat farm of Laiwu black breed was chosen as a basis of this study. Forty-eight kids belonging to eight-time points (1, 7, 14, 28, 42, 56, 70, and 84 d; 6 kids for each) were selected and slaughtered. The body weight, body size indices, rumen pH, and stomach parts were determined and fitted to the polynomial and sigmoidal models. In terms of goodness of fit criteria, the Gompertz model was the best model for body weight, body oblique length, tube, and rumen weight. Moreover, the Logistic model was the best model for carcass weight, body height, and chest circumference. In addition, the Quadratic model showed the best fit for dressing percentage, omasum weight, abomasum weight, and rumen volume. Moreover, the cubic model best fitted the ruminal pH and reticulum percentage. The Weibull model was the best model for the reticulum weight and omasum percentage, while the MMF model was the best model describing the growth of chest depth, rumen percentage, and abomasum percentage. The model parameters, R squared, inflection points, area under curve varied among the different dependent variables. The Pearson correlation showed that the digestive tract development was more correlated with age than body weight, but the other variables were more correlated with body weight than age. The study demonstrated the use of empirical sigmoidal and polynomial models to predict growth rates of the digestive tract at relevant age efficiently.

Meat production traits of Angora goat 1: fattening, slaughter, and carcass characteristics of intact and castrated kids

Fattening performance, slaughter, and carcass traits of intact and castrated Angora goat kids slaughtered at different slaughter weights were examined. A total of 96 (48 intact, I-kids, and 48 castrated, C-kids) single Angora kids were fattened, and 48 of them (24 I-kids and 24 C-kids) were slaughtered at slaughter weights (SW) of 20, 25, and 30 kg. Castration negatively affected fattening performance, and the kids showed rising daily weight gain with increasing SW. While dressing percentages were not affected by castration, it caused a tendency for a decrease in offal. However, with the increase in SW, dressing percentages increased, and offal decreased. Castration increased non-carcass fat percentages and back fat depth. The proportions of individual cuts did not generally vary because of castration but changed with SW; foreleg percentages decreased (P < 0.001) while neck percentages increased (P < 0.05). Carcass composition was affected by castration; the percentages of carcass lean (P < 0.001) and bone (P < 0.01) dropped, and total fat (P < 0.001) increased. SW had an impact on carcass composition; the percentages of carcass bone (P < 0.001) declined, and lean (P < 0.001) and total fat (P < 0.001) raised as SW increased. The lean/fat ratio was affected by castration (P < 0.001) and increasing SW (P < 0.05). Castration reduced the lean percentage in all cuts. The leg showed the highest lean percentage, whereas the greatest fat ratio was found in the breast+flank in all SW groups. Consequently, castration of Angora male kids negatively affected fattening performance and altered the carcass composition, while the increase in slaughter weight improved fattening performance and slaughter and carcass characteristics.

Prediction of dressing percentage, carcass characteristics and meat yield of goats, and implications for live assessment and carcass-grading systems

Abstract Context Dressing percentage (DP) and meat yield (MY) predictions using live assessments and carcass measurements enable objective valuation of animals and their carcasses. We hypothesised that distribution of goat carcass tissues affects predictive value of live body condition scoring (CS) methods and carcass measurements for these traits. Aims The present paper aimed to assess the value of CS methods for prediction of DP and MY and of carcass measurements for prediction of MY. Methods Correlation and regression analyses from a dataset (n = 1014 goats) highly heterogeneous for factors influencing DP and MY were used to assess (1) the value of live-goat assessments and classifications, including five CS methods, age (dentition), liveweight (LW), sex, fleece characteristics and breed or genotype to predict DP and MY, and (2) the value of hot standard carcass weight (HSCW) and carcass GR (soft tissue over the 12th rib) tissue depth, eye-muscle depth and eye-muscle area to predict MY. Key results Among kids, LW accounted for 1% (residual standard deviation of 2.6%) of variation in DP, 22% (2.3%) in MY (% LW) and 34% (2.5%) in MY (% HSCW). LW plus the best CS method accounted for 24% (2.3%) of variation in DP, 58% (1.7%) in MY (% LW) and 61% (2.0%) in MY (% HSCW). Among all goats, LW plus CS accounted for up to 21% (3.2%), 39% (2.1%) and 45% (2.2%) of variation in these traits. Regression models that included age, sex, fleece type, breed or genotype, LW and CS accounted for 67% (2.5%), 72% (1.9%) and 72% (2.1%) of variation in DP, MY (% LW) and MY (% HSCW). Among carcass measurements, HSCW plus eye-muscle depth had best predictive value, accounting for 61% (2.3%) of variation in MY (% HSCW) for kids and 40% (2.9%) for all goats. Conclusions The body condition-score methods that best relate to DP and MY (% LW or % HSCW) assessed the shape of M. longissimus lumborum (eye muscle) in the lumbar region, which relates to muscularity of goats, rather than subcutaneous fat depth such as assessed at the GR-site. Implications The results guide potential targets for future developments in live-goat assessment, carcass classification and grading, and trading languages underpinned by value-based marketing.

Advances in Sheep and Goat Meat Products Research

The main goal of this chapter was to review the state of the art in the recent advances in sheep and goat meat products research. Research and innovation have been playing an important role in sheep and goat meat production and meat processing as well as food safety. Special emphasis will be placed on the imaging and spectroscopic methods for predicting body composition, carcass and meat quality. The physicochemical and sensory quality as well as food safety will be referenced to the new sheep and goat meat products. Finally, the future trends in sheep and goat meat products research will be pointed out.

Pre-slaughter cattle welfare indicators for use in commercial abattoirs with voluntary monitoring systems: A systematic review

Animal welfare has become an important subject of public, economic and political concern, leading to the need to validate indicators that are feasible to use at abattoirs. A systematic review was carried out, which identified 72 cattle welfare indicators (CWI) that were classified into four categories (physiological, morphometric, behavioral and meat quality). Their validity and feasibility for use in abattoirs were evaluated as potential measures of cattle welfare during transportation to the abattoir and at the abattoir itself. Several highly valid indicators were identified that are useful to assess welfare at abattoirs, including body condition score, human-animal interactions, vocalizations, falling, carcass bruising, and meat pH. In addition, some intermediate valid indicators are useful and should be investigated further. Information along the food chain could be used systematically to provide a basis for a more-risk-based meat inspection. An integrated system based on the use of key indicators defined for each inspection step with the setting of alarm thresholds could be implemented.