The objective of this study was to determine post-mortem measurements for predicting carcass traits in growing rabbits. A total of 50 clinically healthy New Zealand White × Californian male rabbits with a body weight (BW) of 1351 ± 347 g between 60 to 80 days of age were used. Body weight was recorded 12 h before slaughtering. Data recorded at slaughtering included carcass weights (HCW). After cooling at 4 °C for 24 h, carcasses were weighed (CCW) and then were carefully split longitudinally with a band saw to obtain left and right halves. In the right half carcass, the following measurements were recorded using a tape measure: dorsal length (DL), thoracic depth (TD), thigh length (TL), carcass length (CL), lumbar circumference (LC). The compactness index (CCI) was calculated as the CCW divided by the CL. Thereafter, the right half carcass was weighed and manually deboned to record weights of muscle (TCM), and bone (TCB). The CCI explained of 93% of variation for TCM (R² = 0.93 and a CV = 9.30%). In addition, the DL was the best predictor (p < 0.001) for TCB (R² = 0.60 and a CV = 18.9%). Our results indicated that the use of carcass measurements could accurately and precisely (R² = ≥ 0.60 and ≤0.95) be used as alternatives to predict the carcass tissues composition in growing rabbits.

The aim of this study is to evaluate the model performance in the classification of FAMACHA© scores using Support Vector Machines (SVMs) with a focus on the estimation of the FAMACHA© scoring system used for early diagnosis and treatment management of parasitic infections. FAMACHA© scores are a color-based visual assessment system used to determine parasite load in animals, and in this study, the accuracy of the model was investigated. The model’s accuracy rate was analyzed in detail with metrics such as sensitivity, specificity, and positive/negative predictive values. The results showed that the model had high sensitivity and specificity rates for class 1 and class 3, while the performance was relatively low for class 2. These findings not only demonstrate that SVM is an effective method for classifying FAMACHA© scores but also highlight the need for improvement for class 2. In particular, the high accuracy rate (97.26%) and high kappa value (0.9588) of the model indicate that SVM is a reliable tool for FAMACHA© score estimation. In conclusion, this study demonstrates the potential of SVM technology in veterinary epidemiology and provides important information for future applications. These results may contribute to efforts to improve scientific approaches for the management of parasitic infections.

El objetivo de este trabajo fue revisar la información acerca de la investigación sobre características de la canal y composición del tejido de la canal en ovinos de pelo en el sureste de México. La información disponible reveló que se han investigado los principales métodos, "medidas in vivo", como las medidas de ultrasonografía y las medidas biométricas; y "mediciones post-mortem" tales como disección de cortes primarios (cuello, hombro y sección de costillas 9-11) y mediciones de canales de razas ovinas de pelo criadas en el sureste de México.

Background. As body weight (BW) is rarely determined on fasted animals in most production systems, the need to develop accurate mathematical models for adjusting BW has been identified. Objective. To evaluate models for estimating shrunk body weight (SBW) and empty body weight (EBW) in Black Belly growing ewe lambs raised in tropical conditions. Methodology. Data of sixty Black Belly ewe lambs, between four to eight months of age with a mean BW of 26.55± 3.92 kg (± SD) were used. The SBW was estimated based on the BW without fasting (fed) and the EBW based on SBW through three models: 1. Linear; 2.-Linear without intercept and 3.- Exponential. The predictive capacity of the models was evaluated by cross-validation. Results. The correlation coefficients between BW and SBW and SB and EBW were high (r = 0.94; <0.0001). The coefficients of determination (r2) for the equations between BW and SBW were 0.89 (P<0.001); and 0.86 (P<0.001) for the relationship between SBW and EBW. Based on the AIC (26.81) Eq. 2 described the relationship between BW and SBW better than Eq. 1, (AIC = 28.44) and Eq. 3 (AIC =28.35). The final model to estimate SBW as a function of BW was: SBW (kg): 0.96 (±0.001***) × BW. With respect to the SBW and EBW ratio, Equation 5, was better than the linear Eq. 4, (AIC = 33.01) and Eq. 6 (AIC = 33.35). The following model was: EBW (kg): 0.81 (± 0.06***) × SBW. The cross-validation of the equation of the relationship between BW and SBW showed that Eq. 1 had a higher r2 (0.87), and lower RMSEP and MAE than Eq. 2 and Eq. 3. Also, the equation of the relationship between SBW and EBW the cross-validation revealed that Eqs. 4 and 6 had the higher r2 (0.82), and lower RMSEP and MAE and tended to be more accurate than Eq. 5. Implications. The results obtained in present study contributes to the development of mathematical models for more accurate body weight adjustments in tropical sheep. Conclusion. The equations developed and evaluated in the present study revealed that the linear relationship between BW and SBW, and the linear and exponential relationship between SBW and EBW can be used to body weight adjustments in growing Black Belly ewe lambs. It's worth noting that this confirms that the SBW can be calculated using an adjustment factor of 0.96 FBW.