Background and Aim: The Kalahari Red goat breed is the finest meat-producing species in South Africa, and its coat color ranges from light to dark red-brown. A practical approach to estimating their body weight (BW) using linear body measurements is still scarce. Therefore, this study aimed to determine the best data mining technique among classification and regression trees (CART), Chi-square automatic interaction detection (CHAID), and exhaustive CHAID (Ex-CHAID) for predicting the BW of Kalahari Red goats. Materials and Methods: This study included 50 Kalahari Red goats (does = 42 and bucks = 8) aged 3–5 years. Body length (BL), heart girth (HG), rump height (RH), height at withers (WH), sex, and age were the essential indicators to estimate BW. The best model was chosen based on the goodness of fit, such as adjusted coefficient of determination (Adj. R2), coefficient of determination (R2), root mean square error (RMSE), standard deviation ratio (SD ratio), mean absolute percentage error, Akaike information criteria, relative approximation error, and coefficient of variation. Results: The SD values of the ratio ranged from 0.32 (CART) to 0.40 (Ex-CHAID). The greatest R2 (%) was established for CART (89.23), followed by CHAID (81.99), and the lowest was established for Ex-CHAID (81.70). CART was established as the preferred algorithm with BL, HG, and WH as critical predictors. The heaviest BW (73.50 kg) was established in four goats with BL higher than 92.5 cm. Conclusion: This study reveals that CART is the optimum model with BL, HG, and WH as the essential linear body measurements for estimating BW for Kalahari Red goats. The updated records will assist the rural farmers in making precise judgments for various objectives, such as marketing, breeding, feeding, and veterinary services in remote areas where weighing scales are unavailable.

The association between live body weight and morphometric traits plays a major role in the daily management and improvement of poultry. The objective of the current study was to determine the association between morphometric features and live body weight, as well as to investigate the direct and indirect effects of morphometric traits on White Leghorn laying hens' live body weight. Live body weight (BW) and morphometric traits including shank circumference (SC), body length (BL), wing length (WL), shank length (SL), toe to back length (TBL), beak length (BKL), beak to comb length (BCL), height (CH) and chest girth (CG) were collected from one hundred (n = 100) White Leghorn laying hens aged 40 weeks. The correlation findings showed that BW was positively correlated to SL, WL, SC, BL, and CH (p < 0.05). The Path analysis results reported that BL (0.45) had the highest direct effect on BW while WL (0.14) had the highest indirect effect on BW via BL. Correlation results propose that improvement of BL, SL, SC, CG and CH might increase the BW of White Leghorn hens. Path analysis results on the other hand, imply that BL and SC may be used as selection basis during breeding to improve BW in chickens. This study suggests that BW of White Leghorn is correlated with some morphometric traits that might be used during breeding. The findings also suggest that body length directly influence the live body weight of White Leghorn chicken breed. Keywords: Correlation matrix, Morphometric traits, Regression, Shank circumference, Wing length.

Insulin-like growth factor 1 gene (IGF-1) is associated with growth traits in different livestock, but its association with growth traits in South African indigenous goat breeds is poorly understood. The study's objective was to identify the single nucleotide polymorphisms (SNPs) of IGF-1 in the Kalahari Red goat breed and their association with the growth traits. Two genotypes (KK and KM) were identified using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The frequency of genotype KK and KM was 0.56 and 0.44 respectively. The allelic frequency of allele K and M was 0.78 and 0.22 respectively. The studied Kalahari Red goat breed population was not in Hardy Weinberg equilibrium (HWE) as revealed by the Chi-square test (χ2 = 0.39). Marker-trait association indicated that the identified genotypes had no association (P > 0.05) with the measured growth traits. In conclusion, the current study suggests that two identified genotypes of IGF-1 might not be used as potential genetic markers during selection to improve growth traits. Further studies need to be conducted on SNPs of IGF-1 and their association with growth traits using a larger sample, more growth traits and targeting more exons of Kalahari Red goats. Keywords: Single nucleotide polymorphisms, Body weight, Genetic markers, Hardy Weinberg equilibrium

In Lepelle-Nkumbi Local Municipality of South Africa, 200 none-descript indigenous goats ranging in age from one to five years were the subjects of a study that compared the live body weight predictions made by stepwise linear regression, Classification Regression Tree (CART), and Multivariate Adaptive Splines (MARS) models. Several bodily measurements, such as canonical circumference (CC), sternum height (SH), body length (BL), ear length (EL), head length (HL), head width (HW), rump length (RL), rump height (RH), and rump width (RW). The evaluation criteria included the root mean square error (RMSE), coefficient of determination (R²), to decide which model was the best. According to the results, CART outperformed the others, obtaining the lowest RMSE (3.65) and the greatest R² (0.80). The stepwise regression model outperformed data mining algorithms in male goats. According to the study, CART is a useful statistical technique for defining requirements for producing indigenous goats that are not very special. In addition, when predicting live body weight from body measuring features, the stepwise regression model should be considered.