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The study was performed to determine the association between body weight and biometric traits and to examine the effect of biometric traits on the live body weight of Nguni cows using Multivariate Adaptive Regression Splines (MARS) and Classification and Regression Tree (CART) data mining algorithms. In total, 105 Nguni cows aged three to four years were used for body weight (BW) and biometric traits viz; head width (HW), head length (HL), ear length (EL), body length (BL), rump height (RH), withers height (WH), sternum height (SH), body depth (BD), bicoastal diameter (BCD), rump width (RW), rump length (RL) and heart girth (HG). Coefficient of determination (R 2 ), adjusted coefficient of determination (Adj.R 2 ), root-mean square error (RMSE), standard deviation ratio (SD ratio) and Pearson correlation between actual and predicted values were predicted to find out the best fit models. MARS models in prediction of BW presented as the best fit as compare with CART model with higher R 2  = 0.993 and Adj.R 2  = 0.991 with the lowest RMSE = 5.97 and SD ratio = 0.081. In this study, MARS models established are the significant statistical tools that might be used for describing studied breed standards for breeding purposes.

The Savanna goat breed is an indigenous goat breed in South Africa that is reared for meat production. Live body weight is an important tool for livestock management, selection and feeding. The use of multivariate adaptive regression splines (MARS) to predict the live body weight of Savanna goats remains poorly understood. The study was conducted to investigate the influence of linear body measurements on the body weight of Savanna goats using MARS. In total, 173 Savanna goats between the ages of two and five years were used to collect body weight (BW), body length (BL), heart girth (HG), rump height (RH) and withers height (WH). MARS was used as a data mining algorithm for data analysis. The best predictive model was achieved from the training dataset with the highest coefficient of determination and Pearson’s correlation coefficient (0.959 and 0.961), respectively. BW was influenced positively when WH > 63 cm and HG >100 cm with a coefficient of 0.51 and 2.71, respectively. The interaction of WH > 63 cm and BL < 75 cm, WH < 68 cm and HG < 100 cm with a coefficient of 0.28 and 0.02 had a positive influence on Savanna goat BW, while male goats had a negative influence (−4.57). The findings of the study suggest that MARS can be used to estimate the BW in Savanna goats. This finding will be helpful to farmers in the selection of breeding stock and precision in the day-to-day activities such as feeding, marketing and veterinary services.

This work was conducted to examine the association between body weight (BW) and biometric traits viz. head width (HW), head length (HL), ear length (EL), body length (BL), rump height (RH), withers height (WH), sternum height (SH), rump width (RW), and heart girth (HG) and to determine the direct and indirect effects of biometric traits on BW. Sixty female and twenty male Nguni cattle between the ages of one to four years were used. Pearson correlation and path analysis were used for data analysis. Correlation results recognized that BW had a positive highly significant correlation with RW ( = 0.70**), RH ( = 0.90**), HG ( = 0.90**), SH ( = 0.90**), and WH ( = 0.93**) in male, whereas SH ( = 0.34**), WH ( = 0.55**), RH ( = 0.70**), and HG ( = 0.76**) had a positive highly significant correlation with BW of female Nguni cattle. Path analysis showed that RW (13.35) had the highest direct effect, whereas SH had an indirect effect on BW of male Nguni cattle. In female Nguni cattle, RH (4.87) had the highest direct effect, whereas HL had an indirect effect on BW. Association findings suggest that improvement of RW, RH, HG, SH, HG, and WH might result in the increase in BW of Nguni cattle. Path analysis results suggest that RW and RH might be used as a selection criterion during breeding to increase BW of Nguni cattle. The results of the current study might be used by cattle farmers to estimate BW using biometric traits.

ABSTRACT An egg is a reproduction tool in chickens and a valuable food source for humans. The objective of this study was to examine the effect of egg weight (EW) on egg quality traits such as egg length (EL), egg diameter (ED), yolk weight (YW), albumen weight (AW), shell weight (SW), shell index (SI), yolk ratio (YR), albumen ratio (AR) and shell ratio (SR). Potchefstroom Koekoek layer genotype eggs (n = 200) were used. Pearson correlation and analysis of variance (ANOVA) were used for analysis. Correlation results indicated that egg weight had a statistical significant correlation (P < 0.05) with egg quality traits. Egg weight displayed a positive highly significant correlation with EL (0.82), AW (0.67) and SW (0.62), respectively. The findings suggest that EL, AW and SW might be used in selection to improve EW of Potchefstroom Koekoek chicken genotype. ANOVA results showed that egg weight had a statistical significant difference (P < 0.05) with egg quality traits except for albumen ratio and yolk ratio (P > 0.05). Moreover, the findings revealed that small eggs weight had a longer egg length, yolk weight, shell weight, shell ratio and albumen weight than medium and large eggs. While large eggs had a higher egg diameter and shell index.