Classification and regression tree analysis is a powerful statistical technique which helps to determine the most important variables in a particular dataset and helps to create a model. The study was conducted to identify linear body measurement traits (beak length, body length, keel length, chest circumference, toe length, body girth, shank length, back length, shank circumference and wing length) which could be employed in developing an effective prediction equation for body weight of Potchefstroom Koekoek laying hens. Eighty Potchefstroom Koekoek laying hens at twenty two weeks old were used. Pearson's correlation together with classification and regression tree (CRT) methods were used for analysis. Descriptive statistics indicated that mean of body weight was 1.50 kg. Correlation findings revealed that body weight was positively significantly correlated (P < 0.05) with beak length (r = 0.23) and toe length (r = 0.21), respectively. CRT results demonstrated that beak length, wing length and back length play an important role in the body weight of Potchefstroom Koekoek laying hen chickens. This study suggests that body weight of laying hens could be estimated by some linear body measurement traits. The models established in the current study might be employed by chicken farmers when making selection during breeding to improve body weight. However, further studies need to be done to validate the use of classification and regression tree analysis in prediction of body weight from linear body measurement traits of chickens.
| Multivariate Adaptive Regression Splines (MARS) data mining algorithm is a non-parametric regression method employed to obtain the prediction of live weight by using body measurements. The study was conducted to investigate the relationship between body weight, linear body measurement traits and the effect of linear body measurement traits on body weight of Hy-Line silver brown commercial layer. A total of one hundred (n= 100) Hy-Line silver brown commercial layers aged 22 weeks were used for body measurements viz; body weight (BW) in kilograms, Beak Length (BK), Body Length (BL), Body Girth (BG), Shank Length (SL) and Wing Length (WL) in centimetres. Furthermore, Pearson correlation and MARS methods were used for data analysis. Correlation results revealed that BW had a negative statistically high significant correlation with WL (r=-0.48**) and BL (r=-0.61**). MARS results developed a non-parametric regression model with coefficient of determination (R 2) = 1, adjusted coefficient of determination (R 2 adj.)= 1, standard deviation ration (SD ratio) = 0.006, root mean square error (RMSE) = 0.001 and Pearson correlation (r) = 1 between predicted and actual values (P < 0.01) of body weight. MARS model revealed that WL and BL had an effect on BW of Hy-Line silver brown commercial layer. The findings suggest that WL and BL had an effect on BW, therefore chicken layer farmers might use WL and BL for selection during breeding to improve BW. In conclusion, MARS models developed in this study might be used by chicken layer farmers for selection during breeding.
A total of 72 Boer goats (females = 58 and males = 14) from the age of one to five years were used to determine the association between Body Weight (BW) and linear body measurement traits viz. Body Length (BL), Heart Girth (HG), Rump Height (RH), Rump Width (RW), Ear Length (EL), Cannon Circumference (CC) and Heard Width (HW) and to establish a model for the prediction of BW using linear body measurement traits. Pearson correlation results indicated that BW in Boer goats had a positively high statistically association (P<0.01) with BL (r = 0.86**), HG (r = 0.89**), RH (r = 0.75**), CC (r = 0.58**) and HW (r = 0.65**). Furthermore, the results showed that BW in bucks had a positively high statistical correlation (P<0.01) with BL (r = 0.62**), HG (r = 0.83**), RH (r = 0. 56**) and HW (r = 0.51**) and a positive statistical correlation (P<0.05) with RW (r = 0.31*) and CC (r = 0.36*), as well as a negative statistical association (P<0.05) with EL (r = -0.25*). The regression results suggest that improving BL and HG might result in the improvement of BW in Boer goats.
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.
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