Author/Authors :
Louis Tyasi, Thobela Department of Agricultural Economics and Animal Production - School of Agricultural and Environmental Sciences - University of Limpopo, Limpopo, South Africa , Maaposo Makgowo, Kgotlelelo Department of Agricultural Economics and Animal Production - School of Agricultural and Environmental Sciences - University of Limpopo, Limpopo, South Africa , Mokoena, Kwena Department of Agricultural Economics and Animal Production - School of Agricultural and Environmental Sciences - University of Limpopo, Limpopo, South Africa , Trudy Rashijane, Lebo Department of Agricultural Economics and Animal Production - School of Agricultural and Environmental Sciences - University of Limpopo, Limpopo, South Africa , Cyril Mathapo, Madumetja Department of Agricultural Economics and Animal Production - School of Agricultural and Environmental Sciences - University of Limpopo, Limpopo, South Africa , William Danguru, Lebogang Department of Agricultural Economics and Animal Production - School of Agricultural and Environmental Sciences - University of Limpopo, Limpopo, South Africa , Madikadike Molabe, Kagisho Department of Agricultural Economics and Animal Production - School of Agricultural and Environmental Sciences - University of Limpopo, Limpopo, South Africa , Mogowe Bopape, Paul Department of Agricultural Economics and Animal Production - School of Agricultural and Environmental Sciences - University of Limpopo, Limpopo, South Africa , Divine Mathye, Nhlakanipho Department of Agricultural Economics and Animal Production - School of Agricultural and Environmental Sciences - University of Limpopo, Limpopo, South Africa , Maluleke, Dannis Department of Agricultural Economics and Animal Production - School of Agricultural and Environmental Sciences - University of Limpopo, Limpopo, South Africa
Abstract :
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 (R2
) = 1, adjusted
coefficient of determination (R2
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.
Keywords :
Body weight , Correlation , Data mining algorithm , Layer , Wing length
