Author/Authors :
Louis Tyasi, Thobela Department of Agricultural Economics and Animal Production - School of Agricultural & Environmental Sciences - University of Limpopo, Sovenga, Limpopo, South Africa , Maaposo Makgowo, Kgotlelelo Department of Agricultural Economics and Animal Production - School of Agricultural & Environmental Sciences - University of Limpopo, Sovenga, Limpopo, South Africa , Mokoena, Kwena Department of Agricultural Economics and Animal Production - School of Agricultural & Environmental Sciences - University of Limpopo, Sovenga, Limpopo, South Africa , Trudy Rashijane, Lebo Department of Agricultural Economics and Animal Production - School of Agricultural & Environmental Sciences - University of Limpopo, Sovenga, Limpopo, South Africa , Cyril Mathapo, Madumetja Department of Agricultural Economics and Animal Production - School of Agricultural & Environmental Sciences - University of Limpopo, Sovenga, Limpopo, South Africa , Danguru, Lebogang William Department of Agricultural Economics and Animal Production - School of Agricultural & Environmental Sciences - University of Limpopo, Sovenga, Limpopo, South Africa , Madikadike Molabe, Kagisho Department of Agricultural Economics and Animal Production - School of Agricultural & Environmental Sciences - University of Limpopo, Sovenga, Limpopo, South Africa , Mogowe Bopape, Paul Department of Agricultural Economics and Animal Production - School of Agricultural & Environmental Sciences - University of Limpopo, Sovenga, Limpopo, South Africa , Divine Mathye, Nhlakanipho Department of Agricultural Economics and Animal Production - School of Agricultural & Environmental Sciences - University of Limpopo, Sovenga, Limpopo, South Africa , Maluleke, Dannis Department of Agricultural Economics and Animal Production - School of Agricultural & Environmental Sciences - University of Limpopo, Sovenga, Limpopo, South Africa , Gunya, Busisiwe Department of Agricultural Economics and Animal Production - School of Agricultural & Environmental Sciences - University of Limpopo, Sovenga, Limpopo, South Africa , Gxasheka, Masibonge Department of Plant Production Soil Science & Agricultural Engineering - School of Agricultural & Environmental Sciences - University of Limpopo, Sovenga, Limpopo, South Africa
Abstract :
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
