Predicting live weight of rural African goats using body measurements

Authors: CHINCHILLA-VARGASA, JOSUE - Iowa State University; Woodward-Greene, Jennifer; Van Tassell, Curtis - Curt; MASIGA, CLET - Tropical Institute Of Development Innovation (TRIDI); ROTHSCHILD, MAX - Iowa State University

Submitted to: Livestock Research for Rural Development
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/6/2018
Publication Date: 7/15/2018
Citation: Chinchilla-Vargasa, J., Woodward Greene, M.J., Van Tassell, C.P., Masiga, C.W., Rothschild, M.F. 2018. Predicting live weight of rural African goats using body measurements. Livestock Research for Rural Development. 30(7):123.

Interpretive Summary: The goal of the current study was to develop simple regression-based equations that allow small-scale producers to use simple body measurements to accurately predict live weight of typical African goats. The data used in this study were recorded in five African countries, and was composed of 814 individuals of 40 indigenous breeds or populations and crosses that included 158 males and 656 females. Records included the live weight measured with a hanging scale, linear body measurements, country, breed, owner, and age. Country, breed, age, chest girth, height at withers, body length, and shoulder width had large effects (p<0.05) on live weight. One linear model and two quadratic models were developed to predict weight from body measurements. The mean of the absolute value of the differences (mean absolute difference) between predicted and observed weights were compared to a standard body measurement (BM) method live weight predictions.Different models were optimal under different conditions, but in all cases, the models produced smaller mean prediction errors than the BM method.

Technical Abstract: The goal of the current study was to develop simple regression-based equations that allow small-scale producers to use simple body measurements to accurately predict live weight of typical African goats. The data used in this study were recorded in five African countries, and was composed of 814 individuals of 40 indigenous breeds or populations and crosses that included 158 males and 656 females. Records included the live weight measured with a hanging scale, linear body measurements, country, breed, owner, and age. Country, breed, age, chest girth, height at withers, body length, and shoulder width had large effects (p<0.05) on live weight. One linear model and two quadratic models were developed to predict weight from body measurements. The mean of the absolute value of the differences (mean absolute difference) between predicted and observed weights were compared to a standard body measurement (BM) method live weight predictions. Based on the improved fit of the predictions, animals were divided into three chest girth classes. For the animals with chest girth of <55 cm the prediction model with linear terms for chest girth, body length, shoulder width and height at withers and chest girth and body length as a quadratic term was selected as the most accurate. For animals with chest girths of 56-75 cm and >76 cm, the prediction model selected that included linear terms for chest girth, body length, shoulder width and height at withers plus a quadratic term for chest girth was selected as the most accurate. When analyzed within country from Uganda and Zimbabwe, animals with chest girth < 55cm the linear model with additional quadratic terms for chest girth and body length was selected. For animals with chest girth 55-75cm the linear model with the added quadratic terms for chest girth and body length was selected for animals from Malawi and Zimbabwe while the linear model with a quadratic term for chest girth was selected for Mozambique, Tanzania and Uganda. For animals with chest girth of >76 cm the linear model with a quadratic term for chest girth was chosen for Tanzania, while for the other countries the linear model with quadratic terms for chest girth and body length was most accurate. In all cases, the models produced smaller mean prediction errors than the BM method.