A tool for predicting live weight in Huacaya alpacas from southern Peru

Abstract

The aim of this study was to identify the most suitable model for predicting live weight using both original body measurements (BM) and principal component scores (PC), and to assess the relationship between body measurements (BM) and live weight (LW). LW and BM of Huacaya alpacas (n = 117) were collected from the Quimsachata Center of the National Institute of Agricultural Innovation in Peru. The following BM, including LW, withers height (WH), croup height (CH), thoracic circumference (TC), abdominal circumference (AC), cannon-bone length (CL), neck-base circumference (NBC), cannon circumference (CC), tail insertion length (TIL), rump width (RW), tail insertion circumference (TIC), body width (BDW), rump length (RL), forelimb length (FL), body length (BDL), back length (BKL), were taken. Principal component analysis (PCA) was utilized to extract and clarify the correlation between LW and their BMs. Regression equations relating LW to BM and their PCs were computed. Additionally, the root mean squared error (RMSE), Bayesian information criterion (BIC), Akaike information criterion (AIC), and coefficients of multiple determination (R²) were used to assess the models. Four components were extracted from the PCA of BM and LW, which accounted for 67.3% of the total variance. The prediction model for LW, utilizing two PCs, showed the highest R² as well as the lowest BIC, AIC and RMSE values in contrast to models based on the original measurements. The findings suggest that this method is a viable alternative for predicting live weight and may be useful in breeding programs, as well as in the design of management and selection strategies for Huacaya alpacas from Peru.