The aims of this study were to evaluate the effect of genetic and non-genetic factors on the growth of Socorro Island Merino lambs and to model their growth curve using nonlinear mathematical models. The weight of 41 Socorro Island Merino lambs was recorded at birth and at 45, 90, 135, 180, 225, 270, 315 and 365 days of life from May 2019 to September 2021. The effects on growth of sex, genotype, year of birth, and parity of the dam were analyzed. Four non-linear models (Brody, Logistic, Gompertz and von Bertalanffy) were fitted for determining the best model to describe growth curve. Birth weight and pre-weaning growth rate were not affected (P 0.05) by any of the factors studied, while weaning weight was only significantly affected (P 0.05) by sex. Year of birth significantly affected (P 0.05) post-weaning growth rate and weight of the lamb from 270 days, while sex significantly affected (P 0.05) weight at 315 days. The Gompertz and Brody models were the best fitted to describe growth curves of lambs. Purebred males showed a larger response to increasing levels of energy-protein supplementation, while they had greater mature weight and lower maturation rate compared to females and crossbred males. In conclusion, knowledge of growth and factors influencing growth pattern can help implement appropriate management strategies and make decisions aimed at the conservation of Socorro Island Merino lambs.

The aim of this study was to describe the morphology and estimate live weight from body measurements of Socorro Island Merino lambs. A group of Socorro Island Merino lambs was recorded from birth to year for live weight, rump width, rump length, withers height, body length, cannon bone perimeter, and chest girth, width, and depth. The effect of the lamb type on body measurements and live weight was analyzed using ANOVA, Pearson’s correlation analysis was performed to estimate the relationship between body measurements and live weight, multiple linear regressions were fitted to obtain prediction equations of live weight from the body measurements and finally, chest girth was used to generate prediction equations using linear and exponential models. At birth and at year, differences were observed in body measurements, especially those related to the thoracic region, with crossbred males showing the highest values. Live weight was correlated with almost all the body measurements, with the highest coefficients observed with chest girth, chest width, and chest depth. Live weight can be accurately predicted from multiple regression equations using several body measurements, but using only chest girth (CG) as a predictor, the exponential equation W0–365 = 0.9142 exp(0.0462 CG) showed the best accuracy.