effect of non genetic factors affecting the survival of lori-bakhtiari lambs from birth to yearling, utilizing linear and nonlinear models

data utilized in this study were 6800 records of lambs’ life longevity, and survival rate from 263 sires and 1839 dams collected from 1989 through 2009, from the lori-bakhtiari flock at shooli station in shahrekord. the data were analyzed, using linear and hazard ratio models along with weibul function. these models included such non genetic factors as year and month of birth, sex of lamb, type of birth, age of dam and lambs birth weight as linear and quadratic covariates. results revealed that the overall mean of lamb’s life longevity and cumulative survival rate, up to yearling, were 301.60 days vs. 78.68%, respectively. the effect of non genetic factors on survival rate resulted from linear model were almost the same as hazard ratio model of wiebull function. the effect of year and month of birth, sex of lamb and lamb’s birth weight as quadratic covariate were significant (p0.05) on the longevity of lamb’s life as well as survival rate. the survival rate and hazard ratio of lambs born in the first vs. the second months were higher vs. lower than those in the third month respectively. the male lambs suffered from a lower survival rate and higher hazard ratio than the females. lambs with medium birth weight benefited from a higher survival rate and lower hazard ratio than lambs with either too low or too high birth weights. although, lambs born from younger and older dams suffered from lower survival rates and higher hazard ratios, but they did not any significant differences with lambs born from middle aged dams. twin lambs suffered from lower survival rates and higher hazard ratios than single lambs, but the differences were not significant. according to the results obtained in this study, the effect of non genetic factors on lamb’s survival rate to yearling age, achieved from liner and non linear models were similar and to increase the survival rate by correcting the non genetic factors, one could use the results obtained from either linear or non linear models.