@article{MAKHILLJAVA200763427,
    title = {Indigenous Kids` Weight Variation with Respect to non Genetic Factors under Pastoral Mode in Tunisian Arid Region},
    journal = {Journal of Animal and Veterinary Advances},
    volume = {6},
    number = {3},
    pages = {441-450},
    year = {2007},
    issn = {1680-5593},
    doi = {javaa.2007.441.450},
    url = {https://makhillpublications.co/view-article.php?issn=1680-5593&doi=javaa.2007.441.450},
    author = {Najari Sghaier,Gaddour Amor,Ouni Mabrouk,Abdennabi Mouldi and},
    keywords = {Kids weight,local population,arid environment,non-genetic factors,pastoral breeding,Tunisia},
    abstract = {During four years, a weighing program allowed collect about 4900 of data of 722 indigenous kids raised in 9 herds in the Tunisian arid zone under pastoral husbandry mode. A Gompertz model was applied to estimate kids` weights at some standard ages 10, 30, 60, 90, 120, 150 and 180 days. A GLM procedure was applied to decompose the total variance of the kid&#8217;s traits. A means comparison test (SNK,   = 0.05) was applied to identify homogenous class by factor. Results show that the GLM determination coefficient R² remains lower than 87% of all studied traits due to the observed data structure. All traits seem to be affected by the significant effects (p< 0.001 or 0.05) of the factors related to the restrictions and the irregularity of the technical and natural environment of pastoral husbandry. The non-genetic factors impact increases with kids&#8217; age and requirements. Growth traits varied with year, herd, month of birth and natural region. The year factor plays an important role upon the kid&#8217;s weight and it evolution till 6 months age. The sex, birth mode and mother age acts only during first 2 months age. Regarding the birth season effect, regrouping kidding period allows improving herd productivity. So, arid environment affects both quantitatively and qualitatively individual kid&#8217;s growing behaviour and have to be considered for local goat rational genetic improvement modelling and planning.}
    }