Lamb survival is influenced by the culmination of a sequence of often interrelated events including genetics, physiology, behaviour and nutrition, with the environment providing an overarching complication. Machine learning algorithms offer great flexibility with regard to problems of complex interactions among variables. The objective of this study was to use machine learning algorithms to identify factors affecting the lamb survival in high altitudes and cold climates. Lambing records were obtained from three native breed of sheep (Awassi = 50, Morkaraman = 50, Tuj = 50) managed in semi intensive systems. The data set included 193 spring born lambs out of which 106 lambs were sired by indigenous rams (n = 10), and 87 lambs were sired by Romanov Rams (n = 10).

Factors included were dam body weight at lambing, age of dam, litter size at birth, maternal and lamb behaviors, and lamb sex. Individual and cohort data were combined into an original dataset containing 1351 event records from 193 individual lambs and 750 event records from 150 individual ewes. Classification algorithms applied for lamb survival were Bayesian Methods, Artificial Neural Networks, Support Vector Machine and Decision Trees. Variables were categorized for lamb survival, lamb behavior, and mothering ability. RandomForest performed very well in their classification of the mothering ability while SMO was found best in predicting lamb behavior. REPtree tree visualization showed that grooming behavior is the first determinant for mothering ability. Classification Trees performed best in lamb survival. Our results showed that Classification Trees clearly outperform others in all traits included in this study.

羔羊的生存受到一系列经常相互关联的事件（包括遗传学，生理学，行为和营养）的影响，而环境则是最复杂的事情。机器学习算法为变量之间复杂的交互问题提供了极大的灵活性。这项研究的目的是使用机器学习算法来确定影响高海拔和寒冷气候下羔羊生存的因素。从在半精养系统中管理的三种本地绵羊品种（Awassi = 50，Morkaraman = 50，Tuj = 50）获得羔羊记录。数据集包括193只春季出生的羔羊，其中106只羔羊由本地公羊饲养（n = 10），而87只羔羊由Romanov Rams（n = 10）饲养。

包括的因素有：产羔时的水坝体重，水坝的年龄，出生时的产仔数，产妇和羔羊的行为以及羔羊的性别。将个体数据和队列数据合并到原始数据集中，该原始数据集包含来自193只羔羊的1351个事件记录和来自150个母羊的750个事件记录。用于羔羊生存的分类算法是贝叶斯方法，人工神经网络，支持向量机和决策树。根据羔羊存活率，羔羊行为和母体能力对变量进行分类。在他们对母体能力的分类中，RandomForest表现非常出色，而SMO在预测羔羊行为方面表现最佳。REPtree树可视化显示，修饰行为是决定母性能力的首要因素。分类树在羔羊存活方面表现最佳。