Salamanca-Carreño Arcesio, Vélez-Terranova Mauricio, Parés-Casanova Pere M, Toalombo-Vargas Paula A, Rangel-Pachón David E, Castillo-Pérez Andrés F
Facultad de Medicina Veterinaria y Zootecnia, Universidad Cooperativa de Colombia, Villavicencio 50001, Colombia.
Facultad de Ciencias Agropecuarias, Universidad Nacional de Colombia, Palmira 763531, Colombia.
Life (Basel). 2025 Apr 24;15(5):693. doi: 10.3390/life15050693.
Creole pigs (), descendants of Iberian breeds, possess significant genetic and cultural importance but are under-researched and at risk due to the dominance of improved breeds for commercial production. The aim of this study was to identify the most representative body morphometric measurements for the differentiation of two Creole pig breeds, using statistical and machine learning methods. A sample of "Casco de Mula" ( = 54) and San Pedreño ( = 30) Creole pigs, aged between 2 and 6 months, belonging to seven traditional farms located in the department of Meta (Colombia), was studied. A total of 14 morphometric variables were recorded, as well as the animal's sex. Four algorithms-linear discriminant analysis, quadratic discriminant analysis, logistic regression, and classification trees-were used to classify the breeds. The results indicated that head width, height at the withers, and right ear length measurements could be used to differentiate the "Casco de Mula" and San Pedreño Creole pigs. The decision tree was the most accurate algorithm (accuracy = 92%, sensitivity = 96%, specificity = 83%, and Matthews correlation coefficient = 0.82), and its performance can be improved by increasing the number of animals. Non-parametric supervised learning methods like decision trees can be used to morphometrically differentiate Creole pigs raised in the same or different environments in order to characterize animal genetic resources.
克里奥尔猪是伊比利亚品种的后代，具有重要的遗传和文化价值，但由于商业生产中改良品种占据主导地位，它们的研究不足且面临风险。本研究的目的是使用统计和机器学习方法，确定用于区分两个克里奥尔猪品种的最具代表性的身体形态测量指标。对来自哥伦比亚梅塔省七个传统农场的54头“卡斯克·德·穆拉”克里奥尔猪和30头圣佩德雷尼奥克里奥尔猪进行了研究，这些猪年龄在2至6个月之间。记录了总共14个形态测量变量以及动物的性别。使用四种算法——线性判别分析、二次判别分析、逻辑回归和分类树——对品种进行分类。结果表明，头部宽度、肩高和右耳长度测量值可用于区分“卡斯克·德·穆拉”和圣佩德雷尼奥克里奥尔猪。决策树是最准确的算法（准确率=92%，灵敏度=96%，特异性=83%，马修斯相关系数=0.82），增加动物数量可以提高其性能。像决策树这样的非参数监督学习方法可用于对在相同或不同环境中饲养的克里奥尔猪进行形态学区分，以表征动物遗传资源。
