Assessment of Body Morphometry to Classify Two Colombian Creole Pigs Using Statistical and Machine Learning Methods.

Creole pigs (Sus scrofa domestica), 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" (n = 54) and San Pedreño (n = 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.