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Feature Papers in Animal Science: 4th Edition

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Special Issue Editor

Special Issue Information

Dear Colleagues,

This Special Issue aims to collate high-quality papers related to all aspects of animal science, including anatomy, behavior, embryo manipulation, feeding, nutrition, physiology, histology, diseases, animal management, health and welfare, genetics and breeding, gut development, functional food, microbial ecology, gut microbiome, next-generation sequencing, and immunology. We encourage researchers from various fields within Life’s scope to contribute papers highlighting the latest developments in their research field or to invite relevant experts and colleagues to do so.

Original research, reviews, communications, case reports, and other types of articles are invited to kindly be submitted in order to improve our knowledge and practice regarding animal science.

Prof. Dr. Einar Ringø
Guest Editor

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

fish
microbial ecology
lipid nutrition
electron microscopy
gut microbiota
proteomics
next-generation sequencing
arthropod parasites
protease inhibitors
disease vectors
cystatin
serpin
Kunitz
tick
insect
vector–host interaction
innate immunity
shellfish immunology
bivalve molluscs
transcriptomics
diseases
hemocytes
microscopy

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Research

11 pages, 1188 KiB

Open AccessArticle

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

by Arcesio Salamanca-Carreño, Mauricio Vélez-Terranova, Pere M. Parés-Casanova, Paula A. Toalombo-Vargas, David E. Rangel-Pachón and Andrés F. Castillo-Pérez

Life 2025, 15(5), 693; https://doi.org/10.3390/life15050693 - 24 Apr 2025

Viewed by 156

Abstract

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. Full article

(This article belongs to the Special Issue Feature Papers in Animal Science: 4th Edition)

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