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Prediction of Subcutaneous Fat Thickness (SFT) in Pantaneiro Lambs: A Model Based on Adipometer and Body Measurements for Android Application

dc.contributor.authorAdrielly Lais Alves da Silva
dc.contributor.authorMarcus Vinicius Porto dos Santos
dc.contributor.authorMarcelo Corrêa da Silva
dc.contributor.authorHélio Almeida Ricardo
dc.contributor.authorMarcio Rodrigues de Souza
dc.contributor.authorNúbia Michelle Vieira da Silva
dc.contributor.authorFernando Miranda de Vargas Junior
dc.contributor.otherFaculty of Agrarian Sciences, Federal University of Grande Dourados, Rodovia Dourados–Itahum, km 12, Dourados 79804-970, MS, Brazil
dc.contributor.otherFaculty of Agrarian Sciences, Federal University of Grande Dourados, Rodovia Dourados–Itahum, km 12, Dourados 79804-970, MS, Brazil
dc.contributor.otherFaculty of Agrarian Sciences, Federal University of Grande Dourados, Rodovia Dourados–Itahum, km 12, Dourados 79804-970, MS, Brazil
dc.contributor.otherFaculty of Agrarian Sciences, Federal University of Grande Dourados, Rodovia Dourados–Itahum, km 12, Dourados 79804-970, MS, Brazil
dc.contributor.otherFederal Institute of Education Science and Technology of Mato Grosso do Sul, Campus Dourados, Dourados 79833-520, MS, Brazil
dc.contributor.otherFaculty of Agrarian Sciences, Federal University of Grande Dourados, Rodovia Dourados–Itahum, km 12, Dourados 79804-970, MS, Brazil
dc.contributor.otherFaculty of Agrarian Sciences, Federal University of Grande Dourados, Rodovia Dourados–Itahum, km 12, Dourados 79804-970, MS, Brazil
dc.date.accessioned2025-08-27T14:00:30Z
dc.date.accessioned2025-10-08T08:36:10Z
dc.date.available2025-10-08T08:36:10Z
dc.date.issued01-08-2025
dc.identifier.urihttp://digilib.fisipol.ugm.ac.id/repo/handle/15717717/36223
dc.description.abstractThe increasing adoption of digital technologies in the agriculture sector has significantly contributed to optimizing on-farm routines, especially in data-driven decision-making. This study aimed to develop an application to determine the slaughter point of lambs by predicting subcutaneous fat thickness (SFT) using pre-slaughter parameters such as body weight (BW), body condition score (BCS), and skinfold measurements at the brisket (BST), lumbar (LST), and tail base (TST), obtained using an adipometer. A total of 45 Pantaneiros lambs were evaluated, finished in feedlot, and slaughtered at different body weights. Each pre-slaughter weight class showed a distinct carcass pattern when all parameters were included in the model. Exploratory analysis revealed statistical significance for all variables (<i>p</i> < 0.001). BW and LST were selected to construct the predictive equation (R<sup>2</sup> = 55.44%). The regression equations were integrated into the developed application, allowing for in-field estimation of SFT based on simple measurements. Compared to conventional techniques such as ultrasound or visual scoring, this tool offers advantages in portability, objectivity, and immediate decision-making support. In conclusion, combining accessible technologies (e.g., adipometer) with traditional variables (e.g., body weight), represents an effective alternative for production systems aimed at optimizing and enhancing the value of lamb carcasses.
dc.language.isoEN
dc.publisherMDPI AG
dc.subject.lccAgriculture (General)
dc.titlePrediction of Subcutaneous Fat Thickness (SFT) in Pantaneiro Lambs: A Model Based on Adipometer and Body Measurements for Android Application
dc.typeArticle
dc.description.keywordsconformation
dc.description.keywordsovine
dc.description.keywordssmartphone-based device
dc.description.doi10.3390/agriengineering7080251
dc.title.journalAgriEngineering
dc.identifier.e-issn2624-7402
dc.identifier.oaioai:doaj.org/journal:ca02ffa036794ee685387a4e63c4b953
dc.journal.infoVolume 7, Issue 8


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