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Machine learning models for crude protein prediction in Tamani grass pastures

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datacite.subject.fosCiências Agrárias::Agricultura, Silvicultura e Pescas
datacite.subject.fosCiências Agrárias::Biotecnologia Agrária e Alimentar
datacite.subject.sdg01:Erradicar a Pobreza
datacite.subject.sdg13:Ação Climática
datacite.subject.sdg02:Erradicar a Fome
dc.contributor.authorMonteiro, Gabriela Oliveira de Aquino
dc.contributor.authorDifante, Gelson dos Santos
dc.contributor.authorMontagner, Denise Baptaglin
dc.contributor.authorEuclides, Valéria Pacheco Batista
dc.contributor.authorCastro, Marina
dc.contributor.authorRodrigues, Jéssica Gomes
dc.contributor.authorPereira, Marislayne de Gusmão
dc.contributor.authorSantana, Juliana Caroline Santos
dc.contributor.authorItavo, Luis Carlos Vinhas
dc.contributor.authorNantes, Rafael Torres
dc.contributor.authorCampos, Jecelen Adriane
dc.contributor.authorCosta, Anderson Bessa da
dc.contributor.authorMatsubara, Edson Takashi
dc.date.accessioned2026-02-23T09:45:46Z
dc.date.available2026-02-23T09:45:46Z
dc.date.issued2026
dc.description.abstractUnderstanding forage quality is essential for meeting animal demands and optimizing production. This study aimed to: (i) test the applicability of machine learning models with tabular data such as climate variables, light interception (LI), nitrogen dose (N dose), interval between grazing (GI), and pre- (HPRE) and post-grazing height (HPOST) to predict leaf crude protein (CP) content of tamani grass pastures; (ii) identify which variables contribute most to CP prediction. A set of 90 instances was used with 80% for training and validation and 20% for testing. The hyperparameters were adjusted with grid-search on the training set. We tested Linear Regression (LR), Multilayer Perceptron (MLP), Decision Trees(DT), Random Forest (RF), and XGBoost. The MLP (r=0.75, R2 =44.18%, MAE=1.55), RF (r=0.78, R2 =49.07%, MAE=1.59) and XGBoost (r=0.78, R2 =56.65% MAE=1.45) models presented the best prediction results (p<0.001). The variables most important in predicting CP content were GI, followed by N dose, HPRE and HPOST. XGBoost outperformed other tested models (p<0.001). Tabular data, including N dose, GI, HPRE, HPOST, LI, and climatic variables, is a viable alternative for predicting CP. In conclusion, the results of this study suggest that management practices may have a greater influence on the chemical composition of Tamani grass than environmental conditions, although further research with larger and more diverse datasets is needed to confirm these findings.eng
dc.description.sponsorshipThe authors thank the Embrapa Beef Cattle, Federal University of Mato Grosso do Sul Foundation, through the Postgraduate Program in Animal Science, the National Council for Scientific and Techno- logical Development (CNPq), the Higher Education Personnel Improvement Coordination (CAPES, Finance Code 001) and the Foundation for the Support of the Development of Education, Science and Technology of the State of Mato Grosso do Sul (FUNDECT)
dc.identifier.doi10.1038/s41598-026-36949-6
dc.identifier.issn2045-2322
dc.identifier.urihttp://hdl.handle.net/10198/35819
dc.language.isoeng
dc.peerreviewedyes
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectMachine learning
dc.subjectPanicum maximum
dc.subjectPasture management
dc.subjectPrecision livestock farming ng
dc.titleMachine learning models for crude protein prediction in Tamani grass pastureseng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage13
oaire.citation.issue1
oaire.citation.startPage1
oaire.citation.titleScientific Reports
oaire.citation.volume16
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameCastro
person.givenNameMarina
person.identifier2303569
person.identifier.ciencia-id6417-7D8D-FD7E
person.identifier.orcid0000-0002-6368-8098
person.identifier.ridB-5197-2016
person.identifier.scopus-author-id56612728000
relation.isAuthorOfPublicationa7a3b08e-9d22-4faf-9224-36924d8ce7c8
relation.isAuthorOfPublication.latestForDiscoverya7a3b08e-9d22-4faf-9224-36924d8ce7c8

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