Comparative analysis of machine learning models for predicting forage grass digestibility using chemical composition and management data.

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dc.contributorJULIANA CAROLINE SANTOS SANTANA, UNIVERSIDADE FEDERAL DO RIO GRANDE DO NORTE; GELSON DOS SANTOS DIFANTE, UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL; VALERIA PACHECO BATISTA EUCLIDES, CNPGC; DENISE BAPTAGLIN MONTAGNER, CNPGC; ALEXANDRE ROMEIRO DE ARAUJO, CNPGC; LARISSA PEREIRA RIBEIRO TEODORO, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; PAULO EDUARDO TEODORO, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; CAROLINA DE ARRUDA QUEIRÓZ TAIRA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; ITÂNIA MARIA MEDEIROS DE ARAÚJO, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; GABRIELA DE AQUINO MONTEIRO, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; JÉSSICA GOMES RODRIGUES, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL; MARISLAYNE DE GUSMÃO PEREIRA, UNIVERSIDADE FEDERAL DE MATO GROSSO DO SUL.
dc.creatorSANTANA, J. C. S.
dc.creatorDIFANTE, G. dos S.
dc.creatorEUCLIDES, V. P. B.
dc.creatorMONTAGNER, D. B.
dc.creatorARAUJO, A. R. de
dc.creatorTEODORO, L. P. R.
dc.creatorTEODORO, P. E.
dc.creatorTAÍRA, C. de A. Q.
dc.creatorARAÚJO, I. M. M. de
dc.creatorMONTEIRO, G. de A.
dc.creatorRODRIGUES, J. G.
dc.creatorPEREIRA, M. de G.
dc.date2025-12-09T16:49:11Z
dc.date2025-12-09T16:49:11Z
dc.date2025-12-09
dc.date2025
dc.date.accessioned2026-07-07T03:43:21Z
dc.descriptionAccurate prediction of forage digestibility is essential for efficient livestock management and feed formulation. This study evaluated the performance of machine learning (ML) models to estimate the in vitro digestibility of leaf and stem components of Brachiaria hybrid cv. Ipyporã, using three datasets composed of pasture management variables, chemical composition variables, and their combination. Artificial neural network (Multilayer Perceptron, MLP), decision trees (REPTree and M5P), Random Forest (RF), and Multiple Linear Regression (LR) were tested. The principal component analysis revealed that 61.3% of the total variance was explained by two components, highlighting a strong association between digestibility and crude protein content and an opposite relationship with lignin and neutral detergent fiber. Among the evaluated models, MLP, LR, and RF achieved the best performance for leaf digestibility (r = 0.76), while for stem digestibility the highest accuracy was obtained with the LR model (r = 0.79; MAE = 2.42; RMAE = 2.87). The REPTree algorithm presented the lowest predictive performance regardless of the input data. The results indicate that chemical composition variables alone are sufficient to develop reliable prediction models. These findings demonstrate the potential of ML techniques as a non-destructive and cost-effective approach to predict the nutritional quality of tropical forage grasses and support precision livestock management.
dc.identifierAgriEngineering, v. 7, 412, 2025.
dc.identifier2624-7402
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1182556
dc.identifierhttps://doi.org/10.3390/agriengineering7120412
dc.identifier.urihttp://hdl.handle.net/123456789/441623
dc.languageeng
dc.rightsopenAccess
dc.subjectAgricultura de Precisão
dc.subjectBrachiaria Brizantha
dc.subjectBrachiaria Ruziziensis
dc.subjectDigestibilidade In Vitro
dc.subjectProteína Bruta
dc.subjectCrude protein
dc.subjectForage grasses
dc.subjectIn vitro digestibility
dc.subjectPasture management
dc.subjectPrecision agriculture
dc.titleComparative analysis of machine learning models for predicting forage grass digestibility using chemical composition and management data.
dc.typeArtigo de periódico

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