Chen, Huilong; Feng, Shuyan; Wan, Junliang; Yang, Keen; Wang, Ying; Lv, Yongzi; Sun, Changyao; Lei, Biai; Nussio, Luiz Gustavo 
; Yang, Fuyu; Zhang, Yingchao; Wang, Xuekai
Source: Bioresource Technology. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, COMPOSIÇÃO QUÍMICA, FERMENTAÇÃO ANAERÓBICA, MILHO, MINERAÇÃO DE DADOS, MODELAGEM DE DADOS, SILAGEM
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CHEN, Huilong et al. Predicting chemical composition of corn silage using tree-based machine learning models: model development, feature analysis, and practical application. Bioresource Technology, v. 441, p. 1-9, 2026Tradução . . Disponível em: https://doi.org/10.1016/j.biortech.2025.133542. Acesso em: 01 dez. 2025.APA
Chen, H., Feng, S., Wan, J., Yang, K., Wang, Y., Lv, Y., et al. (2026). Predicting chemical composition of corn silage using tree-based machine learning models: model development, feature analysis, and practical application. Bioresource Technology, 441, 1-9. doi:10.1016/j.biortech.2025.133542NLM
Chen H, Feng S, Wan J, Yang K, Wang Y, Lv Y, Sun C, Lei B, Nussio LG, Yang F, Zhang Y, Wang X. Predicting chemical composition of corn silage using tree-based machine learning models: model development, feature analysis, and practical application [Internet]. Bioresource Technology. 2026 ; 441 1-9.[citado 2025 dez. 01 ] Available from: https://doi.org/10.1016/j.biortech.2025.133542Vancouver
Chen H, Feng S, Wan J, Yang K, Wang Y, Lv Y, Sun C, Lei B, Nussio LG, Yang F, Zhang Y, Wang X. Predicting chemical composition of corn silage using tree-based machine learning models: model development, feature analysis, and practical application [Internet]. Bioresource Technology. 2026 ; 441 1-9.[citado 2025 dez. 01 ] Available from: https://doi.org/10.1016/j.biortech.2025.133542
