Microbiome-driven machine learning for predicting suppressiveness to Rhizoctonia solani in organic-amended soils (2025)
- Authors:
- Autor USP: MENDES, LUCAS WILLIAM - CENA
- Unidade: CENA
- DOI: 10.1016/j.apsoil.2025.106409
- Subjects: ECOLOGIA MICROBIANA; REDES NEURAIS; SOLOS; LINGUAGEM DE MÁQUINA
- Keywords: Floresta aleatória; Índice de biocontrole; Microbioma do solo
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Applied Soil Ecology
- ISSN: 0929-1393
- Volume/Número/Paginação/Ano: v. 214, art. 106409, p. 1-9, 2025
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
BUTTRÓS, Victor Hugo Teixeira et al. Microbiome-driven machine learning for predicting suppressiveness to Rhizoctonia solani in organic-amended soils. Applied Soil Ecology, v. 214, p. 1-9, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.apsoil.2025.106409. Acesso em: 03 mar. 2026. -
APA
Buttrós, V. H. T., Kurm, V., Lacerda, W. S., Guimarães, P. H. S., Mendes, L. W., & Dória, J. (2025). Microbiome-driven machine learning for predicting suppressiveness to Rhizoctonia solani in organic-amended soils. Applied Soil Ecology, 214, 1-9. doi:10.1016/j.apsoil.2025.106409 -
NLM
Buttrós VHT, Kurm V, Lacerda WS, Guimarães PHS, Mendes LW, Dória J. Microbiome-driven machine learning for predicting suppressiveness to Rhizoctonia solani in organic-amended soils [Internet]. Applied Soil Ecology. 2025 ; 214 1-9.[citado 2026 mar. 03 ] Available from: https://doi.org/10.1016/j.apsoil.2025.106409 -
Vancouver
Buttrós VHT, Kurm V, Lacerda WS, Guimarães PHS, Mendes LW, Dória J. Microbiome-driven machine learning for predicting suppressiveness to Rhizoctonia solani in organic-amended soils [Internet]. Applied Soil Ecology. 2025 ; 214 1-9.[citado 2026 mar. 03 ] Available from: https://doi.org/10.1016/j.apsoil.2025.106409 - Microbial enzymatic stoichiometry and the acquisition of C, N, and P in soils under different land-use types in Brazilian semiarid
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Informações sobre o DOI: 10.1016/j.apsoil.2025.106409 (Fonte: oaDOI API)
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