A soil productivity system reveals most Brazilian agricultural lands are below their maximum potential (2023)
- Authors:
- USP affiliated authors: DEMATTE, JOSE ALEXANDRE MELO - ESALQ ; SILVERO, NÉLIDA ELIZABET QUIÑONEZ - ESALQ ; GRESCHUK, LUCAS TADEU - ESALQ ; ROSIN, NÍCOLAS AUGUSTO - ESALQ
- Unidade: ESALQ
- DOI: 10.1038/s41598-023-39981-y
- Assunto: SOLO AGRÍCOLA
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Scientific Reports
- ISSN: 2045-2322
- Volume/Número/Paginação/Ano: v. 13, art. 14103, p. 1-14, 2023
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
GRESCHUK, Lucas Tadeu et al. A soil productivity system reveals most Brazilian agricultural lands are below their maximum potential. Scientific Reports, v. 13, p. 1-14, 2023Tradução . . Disponível em: https://doi.org/10.1038/s41598-023-39981-y. Acesso em: 09 fev. 2026. -
APA
Greschuk, L. T., Demattê, J. A. M., Silvero, N. E. Q., & Rosin, N. A. (2023). A soil productivity system reveals most Brazilian agricultural lands are below their maximum potential. Scientific Reports, 13, 1-14. doi:10.1038/s41598-023-39981-y -
NLM
Greschuk LT, Demattê JAM, Silvero NEQ, Rosin NA. A soil productivity system reveals most Brazilian agricultural lands are below their maximum potential [Internet]. Scientific Reports. 2023 ; 13 1-14.[citado 2026 fev. 09 ] Available from: https://doi.org/10.1038/s41598-023-39981-y -
Vancouver
Greschuk LT, Demattê JAM, Silvero NEQ, Rosin NA. A soil productivity system reveals most Brazilian agricultural lands are below their maximum potential [Internet]. Scientific Reports. 2023 ; 13 1-14.[citado 2026 fev. 09 ] Available from: https://doi.org/10.1038/s41598-023-39981-y - Mapping Brazilian soil mineralogy using proximal and remote sensing data
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- Pedometria: histórico, princípios e aplicações
- The fundamental of the effects of water, organic matter, and iron forms on the pXRF information in soil analyses
- Potential of soil minerals to sequester soil organic carbon
- Mapping soil thickness using a mechanistic model and machine learning approaches
- Detection of bare soils in sugarcane areas by temporal satellite images: a monitoring technique for soil security
- The Brazilian program of soil analysis via spectroscopy (ProBASE): combining spectroscopy and wet laboratories to understand new technologies
- Improving the monitoring of sugarcane residues in a tropical environment based on laboratory and Sentinel-2 data
Informações sobre o DOI: 10.1038/s41598-023-39981-y (Fonte: oaDOI API)
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