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Artigo de periodico
Quality Indicators to Subsidize Soil Conservation Under Pasture in Brazil (2025)
Costa, Adriana Monteiro da; Fernandes, Luís Filipe Sanches; Pacheco, Fernando António Leal ; Valera, Carlos Alberto
Source: Land Degradation & Development. Unidade: ESALQ
Subjects: CONSERVAÇÃO DO SOLO, PASTAGENS, QUALIDADE DO SOLO
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ABNT
COSTA, Adriana Monteiro da et al. Quality Indicators to Subsidize Soil Conservation Under Pasture in Brazil. Land Degradation & Development, p. 1-28, 2025Tradução . . Disponível em: https://doi.org/10.1002/ldr.5504. Acesso em: 08 out. 2025.
APA
Costa, A. M. da, Fernandes, L. F. S., Pacheco, F. A. L., & Valera, C. A. (2025). Quality Indicators to Subsidize Soil Conservation Under Pasture in Brazil. Land Degradation & Development, 1-28. doi:10.1002/ldr.5504
NLM
Costa AM da, Fernandes LFS, Pacheco FAL, Valera CA. Quality Indicators to Subsidize Soil Conservation Under Pasture in Brazil [Internet]. Land Degradation & Development. 2025 ; 1-28.[citado 2025 out. 08 ] Available from: https://doi.org/10.1002/ldr.5504
Vancouver
Costa AM da, Fernandes LFS, Pacheco FAL, Valera CA. Quality Indicators to Subsidize Soil Conservation Under Pasture in Brazil [Internet]. Land Degradation & Development. 2025 ; 1-28.[citado 2025 out. 08 ] Available from: https://doi.org/10.1002/ldr.5504
Artigo de periodico
Mapping soil thickness using a mechanistic model and machine learning approaches (2025)
Rosin, Nícolas Augusto ; Mello, Danilo César de; Bonfatti, Benito R; Hartemink, Alfred E; Ferreira, Tiago Osorio ; Silvero, Nelida Elizabet Quiñonez ; Poppiel, Raul Roberto ; Mendes, Wanderson de S; Veloso, Gustavo Vieira; Francelino, Márcio Rocha; Alves, Marcelo Rodrigo; Falcioni, Renan; Demattê, José Alexandre Melo
Source: Catena. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, MAPEAMENTO DO SOLO, MORFOLOGIA DO SOLO, SOLOS
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ABNT
ROSIN, Nícolas Augusto et al. Mapping soil thickness using a mechanistic model and machine learning approaches. Catena, v. 249, p. 1-17, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.catena.2024.108621. Acesso em: 08 out. 2025.
APA
Rosin, N. A., Mello, D. C. de, Bonfatti, B. R., Hartemink, A. E., Ferreira, T. O., Silvero, N. E. Q., et al. (2025). Mapping soil thickness using a mechanistic model and machine learning approaches. Catena, 249, 1-17. doi:10.1016/j.catena.2024.108621
NLM
Rosin NA, Mello DC de, Bonfatti BR, Hartemink AE, Ferreira TO, Silvero NEQ, Poppiel RR, Mendes W de S, Veloso GV, Francelino MR, Alves MR, Falcioni R, Demattê JAM. Mapping soil thickness using a mechanistic model and machine learning approaches [Internet]. Catena. 2025 ; 249 1-17.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.catena.2024.108621
Vancouver
Rosin NA, Mello DC de, Bonfatti BR, Hartemink AE, Ferreira TO, Silvero NEQ, Poppiel RR, Mendes W de S, Veloso GV, Francelino MR, Alves MR, Falcioni R, Demattê JAM. Mapping soil thickness using a mechanistic model and machine learning approaches [Internet]. Catena. 2025 ; 249 1-17.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/j.catena.2024.108621
Artigo de periodico
Analysis of organic and mineral nitrogen, total organic carbon and humic fractions in Ferralsols: an approach using Vis-NIR-SWIR, MIR and X-ray fluorescence spectroscopy (2024)
Lima, Bruna Coelho de; Santos, Carlos H. dos; Tiritan, Carlos S; Demattê, José Alexandre Melo ; Gomez, Andres Mauricio Rico ; Albarracín, Heidy Soledad Rodríguez ; Bartsch, Bruno dos Anjos
Source: Discover Environment. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, CARBONO, ESPECTROSCOPIA, HÚMUS, MATÉRIA ORGÂNICA DO SOLO, NITROGÊNIO, SOLO ARENOSO
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ABNT
LIMA, Bruna Coelho de et al. Analysis of organic and mineral nitrogen, total organic carbon and humic fractions in Ferralsols: an approach using Vis-NIR-SWIR, MIR and X-ray fluorescence spectroscopy. Discover Environment, v. 2, p. 1-19, 2024Tradução . . Disponível em: https://doi.org/10.1007/s44274-024-00097-3. Acesso em: 08 out. 2025.
APA
Lima, B. C. de, Santos, C. H. dos, Tiritan, C. S., Demattê, J. A. M., Gomez, A. M. R., Albarracín, H. S. R., & Bartsch, B. dos A. (2024). Analysis of organic and mineral nitrogen, total organic carbon and humic fractions in Ferralsols: an approach using Vis-NIR-SWIR, MIR and X-ray fluorescence spectroscopy. Discover Environment, 2, 1-19. doi:10.1007/s44274-024-00097-3
NLM
Lima BC de, Santos CH dos, Tiritan CS, Demattê JAM, Gomez AMR, Albarracín HSR, Bartsch B dos A. Analysis of organic and mineral nitrogen, total organic carbon and humic fractions in Ferralsols: an approach using Vis-NIR-SWIR, MIR and X-ray fluorescence spectroscopy [Internet]. Discover Environment. 2024 ; 2 1-19.[citado 2025 out. 08 ] Available from: https://doi.org/10.1007/s44274-024-00097-3
Vancouver
Lima BC de, Santos CH dos, Tiritan CS, Demattê JAM, Gomez AMR, Albarracín HSR, Bartsch B dos A. Analysis of organic and mineral nitrogen, total organic carbon and humic fractions in Ferralsols: an approach using Vis-NIR-SWIR, MIR and X-ray fluorescence spectroscopy [Internet]. Discover Environment. 2024 ; 2 1-19.[citado 2025 out. 08 ] Available from: https://doi.org/10.1007/s44274-024-00097-3
Artigo de periodico
The Brazilian Soil Spectral Library data opening (2024)
Novais, Jean de Jesus Macedo ; Rosin, Nícolas Augusto ; Rosas, Jorge Tadeu Fim ; Poppiel, Raul Roberto ; Dotto, André Carnieletto ; Paiva, Ariane Francine da Silveira ; Bellinaso, Henrique ; Albarracín, Heidy Soledad Rodríguez ; Amorim, Merilyn Taynara Accorsi ; Bartsch, Bruno dos Anjos ; Vogel, Letícia Guadagnin ; Mello, Danilo César de ; Francelino, M. R; Alves, M. R; Falcioni, R; Demattê, Jose Alexandre Melo
Source: Dokuchaev Soil Bulletin. Unidade: ESALQ
Subjects: ANÁLISE DO SOLO, BASES DE DADOS, ESPECTROSCOPIA
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ABNT
NOVAIS, Jean de Jesus Macedo et al. The Brazilian Soil Spectral Library data opening. Dokuchaev Soil Bulletin, v. 119, p. 261-305, 2024Tradução . . Disponível em: https://doi.org/10.19047/0136-1694-2024-119-261-305. Acesso em: 08 out. 2025.
APA
Novais, J. de J. M., Rosin, N. A., Rosas, J. T. F., Poppiel, R. R., Dotto, A. C., Paiva, A. F. da S., et al. (2024). The Brazilian Soil Spectral Library data opening. Dokuchaev Soil Bulletin, 119, 261-305. doi:10.19047/0136-1694-2024-119-261-305
NLM
Novais J de JM, Rosin NA, Rosas JTF, Poppiel RR, Dotto AC, Paiva AF da S, Bellinaso H, Albarracín HSR, Amorim MTA, Bartsch B dos A, Vogel LG, Mello DC de, Francelino MR, Alves MR, Falcioni R, Demattê JAM. The Brazilian Soil Spectral Library data opening [Internet]. Dokuchaev Soil Bulletin. 2024 ; 119 261-305.[citado 2025 out. 08 ] Available from: https://doi.org/10.19047/0136-1694-2024-119-261-305
Vancouver
Novais J de JM, Rosin NA, Rosas JTF, Poppiel RR, Dotto AC, Paiva AF da S, Bellinaso H, Albarracín HSR, Amorim MTA, Bartsch B dos A, Vogel LG, Mello DC de, Francelino MR, Alves MR, Falcioni R, Demattê JAM. The Brazilian Soil Spectral Library data opening [Internet]. Dokuchaev Soil Bulletin. 2024 ; 119 261-305.[citado 2025 out. 08 ] Available from: https://doi.org/10.19047/0136-1694-2024-119-261-305
Artigo de periodico
Sensing technologies for characterizing and monitoring soil functions: a review (2022)
Silvero, Nélida Elizabet Quinõnez ; Demattê, José Alexandre Melo ; Minasny, Budiman; Rosin, Nícolas Augusto ; Nascimento, Jessica García; Albarracín, Heidy Soledad Rodríguez ; Bellinaso, Henrique ; Gómez, Andrés Mauricio Rico
Source: Advances in Agronomy. Unidade: ESALQ
Subjects: ESPECTROSCOPIA, MONITORAÇÃO DOS SOLOS, SENSORIAMENTO REMOTO, SOLOS, VARIABILIDADE ESPACIAL
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ABNT
SILVERO, Nélida Elizabet Quinõnez et al. Sensing technologies for characterizing and monitoring soil functions: a review. Advances in Agronomy, p. 1-44, 2022Tradução . . Disponível em: https://doi.org/10.1016/bs.agron.2022.08.002. Acesso em: 08 out. 2025.
APA
Silvero, N. E. Q., Demattê, J. A. M., Minasny, B., Rosin, N. A., Nascimento, J. G., Albarracín, H. S. R., et al. (2022). Sensing technologies for characterizing and monitoring soil functions: a review. Advances in Agronomy, 1-44. doi:10.1016/bs.agron.2022.08.002
NLM
Silvero NEQ, Demattê JAM, Minasny B, Rosin NA, Nascimento JG, Albarracín HSR, Bellinaso H, Gómez AMR. Sensing technologies for characterizing and monitoring soil functions: a review [Internet]. Advances in Agronomy. 2022 ; 1-44.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/bs.agron.2022.08.002
Vancouver
Silvero NEQ, Demattê JAM, Minasny B, Rosin NA, Nascimento JG, Albarracín HSR, Bellinaso H, Gómez AMR. Sensing technologies for characterizing and monitoring soil functions: a review [Internet]. Advances in Agronomy. 2022 ; 1-44.[citado 2025 out. 08 ] Available from: https://doi.org/10.1016/bs.agron.2022.08.002
Artigo de periodico
Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning (2020)
Ng, Wartini; Minasny, Budiman; Mendes, Wanderson de Sousa ; Dematte, Jose Alexandre Melo
Source: Soil Discussions. Unidade: ESALQ
Subjects: ANÁLISE DO SOLO, APRENDIZADO COMPUTACIONAL, ESPECTROSCOPIA INFRAVERMELHA, REDES NEURAIS, SOLOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
NG, Wartini et al. Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning. Soil Discussions, v. 6, p. 565–578, 2020Tradução . . Disponível em: https://soil.copernicus.org/articles/6/565/2020/soil-6-565-2020.pdf. Acesso em: 08 out. 2025.
APA
Ng, W., Minasny, B., Mendes, W. de S., & Dematte, J. A. M. (2020). Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning. Soil Discussions, 6, 565–578. doi:10.5194/soil-2019-48
NLM
Ng W, Minasny B, Mendes W de S, Dematte JAM. Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning [Internet]. Soil Discussions. 2020 ; 6 565–578.[citado 2025 out. 08 ] Available from: https://soil.copernicus.org/articles/6/565/2020/soil-6-565-2020.pdf
Vancouver
Ng W, Minasny B, Mendes W de S, Dematte JAM. Estimation of effective calibration sample size using visible near infrared spectroscopy: deep learning vs machine learning [Internet]. Soil Discussions. 2020 ; 6 565–578.[citado 2025 out. 08 ] Available from: https://soil.copernicus.org/articles/6/565/2020/soil-6-565-2020.pdf

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