Register      Login
Soil Research Soil Research Society
Soil, land care and environmental research

Articles citing this paper

Tree-based techniques to predict soil units

H. S. K. Pinheiro A D , P. R. Owens B , L. H. C. Anjos A , W. Carvalho Júnior C and C. S. Chagas C
+ Author Affiliations
- Author Affiliations

A Agronomy Institute – Soil Department, Federal Rural University of Rio de Janeiro, Rodovia BR 465, Km 7, Campus Universitário, Zona Rural, 23897-000 Seropédica, RJ, Brazil.

B USDA Dale Bumpers Small Farms Research Center, 6883 S State Highway 23, Booneville, AR 72927, USA.

C Embrapa Solos (National Center of Soil Research), R. Jardim Botânico 1024, Rio de Janeiro, RJ, Brazil.

D Corresponding author. Email: lenask@gmail.com

Soil Research 55(8) 788-798 https://doi.org/10.1071/SR16060
Submitted: 5 March 2016  Accepted: 24 April 2017   Published: 1 June 2017



5 articles found in Crossref database.

Prediction of mechanical properties of carbon fiber based on cross-scale FEM and machine learning
Qi Zhenchao, Zhang Nanxi, Liu Yong, Chen Wenliang
Composite Structures. 2019 212 p.199
Digital soil mapping for soil types using machine learning approaches at the landscape scale in the arid regions of Iran
Manteghi Shaho, Moravej Kamran, Mousavi Seyed Roohollah, Delavar Mohammad Amir, Mastinu Andrea
Advances in Space Research. 2024
A multisensoral approach for high-resolution land cover and pasture degradation mapping in the humid tropics: A case study of the fragmented landscape of Rio de Janeiro
Naegeli de Torres Friederike, Richter Ronny, Vohland Michael
International Journal of Applied Earth Observation and Geoinformation. 2019 78 p.189
Assessment of Phytoecological Variability by Red-Edge Spectral Indices and Soil-Landscape Relationships
Pinheiro Helena S. K., Barbosa Theresa P. R., Antunes Mauro A. H., Carvalho Daniel Costa de, Nummer Alexis R., Carvalho Junior Waldir de, Chagas Cesar da Silva, Fernandes-Filho Elpídio I., Pereira Marcos Gervasio
Remote Sensing. 2019 11(20). p.2448
Assessing the influence of environmental factors and datasets on soil type prediction with two machine learning algorithms in a heterogeneous area in the Rur catchment, Germany
Kramm Tanja, Hoffmeister Dirk
Geoderma Regional. 2020 22 p.e00316

Committee on Publication Ethics


Abstract Export Citation Get Permission