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Spectral data of tropical soils using dry-chemistry techniques (VNIR, XRF, and LIBS): A dataset for soil fertility prediction.
Tavares, Tiago Rodrigues; Molin, José Paulo; Nunes, Lidiane Cristina; Alves, Elton Eduardo Novais; Krug, Francisco José; de Carvalho, Hudson Wallace Pereira.
Afiliação
  • Tavares TR; Department of Biosystems Engineering, "Luiz de Queiroz" College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba, São Paulo 13418900, Brazil.
  • Molin JP; Center for Nuclear Energy in Agriculture (CENA), University of São Paulo (USP), Piracicaba, São Paulo 13416000, Brazil.
  • Nunes LC; Department of Biosystems Engineering, "Luiz de Queiroz" College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba, São Paulo 13418900, Brazil.
  • Alves EEN; Center for Nuclear Energy in Agriculture (CENA), University of São Paulo (USP), Piracicaba, São Paulo 13416000, Brazil.
  • Krug FJ; Department of Chemistry, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, 31270-901, Brazil.
  • de Carvalho HWP; CESFRA, AgroBioSciences, Mohammed VI Polytechnic University, BenGuerir 43150, Morocco.
Data Brief ; 41: 108004, 2022 Apr.
Article em En | MEDLINE | ID: mdl-35274030
Proximal soil sensing technologies, such as visible and near infrared diffuse reflectance spectroscopy (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS), are dry-chemistry techniques that enable rapid and environmentally friendly soil fertility analyses. The application of XRF and LIBS sensors in an individual or combined manner for soil fertility prediction is quite recent, especially in tropical soils. The shared dataset presents spectral data of VNIR, XRF, and LIBS sensors, even as the characterization of key soil fertility attributes (clay, organic matter, cation exchange capacity, pH, base saturation, and exchangeable P, K, Ca, and Mg) of 102 soil samples. The samples were obtained from two Brazilian agricultural areas and have a wide variation of chemical and textural attributes. This is a pioneer dataset of tropical soils, with potential to be reused for comparative studies with other datasets, e.g., comparing the performance of sensors, instrumental conditions, and/or predictive models on different soil types, soil origin, concentration range, and agricultural practices. Moreover, it can also be applied to compose soil spectral libraries that use spectral data collected under similar instrumental conditions.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Data Brief Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Brasil País de publicação: Holanda
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Data Brief Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Brasil País de publicação: Holanda