Rapid soil fertility prediction using X-ray fluorescence data and machine learning algorithms

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorBenedet, Lucas-
Autor(es): dc.creatorAcuña-Guzman, Salvador F.-
Autor(es): dc.creatorFaria, Wilson Missina-
Autor(es): dc.creatorSilva, Sérgio Henrique Godinho-
Autor(es): dc.creatorMancini, Marcelo-
Autor(es): dc.creatorTeixeira, Anita Fernanda dos Santos-
Autor(es): dc.creatorPierangeli, Luiza Maria Pereira-
Autor(es): dc.creatorAcerbi Júnior, Fausto Weimar-
Autor(es): dc.creatorGomide, Lucas Rezende-
Autor(es): dc.creatorPádua Júnior, Alceu Linares-
Autor(es): dc.creatorSouza, Igor Alexandre de-
Autor(es): dc.creatorMenezes, Michele Duarte de-
Autor(es): dc.creatorMarques, João José-
Autor(es): dc.creatorGuilherme, Luiz Roberto Guimarães-
Autor(es): dc.creatorCuri, Nilton-
Data de aceite: dc.date.accessioned2026-02-09T11:27:23Z-
Data de disponibilização: dc.date.available2026-02-09T11:27:23Z-
Data de envio: dc.date.issued2022-01-30-
Data de envio: dc.date.issued2022-01-30-
Data de envio: dc.date.issued2021-01-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/49104-
Fonte completa do material: dc.identifierhttps://doi.org/10.1016/j.catena.2020.105003-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1141169-
Descrição: dc.descriptionConventional soil fertility analyses are laborious, costly, time-consuming, and produce hazardous waste. The high demand of these laboratory-based analyses prompted us to investigate an environment-friendly, rapid, and inexpensive methodology for soil fertility assessment. Portable X-ray fluorescence (pXRF) spectrometry allows the determination of total elemental concentration in soils quickly, simply and without hazardous waste production. However, incipient usage of this technology for the prediction of soil fertility properties has been reported for tropical conditions. Soil samples were collected from seven Brazilian states (n = 1975) aiming to use pXRF data to predict contents of available or exchangeable Ca2+, Mg2+, Al3+, K+ and P by testing different algorithms using 70% of the samples for model training, and the remaining 30% for model validation. In addition to point data predictions, the best performing models were applied to data obtained from a farm within the studied regions with a known cropping history to create soil fertility maps and illustrate another applicability of this approach. The attested use of pXRF data and machine learning algorithms stepwise Generalized Linear Model (GLM) and Random Forest (RF) to predict the contents of relevant soil fertility properties exhibited great potential. Validation of the models corroborated that RF resulted in more accurate predictions than GLM. Validation R2 values ranged from 0.59 to 0.82. Maps created were coherent with expected distributions of soil fertility attributes. This environment-friendly methodology may be used for the assessment of soil fertility properties in a wide range of tropical and subtropical soils with minimum waste generation and reduced costs.-
Idioma: dc.languageen-
Publicador: dc.publisherElsevier-
Direitos: dc.rightsrestrictAccess-
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Palavras-chave: dc.subjectPortable X-ray fluorescence spectrometry-
Palavras-chave: dc.subjectSoil fertility-
Palavras-chave: dc.subjectProximal sensor-
Palavras-chave: dc.subjectTropical soils-
Palavras-chave: dc.subjectSoil spatial variability-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectEspectrometria de fluorescência de raios X portátil-
Palavras-chave: dc.subjectFertilidade do solo-
Palavras-chave: dc.subjectSensor proximal-
Palavras-chave: dc.subjectSolos tropicais-
Palavras-chave: dc.subjectVariabilidade espacial do solo-
Palavras-chave: dc.subjectAprendizado de máquina-
Título: dc.titleRapid soil fertility prediction using X-ray fluorescence data and machine learning algorithms-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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