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dc.creatorAndrade, Renata-
dc.creatorSilva, Sérgio Henrique Godinho-
dc.creatorWeindorf, David C.-
dc.creatorChakraborty, Somsubhra-
dc.creatorFaria, Wilson Missina-
dc.creatorGuilherme, Luiz Roberto Guimarães-
dc.creatorCuri, Nilton-
dc.date.accessioned2022-02-08T19:46:07Z-
dc.date.available2022-02-08T19:46:07Z-
dc.date.issued2021-12-
dc.identifier.citationANDRADE, R. et al. Micronutrients prediction via pXRF spectrometry in Brazil: influence of weathering degree. Geoderma Regional, [S. l.], v. 27, e00431, Dec. 2021. DOI: 10.1016/j.geodrs.2021.e00431.pt_BR
dc.identifier.urihttps://doi.org/10.1016/j.geodrs.2021.e00431pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/49218-
dc.description.abstractManagement of micronutrient levels in soils must be done carefully to avoid their deficiency or toxicity to plants. The laboratory determination of micronutrient contents is time-consuming, expensive and generates chemical wastes, making it difficult for soil surveys required in precision agriculture, especially in tropical countries. While proximal sensors like portable X-ray fluorescence (pXRF) spectrometry have been successfully used to predict contents of soil available macronutrient, little effort has focused on micronutrients, especially involving a large dataset, soils weathering degree and a practical application of the predictions. This study aimed to use pXRF data for the prediction of available micronutrients in 1514 samples from variable soil classes (from Entisols to Oxisols) from seven Brazilian states using machine learning algorithms and to assess the influence of soil weathering degree on such prediction models. The soil samples were collected from both surface (A) and subsurface (B or C) horizons of various soil classes under several land uses, and with varying parent materials. Available B, Cu, Fe, Mn, and Zn were predicted via stepwise multiple linear regression (SMLR), support vector machine (SVM), extreme gradient boosting (XGB), and random forest (RF) algorithms and subsequently validated. The best prediction models were classified according to micronutrient availability classes (categorical validation). Adequate predictions were achieved for Cu: R2 = 0.80; RPD = 2.28; Mn: 0.68; 1.76; and Zn: 0.68; 1.70. Predictions of B, Cu, Fe, Mn, and Zn availability classes yielded overall accuracy of 0.90, 0.65, 0.67, 0.73, and 0.53, respectively. Summarily, pXRF data in conjunction with prediction models can be an effective and rapid method to determine available Cu, Mn, and Zn. Soil weathering degree must be considered on such predictions as they strongly influence model accuracy.pt_BR
dc.languageen_USpt_BR
dc.publisherElsevierpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceGeoderma Regionalpt_BR
dc.subjectMicronutrientspt_BR
dc.subjectSoils of the tropicspt_BR
dc.subjectPrediction modelspt_BR
dc.subjectWeathering-leachingpt_BR
dc.subjectSoil fertilitypt_BR
dc.subjectPlant nutritionpt_BR
dc.subjectMicronutrientespt_BR
dc.subjectSolos dos trópicospt_BR
dc.subjectModelos de previsãopt_BR
dc.subjectLixiviação por intemperismopt_BR
dc.subjectFertilidade do solopt_BR
dc.subjectNutrição de plantaspt_BR
dc.titleMicronutrients prediction via pXRF spectrometry in Brazil: influence of weathering degreept_BR
dc.typeArtigopt_BR
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