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dc.creatorFaria, Alvaro José Gomes de-
dc.creatorSilva, Sérgio Henrique Godinho-
dc.creatorAndrade, Renata-
dc.creatorMancini, Marcelo-
dc.creatorMelo, Leônidas Carrijo Azevedo-
dc.creatorWeindorf, David C.-
dc.creatorGuilherme, Luiz Roberto Guimarães-
dc.creatorCuri, Nilton-
dc.date.accessioned2022-07-01T16:50:53Z-
dc.date.available2022-07-01T16:50:53Z-
dc.date.issued2022-03-
dc.identifier.citationANDRADE, R. et al. Proximal sensor data fusion and auxiliary information for tropical soil property prediction: soil texture. Geoderma, [S.l.], v. 422, p. 1-12, Sept. 2022. DOI: 10.1016/j.geoderma.2022.115936.pt_BR
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S2352009421001061pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/50429-
dc.description.abstractSoil organic matter (SOM) measurement is of great agricultural and environmental importance. Thus, the development of rapid, environmentally-friendly, economical and reliable assessment methods is challenging. Soil proximal sensors have become an important approach for SOM prediction worldwide, but require regional calibration. This work aimed to assess the efficiency of SOM content prediction using the Nix Pro™ color sensor and portable X-ray fluorescence (pXRF) spectrometry, either separately or combined. The type of soil horizon collected (A or B) was used as auxiliary input data. A total of 705 Brazilian variable soil samples were analyzed in the laboratory for SOM content and scanned by Nix Pro™ and pXRF. Via Nix Pro™, samples were analyzed both dry and moist since moisture changes their color. Prediction models were built using 70% of the data via the stepwise multiple linear regression (SMLR), support vector machine with linear kernel (SVM) and random forest (RF). Validation was performed with the remaining 30% of the data through the coefficient of determination (R2), the root mean square error (RMSE) and the residual prediction deviation (RPD). SOM content was predicted with good accuracy (R2 = 0.73, RMSE = 1.09% and RPD = 2.00) using the RF algorithm trained with combined data from the Nix Pro™ and pXRF sensors. Soil horizons and Ca content were the two most important predictor variables. The combination of data obtained by Nix Pro™ and pXRF yielded accurate SOM predictions for a wide variety of Brazilian soils, in addition to being environmentally-friendly, without generating chemical waste.pt_BR
dc.languageen_USpt_BR
dc.publisherElsevierpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceGeodermapt_BR
dc.subjectProximal sensorspt_BR
dc.subjectRandom forestpt_BR
dc.subjectpXRFpt_BR
dc.subjectSoil colorpt_BR
dc.subjectSoil modelingpt_BR
dc.subjectTropical soilspt_BR
dc.titleProximal sensor data fusion and auxiliary information for tropical soil property prediction: soil texturept_BR
dc.typeArtigopt_BR
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