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Tropical soil pH and sorption complex prediction via portable X-ray fluorescence spectrometry

dos Santos Teixeira, Anita Fernanda ; Henrique Procópio Pelegrino, Marcelo ; Missina Faria, Wilson ; Henrique Godinho Silva, Sérgio ; Gabriela Marcolino Gonçalves, Mariana ; Weimar Acerbi Júnior, Fausto ; Rezende Gomide, Lucas ; Linares Pádua Júnior, Alceu ; de Souza, Igor Alexandre ; Chakraborty, Somsubhra ; Weindorf, David C. ; Roberto Guimarães Guilherme, Luiz ; Curi, Nilton

Geoderma, 2020-03, Vol.361, p.114132, Article 114132 [Periódico revisado por pares]

Elsevier B.V

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  • Título:
    Tropical soil pH and sorption complex prediction via portable X-ray fluorescence spectrometry
  • Autor: dos Santos Teixeira, Anita Fernanda ; Henrique Procópio Pelegrino, Marcelo ; Missina Faria, Wilson ; Henrique Godinho Silva, Sérgio ; Gabriela Marcolino Gonçalves, Mariana ; Weimar Acerbi Júnior, Fausto ; Rezende Gomide, Lucas ; Linares Pádua Júnior, Alceu ; de Souza, Igor Alexandre ; Chakraborty, Somsubhra ; Weindorf, David C. ; Roberto Guimarães Guilherme, Luiz ; Curi, Nilton
  • Assuntos: Base saturation ; CEC ; Cubist ; pXRF ; Random forest ; Soil fertility
  • É parte de: Geoderma, 2020-03, Vol.361, p.114132, Article 114132
  • Descrição: •pXRF was used to predict five different soil properties in tropical soils.•Three different algorithms were tested and compared: Cubist, RF and SMLR.•Sum of bases, base saturation percentage, and Alsat achieved better results.•CaO was the most influential variable for soil properties prediction.•pXRF can accurately predict fertility properties for varying Brazilian soils. Portable X-ray fluorescence (pXRF) spectrometry delivers results rapidly, at low-cost, and without generating chemical residues. This study aimed to predict soil pH, sum of bases (SB), base saturation percentage (BSP), cation exchange capacity (CEC), and Al saturation (Alsat) of 2017 contrasting Brazilian soil samples through the association of pXRF and three different algorithms [Cubist, Random forest (RF), and stepwise multiple linear regression (SMLR)]. Soil samples were collected from the surface (SURF) and subsurface (SUB) horizons in seven Brazilian states. The prediction models were generated for the SURF and SUB horizons separately and combined (SURF + SUB dataset). Overall, the best predictions were achieved via Cubist followed by RF. For the pH predictions, the model combining SURF and SUB horizons data presented better results. Satisfactory results were achieved for the predictions of SB (validation R2 = 0.86), BSP (validation R2 = 0.81) and Alsat (R2 = 0.76). Moreover, promising results were obtained for predicting pH (R2 = 0.63). Notably, CaO appeared as the most influential variable for soil property prediction models. Overall, pXRF showed great potential for predicting soil fertility properties for diversified tropical soils with low cost, rapidity, and without chemical waste generation.
  • Editor: Elsevier B.V
  • Idioma: Inglês
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