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

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

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


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.

Original languageEnglish
Article number114132
StatePublished - Mar 1 2020


  • Base saturation
  • CEC
  • Cubist
  • Random forest
  • Soil fertility
  • pXRF


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