Prediction of soil fertility via portable X-ray fluorescence (pXRF) spectrometry and soil texture in the Brazilian Coastal Plains

Renata Andrade, Wilson Missina Faria, Sérgio Henrique Godinho Silva, Somsubhra Chakraborty, David C. Weindorf, Luiz Felipe Mesquita, Luiz Roberto Guimarães Guilherme, Nilton Curi

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


Traditional methods of soil chemical analysis are time consuming, costly, and generate chemical waste. Proximal sensors, such as portable X-ray fluorescence (pXRF) spectrometry, may help to overcome these issues since they have been shown to produce accurate predictions of many soil properties. However, such processes need to be further investigated in Brazilian soils. This work aimed to assess the influence of soil management and mineralogy on elemental composition of soils and predict exchangeable Al3+, Ca2+, Mg2+, and available K+, and P contents from pXRF data alone and associated with soil texture through machine learning algorithms [stepwise generalized linear models (SGLM), and random forest (RF)] in soils of the Brazilian Coastal Plains (BCP). A total of 285 soil samples were collected from the A (n = 123) and B (n = 162) horizons and subjected to laboratory analyses and pXRF scans. Samples were randomly separated into 70% for modeling and 30% for validation. Soil mineralogy and management mainly influenced Al, and Ca and K total content, respectively. In general, the inclusion of the auxiliary input data of soil texture did not change the predictive power of the models. The best results highlight a considerable promise of pXRF technique for rapidly assessing exchangeable Ca2+ (RMSE = 176.3 mg kg 1, R2 = 0.71), Mg2+ (37.7 mg kg 1, 0.60), and available K+ (27.46 mg kg 1, 0.67). The algorithms could not generate reliable models to predict exchangeable Al3+ (30.6 mg kg 1, 0.47) and available P (19.9 mg kg 1, 0.14). In sum, pXRF can be used to reasonably predict soil fertility properties in the BCP soils. Further studies may extend predictions to other soil properties.

Original languageEnglish
Article number113960
StatePublished - Jan 1 2020


  • Kaolinitic soils
  • Machine learning algorithms
  • Proximal sensors
  • Soil texture
  • Spectroscopy


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