Influence of auxiliary soil variables to improve PXRF-based soil fertility evaluation in India

Shubhadip Dasgupta, Somsubhra Chakraborty, David C. Weindorf, Bin Li, Sérgio Henrique Godinho Silva, Kallol Bhattacharyya

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Portable X-ray fluorescence (PXRF)spectrometry has already been established as a rapid and cost-effective tool for predicting various soil physicochemical properties. This study used PXRF in combination with physiographic, agro-climatic, soil parent-material, and physicochemical attributes (pH, electrical conductivity (EC), loss on ignition organic matter, and organic carbon) as auxiliary properties to predict multiple soil fertility indicators [available K, Ca, Mg, Fe, Cu, Zn, Mn, B, K/Mg ratio, total exchangeable bases (TEB), and sulfur availability index (SAI)] via four machine-learning algorithms (random forest, support vector regression, stepwise multiple linear regression, and an averaged model). Principal component analysis (PCA) indicated the links between PXRF-reported elements, agro-climatic zones, and soil parent materials. Although no universal prediction model can be selected to suit all 11 soil fertility parameters, three parameters (available Ca, Fe, and TEB) produced reasonable model performance with an R2 > 0.50 for most prediction model-dataset combinations. Concatenation of auxiliary soil parameters with PXRF data showed relative improvement in model accuracy compared to PXRF in isolation. Notably, the agro-climatic zone appeared influential for predicting available K, Mg, Zn, Fe, Mn, B, K/Mg ratio, and TEB. For potential fertilizer recommendation, six parameters (available K, Ca, Mg, Cu, Mn, and B) produced reasonable classification performance via the averaged model using all auxiliary predictors (κ > 0.30). The same categorical model was used, as an instance, for delineating a conceptualized framework for (PXRF+ auxiliary properties)-based fertilizer recommendation facilitating site-specific nutrient management. More research is needed to enhance model prediction/classification accuracy by including a well-balanced dataset and other relevant auxiliary variables with PXRF. Nevertheless, the importance of adding auxiliary soil properties with PXRF elemental data for cost-effective and accessible nutrient management in resource-poor countries seems promising.

Original languageEnglish
Article numbere00557
JournalGeoderma Regional
StatePublished - Sep 2022


  • Classification
  • Entisols
  • Fertilizer recommendation
  • Inceptisols
  • PXRF
  • Random forest
  • STCR
  • Soil fertility
  • Support vector regression


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