Analysis of organic and mineral nitrogen, total organic carbon and humic fractions in Ferralsols: an approach using Vis-NIR-SWIR, MIR and X-ray fluorescence spectroscopy

Abstract

Abstract This work aimed to develop suitable predictive models for ammonium, nitrate, total nitrogen, total organic carbon and soil humic fractions, for Ferralsols, using Vis-NIR-SWIR, MIR and X-ray fluorescence spectroscopic techniques in conjunction with machine learning algorithms, Cubist, PLSR, Random Forest and Support Vector Machine. Chemical analyzes were carried out to determine nitrate, total nitrogen, total organic carbon and chemical fractionation of soil organic matter, as well as spectral analyzes using Vis-NIR-SWIR spectroscopy, MIR and X-ray fluorescence. The spectroscopy results were processed using RStudio v. 4.1.3, applying Cusbist, PLSR, Random Forest and Support Vector Machine machine learning algorithms to create predictive models and describe spectral curves and Pearson correlation. Of the prediction models developed for nitrogen, total organic carbon and humic fractions, the PLSR and Support Vector Machine algorithms presented the best predictive performances. The descriptive analysis of the spectra identified the main absorption bands and the location of the bands sensitive to the attributes of interest. The correlation analysis proposed that the use of Vis-NIR-SWIR, MIR and XRF spectroscopic techniques were effective in predicting the contents of nitrogen, total organic carbon and humic fractions in soil with a medium sandy texture. However, it is important to highlight that each technique has its characteristic mechanism of action, Vis-NIR-SWIR and MIR detect the element based on overtones and fundamental tones, while XRF is based on the atomic number of the elements or elemental association.

Keywords

• Supervised machine learning
• Spectroscopic techniques
• Accurate fertilization Sandy soil
• Ammonium and nitrate quantification
• Organic matter quantification
• Soil health