Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains

• Published in: Land (Basel) 2022-03, Vol.11 (3), p.363
• Main Authors: Mammadov, Elton, Denk, Michael, Riedel, Frank, Kaźmierowski, Cezary, Lewinska, Karolina, Łukowiak, Remigiusz, Grzebisz, Witold, Mamedov, Amrakh I., Glaesser, Cornelia
• Format: Article
• Language: English
• Subjects: Agricultural land; Agriculture; Cadmium; Calcium carbonate; Calibration; Cations; Caucasus Mountains; Chemicals; Clay; Clay minerals; Clay soils; Copper; Farming; Feasibility studies; Forecasting; Infrared spectroscopy; Iron; Land degradation; Land use; Land use planning; Lead; Least squares method; Manganese; Mehlich 3 extractable elements; Metals; Mineralogy; Mountain regions; Mountain soils; Mountains; Near infrared radiation; Nutrients; Organic carbon; Organic matter; Organic soils; partial least squares regression; pH effects; prediction mechanisms; Prediction models; Predictions; Regression analysis; Root-mean-square errors; Sand; Silt; Soil erosion; Soil fertility; Soil organic matter; Soil properties; Soil quality; Soil testing; Spectra; Spectroscopic analysis; Spectrum analysis; Topsoil; VIS-NIR reflectance spectroscopy; Water quality; Zinc
• ISSN: 2073-445X; 2073-445X
• DOI: 10.3390/land11030363

Soil spectroscopy is a promising alternative to evaluate and monitor soil and water quality, particularly in mountainous agricultural lands characterized by intense degradation and limited soil tests reports; a few studies have evaluated the feasibility of VIS-NIR spectroscopy to predict Mehlich 3 (M3) extractable nutrients. This study aimed to (i) examine the potential of VIS-NIR spectroscopy in combination with partial least squares regression to predict M3-extractable elements (Ca, K, Mg, P, Fe, Cd, Cu, Mn, Pb, and Zn) and basic soil properties (clay, silt, sand, CaCO3, pH, and soil organic carbon-SOC), (ii) find optimal pre-processing techniques, and (iii) determine primary prediction mechanisms for spectrally featureless soil properties. Topsoil samples were collected from a representative area (114 samples from 525 ha) located in the mountainous region of NW Azerbaijan. A series of pre-processing steps and transformations were applied to the spectral data, and the models were calibrated and evaluated based on the coefficient of determination (R2), root mean square error (RMSE), and the residual prediction deviation (RPD). The leave-one-out cross-validated predictions showed that the first derivative spectra produce higher prediction accuracies (R2 = 0.51–0.91; RPD = 1.20–2.29) for most soil properties. The evaluation of the model performance with optimal pre-processing techniques revealed that both calibration and validation models produce considerable differences in RPD values associated with sample size and the random partition of the calibration or validation subsets. The prediction models were excellent or very good (RPD > 2.0) for CaCO3, SOC, sand, silt, Ca, and Pb, good or fair (1.4 < RPD < 2.0) for clay, K, Cd, pH, Fe, Mn, and Cu, and poor (1.0 < RPD < 1.4) for Mg, P, and Zn. Principal component and correlation, stepwise regression analysis, and variable importance in projection procedures allowed to elucidate the underlying prediction mechanisms. Unlike the previous studies, the spectral estimations of pH, Ca, Mg, P, Fe, Pb, and Cd concentrations were linked to their correlation with CaCO3 rather than soil organic matter, whereas Mg and P concentrations were also connected to Fe-oxides. Soil particle sizes contributed to predicting K concentration but confounded the prediction of P and Zn concentration. The weaker correlations of Mn, Cu or Zn with CaCO3, particle sizes, SOC, Fe, and spectral data yielded to their lower prediction accuracy. Th