Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains
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 (...
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MDPI AG
2022-03-01
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| Online Access: | https://www.mdpi.com/2073-445X/11/3/363 |
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| author | Elton Mammadov Michael Denk Frank Riedel Cezary Kaźmierowski Karolina Lewinska Remigiusz Łukowiak Witold Grzebisz Amrakh I. Mamedov Cornelia Glaesser |
| author_facet | Elton Mammadov Michael Denk Frank Riedel Cezary Kaźmierowski Karolina Lewinska Remigiusz Łukowiak Witold Grzebisz Amrakh I. Mamedov Cornelia Glaesser |
| author_sort | Elton Mammadov |
| collection | DOAJ |
| description | 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, CaCO<sub>3</sub>, 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 (R<sup>2</sup>), 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 (R<sup>2</sup> = 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 CaCO<sub>3,</sub> 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 CaCO<sub>3</sub> 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 CaCO<sub>3</sub>, particle sizes, SOC, Fe, and spectral data yielded to their lower prediction accuracy. The major prediction mechanisms for M3-extractable elements relied on their relations with CaCO<sub>3</sub>, pH, clay content and mineralogy, and exchangeable cations in the context of their association with land use. The results can be used in mountain lands to evaluate and control the effect of management on soil quality indices and land degradation neutrality. Further studies are needed to develop most advantageous sampling schemes and modeling. |
| first_indexed | 2024-03-09T13:37:34Z |
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| last_indexed | 2024-03-09T13:37:34Z |
| publishDate | 2022-03-01 |
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| series | Land |
| spelling | doaj.art-e8b4ed23502f4c219550963c81ed820d2023-11-30T21:10:48ZengMDPI AGLand2073-445X2022-03-0111336310.3390/land11030363Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus MountainsElton Mammadov0Michael Denk1Frank Riedel2Cezary Kaźmierowski3Karolina Lewinska4Remigiusz Łukowiak5Witold Grzebisz6Amrakh I. Mamedov7Cornelia Glaesser8Institute of Soil Science and Agrochemistry, Azerbaijan National Academy of Sciences, 5 M. Rahim, Baku 1073, AzerbaijanInstitute of Geosciences and Geography, Martin Luther University Halle-Wittenberg, Von-Seckendorff Platz 4, 06120 Halle (Saale), GermanyInstitute of Geosciences and Geography, Martin Luther University Halle-Wittenberg, Von-Seckendorff Platz 4, 06120 Halle (Saale), GermanyDepartment of Environmental Remote Sensing and Soil Science, Adam Mickiewicz University in Poznan, Krygowskiego 10, 61-680 Poznan, PolandDepartment of Environmental Remote Sensing and Soil Science, Adam Mickiewicz University in Poznan, Krygowskiego 10, 61-680 Poznan, PolandDepartment of Agricultural Chemistry and Environmental Biogeochemistry, Nature University of Poznan, Wojska Polskiego 38/42, 60-625 Poznan, PolandDepartment of Agricultural Chemistry and Environmental Biogeochemistry, Nature University of Poznan, Wojska Polskiego 38/42, 60-625 Poznan, PolandFaculty of Agriculture, ALRC, Tottori University, 1390 Hamasaka, Tottori 680-0001, JapanInstitute of Geosciences and Geography, Martin Luther University Halle-Wittenberg, Von-Seckendorff Platz 4, 06120 Halle (Saale), GermanySoil 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, CaCO<sub>3</sub>, 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 (R<sup>2</sup>), 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 (R<sup>2</sup> = 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 CaCO<sub>3,</sub> 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 CaCO<sub>3</sub> 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 CaCO<sub>3</sub>, particle sizes, SOC, Fe, and spectral data yielded to their lower prediction accuracy. The major prediction mechanisms for M3-extractable elements relied on their relations with CaCO<sub>3</sub>, pH, clay content and mineralogy, and exchangeable cations in the context of their association with land use. The results can be used in mountain lands to evaluate and control the effect of management on soil quality indices and land degradation neutrality. Further studies are needed to develop most advantageous sampling schemes and modeling.https://www.mdpi.com/2073-445X/11/3/363VIS-NIR reflectance spectroscopyMehlich 3 extractable elementspartial least squares regressionprediction mechanismsCaucasus Mountains |
| spellingShingle | Elton Mammadov Michael Denk Frank Riedel Cezary Kaźmierowski Karolina Lewinska Remigiusz Łukowiak Witold Grzebisz Amrakh I. Mamedov Cornelia Glaesser Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains Land VIS-NIR reflectance spectroscopy Mehlich 3 extractable elements partial least squares regression prediction mechanisms Caucasus Mountains |
| title | Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains |
| title_full | Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains |
| title_fullStr | Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains |
| title_full_unstemmed | Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains |
| title_short | Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains |
| title_sort | determination of mehlich 3 extractable elements with visible and near infrared spectroscopy in a mountainous agricultural land the caucasus mountains |
| topic | VIS-NIR reflectance spectroscopy Mehlich 3 extractable elements partial least squares regression prediction mechanisms Caucasus Mountains |
| url | https://www.mdpi.com/2073-445X/11/3/363 |
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