A soil quality index using Vis-NIR and pXRF spectra of a soil profile
•Soil profile had a considerable variation of soil properties and SQI with depth and within horizons.•Cubist model can be used to predict SQI.•The grid soil sampling can be used to characterize soil properties and SQI in soil profile wall.•Combined Vis-NIR and pXRF spectra had better prediction perf...
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| Published in | Catena (Giessen) Vol. 211; p. 105954 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.04.2022
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| Abstract | •Soil profile had a considerable variation of soil properties and SQI with depth and within horizons.•Cubist model can be used to predict SQI.•The grid soil sampling can be used to characterize soil properties and SQI in soil profile wall.•Combined Vis-NIR and pXRF spectra had better prediction performance than individual Vis-NIR and pXRF spectra.
A soil quality index (SQI) can be used to manage the soil for potential uses and remediate limitations of soil for sustainable agriculture and ecology. The objectives of this study were to (i) characterize vertical and horizontal variations of some soil properties and SQI in a Fluventic Haploxerepts profile in Eskişehir, Turkey, (ii) compare interpolation methods for mapping the distribution of soil properties and SQI in the profile wall, and (iii) assess cubist model for predicting soil properties and SQI using individual and combined Vis-NIR and pXRF spectra in the profile. The soil profile wall (1 m deep × 1 m wide) was divided into a raster of 10 × 10 cm intervals for collecting a total of 100 soil samples. The soil samples were analyzed for soil physical, chemical properties, and elemental concentrations and scanned using Vis-NIR (350–2500 nm) and pXRF (0–8 and 0–45 kV) spectrometers. Standard scoring function was used for standardization of indicators. Weighted linear combination method was used to calculate SQI and F-AHP for assigning weights of indicators. The 100 samples were split into calibration (70%) and validation (30%) datasets. Individual or combined Vis-NIR and pXRF spectra were used in cubist model to evaluate the feasibility for predicting soil properties and SQI in the Inceptisol. Results suggested that the soil profile had a considerable variation of soil properties and SQI with depth and within horizons. The A horizon had the highest SQI. In addition, Vis-NIR had better prediction performances for pH, SOM, Fe, and Mn (R2 = 0.42–0.88), whereas pXRF had better prediction performances for EC, CaCO3, P, and Zn (R2 = 0.18–0.90). In addition, combining Vis-NIR, pXRF8, and pXRF45 had better prediction performance for sand, silt, clay, K, Cu, and SQI (R2 = 0.81–0.97). We concluded that Vis-NIR and pXRF spectra can be successfully used to predict SQI in Fluventic Haploxerepts. Combined Vis-NIR and pXRF spectra had better prediction performance for SQI. |
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| AbstractList | •Soil profile had a considerable variation of soil properties and SQI with depth and within horizons.•Cubist model can be used to predict SQI.•The grid soil sampling can be used to characterize soil properties and SQI in soil profile wall.•Combined Vis-NIR and pXRF spectra had better prediction performance than individual Vis-NIR and pXRF spectra.
A soil quality index (SQI) can be used to manage the soil for potential uses and remediate limitations of soil for sustainable agriculture and ecology. The objectives of this study were to (i) characterize vertical and horizontal variations of some soil properties and SQI in a Fluventic Haploxerepts profile in Eskişehir, Turkey, (ii) compare interpolation methods for mapping the distribution of soil properties and SQI in the profile wall, and (iii) assess cubist model for predicting soil properties and SQI using individual and combined Vis-NIR and pXRF spectra in the profile. The soil profile wall (1 m deep × 1 m wide) was divided into a raster of 10 × 10 cm intervals for collecting a total of 100 soil samples. The soil samples were analyzed for soil physical, chemical properties, and elemental concentrations and scanned using Vis-NIR (350–2500 nm) and pXRF (0–8 and 0–45 kV) spectrometers. Standard scoring function was used for standardization of indicators. Weighted linear combination method was used to calculate SQI and F-AHP for assigning weights of indicators. The 100 samples were split into calibration (70%) and validation (30%) datasets. Individual or combined Vis-NIR and pXRF spectra were used in cubist model to evaluate the feasibility for predicting soil properties and SQI in the Inceptisol. Results suggested that the soil profile had a considerable variation of soil properties and SQI with depth and within horizons. The A horizon had the highest SQI. In addition, Vis-NIR had better prediction performances for pH, SOM, Fe, and Mn (R2 = 0.42–0.88), whereas pXRF had better prediction performances for EC, CaCO3, P, and Zn (R2 = 0.18–0.90). In addition, combining Vis-NIR, pXRF8, and pXRF45 had better prediction performance for sand, silt, clay, K, Cu, and SQI (R2 = 0.81–0.97). We concluded that Vis-NIR and pXRF spectra can be successfully used to predict SQI in Fluventic Haploxerepts. Combined Vis-NIR and pXRF spectra had better prediction performance for SQI. A soil quality index (SQI) can be used to manage the soil for potential uses and remediate limitations of soil for sustainable agriculture and ecology. The objectives of this study were to (i) characterize vertical and horizontal variations of some soil properties and SQI in a Fluventic Haploxerepts profile in Eskişehir, Turkey, (ii) compare interpolation methods for mapping the distribution of soil properties and SQI in the profile wall, and (iii) assess cubist model for predicting soil properties and SQI using individual and combined Vis-NIR and pXRF spectra in the profile. The soil profile wall (1 m deep × 1 m wide) was divided into a raster of 10 × 10 cm intervals for collecting a total of 100 soil samples. The soil samples were analyzed for soil physical, chemical properties, and elemental concentrations and scanned using Vis-NIR (350–2500 nm) and pXRF (0–8 and 0–45 kV) spectrometers. Standard scoring function was used for standardization of indicators. Weighted linear combination method was used to calculate SQI and F-AHP for assigning weights of indicators. The 100 samples were split into calibration (70%) and validation (30%) datasets. Individual or combined Vis-NIR and pXRF spectra were used in cubist model to evaluate the feasibility for predicting soil properties and SQI in the Inceptisol. Results suggested that the soil profile had a considerable variation of soil properties and SQI with depth and within horizons. The A horizon had the highest SQI. In addition, Vis-NIR had better prediction performances for pH, SOM, Fe, and Mn (R² = 0.42–0.88), whereas pXRF had better prediction performances for EC, CaCO₃, P, and Zn (R² = 0.18–0.90). In addition, combining Vis-NIR, pXRF8, and pXRF45 had better prediction performance for sand, silt, clay, K, Cu, and SQI (R² = 0.81–0.97). We concluded that Vis-NIR and pXRF spectra can be successfully used to predict SQI in Fluventic Haploxerepts. Combined Vis-NIR and pXRF spectra had better prediction performance for SQI. |
| ArticleNumber | 105954 |
| Author | Acar, Mert Dengiz, Orhan Hartemink, Alfred E. Ozlu, Ekrem Gozukara, Gafur Zhang, Yakun |
| Author_xml | – sequence: 1 givenname: Gafur surname: Gozukara fullname: Gozukara, Gafur email: ggozukara@ogu.edu.tr organization: Eskişehir Osmangazi University, Department of Soil Science and Plant Nutrition, Eskişehir 26160, Turkey – sequence: 2 givenname: Mert surname: Acar fullname: Acar, Mert organization: Cukurova University, Department of Soil Science and Plant Nutrition, Adana 01330, Turkey – sequence: 3 givenname: Ekrem surname: Ozlu fullname: Ozlu, Ekrem organization: Michigan State University, Great Lakes Bioenergy Research Center, W. K. Kellogg Biological Station, and Department of Plant, Soil and Microbial Sciences, East Lansing, MI, USA – sequence: 4 givenname: Orhan surname: Dengiz fullname: Dengiz, Orhan organization: Ondokuz Mayıs University, Department of Soil Science and Plant Nutrition, Samsun 55200, Turkey – sequence: 5 givenname: Alfred E. surname: Hartemink fullname: Hartemink, Alfred E. organization: University of Wisconsin-Madison, Department of Soil Science, FD Hole Soils Lab, 1525 Observatory Drive, Madison, WI 53706, USA – sequence: 6 givenname: Yakun surname: Zhang fullname: Zhang, Yakun organization: University of Wisconsin-Madison, Department of Soil Science, FD Hole Soils Lab, 1525 Observatory Drive, Madison, WI 53706, USA |
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| Keywords | Soil spectra Cubist model Soil elements Digital soil mapping Soil quality index |
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| Snippet | •Soil profile had a considerable variation of soil properties and SQI with depth and within horizons.•Cubist model can be used to predict SQI.•The grid soil... A soil quality index (SQI) can be used to manage the soil for potential uses and remediate limitations of soil for sustainable agriculture and ecology. The... |
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| SubjectTerms | catenas clay Cubist model data collection Digital soil mapping ecology Haploxerepts prediction sand silt Soil elements soil profiles soil quality Soil quality index Soil spectra sustainable agriculture |
| Title | A soil quality index using Vis-NIR and pXRF spectra of a soil profile |
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