A Chemometric Analysis of Soil Health Indicators Derived from Mid-Infrared Spectra
Significant models predicting Soil Organic Carbon (SOC) and other chemical and biological indicators of soil health in an experimental farm with semi-arid Mediterranean Calcisol have been obtained by partial least squares (PLS) regression, with mid-infrared (MIR) spectra of whole soil samples used a...
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MDPI AG
2025-06-01
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| author | Gonzalo Almendros Antonio López-Pérez Zulimar Hernández |
| author_facet | Gonzalo Almendros Antonio López-Pérez Zulimar Hernández |
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| description | Significant models predicting Soil Organic Carbon (SOC) and other chemical and biological indicators of soil health in an experimental farm with semi-arid Mediterranean Calcisol have been obtained by partial least squares (PLS) regression, with mid-infrared (MIR) spectra of whole soil samples used as independent variables (IVs). The dependent variables (DVs) included SOC, pH, electric conductivity, N, P<sub>2</sub>O<sub>5</sub>, K, Ca<sup>2+</sup>, Mg<sup>2+</sup>, Na<sup>+</sup>, Fe, Mn, Cu and Zn. The DVs also included free-living nematodes and microbivores, such as Rhabditids and Cephalobids, and phytoparasitics, such as <i>Xiphinema</i> spp. and other Dorylaimids. More importantly, an attempt was made to determine which spectral patterns allowed each dependent variable (DV) to be predicted. For this purpose, a number of statistical indices were plotted between 4000 and 450 cm<sup>−1</sup>, e.g., variable importance for prediction (VIP) and beta coefficients from PLS, loading factors from principal component analysis (PCA) and correlation and determination indices. The most effective plots, however, were the “scaled subtraction spectra” (SSS) obtained by subtracting the averages of groups of spectra in order to reproduce the spectral patterns typical in soils where the values of each DV are higher, or vice versa. For instance, distinct SSS resembled the spectra of carbonate, clay, oxides and SOC, whose varying concentrations enabled the prediction of the different DVs. |
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| institution | Kabale University |
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| spelling | doaj-art-6dde0af9e97246dfb26ffdfd838c28642025-08-20T03:32:31ZengMDPI AGAgronomy2073-43952025-06-01157159210.3390/agronomy15071592A Chemometric Analysis of Soil Health Indicators Derived from Mid-Infrared SpectraGonzalo Almendros0Antonio López-Pérez1Zulimar Hernández2Museo Nacional de Ciencias Naturales (MNCN-CSIC), Serrano 115-B, 28006 Madrid, SpainInstituto Regional de Investigación y Desarrollo Agroalimentario y Forestal (IRIAF), Centro Investigaciones Apícola y Agroambiental (CIAPA), Junta de Comunidades de Castilla-La Mancha, Cam. de San Martín, s/n, 19180 Marchamalo, SpainCIMO, LA SusTEC, Instituto Politécnico de Bragança (IPB), Campus de Santa Apolónia, 5300-253 Bragança, PortugalSignificant models predicting Soil Organic Carbon (SOC) and other chemical and biological indicators of soil health in an experimental farm with semi-arid Mediterranean Calcisol have been obtained by partial least squares (PLS) regression, with mid-infrared (MIR) spectra of whole soil samples used as independent variables (IVs). The dependent variables (DVs) included SOC, pH, electric conductivity, N, P<sub>2</sub>O<sub>5</sub>, K, Ca<sup>2+</sup>, Mg<sup>2+</sup>, Na<sup>+</sup>, Fe, Mn, Cu and Zn. The DVs also included free-living nematodes and microbivores, such as Rhabditids and Cephalobids, and phytoparasitics, such as <i>Xiphinema</i> spp. and other Dorylaimids. More importantly, an attempt was made to determine which spectral patterns allowed each dependent variable (DV) to be predicted. For this purpose, a number of statistical indices were plotted between 4000 and 450 cm<sup>−1</sup>, e.g., variable importance for prediction (VIP) and beta coefficients from PLS, loading factors from principal component analysis (PCA) and correlation and determination indices. The most effective plots, however, were the “scaled subtraction spectra” (SSS) obtained by subtracting the averages of groups of spectra in order to reproduce the spectral patterns typical in soils where the values of each DV are higher, or vice versa. For instance, distinct SSS resembled the spectra of carbonate, clay, oxides and SOC, whose varying concentrations enabled the prediction of the different DVs.https://www.mdpi.com/2073-4395/15/7/1592infrared spectroscopypartial least squaresphytoparasitessoil organic carbon |
| spellingShingle | Gonzalo Almendros Antonio López-Pérez Zulimar Hernández A Chemometric Analysis of Soil Health Indicators Derived from Mid-Infrared Spectra Agronomy infrared spectroscopy partial least squares phytoparasites soil organic carbon |
| title | A Chemometric Analysis of Soil Health Indicators Derived from Mid-Infrared Spectra |
| title_full | A Chemometric Analysis of Soil Health Indicators Derived from Mid-Infrared Spectra |
| title_fullStr | A Chemometric Analysis of Soil Health Indicators Derived from Mid-Infrared Spectra |
| title_full_unstemmed | A Chemometric Analysis of Soil Health Indicators Derived from Mid-Infrared Spectra |
| title_short | A Chemometric Analysis of Soil Health Indicators Derived from Mid-Infrared Spectra |
| title_sort | chemometric analysis of soil health indicators derived from mid infrared spectra |
| topic | infrared spectroscopy partial least squares phytoparasites soil organic carbon |
| url | https://www.mdpi.com/2073-4395/15/7/1592 |
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