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Assessment of soil quality of arable soils in Hungary using DRIFT spectroscopy and chemometrics
Institution:1. CNR, Institute for Agricultural and Forest Systems in the Mediterranean (ISAFOM), Rende, CS, Italy;2. CRA — Consiglio per la Ricerca e la Sperimentazione in Agricoltura, Bari, Italy;3. DiBEST, University of Calabria, Rende, CS, Italy;1. Geoinformatics and Remote Sensing, Institute for Geography, Leipzig University, Leipzig, Germany;2. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Germany;3. Department of Environmental Chemistry, Organic Agricultural Sciences, Kassel University, Witzenhausen, Germany;4. Soil Science, Faculty of Regional and Environmental Sciences, Trier University, Trier, Germany;1. World Agroforestry Centre (ICRAF), P.O. Box 30677-00100 GPO, Nairobi, Kenya;2. School of Mathematics, University of Nairobi, P.O Box 30196-00100 GPO, Nairobi, Kenya
Abstract:Nowadays, there is a great demand for precise, sensitive and adequate indicators for evaluating the quality of soils. In spite of recent developments in this field, a fast, non-destructive method for soil quality assessment has not yet been evaluated. The objective of this study was to investigate the possibility of using diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy to estimate soil quality in the form of soil quality index (SQI). A set of soil samples (n = 89) was scanned and regression was carried out using a combination of DRIFT spectroscopy and partial least-squares (PLS). The reliability of the DRIFT-PLS calibration model (n = 53) was acceptable (coefficient of determination, R2 = 0.49; residual prediction deviation, RPD = 1.4) for the estimating of the SQI values. The validation of the calibration model using a validation set (n = 36) of unknown samples also resulted in good acceptability with R2 = 0.68 and RPD = 1.85. The DRIFT-PLS based model could provide a rapid, cheap estimate of SQI values and subsequently of soil quality by taking into account the integrated effects of the mineralogical and organic components of the soil. This approach could be useful to monitor soil quality under conditions where the analysis of a large number of soil samples is required.
Keywords:Arable soil  Diffuse reflectance IR spectroscopy  Chemometrics  PLS  Soil quality  SQI
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