Comparison of infrared spectroscopy and laser granulometry as alternative methods to estimate soil aggregate stability in Mediterranean badlands

Erktan, A. ; Legout, C. ; De Danieli, S. ; Daumergue, N. ; Cecillon, L.

Type de document
Article de revue scientifique à comité de lecture
Langue
Anglais
Affiliation de l'auteur
IRSTEA GRENOBLE UR EMGR FRA ; UNIVERSITE GRENOBLE ALPES UMR 5564 LTHE FRA ; IRSTEA GRENOBLE UR EMGR FRA ; IRSTEA GRENOBLE UR EMGR FRA ; IRSTEA GRENOBLE UR EMGR FRA
Année
2016
Résumé / Abstract
Soil aggregate stability is a key indicator of soil resistance to erosion, but its measurement remains fastidious for large scale uses. Alternative time and cost-effective methods are thus needed. Our objective was to assess and compare the efficiency of laser granulometry (LG) and soil mid- and near-infrared spectroscopy (MIR/NIR) as alternative methods to assess soil aggregate stability in Mediterranean badland soils. A collection of 75 badland soil samples was used, showing wide variations in soil aggregate stability. Three different categories of measurements were performed: (i) the aggregate breakdown kinetics of the [<1 mm] size fraction under stirring and sonication, tracked by repeated particle size distribution measurements, using LG, (ii) mid-(diffuse-MIR-DR and attenuate transmitted reflectance MIR-ATR) and near-(NIR-DR) infrared spectra of the fine soil fraction [<2 mm] and (iii) the soil aggregate [3-5 mm] stability, using the standardized method (ISO/FDIS 10930, 2012). Partial least squares regression models were used to predict soil aggregate stability using LG data and infrared spectra. Results showed that NIR-DR and MIR-ATR data provided the best prediction model for soil aggregate stability values (RPD = 2.61 & 2.74; R-2 = 0.85 & 810.87), followed by MIR-DR data (RPD = 2.24; R-2 = 0.89) and finally LG data (RPD = 2.12; R-2 = 0.80). For a quantitative use of the models to assign soil samples to standardized soil aggregate stability classes (ISO/FDIS 10930, 2012), infrared spectra also provided the best accuracy, with a misclassification rate below 30% for NIR-DR and MIR-ATR models, while it reached 43% with the LG-based model. The combination of IR and LG data did not yield a better prediction model for soil aggregate stability values and classes, Infrared-based method also provided best results in terms of time-saving strategy, reducing the measurement time to 8 min only. To conclude, infrared spectra (NIR-DR and MIR-ATR) outperformed LG-data to predict soil aggregate stability. Further development of this technique would require calibrating a set of soil-type specific prediction models for a wide range of soil types.
Source
Geoderma, vol. 271, p. 225 - 233

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