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Use of near infrared spectroscopy to determine biological and chemical characteristics of organic layers under spruce and beech stands
Author(s) -
Chodak Marcin,
Ludwig Bernard,
Khanna Partap,
Beese Friedrich
Publication year - 2002
Publication title -
journal of plant nutrition and soil science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.644
H-Index - 87
eISSN - 1522-2624
pISSN - 1436-8730
DOI - 10.1002/1522-2624(200202)165:1<27::aid-jpln27>3.0.co;2-a
Subject(s) - beech , humus , chemistry , soil water , chemical composition , analytical chemistry (journal) , environmental chemistry , soil science , botany , environmental science , organic chemistry , biology
The chemical composition of organic layers of forest soils shows a high spatial variability and fast methods may be required for its study at a landscape level. The objective was to assess the applicability of near infrared spectroscopy (NIRS) to measure several chemical and biological properties of organic layers in spruce, beech, and mixed spruce‐beech stands. Spectra in the VIS‐NIR region (400—2500 nm) were recorded for 406 samples representing Oi, Oe, and Oa layers of forest soils from Solling (Germany), 195 of them were used for calibration and 211 for validation. The calibration equations for each constituent were developed using the whole spectrum (0 th to 3 rd derivative). Humus samples were analyzed for contents of C and N and contents of P, S, Na, K, Ca, Mg, Mn, Fe, and Al after pressure digestion in HNO 3 . Additionally, basal respiration and microbial C (C mic ) were measured. NIRS predicted well the contents of C, N, P, S, Ca, Na, K, Fe, and Al and C/N and C/P ratios: the regression coefficients (a) of a linear regression (measured against predicted values) ranged from 0.9 to 1.1, and the correlation coefficients (r) were greater or equal 0.9. C mic (a = 0.87, r = 0.83) was predicted satisfactorily, whereas the prediction of the basal respiration (a = 0.74, r = 0.87) was less satisfactory. Due to liming of some of the plots NIRS failed to predict contents of Mg (a = 1.27, r = 0.68). For all chemical and biological characteristics the best prediction performances were achieved using the whole sample population. Splitting the samples into smaller groups according to a dominant tree species or an organic layer did not improve the predictions.