Bornemann, Ludger Clemens: Soil organic carbon pools and their spatial patterns : rapid assessment using mid-infrared spectroscopy. - Bonn, 2011. - Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn.
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author = {{Ludger Clemens Bornemann}},
title = {Soil organic carbon pools and their spatial patterns : rapid assessment using mid-infrared spectroscopy},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2011,
month = feb,

note = {Soil organic carbon (SOC) plays an important role in global C cycling. Until today, the spatial patterns of individual SOC fractions are, however, largely undiscovered as traditional methods for their determination are too time consuming. In consequence, also the interaction of regulating parameters governing SOC turnover on the field scale remains unresolved.
The aim of my work was to elucidate the potential of mid-infrared spectroscopy (MIRS) for time- and cost-effective quantification of constitutive parameters regulating SOC turnover, and to identify effective control parameters regulating the spatial heterogeneity of SOC dynamics on the field scale. In addition to SOC quantification, I determined amounts of black carbon (BC), and particulate organic matter (POM) of three size classes in sample sets of different regional provenance. Quantitative prediction models for parameter estimation of the measured values were derived from MIR spectra. Mineral-bound SOC was calculated by difference. Further on, I identified effective control parameters regulating the spatial heterogeneity of SOC dynamics by statistical structure analyses and geo-statistical instrumentation. Employing samples of various arable and grassland soils from loess regions all across the world (n = 309), I was able to conduct quantitative and qualitative determination of SOC and BC from MIR spectra for the first time. Black carbon contents were determined by a molecular marker method (benzene polycarboxylic acids, BPCA). The MIRS-based BC characterization was validated employing individual samples of charred organic matter which represented different stages of combustion. With regard to the results of the SOC and BC predictions, I employed regionalized sample sets for the MIRS-based determination of POM of three size classes (POM1: 2000.250 μm; POM2: 250.53 μm; POM3: 53.20 μm) for 129 subsites of a 1.3 ha test site (R2 = 0.77. 0.96). At this, primarily analyses of the lignin contents were used for validation of the individual prediction models.
The stone content, texture of the fine earth, pedogenic oxides, hill slope, elevation above sea level, 137Cs-activity as proxy for erosive translocation, as well as the soil moisture were considered as effective parameters regulating SOC turnover, and determined for all 129 subsites of the investigated test site. The statistical instrumentation for the identification of effective parameters for SOC turnover comprised multidimensional scaling of a fuzzy-kappa similarity matrix, principal component analysis, correlation analysis, multiple regression models, as well as analyses of semivariance.
All investigated SOC fractions were successfully determined by MIRS predictions. About 99 % of total SOC variability was explained by local calibrations. The precision of BC prediction was lower (R2 > 0.8), partly reflecting different BC quality. A measure of the latter is the mellitic acid-C percentage, which also correlated with MIRS patterns (R2 . 0.6).
Coefficients of determination for the predictions of POM of three size classes ranged between 0.77 and 0.96. The prediction model for POM1 chiefly relied on specific signals of lignin and cellulose; contents of POM2 were estimated by spectral bands assigned to degradation products as aliphatic C.H groups and aromatic moieties. Carboxylic groups essentially contributed to the prediction of POM3. There was a close spatial relation between the coarse POM1 and POM2 fractions and lignin, which largely also explained variations in bulk SOC. In contrast, POM3 exhibited a less deterministic pattern in the field, thus contributing little to spatial variation of the SOC content.
With exception of POM3 (R2 = 0.20), multiple regression models employing the stone content, contents of pedogenic oxides, as well as the hillslope, successfully predicted the spatial distribution of all investigated SOC fractions (R2 = The highly variable stone content (4.60 %) proved to be the dominating factor regulating SOC dynamics on the investigated test site. The spatial distribution of BC was additionally affected by erosive translocation.
In summary, MIRS predictions facilitate a time- and cost-effective determination of spatial distributions of SOC, BC, and POM within landscapes. On the investigated test site, the observed variability is chiefly deterministic and can be attributed to saturation processes, caused by disproportionately high input of plant debris as amounts of fine earth are reduced by increasing stone contents. Especially in soils that comprise highly variable stone contents, the coarse texture thus necessarily needs to be considered in case effective parameters of SOC turnover are to be identified . even though only rarely considered in conventional soil analysis (soil sieved to grain sizes of 2 mm).},

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