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  • 1
    Language: English
    In: Scientific Reports, 01 September 2018, Vol.8(1), pp.1-13
    Description: Abstract Subsoil organic carbon (OC) is generally lower in content and more heterogeneous than topsoil OC, rendering it difficult to detect significant differences in subsoil OC storage. We tested the application of laboratory hyperspectral imaging with a variety of machine learning approaches to predict OC distribution in undisturbed soil cores. Using a bias-corrected random forest we were able to reproduce the OC distribution in the soil cores with very good to excellent model goodness-of-fit, enabling us to map the spatial distribution of OC in the soil cores at very high resolution (~53 × 53 µm). Despite a large increase in variance and reduction in OC content with increasing depth, the high resolution of the images enabled statistically powerful analysis in spatial distribution of OC in the soil cores. In contrast to the relatively homogeneous distribution of OC in the plough horizon, the subsoil was characterized by distinct regions of OC enrichment and depletion, including biopores which contained ~2–10 times higher SOC contents than the soil matrix in close proximity. Laboratory hyperspectral imaging enables powerful, fine-scale investigations of the vertical distribution of soil OC as well as hotspots of OC storage in undisturbed samples, overcoming limitations of traditional soil sampling campaigns.
    Keywords: Biology
    ISSN: Scientific Reports
    E-ISSN: 2045-2322
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  • 2
    Language: English
    In: Sci Rep, 2016, Vol.6(1), pp.29478-29478
    Description: Soil organic carbon (SOC) from aboveground and belowground sources has rarely been differentiated although it may drive SOC turnover and stabilization due to a presumed differing source dependent degradability. It is thus crucial to better identify the location of SOC from different sources for the parameterization of SOC models, especially in the less investigated subsoils. The aim of this study was to spatially assess contributions of organic carbon from aboveground and belowground parts of beech trees to subsoil organic carbon in a Dystric Cambisol. Different sources of SOC were distinguished by solvent-extractable and hydrolysable lipid biomarkers aided by C analyses of soil compartments 〈63 μm. We found no effect of the distance to the trees on the investigated parameters. Instead, a vertical zonation of the subsoil was detected. A high contribution of fresh leaf- and root-derived organic carbon to the upper subsoil (leaf- and root-affected zone) indicate that supposedly fast-cycling, leaf-derived SOC may still be of considerable importance below the A-horizon. In the deeper subsoil (root-affected zone), roots were an important source of fresh SOC. Simultaneously, strongly increasing apparent C ages (3860 yrs BP) indicate considerable contribution of SOC that may be inherited from the Pleistocene parent material.
    Keywords: Biology;
    ISSN: 2045-2322
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  • 3
    Language: English
    In: Scientific Reports, 2018, Vol.8(1), p.6852
    Description: Biochar (BC) amendments may be suitable to increase the ecosystems resistance to drought due to their positive effects on soil water retention and availability. We investigated the effect of BC in situ ageing on water availability and microbial parameters of a grassland soil. We used soil...
    Keywords: Life Sciences ; Biology
    ISSN: 2045-2322
    E-ISSN: 2045-2322
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  • 4
    In: Scientific Reports, 2016, Vol.6
    Description: Climate change and stagnating crop yields may cause a decline of SOC stocks in agricultural soils leading to considerable CO2 emissions and reduced agricultural productivity. Regional model-based SOC projections are needed to evaluate these potential risks. In this study, we simulated the future SOC development in cropland and grassland soils of Bavaria in the 21(st) century. Soils from 51 study sites representing the most important soil classes of Central Europe were fractionated and derived SOC pools were used to initialize the RothC soil carbon model. For each site, long-term C inputs were determined using the C allocation method. Model runs were performed for three different C input scenarios as a realistic range of projected yield development. Our modelling approach revealed substantial SOC decreases of 11-16% under an expected mean temperature increase of 3.3 °C assuming unchanged C inputs. For the scenario of 20% reduced C inputs, agricultural SOC stocks are projected to decline by 19-24%. Remarkably, even the optimistic scenario of 20% increased C inputs led to SOC decreases of 3-8%. Projected SOC changes largely differed among investigated soil classes. Our results indicated that C inputs have to increase by 29% to maintain present SOC stocks in agricultural soils.
    Keywords: Biology;
    E-ISSN: 2045-2322
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