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  • 1
    Language: English
    In: Proceedings of the National Academy of Sciences of the United States of America, 17 August 2010, Vol.107(33), pp.14926-30
    Description: Agriculture is considered to be among the economic sectors having the greatest greenhouse gas mitigation potential, largely via soil organic carbon (SOC) sequestration. However, it remains a challenge to accurately quantify SOC stock changes at regional to national scales. SOC stock changes resulting from SOC inventory systems are only available for a few countries and the trends vary widely between studies. Process-based models can provide insight in the drivers of SOC changes, but accurate input data are currently not available at these spatial scales. Here we use measurements from a soil inventory dating from the 1960s and resampled in 2006 covering the major soil types and agricultural regions in Belgium together with region-specific land use and management data and a process-based model. The largest decreases in SOC stocks occurred in poorly drained grassland soils (clays and floodplain soils), consistent with drainage improvements since 1960. Large increases in SOC in well drained grassland soils appear to be a legacy effect of widespread conversion of cropland to grassland before 1960. SOC in cropland increased only in sandy lowland soils, driven by increasing manure additions. Modeled land use and management impacts accounted for more than 70% of the variation in observed SOC changes, and no bias could be demonstrated. There was no significant effect of climate trends since 1960 on observed SOC changes. SOC monitoring networks are being established in many countries. Our results demonstrate that detailed and long-term land management data are crucial to explain the observed SOC changes for such networks.
    Keywords: Agriculture -- Methods ; Carbon -- Metabolism ; Crops, Agricultural -- Metabolism ; Poaceae -- Metabolism ; Soil -- Analysis
    ISSN: 00278424
    E-ISSN: 1091-6490
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  • 2
    Language: English
    In: Global change biology, 2011, Vol.17(7), pp.2415-2427
    Description: Land-use change (LUC) is a major driving factor for the balance of soil organic carbon (SOC) stocks and the global carbon cycle. The temporal dynamic of SOC after LUC is especially important in temperate systems with a long reaction time. On the basis of 95 compiled studies covering 322 sites in the temperate zone, carbon response functions (CRFs) were derived to model the temporal dynamic of SOC after five different LUC types (mean soil depth of 30±6 cm). Grassland establishment caused a long lasting carbon sink with a relative stock change of 128±23% and afforestation on former cropland a sink of 116±54%, 100 years after LUC (mean±95% confidence interval). No new equilibrium was reached within 120 years. In contrast, there was no SOC sink following afforestation of grasslands and 75% of all observations showed SOC losses, even after 100 years. Only in the forest floor, there was carbon accumulation of 0.38±0.04 Mg ha⁻¹ yr⁻¹ in afforestations adding up to 38±4 Mg ha⁻¹ labile carbon after 100 years. Carbon loss after deforestation (−32±20%) and grassland conversion to cropland (−36±5%), was rapid with a new SOC equilibrium being reached after 23 and 17 years, respectively. The change rate of SOC increased with temperature and precipitation but decreased with soil depth and clay content. Subsoil SOC changes followed the trend of the topsoil SOC changes but were smaller (25±5% of the total SOC changes) and with a high uncertainty due to a limited number of datasets. As a simple and robust model approach, the developed CRFs provide an easily applicable tool to estimate SOC stock changes after LUC to improve greenhouse gas reporting in the framework of UNFCCC. ; p. 2415-2427.
    Keywords: Soil Organic Carbon ; Clay ; Data Collection ; Land Use Change ; Topsoil ; Confidence Interval ; Temperate Zones ; Temperature ; Forest Litter ; Soil Depth ; Afforestation ; Carbon Sinks ; Cropland ; Carbon ; Grasslands ; Greenhouse Gases ; Carbon Cycle ; Deforestation ; Uncertainty
    ISSN: 1354-1013
    E-ISSN: 13652486
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  • 3
    In: Global Change Biology, July 2012, Vol.18(7), pp.2218-2232
    Description: Recent studies have highlighted the tight coupling between geomorphic processes and soil carbon (C) turnover and suggested that eroding landscapes can stabilize more C than their non‐eroding counterparts. However, large uncertainties remain and a mechanistic understanding of geomorphic effects on C storage in soils is still lacking. Here, we quantified the soil organic carbon (SOC) stock and pool distribution along geomorphic gradients and combined data derived from soil organic matter fractionation and incubation experiments. The size and composition of the SOC pools were strongly related to geomorphic position: 1.6 to 6.2 times more C was stabilized in the subsoils (25–100cm) of depositional profiles than in those of eroding profiles. Subsoil C of depositional profiles is predominantly associated with microaggregates and silt‐sized particles which are associated with pools of intermediate stability. We observed a significantly higher mean residence time for the fast and intermediate turnover pools of buried C at depositional positions, relative to non‐eroding and eroding positions, resulting from the physical protection of C associated with microaggregates and silt particles. Conversely, significant amounts of C were replaced at eroding positions but the lower degree of decomposition and the lack of physically protected C, resulted in higher respiration rates. By considering C cycling at non‐eroding, eroding and depositional positions, we found that the eroding landscapes studied store up to 10% more C due to soil redistribution processes than non‐eroding landscapes. This is the result of the stabilization of C in former subsoil at eroding positions and partial preservation of buried C in pools of intermediate turnover at depositional positions. However, the sink strength was limited by significant losses of buried C as only a small fraction of the C was associated with stable pools.
    Keywords: Carbon Cycling ; Carbon Erosion ; Carbon Re‐Distribution ; Soil Organic Carbon ; Soil Respiration
    ISSN: 1354-1013
    E-ISSN: 1365-2486
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  • 4
    Language: English
    In: Global change biology, August 2015, Vol.21(8), pp.3181-92
    Description: Agricultural management has received increased attention over the last decades due to its central role in carbon (C) sequestration and greenhouse gas mitigation. Yet, regardless of the large body of literature on the effects of soil erosion by tillage and water on soil organic carbon (SOC) stocks in agricultural landscapes, the significance of soil redistribution for the overall C budget and the C sequestration potential of land management options remains poorly quantified. In this study, we explore the role of lateral SOC fluxes in regional scale modelling of SOC stocks under three different agricultural management practices in central Belgium: conventional tillage (CT), reduced tillage (RT) and reduced tillage with additional carbon input (RT+i). We assessed each management scenario twice: using a conventional approach that did not account for lateral fluxes and an alternative approach that included soil erosion-induced lateral SOC fluxes. The results show that accounting for lateral fluxes increased C sequestration rates by 2.7, 2.5 and 1.5 g C m(-2)  yr(-1) for CT, RT and RT+i, respectively, relative to the conventional approach. Soil redistribution also led to a reduction of SOC concentration in the plough layer and increased the spatial variability of SOC stocks, suggesting that C sequestration studies relying on changes in the plough layer may underestimate the soil's C sequestration potential due to the effects of soil erosion. Additionally, lateral C export from cropland was in the same of order of magnitude as C sequestration; hence, the fate of C exported from cropland into other land uses is crucial to determine the ultimate impact of management and erosion on the landscape C balance. Consequently, soil management strategies targeting C sequestration will be most effective when accompanied by measures that reduce soil erosion given that erosion loss can balance potential C uptake, particularly in sloping areas.
    Keywords: Agricultural Management ; Carbon Sequestration ; Soil Erosion ; Soil Organic Matter ; Spatial Modelling ; Models, Theoretical ; Agriculture -- Methods ; Carbon -- Analysis ; Soil -- Chemistry
    ISSN: 13541013
    E-ISSN: 1365-2486
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  • 5
    Language: English
    In: Agriculture, Ecosystems and Environment, 15 October 2014, Vol.196, pp.1-9
    Description: Detecting soil organic carbon (SOC) gain or loss is challenging due to large uncertainties deriving from temporal and spatial variability of organic carbon concentrations, even at the field scale. In order to reduce these uncertainties, we used the organic carbon associated with clay and fine silt particles (fine fraction) rather than SOC in bulk soil for assessing decadal changes. This approach reduces the impact of the inherent variability of labile carbon on SOC estimates. We analysed archived soil samples taken in 1970 and recent ones taken in 2012 from an on-going long-term field trial in the Hesbaye region in Belgium. The experiment started in 1959 and contains three contrasting management practices (3 × 6 replicates): residue export (RE), farmyard manure (FYM) and residue restitution (RR). After 42 years, there are no significant differences in bulk soil organic carbon concentrations between treatments (RE = 9.2 g C kg soil; FYM = 10.4 g C kg soil; RR = 10.1 g C kg soil). In contrast, there are significant differences ( 〈 0.05) in stable carbon concentration (C associated to the fine fraction) between treatments over the same time period (RE = 13.2 g C kg clay and fine silt; FYM = 16.6 g C kg clay and fine silt; RR = 15.4 g C kg clay and fine silt). Moreover, we can be 99% confident that stable carbon in the fine fraction increased between 1970 and 2012 in FYM (+19%, 〈 0.01) and RR plots (+14%, 〈 0.01). There was a small, but significant, change of stable carbon in RE plots over the same period. In 1970, no differences in stable carbon concentration were detected between residue treatments. Labile carbon did not change significantly from 1970 to 2012 but its variability increased for all plots except for the RE treatment. We used the Rothamsted carbon model (RothC-26.3) to describe SOC changes under the different residue treatments. For bulk soil, observed trends in FYM and RR SOC concentrations are in line with the ones predicted. Modeled SOC changes from 1962 to 2012 are −14% (RE) and +10% (FYM). We also used RothC-26.3 to understand the evolution of the sensitive and slow fractions over time. On the one hand, we found that RothC was not capable to simulate the range of observed SOC concentrations inter-annual variability. On the other hand, the increase of the RothC pool with slow decomposition (HUM) was similar to the trend in the carbon associated with the fine fraction observed in the FYM and RR plots. This finding highlights that residue management can increase the storage of C in more stable fractions in agricultural soils, even when no changes are detected in bulk soil C.
    Keywords: Soil Organic Carbon ; Carbon Stabilization ; Residue Management Practices ; Physical ; Fractionation ; Long-Term Experiment ; Agriculture ; Environmental Sciences
    ISSN: 0167-8809
    E-ISSN: 1873-2305
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  • 6
    Language: English
    In: PLoS ONE, 01 January 2013, Vol.8(6), p.e66409
    Description: Soil organic carbon is a key soil property related to soil fertility, aggregate stability and the exchange of CO2 with the atmosphere. Existing soil maps and inventories can rarely be used to monitor the state and evolution in soil organic carbon content due to their poor spatial resolution, lack of consistency and high updating costs. Visible and Near Infrared diffuse reflectance spectroscopy is an alternative method to provide cheap and high-density soil data. However, there are still some uncertainties on its capacity to produce reliable predictions for areas characterized by large soil diversity. Using a large-scale EU soil survey of about 20,000 samples and covering 23 countries, we assessed the performance of reflectance spectroscopy for the prediction of soil organic carbon content. The best calibrations achieved a root mean square error ranging from 4 to 15 g C kg(-1) for mineral soils and a root mean square error of 50 g C kg(-1) for organic soil materials. Model errors are shown to be related to the levels of soil organic carbon and variations in other soil properties such as sand and clay content. Although errors are ∼5 times larger than the reproducibility error of the laboratory method, reflectance spectroscopy provides unbiased predictions of the soil organic carbon content. Such estimates could be used for assessing the mean soil organic carbon content of large geographical entities or countries. This study is a first step towards providing uniform continental-scale spectroscopic estimations of soil organic carbon, meeting an increasing demand for information on the state of the soil that can be used in biogeochemical models and the monitoring of soil degradation.
    Keywords: Sciences (General)
    E-ISSN: 1932-6203
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  • 7
    Language: English
    In: Agriculture, Ecosystems and Environment, Jan 1, 2015, Vol.199, p.114(10)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.agee.2014.08.027 Byline: Miguel A. Gabarron-Galeote, Sylvain Trigalet, Bas van Wesemael Abstract: * We study soil organic carbon after land abandonment in a precipitation gradient. * We compare soil organic carbon and vegetation recovery. * Vegetation recovery explained well the SOC responses. * Precipitation affects the rate of recovery of soil organic carbon. * Low temperature constrains primary production and hence soil organic carbon recovery. Author Affiliation: Georges Lemaitre Centre for Earth and Climate Research, Earth and Life Institute, Universite Catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium Article History: Received 3 June 2014; Revised 27 August 2014; Accepted 28 August 2014
    Keywords: Precipitation (Meteorology) ; Soil Carbon ; Evolution (Biology)
    ISSN: 0167-8809
    Source: Cengage Learning, Inc.
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  • 8
    Language: English
    In: Science, 07 December 2001, Vol.294(5549), pp.2094-2095
    Keywords: Biological sciences -- Biology -- Conservation biology ; Health sciences -- Medical sciences -- Medical research ; Business -- Industry -- Industrial sectors ; Biological sciences -- Ecology -- Population ecology ; Biological sciences -- Biology -- Conservation biology ; Biological sciences -- Agriculture -- Agricultural sciences ; Environmental studies -- Environmental sciences -- Climate change ; Biological sciences -- Biology -- Botany ; Biological sciences -- Ecology -- Applied ecology ; Biological sciences -- Biology -- Botany
    ISSN: 00368075
    E-ISSN: 10959203
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  • 9
    Language: English
    In: Soil Biology and Biochemistry, January 2014, Vol.68, pp.337-347
    Description: Due to the large spatial variation of soil organic carbon (SOC) content, assessing the current state of SOC for large areas is costly and time consuming. Visible and Near Infrared Diffuse Reflectance Spectroscopy (Vis-NIR DRS) is a fast and cheap tool for measuring SOC based on empirical equations and spectral libraries. While the approach has been demonstrated to yield accurate predictions for databases containing samples belonging to soils with similar characteristics such as mineralogy, texture, iron, and CaCO content, spectroscopic calibrations have been less successful when applied to large and diverse soil spectral libraries. The scope of this study was to predict SOC using a local partial least square regression approach. In total, 19,969 topsoil (0–20 cm) samples collected all over the European Union were analyzed for physical and chemical properties, and scanned with a Vis-NIR spectrometer in a single laboratory. The local regression method builds a different multivariate model for each sample to predict. Each local model is trained with neighbours' samples selected from a large spectral library, based on their spectral similarity with the sample to predict. We modified the local regression procedure by including other covariates (geographical and texture information) in the computation of the distance between samples. The results showed good prediction ability for mineral soils under cropland (RMSE = 3.6 g C kg ) and grassland (RMSE = 7.2 g C kg ). Predictions of mineral soils under woodland (RMSE = 11.9 g C kg ) and organic soils (RMSE = 51.1 g C kg ) were less accurate. The use of sand content in the computation of the sample similarities provided the most accurate SOC predictions due to its influence on light scattering properties of soils. In large datasets, using additional soil or environmental information allows to select neighbours that have overall the same soil composition as the samples to predict, resulting in more accurate models. This study shows that (i) it is possible to realize low-cost estimations of SOC at continental scale using large spectral libraries with a reasonable accuracy, and (ii) the local approach is a valuable tool to deal with large datasets, especially if existing soil property maps or soil legacy data could be used as covariates in the SOC prediction models.
    Keywords: Soil Organic Carbon ; Vis-Nir Spectroscopy ; Local Approach ; Spectral Library ; Sand Content ; Europe ; Agriculture ; Chemistry
    ISSN: 0038-0717
    E-ISSN: 1879-3428
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  • 10
    Language: English
    In: Geoderma, 2015, Vol.259-260, p.93(11)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.geoderma.2015.05.008 Byline: Francois Stevens, Patrick Bogaert, Bas van Wesemael Abstract: Evidences exist that the spatial variability of soil organic carbon (SOC) in cropland is partially controlled by environmental or human factors acting on a field basis (e.g., agricultural management, landuse history, landscape structure). However, few studies have quantified the relative importance of the fields-related variability at the regional scale. Recent airborne hyperspectral imagery methods provide SOC estimates at high resolution and over large surfaces. They may be used to quantify and explain the spatial variation of SOC. In this study we used a SOC hyperspectral image over Luxembourg to separate SOC variation in three components: the effect of the texture class (as defined by a texture map), the effect of fields (as defined by a cadastral map) and spatially dependent residuals. The relative variance of these components and the spatial structure of the residuals were rigorously assessed by restricted maximum likelihood (REML). Results indicated that 65.7[+ or -]0.3% of the variance of SOC in the study area was explained by texture classes. The intensity of the field effect was largely dependent on the location. In some sub-areas of homogeneous texture class, no significant effect could be observed while in others, field explained up to 68.8[+ or -]12.0% of the variance. In contrast with other methods like ANOVA, the method developed here measures the variation related to spatial units (soil map units or fields) while taking explicitly into account spatial dependencies. As soon as the boundaries of these spatial units and a spatially extensive (or at least very dense) knowledge of a soil property over a region of interest are available, it allows rigorously determining if the soil property depends on these spatial units. In the present application, findings pointed out the importance of considering fields-related variability in SOC modeling and mapping studies. Article History: Received 5 February 2015; Revised 11 May 2015; Accepted 14 May 2015
    Keywords: Soil Carbon – Analysis ; Soil Carbon – Models
    ISSN: 0016-7061
    Source: Cengage Learning, Inc.
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