Dynamics of C, N, P and S in grassland soils: a model

Parton, William J.; Stewart, John; Cole, C. V.

doi:10.1007/bf02180320

Cited by 1,306 publications

(838 citation statements)

References 28 publications

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“…This study was done using a recently developed simulation platform [16], coupling the well-known Century agroecosystem model [17] with several spatial and numerical databases at European level (EU + Serbia, Bosnia and Herzegovina, Montenegro, Albania, Former Yugoslav Republic of Macedonia and Norway). A comprehensive description of input data management, model structure and scenarios simulated is given in the following paragraphs, referring to Lugato et al [16] for specific details.…”

Section: Methodsmentioning

confidence: 99%

“…Century is a process-based model designed to simulate carbon (C), nitrogen (N), phosphorous (P) and sulphur (S) dynamics in natural or cultivated systems, using a monthly time step [17]. The soil organic matter sub-model includes three SOC pools, namely active, slow and passive, along with two fresh residue pools, structural and metabolic, each with a different turnover rate.…”

Section: Modelmentioning

confidence: 99%

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Modelling Soil Organic Carbon Changes Under Different Maize Cropping Scenarios for Cellulosic Ethanol in Europe

Lugato

Jones

2014

Bioenerg. Res.

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The utilization of crop residues in the production of second-generation biofuels has the potential to boost the bioenergy sector without affecting food commodity prices. However, policies leading to large-scale biomass removal should carefully balance the consequences, both environmental and in terms of emissions, on soil organic carbon (SOC) stocks depletion. Using a recently developed simulation platform, SOC changes were estimated at European level (EU + candidate and potential candidate countries) under two scenarios of low (R30) and high (R90) maize stover removal for cellulosic ethanol production (i.e. 30 and 90 % of stover removal, respectively). Additionally, mitigation practices for SOC preservation, namely the introduction of a ryegrass cover crop (R90_C) and biodigestate return to soil (R90_B), were explored under the highest rate of stover removal. The results showed that 15.3 to 50.6 Mt year −1 of stover (dry matter) would be potentially available for ethanol production under the lower and high removal rates considered. However, largescale exploitation of maize residues will lead to a SOC depletion corresponding to 39.7-135.4 Mt CO 2 eq. by 2020 (under R30 and R90, respectively) with greater losses in the long term. In particular, every tonne of C residue converted to bioethanol was predicted to have an additional impact on SOC loss almost ranging from 0.2 to 0.5 CO 2 eq. ha −1 year −1 , considering a continuous biofuel scenario by 2050. The mitigation practices evaluated could more than halve SOC losses compared to R90, but not totally offsetting the negative soil C balance. There is a pressing need to design policies at EU level for optimum maize biofuel cultivations that will preserve the current SOC stock or even generate C credits.

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Section: Methodsmentioning

confidence: 99%

Section: Modelmentioning

confidence: 99%

Modelling Soil Organic Carbon Changes Under Different Maize Cropping Scenarios for Cellulosic Ethanol in Europe

Lugato

Jones

2014

Bioenerg. Res.

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“…SOM can store C in the soil for hundreds or thousands of years before it is broken down into CO 2 through microbial respiration (Luo and Zhou, 2006). This series of C cycle processes has been represented in most ecosystem models with multiple pools linked by C transfers among them (Jenkinson et al, 1987;Parton et al, 1987Parton et al, , 1988Parton et al, , 1993, including those embedded in Earth system models (Ciais et al, 2013).…”

Section: Mathematical Representation Of Terrestrial C Cyclementioning

confidence: 99%

Transient dynamics of terrestrial carbon storage: mathematical foundation and its applications

et al. 2017

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Abstract. Terrestrial ecosystems have absorbed roughly 30 % of anthropogenic CO 2 emissions over the past decades, but it is unclear whether this carbon (C) sink will endure into the future. Despite extensive modeling and experimental and observational studies, what fundamentally determines transient dynamics of terrestrial C storage under global change is still not very clear. Here we develop a new framework for understanding transient dynamics of terrestrial C storage through mathematical analysis and numerical experiments. Our analysis indicates that the ultimate force driving ecosystem C storage change is the C storage capacity, which is jointly determined by ecosystem C input (e.g., net primary production, NPP) and residence time. Since both C input and residence time vary with time, the C storage capacity is timedependent and acts as a moving attractor that actual C storage chases. The rate of change in C storage is proportional to the C storage potential, which is the difference between the current storage and the storage capacity. The C storage capacity represents instantaneous responses of the land C cycle to external forcing, whereas the C storage potential represents the internal capability of the land C cycle to influence the C change trajectory in the next time step. The influence happens through redistribution of net C pool changes in a network of pools with different residence times.Moreover, this and our other studies have demonstrated that one matrix equation can replicate simulations of most land C cycle models (i.e., physical emulators). As a result, simulation outputs of those models can be placed into a threedimensional (3-D) parameter space to measure their differences. The latter can be decomposed into traceable components to track the origins of model uncertainty. In addition, the physical emulators make data assimilation computationPublished by Copernicus Publications on behalf of the European Geosciences Union. 146Y. Luo et al.: Land carbon storage dynamics ally feasible so that both C flux-and pool-related datasets can be used to better constrain model predictions of land C sequestration. Overall, this new mathematical framework offers new approaches to understanding, evaluating, diagnosing, and improving land C cycle models.

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“…There have been attempts to address this issue using single-point models with varying degrees of complexity (Harden et al, 1999;Manies et al, 2001;Liu et al, 2003;Billings et al, 2010). These models apply prescribed carbon erosion and/or deposition rates and simulate the resulting effects on the soil organic carbon (SOC) profile using CEN-TURY (Parton et al, 1988) parameterizations. Recently, spatially explicit models that combine erosion models with models of carbon dynamics have been developed (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Current models addressing erosion (e.g. CENTURY: Parton et al, 1988;EPIC: Williams, 1995;WaTEM: Van Oost et al, 2000;EDCM: Liu et al, 2003) use a constant average annual soil erosion rate by assuming uniformity. However, empirical observations indicate that soil erosion and sediment delivery are to a large extent controlled by extreme events (Fiener and Auerswald, 2007).…”

Section: Introductionmentioning

confidence: 99%

Modelling a century of soil redistribution processes and carbon delivery from small watersheds using a multi-class sediment transport model

2017

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Abstract.Over the last few decades, soil erosion and carbon redistribution modelling has received a lot of attention due to large uncertainties and conflicting results. For a physically based representation of event dynamics, coupled soil and carbon erosion models have been developed. However, there is a lack of research utilizing models which physically represent preferential erosion and transport of different carbon fractions (i.e. mineral bound carbon, carbon encapsulated by aggregates and particulate organic carbon). Furthermore, most of the models that have a high temporal resolution are applied to relatively short time series (< 10 yr −1 ), which might not cover the episodic nature of soil erosion. We applied the event-based multi-class sediment transport (MCST) model to a 100-year time series of rainfall observation. The study area was a small agricultural catchment (3 ha) located in the Belgium loess belt about 15 km southwest of Leuven, with a rolling topography of slopes up to 14 %. Our modelling analysis indicates (i) that interrill erosion is a selective process which entrains primary particles, while (ii) rill erosion is non-selective and entrains aggregates, (iii) that particulate organic matter is predominantly encapsulated in aggregates, and (iv) that the export enrichment in carbon is highest during events dominated by interrill erosion and decreases with event size.

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Dynamics of C, N, P and S in grassland soils: a model

Cited by 1,306 publications

References 28 publications

Modelling Soil Organic Carbon Changes Under Different Maize Cropping Scenarios for Cellulosic Ethanol in Europe

Modelling Soil Organic Carbon Changes Under Different Maize Cropping Scenarios for Cellulosic Ethanol in Europe

Transient dynamics of terrestrial carbon storage: mathematical foundation and its applications

Modelling a century of soil redistribution processes and carbon delivery from small watersheds using a multi-class sediment transport model

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