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USDA Symposium, 22-24 March 2005, Baltimore. Accounting for black C in the modeling of soil organic matter turnover. Dr Saran Sohi 1,2 , Dr John Gaunt 1,2,3 , Dr Elisa Lopez-Capel 4 , Helen Yates 1 & Prof Johannes Lehmann 2
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USDA Symposium, 22-24 March 2005, Baltimore Accounting for black C in the modeling of soil organic matter turnover Dr Saran Sohi 1,2, Dr John Gaunt 1,2,3, Dr Elisa Lopez-Capel 4, Helen Yates 1 & Prof Johannes Lehmann 2 1 Agriculture & Environment Division, Rothamsted Research, UK2 Dept. of Crop & Soil Sciences, Cornell University, NY 3 GY Associates, Harpenden, UK 4 Civil Eng. & Geosciences, University of Newcastle, UK saran.sohi@bbsrc.ac.uk
IntroductionPurpose & objectives of talk • Placing approaches to SOC modeling in the context of black C Outline: • Why do we need (SOC) models? • What type of SOC models do we have? • Can they be modified to account for black C (BC)? • What other models can be developed to explain the effects of BC?
Purpose of SOC modelsDescription versus prediction • Originally: to make generalizations about land-use impacts on SOC content • Sustainability of production • Descriptive, site-specific, and plot scale • Recently: to predict net changes in SOC from changing agronomic management • diverse landscapes, regional scale • carbon sequestration • So far we are using the same models • Will new models allow mechanisms of stabilization to be elucidated? • What is the impact of black C on SOC turnover
Observed 0 2 4 6 8 10 Why models are requiredRates of decomposition are not simple first-order 100 Constant reactivity (1st order decay curve) 80 % added material remaining in SOC 60 40 20 0 Years after addition Jenkinson, 1990
SOC models Why they are required and how they can be assessed • Organic matter added to soil decomposes • Decomposition is not proportional to what is there i.e., not first-order • How is the actual relationship represented in models • How the relationships are tested • CO2 (measuring where organic matter goes - sensitive in short term) • SOC (measuring what is left - changes slowly, easy to measure) • How are these relationships affected by black C?
Why models are requiredReactivity of substrate declines with time Concept: Rate of carbon ‘release’ from different molecules reflects the balance between the energy gained and the energy expended (in enzyme synthesis) Specific reactivity, k, of a residual substrate Time
Reactivity profiles for SOMInputs & decomposition are continuous & simultaneous Continuously distributed reactivity Proportion of C in soil Reactivity, k
A SOM reactivity profileInferred from thermal analysis Lopez-Capel et al., 2005
Reactivity profilesInferred from thermal analysis Lopez-Capel et al., 2005
Predictive, and versatile descriptive models imply and must embody some: universal or defined reactivity-distribution These distributions will be impacted by the presence of abundant black C
Two pools sufficient for specific descriptions Measured Optimise reactivity and size
0.5 0.4 0.3 CO2 - C (mg/ g soil / day) 0.2 0.1 0.0 300 0 50 100 150 200 250 Days since straw Two pool description of short term CO2 release Liang et al., 2005
Four pool model (RothC)Reliable for general long term predictions Measurable Measured * Reactivity and/or size optimised to work in multiple situations (and to account for soil texture) * * * * Measurable Source: Coleman et al., 2000
Reactivity distributionsImplicit in an established SOC model, RothC 100 “Inert” 90 80 70 60 Proportion of SOC Humus of greater reactivity (%) 50 40 30 20 10 Resistant Decomposable 0 0.0001 0.00001 0.000 0.000 0.001 0.010 -1 Specific reactivity, k (day )
SOC modeling Summary - where we are at Existing models work by allocating SOC to discrete, linked pools of defined & contrasting ‘reactivity’ These pools cannot be measured but from extensive parameterization we know their likely size in typical soils (given information on site, texture and climate) • Descriptions of C accumulation and equilibriums in long term experiments under contrasting conditions • Prediction of C accumulation and equilibriums at sites with known management history • Extended to forest systems • Integrated with GIS for regional scale predictions • These models are not able to automatically account for atypical and/or black soils…..
SOC modeling – Black carbonContext and relevance • Recent studies suggest (Schmidt et al., Skjemstad et al.) • black C is a ubiquitous constituent of soil organic carbon (SOC) in agricultural soils • black C is not only significant, but an often major (or even dominant) constituent of SOC If black C comprises stable or most stable C, its abundance must strongly affect SOM reactivity profiles • SOC accumulation • SOC equilibrium
Many aspects to modeling black CA range of models are required • Context • Soil C stocks • C sequestration • Feedback effects • Description, prediction, spatial • Sensitivity (land-use or agronomic level) • Spatial resolution (soil type or climatic zone) • Time-scale & time-step (field or lab incubation experiments) • Empirical, mechanistic • Model initialization • Assessing intervention • Issues addressed: • Permanency
Modifying a typical four pool modelAssigning black C to the inert (IOM) pool in RothC Measurable Measured * * * * Black C component measured using photo-oxidation method (Skjemstad et al.) Measurable
A revised reactivity profile for ‘black’ soilImplicit in calibration of RothC (Skjemstad, 2004) 100 90 “Inert” 80 70 60 Proportion of SOC of greater reactivity (%) 50 40 Humus 30 20 10 Resistant Decomposable 0 0.000 0.000 0.001 0.010 -1 Specific reactivity, k (day )
Modeling black CUsing an amended four-pool model • Context • C stocks • Descriptive and spatial modeling • Land-use level • Regional scale projections • Time-scale of decades; time-step of months (field data) • Empirical model • Revised ‘rules’ for initialization • Issues addressed: • C-sequestration potential (re-assessed)
Black CCan models gain us a mechanistic understanding? Models based upon relevant (distinct) and verifiable (measurable) pools offer a tool for elucidating underlying mechanisms of C stabilisation • Sohi et al,. 2001 • Our simulations using SOMA would account explicitly for black C present in each pool • Black C appears not to be characterized by a single physical location within the soil matrix (Glaser et al., 2000) • Success would not be entirely dependent upon relevant black C measurement techniques • The impact on the reactivity of each pool can be inferred
Ultrasonic dispersion Density separation Intra-aggregate (light) A procedure to isolate SOM pools suitable for modeling 15 g soil + 90 ml NaI solution (1.80 gcm-3) Density separation Free organic matter (light) Organomineral (heavy) Dissolved organic matter Sohi et al., 2001, SSSAJ 64
Five pool model - SOMAA tool for investigating SOM stabilization? Measured Measurable * Reactivity optimised for several soils of increasing clay content * * Measurable Calculable Measurable * * * * Measurable Sohi et al, in prep. Measurable
Conclusions:Using models to understand BC in soil • Simple modification to existing models e.g. RothC, re-defining the inert/passive fraction • Re-optimizing new mechanistic models e.g. SOMA for soils with/without known amounts/types of black C > Strategies for purposeful amendments that maximise soil C stabilisation