1. 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, UK 2 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 USDA Symposium, 22-24 March 2005, Baltimore

2. Introduction Purpose & 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?

3. Purpose of SOC models Description 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

4. Why models are required Rates of decomposition are not simple first-order % added material remaining in SOC Jenkinson, 1990 Years after addition Observed 0 20 40 60 80 100 0246810 Constant reactivity (1st order decay curve)

5. 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 CO 2 (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?

6. Why models are required Reactivity 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) Time Specific reactivity, k, of a residual substrate

7. Reactivity profiles for SOM Inputs & decomposition are continuous & simultaneous Proportion of C in soil Continuously distributed reactivity Reactivity, k

8. A SOM reactivity profile Inferred from thermal analysis Lopez-Capel et al., 2005

9. Reactivity profiles Inferred from thermal analysis Lopez-Capel et al., 2005

10. 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

11. Two pools sufficient for specific descriptions Optimise reactivity and size Measured

12. Two pool description of short term CO 2 release Liang et al., 2005

13. Four pool model (RothC) Reliable for general long term predictions * Reactivity and/or size optimised to work in multiple situations (and to account for soil texture) Measurable Measured * * * * Measurable Source: Coleman et al., 2000

14. Reactivity distributions Implicit in an established SOC model, RothC Humus “Inert” Resistant Decomposable 0 10 20 30 40 50 60 70 80 90 100 0.000 0.0010.010 Specific reactivity,k (day ) Proportion of SOC of greater reactivity (%) 0.0001 0.00001

15. 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…..

16. SOC modeling – Black carbon Context 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

17. Many aspects to modeling black C A 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

18. Modifying a typical four pool model Assigning black C to the inert (IOM) pool in RothC Black C component measured using photo- oxidation method (Skjemstad et al.) Measurable Measured * * * * Measurable

19. A revised reactivity profile for ‘black’ soil Implicit in calibration of RothC (Skjemstad, 2004) Humus “Inert” Resistant Decomposable 0 10 20 30 40 50 60 70 80 90 100 0.000 0.0010.010 Specific reactivity,k (day ) Proportion of SOC of greater reactivity (%)

20. Modeling black C Using 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)

21. Black C Can 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

22. 15 g soil + 90 ml NaI solution (1.80 gcm -3 ) Dissolved organic matter Organomineral (heavy) Density separation Free organic matter (light) Sohi et al., 2001, SSSAJ 64 Ultrasonic dispersion Density separation Intra-aggregate (light) A procedure to isolate SOM pools suitable for modeling

23. * Reactivity optimised for several soils of increasing clay content Measurable Measured * * * * Measurable Sohi et al, in prep. Measurable * * Five pool model - SOMA A tool for investigating SOM stabilization? Calculable

24. 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