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Soil Organic Carbon (SOC) Equilibrium and Model Initialisation Methods: an Application to the Rothamsted Carbon (RothC) Model

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Abstract

Carbon (C) emissions from anthropogenic land use have accelerated climate change. To reduce C emissions, dynamic models can be used to assess the impact of human drivers on terrestrial C sequestration. Model accuracy requires correct initialisation, since incorrect initialisation can influence the results obtained. Therefore, we sought to improve the initialisation of a process-based SOC model, RothC, which can estimate the effect of climate and land-use change on SOC. The most common initialisation involves running the model until equilibrium (‘spin-up run’), when the SOC pools stabilise (method 1). However, this method does not always produce realistic results. At our experimental sites, the observed SOC was not at equilibrium after 10 years, suggesting that the commonly used spin-up initialisation method assuming equilibrium might be improved. In addition to method 1, we tested two alternative initialisations for RothC that involved adjusting the total or individual SOC pool equilibrium values by regulating the C input during the entire spin-up initialisation period (method 2) and initialising each SOC pool with recently measured SOC values obtained by SOC fractionation (method 3). Analysis of the simulation accuracy for each model initialisation, quantified using the root mean square error (RMSE), indicated that a variant of method 2 that involved adjusting the equilibrium total SOC to observed values (method 2-T) generally showed less variation in the individual SOC pools and total SOC. Furthermore, as total SOC is the sum of all SOC pools, and because total SOC data are more readily available than the individual SOC pool data, we conclude that method 2-T is best for initialising RothC.

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Notes

  1. We validated model results with independently measured total SOC data for all sites. However, due to missing data, we used only one plot (conventional tillage) at Carlow .

  2. Hertfordshire’s total SOC was estimated from the total soil carbon, referring to the site manager’s soil database information.

  3. We validated the accuracy of this estimation with comparing the dead plant weight data regarding 2004.

  4. http://www.carbo-extreme.eu/

  5. If spin-up result of DPM was adjusted to fit to the measured DPM value, labelled method 2-D. Same as method 2-R, -B, -H and -T to highlight that RPM, BIO, HUM pools and Total SOC were adjusted, respectively.

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Acknowledgments

The authors would like to thank Angus Calder and Cheryl Wood (University of St Andrews, UK) who let us use the laboratory facilities to perform the soil fractionation and their helpful support. The authors are also grateful to Olivier Darsonville, Jean- Luc Ollier, Marine Zwicke and Pierre Legoueix for their technical supports at INRA Clermont- Ferrand, UREP, France and also Alex Coad, European Commission - Joint Research Centre for advice on statistics. Funding to support this work was provided by FP7 project GHG- Europe (Grant No. 244122). Pete Smith is a Royal Society-Wolfson Research Merit Award holder.

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Authors and Affiliations

  1. INRA Clermont-Ferrand, UREP, Clermont-Ferrand, France

    Nemo & K. Klumpp

  2. Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, UK

    M. Dondini, A. Hastings, T. Scott, Y. A. Teh & P. Smith

  3. Department of Sustainable Soils and Grassland Systems, Rothamsted Research, Hertfordshire, UK

    K. Coleman & K. Goulding

  4. University College Dublin, School of Biology & Environmental Science, Dublin, Ireland

    B. Osborne & M. Saunders

  5. Department of Botany, Trinity College Dublin, Dublin, Ireland

    Michael. B. Jones

  6. Agroscope Reckenholz-Tänikon, Zürich, Switzerland

    J. Leifeld & M. Saunders

  7. James Hutton Institute, Scotland, UK

    M. Saunders

  8. School of Geography & Geosciences, University of St Andrews, St Andrews, UK

    Y. A. Teh

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Appendix

Appendix

Table 3 Tests of significant differences between contrasting management for each SOC fraction and total SOC (t C ha−1) 0–20 cm. Upper: Estimates (t value) and lower: R2 indicate significant differences of total SOC (t C ha−1) due to management system. For more detail of SOC fractions, see section 2.2
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Fig. 5
figure 5

Concept of adapting SOC fractions to RothC SOC pools (Zimmermann et al. [54])

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Fig. 6
figure 6

Changes in C input (t C ha−1) according to different methods in the method 2, compared to method 1

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Fig. 7
figure 7

Required number of years to attain the equilibrium state with average recent management/weather data for each site. Equilibrium point is SOC changes become <0.0001 (t C ha−1)

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Fig. 8
figure 8

RMSE: model performance accuracy test with each site SOC pools (t C ha−1) data for each initialisation method. Regarding the sites where several years of validation data are available, the result is shown as box plot. Boxes give the median, the 25 and 75 % percentiles. M1 method 1, M3 method 3

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Nemo, Klumpp, K., Coleman, K. et al. Soil Organic Carbon (SOC) Equilibrium and Model Initialisation Methods: an Application to the Rothamsted Carbon (RothC) Model. Environ Model Assess 22, 215–229 (2017). https://doi.org/10.1007/s10666-016-9536-0

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