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A model-data fusion approach to analyse carbon dynamics in managed grasslands

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  • Myrgiotis, Vasileios
  • Blei, Emanuel
  • Clement, Rob
  • Jones, Stephanie K.
  • Keane, Ben
  • Lee, Mark A.
  • Levy, Peter E.
  • Rees, Robert M.
  • Skiba, Ute M.
  • Smallman, Thomas Luke
  • Toet, Sylvia
  • Williams, Mathew

Abstract

Grasslands are an important component of the global carbon (C) cycle, with a strong potential for C sequestration. However, an improved capacity to quantify grassland C stocks and monitor their variation in space and time, particularly in response to management, is needed in order to conserve and enhance grassland C reservoirs. To meet this challenge we outline and test here an approach to combine C cycle modelling with observational data. We implemented an intermediate complexity model, DALEC-Grass, within a probabilistic model-data fusion (MDF) framework, CARDAMOM, at two managed grassland sites (Easter Bush and Crichton) in the UK. We used 3 years (Easter Bush, 2002–2004) of management data and observations of leaf area index (LAI) and Net Ecosystem Exchange (NEE) from eddy covariance to calibrate the distributions of model parameters. Using these refined distributions, we then assimilated the remaining 7 years (Easter Bush, 2005–2010 and Crichton, 2015) of LAI observations and evaluated the simulated NEE, above and below-ground biomass and other C fluxes against independent data from the two grasslands. Our results show that fusing model predictions with LAI observations allowed the CARDAMOM MDF system to diagnose the effects of grazing and cutting realistically. The overlap of MDF-predicted and measured NEE (both sites) and ecosystem respiration (Easter Bush) was 92% and 83% respectively while the correlation coefficient (r) was 0.79 for both variables. This study lays the foundation for using MDF with satellite data on LAI to produce the spatially and temporally-resolved estimates of C cycling needed in shaping and monitoring the implementation of relevant policies and farm-management decisions.

Suggested Citation

  • Myrgiotis, Vasileios & Blei, Emanuel & Clement, Rob & Jones, Stephanie K. & Keane, Ben & Lee, Mark A. & Levy, Peter E. & Rees, Robert M. & Skiba, Ute M. & Smallman, Thomas Luke & Toet, Sylvia & Willia, 2020. "A model-data fusion approach to analyse carbon dynamics in managed grasslands," Agricultural Systems, Elsevier, vol. 184(C).
    • Handle: RePEc:eee:agisys:v:184:y:2020:i:c:s0308521x2030768x
    DOI: 10.1016/j.agsy.2020.102907
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    References listed on IDEAS

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    1. Smit, H.J. & Metzger, M.J. & Ewert, F., 2008. "Spatial distribution of grassland productivity and land use in Europe," Agricultural Systems, Elsevier, vol. 98(3), pages 208-219, October.
    2. Tobias Houska & Philipp Kraft & Alejandro Chamorro-Chavez & Lutz Breuer, 2015. "SPOTting Model Parameters Using a Ready-Made Python Package," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-22, December.
    3. De Oliveira Silva, Rafael & Barioni, Luis Gustavo & Queiroz Pellegrino, Giampaolo & Moran, Dominic, 2018. "The role of agricultural intensification in Brazil's Nationally Determined Contribution on emissions mitigation," Agricultural Systems, Elsevier, vol. 161(C), pages 102-112.
    4. Oenema, Jouke & Burgers, Saskia & van Keulen, Herman & van Ittersum, Martin, 2015. "Stochastic uncertainty and sensitivities of nitrogen flows on dairy farms in The Netherlands," Agricultural Systems, Elsevier, vol. 137(C), pages 126-138.
    5. Yunqing Hao & Zhengwei He, 2019. "Effects of grazing patterns on grassland biomass and soil environments in China: A meta-analysis," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-15, April.
    6. Karl-Heinz Erb & Thomas Kastner & Christoph Plutzar & Anna Liza S. Bais & Nuno Carvalhais & Tamara Fetzel & Simone Gingrich & Helmut Haberl & Christian Lauk & Maria Niedertscheider & Julia Pongratz & , 2018. "Unexpectedly large impact of forest management and grazing on global vegetation biomass," Nature, Nature, vol. 553(7686), pages 73-76, January.
    7. Nicholas Bloom, 2017. "Observations on Uncertainty," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 50(1), pages 79-84, March.
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