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Using a Crop Model to Benchmark Miscanthus and Switchgrass

Author

Listed:
  • Monia El Akkari

    (INRAE, AgroParisTech, Paris-Saclay University, UMR ECOSYS, F-78850 Thiverval-Grignon, France)

  • Fabien Ferchaud

    (BioEcoAgro Joint Research Unit, INRAE, Université de Liège, Université de Lille, Université de Picardie Jules Verne, F-02000 Barenton-Bugny, France)

  • Loïc Strullu

    (BioEcoAgro Joint Research Unit, INRAE, Université de Liège, Université de Lille, Université de Picardie Jules Verne, F-02000 Barenton-Bugny, France)

  • Ian Shield

    (Rothamsted Research, Harpenden AL5 2JQ, UK)

  • Aurélie Perrin

    (ESA, INRAE, USC INRAE-1422 GRAPPE, F-49000, Université Bretagne Loire, Ecole Supérieure d’Agricultures (ESA)-SFR 4207 QUASAV, 55 rue Rabelais, 49007 Angers, France)

  • Jean Louis Drouet

    (INRAE, AgroParisTech, Paris-Saclay University, UMR ECOSYS, F-78850 Thiverval-Grignon, France)

  • Pierre Alain Jayet

    (INRAE, AgroParisTech, UMR Public Economy, F-78850 Thiverval-Grignon, France)

  • Benoît Gabrielle

    (INRAE, AgroParisTech, Paris-Saclay University, UMR ECOSYS, F-78850 Thiverval-Grignon, France)

Abstract

Crop yields are important items in the economic performance and the environmental impacts of second-generation biofuels. Since they strongly depend on crop management and pedoclimatic conditions, it is important to compare candidate feedstocks to select the most appropriate crops in a given context. Agro-ecosystem models offer a prime route to benchmark crops, but have been little tested from this perspective thus far. Here, we tested whether an agro-ecosystem model (CERES-EGC) was specific enough to capture the differences between miscanthus and switchgrass in northern Europe. The model was compared to field observations obtained in seven long-term trials in France and the UK, involving different fertilizer input rates and harvesting dates. At the calibration site (Estrées-Mons), the mean deviations between simulated and observed crop biomass yields for miscanthus varied between −0.3 t DM ha −1 and 4.2 t DM ha −1 . For switchgrass, simulated yields were within 1.0 t DM ha −1 of the experimental data. Observed miscanthus yields were higher than switchgrass yields in most sites and for all treatments, with one exception. Overall, the model captured the differences between both crops adequately, with a mean deviation of 0.46 t DM ha −1 , and could be used to guide feedstock selections over larger biomass supply areas.

Suggested Citation

  • Monia El Akkari & Fabien Ferchaud & Loïc Strullu & Ian Shield & Aurélie Perrin & Jean Louis Drouet & Pierre Alain Jayet & Benoît Gabrielle, 2020. "Using a Crop Model to Benchmark Miscanthus and Switchgrass," Energies, MDPI, vol. 13(15), pages 1-22, August.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:15:p:3942-:d:393209
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    References listed on IDEAS

    as
    1. Iqbal, Y. & Gauder, M. & Claupein, W. & Graeff-Hönninger, S. & Lewandowski, I., 2015. "Yield and quality development comparison between miscanthus and switchgrass over a period of 10 years," Energy, Elsevier, vol. 89(C), pages 268-276.
    2. Laurent, A. & Pelzer, E. & Loyce, C. & Makowski, D., 2015. "Ranking yields of energy crops: A meta-analysis using direct and indirect comparisons," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 41-50.
    3. Mohr, Alison & Raman, Sujatha, 2013. "Lessons from first generation biofuels and implications for the sustainability appraisal of second generation biofuels," Energy Policy, Elsevier, vol. 63(C), pages 114-122.
    4. Izaurralde, R. César & McGill, William B. & Williams, Jimmy R. & Jones, Curtis D. & Link, Robert P. & Manowitz, David H. & Schwab, D. Elisabeth & Zhang, Xuesong & Robertson, G. Philip & Millar, Nevill, 2017. "Simulating microbial denitrification with EPIC: Model description and evaluation," Ecological Modelling, Elsevier, vol. 359(C), pages 349-362.
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    More about this item

    Keywords

    crop modeling; lignocellulosic species; second generation biofuels;
    All these keywords.

    JEL classification:

    • Q - Agricultural and Natural Resource Economics; Environmental and Ecological Economics
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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