Evaluation of Parametric Limitations in Simulating Greenhouse Gas Fluxes from Irish Arable Soils Using Three Process-Based Models

Mohammad I. Khalil, Mohamed Abdalla, Gary Lanigan, Bruce Osborne, Christoph Müller

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Abstract

Globally a large number of process-based models have been assessed for quantification of agricultural greenhouse gas (GHG) emissions. Modelling approaches minimize the presence of spatial variability of biogeochemical processes, leading to improved estimates of GHGs as well as identifying mitigation and policy options. The comparative performance of the three dynamic models (e.g., DNDC v9.4, DailyDayCent and ECOSSE v5+) with minimum numbers of common input parameters was evaluated against measured variables. Simulations were performed on conventionally-tilled spring barley crops receiving N fertilizer at 135 - 159 kg∙N∙ha−1∙yr−1 and crop residues at 3 t∙ha−1∙yr−1. For surface soil nitrate (0 - 10 cm), the ECOSSE and DNDC simulated values showed significant correlations with measured values (R2 = 0.31 - 0.55, p < 0.05). Only the ECOSSE-simulated N2O fluxes showed a significant relationship (R2 = 0.33, p < 0.05) with values measured from fertilized fields, but not with unfertilized ones. The DNDC and DailyDayCent models significantly underestimated seasonal/annual N2O fluxes compared to ECOSSE, with emission factors (EFs), based on an 8-year average, were 0.09%, 0.31% and 0.52%, respectively. Predictions of ecosystem respiration by both DailyDayCent and DNDC showed reasonable agreement with Eddy Covariance data (R2 = 0.34 - 0.41, p < 0.05). Compared to the measured value (3624 kg∙C∙ha−1∙yr−1), the ECOSSE underestimated annual heterotrophic respiration by 7% but this was smaller than the DNDC (50%) and DailyDayCent (24%) estimates. All models simulated CH4 uptake well although the ECOSSE prediction was closer (−29 g∙C∙ha−1∙yr−1) to the measured one (2.9). The site-specific results imply that the ECOSSE model performed better under Irish conditions. However, further refinement and validation of all of the models with a more extensive dataset that includes other land-use and soil types will be required to determine their suitability in providing national estimates.
Original languageEnglish
Pages (from-to)503-520
Number of pages18
JournalAgricultural Sciences
Volume7
Issue number8
DOIs
Publication statusPublished - 16 Aug 2016

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greenhouse gas
soil
respiration
crop residue
eddy covariance
prediction
barley
evaluation
soil type
soil surface
fertilizer
nitrate
land use
crop
ecosystem
modeling
simulation

Keywords

  • Greenhouse Gases
  • Arable Lands
  • Input Parameters
  • Process-Based Models
  • Ireland

Cite this

Evaluation of Parametric Limitations in Simulating Greenhouse Gas Fluxes from Irish Arable Soils Using Three Process-Based Models. / Khalil, Mohammad I.; Abdalla, Mohamed; Lanigan, Gary; Osborne, Bruce; Müller, Christoph.

In: Agricultural Sciences, Vol. 7, No. 8, 16.08.2016, p. 503-520.

Research output: Contribution to journalArticle

Khalil, Mohammad I. ; Abdalla, Mohamed ; Lanigan, Gary ; Osborne, Bruce ; Müller, Christoph. / Evaluation of Parametric Limitations in Simulating Greenhouse Gas Fluxes from Irish Arable Soils Using Three Process-Based Models. In: Agricultural Sciences. 2016 ; Vol. 7, No. 8. pp. 503-520.
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AU - Müller, Christoph

N1 - The senior author gratefully acknowledges the funding by the Science, Technology, Research and Innovation for the Environment (STRIVE) Programme of the Irish Government under the National Development Plan 2007-2013 and the Department of the Environment, Heritage and Local Government. The authors would like to thanks Phillip O’Brien (EPA) for extending technical and relevant support; Mike Williams, Mike Jones and Matt Saunders (TCD), Komsan Rueangritsarakul and Mohamed Helmy (UCD) for supplying experimental data for modelling work; as well as Tom Bolger and Tommy Gallagher (UCD) for providing administrative support.

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N2 - Globally a large number of process-based models have been assessed for quantification of agricultural greenhouse gas (GHG) emissions. Modelling approaches minimize the presence of spatial variability of biogeochemical processes, leading to improved estimates of GHGs as well as identifying mitigation and policy options. The comparative performance of the three dynamic models (e.g., DNDC v9.4, DailyDayCent and ECOSSE v5+) with minimum numbers of common input parameters was evaluated against measured variables. Simulations were performed on conventionally-tilled spring barley crops receiving N fertilizer at 135 - 159 kg∙N∙ha−1∙yr−1 and crop residues at 3 t∙ha−1∙yr−1. For surface soil nitrate (0 - 10 cm), the ECOSSE and DNDC simulated values showed significant correlations with measured values (R2 = 0.31 - 0.55, p < 0.05). Only the ECOSSE-simulated N2O fluxes showed a significant relationship (R2 = 0.33, p < 0.05) with values measured from fertilized fields, but not with unfertilized ones. The DNDC and DailyDayCent models significantly underestimated seasonal/annual N2O fluxes compared to ECOSSE, with emission factors (EFs), based on an 8-year average, were 0.09%, 0.31% and 0.52%, respectively. Predictions of ecosystem respiration by both DailyDayCent and DNDC showed reasonable agreement with Eddy Covariance data (R2 = 0.34 - 0.41, p < 0.05). Compared to the measured value (3624 kg∙C∙ha−1∙yr−1), the ECOSSE underestimated annual heterotrophic respiration by 7% but this was smaller than the DNDC (50%) and DailyDayCent (24%) estimates. All models simulated CH4 uptake well although the ECOSSE prediction was closer (−29 g∙C∙ha−1∙yr−1) to the measured one (2.9). The site-specific results imply that the ECOSSE model performed better under Irish conditions. However, further refinement and validation of all of the models with a more extensive dataset that includes other land-use and soil types will be required to determine their suitability in providing national estimates.

AB - Globally a large number of process-based models have been assessed for quantification of agricultural greenhouse gas (GHG) emissions. Modelling approaches minimize the presence of spatial variability of biogeochemical processes, leading to improved estimates of GHGs as well as identifying mitigation and policy options. The comparative performance of the three dynamic models (e.g., DNDC v9.4, DailyDayCent and ECOSSE v5+) with minimum numbers of common input parameters was evaluated against measured variables. Simulations were performed on conventionally-tilled spring barley crops receiving N fertilizer at 135 - 159 kg∙N∙ha−1∙yr−1 and crop residues at 3 t∙ha−1∙yr−1. For surface soil nitrate (0 - 10 cm), the ECOSSE and DNDC simulated values showed significant correlations with measured values (R2 = 0.31 - 0.55, p < 0.05). Only the ECOSSE-simulated N2O fluxes showed a significant relationship (R2 = 0.33, p < 0.05) with values measured from fertilized fields, but not with unfertilized ones. The DNDC and DailyDayCent models significantly underestimated seasonal/annual N2O fluxes compared to ECOSSE, with emission factors (EFs), based on an 8-year average, were 0.09%, 0.31% and 0.52%, respectively. Predictions of ecosystem respiration by both DailyDayCent and DNDC showed reasonable agreement with Eddy Covariance data (R2 = 0.34 - 0.41, p < 0.05). Compared to the measured value (3624 kg∙C∙ha−1∙yr−1), the ECOSSE underestimated annual heterotrophic respiration by 7% but this was smaller than the DNDC (50%) and DailyDayCent (24%) estimates. All models simulated CH4 uptake well although the ECOSSE prediction was closer (−29 g∙C∙ha−1∙yr−1) to the measured one (2.9). The site-specific results imply that the ECOSSE model performed better under Irish conditions. However, further refinement and validation of all of the models with a more extensive dataset that includes other land-use and soil types will be required to determine their suitability in providing national estimates.

KW - Greenhouse Gases

KW - Arable Lands

KW - Input Parameters

KW - Process-Based Models

KW - Ireland

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M3 - Article

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