Using a crop/soil simulation model and GIS techniques to assess methane emissions from rice fields in Asia. I. Model development

Matthews, Robin; Wassmann, R.; Arah, J. R. M.

doi:10.1007/978-94-010-0898-3_13

Cited by 29 publications

(46 citation statements)

References 36 publications

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“…in units of kg C~ m-2 ) (Wassmann et al, 2000). On average, seasonal emission from permanently flooded irrigated rice is about_ double the emission from rainfed rice, and 20% ~gher than deepwater rice (Wassmann et al, 2000 ).…”

Section: 6 Mitigation Potential Of Rice Agriculturementioning

confidence: 99%

“…China, Japan, Korea, etc. ), 60% of C~ from South and Southeast Asia, and 70-80% of Asian paddy C~ overall (Wassmann et al, 2000 ). China shows that a large scale conversion to midseason drainage is possible.…”

Section: 6 Mitigation Potential Of Rice Agriculturementioning

confidence: 99%

“…It is well known that methane fluxes vary widely by paddy treatment responding to differences in factors such as inundation, soil organic carbon content, rice cultivar, and temperature (Khalil and Shearer, 1991;Khalil et al, 1998;Neue and Roger, 2000,· Shearer and Khalil, 2000, Wassmann et al, 2000Yan et al, 2005 ). One recent survey of published field studies reported fluxes that range~ from 0.1to56 mg CH 4 m-~ hr-1 over a number of paddy systems in monsoon Asia .…”

Section: Introductionmentioning

confidence: 99%

“…To account for this variability, researchers typically organize rice-growing regions into treatments and apply canonical fluxes to the harvested areas over which the treatment is practiced (Matthews et al, 1991;EDGAR, 2008). Alternatively, emissions are estimated using process-level models, which include both physical and biogeochemical processes important to methane production and release, and which compute fluxes directly from factors that characterize a paddy system (Matthews et al, 2000;van Bodegom et al, 2001;Zhang et al, 2002;Yao et al, 2006) Though the heterogeneity and complexity of rice agriculture across systems hinders attempts to estimate emissions at large scales, much effort has been spent in understanding it.…”

Section: Introductionmentioning

confidence: 99%

“…The Decomposition-Denitrification (DNDC) biogeochemical model captures soil redox dynamics by simulating soil Eh and the activity of microbial reducers (Li et al, 2002). Matthews et al (2000/ presented a model (MERES) that simulates soil organic matter decomposition and predicts the amount of carbon substrate available for methanogenesis. It simulates nitrogen transformations in the soil and calculates the uptake of water and nitrogen by the rice crop.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of the sources and sinks of atmospheric methane

Butenhoff¹

2000

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Methane (CfLi) is a potent greenhouse gas and its atmospheric abundance has .nearly tripled since pre-industrial times. With a radiative forcing of 0.48 W m-2 it is second only to carbon dioxide in changing the radiative balance of the atmosphere. The behavior of CH.i has been interesting in recent years. After nearly a century of rapid growth fueled by anthropogenic emissions from sources such as agricultural production and energy, in the past two to three decades the trend of CRi has declined to nearly zero, and its growth today is stalled. There is much interest in understanding why and how CRi changes on decadal time scales, not only to explain past behavior but more importantly to predict where CRi is heading in the future. This has implications not only for future climate change, but also for mitigation policies that aim to reduce emissions of greenhouse gases.The work presented here represents a number of independent studies that investigated various components of the CHi budget, namely the sources and sinks. We The near-constant global burden of CfL seen today hides the changing behavior of the sources and sinks beneath .. Our findings will help us understand whether Cf4 levels will remain stable in the future.2

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Section: 6 Mitigation Potential Of Rice Agriculturementioning

confidence: 99%

Section: 6 Mitigation Potential Of Rice Agriculturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigation of the sources and sinks of atmospheric methane

Butenhoff¹

2000

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The Biogeochemistry of Submerged Soils

Kirk¹

2004

336

306

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Mathematical modeling for improved greenhouse gas balances, agro‐ecosystems, and policy development: lessons from the Australian experience

Moore

Eckard

Thorburn

et al. 2014

WIREs Climate Change

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If the land sector is to make significant contributions to mitigating anthropogenic greenhouse gas (GHG) emissions in coming decades, it must do so while concurrently expanding production of food and fiber. In our view, mathematical modeling will be required to provide scientific guidance to meet this challenge. In order to be useful in GHG mitigation policy measures, models must simultaneously meet scientific, software engineering, and human capacity requirements. They can be used to understand GHG fluxes, to evaluate proposed GHG mitigation actions, and to predict and monitor the effects of specific actions; the latter applications require a change in mindset that has parallels with the shift from research modeling to decision support. We compare and contrast 6 agro-ecosystem models (FullCAM, DayCent, DNDC, APSIM, WNMM, and AgMod), chosen because they are used in Australian agriculture and forestry. Underlying structural similarities in the representations of carbon flows though plants and soils in these models are complemented by a diverse range of emphases and approaches to the subprocesses within the agro-ecosystem. None of these agro-ecosystem models handles all land sector GHG fluxes, and considerable model-based uncertainty exists for soil C fluxes and enteric methane emissions. The models also show diverse approaches to the initialisation of model simulations, software implementation, distribution, licensing, and software quality assurance; each of these will differentially affect their usefulness for policy-driven GHG mitigation prediction and monitoring. Specific requirements imposed on the use of models by Australian mitigation policy settings are discussed, and areas for further scientific development of agro-ecosystem models for use in GHG mitigation policy are proposed.

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Using a crop/soil simulation model and GIS techniques to assess methane emissions from rice fields in Asia. I. Model development

Cited by 29 publications

References 36 publications

Investigation of the sources and sinks of atmospheric methane

Investigation of the sources and sinks of atmospheric methane

The Biogeochemistry of Submerged Soils

Mathematical modeling for improved greenhouse gas balances, agro‐ecosystems, and policy development: lessons from the Australian experience

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