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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Soil Management and Sugarbeet Research » Research » Publications at this Location » Publication #326575

Research Project: Management Practices to Mitigate Global Climate Change, Enhance Bioenergy Production, Increase Soil-C Stocks, and Sustain Soil Productivity and Water Quality

Location: Soil Management and Sugarbeet Research

Title: Modeling GHG Emissions and Carbon Changes in Agricultural and Forest Systems to Guide Mitigation and Adaptation: Synthesis and Future Needs

Author
item Del Grosso, Stephen - Steve
item Ahuja, Lajpat
item Parton, William - Colorado State University

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 8/25/2016
Publication Date: 2/8/2016
Citation: Del Grosso, S.J., Ahuja, L., Parton, W.J. 2016. Modeling GHG Emissions and Carbon Changes in Agricultural and Forest Systems to Guide Mitigation and Adaptation: Synthesis and Future Needs. In: Del Grosso, S.J., Ahuja, L., Parton, W.J., editors. Synthesis and Modeling of Greenhouse Gas Emissions and Carbon Storage in Agricultural and Forest Systems to Guide Mitigation and Adaptation. Madison, WI: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America. p. 305-317. doi: 10.2134/advagricsystmodel6.2013.0008.

Interpretive Summary: Agricultural production systems and land use change for agriculture and forestry are important sources of anthropogenic greenhouse gas (GHG) emissions. Recent commitments by the European Union, the United States, and China to reduce GHG emissions highlight the need to improve estimates of current emissions and mitigation potentials for the agricultural and forestry sectors of the economy. Because it is not feasible to measure GHG emissions and carbon stock changes for these systems beyond the plot scale, models of varying complexity have been applied at regional and larger scales to estimate current emissions and mitigation potentials for different management change scenarios. The chapters in this volume demonstrate that both simple methods and complex models have strengths and weaknesses depending on the stakeholder interest and other factors. Model predictions often agree closely with observed GHG fluxes and C stock changes associated with different land and livestock management practices but uncertainty in model results remains high when they are applied to novel management/landscape combinations. These limitations can be addressed by devoting resources to increasing the quality and quantity of model input data, establishing protocols for modeling methods and making algorithms more transparent, and integrating observations using different approaches.

Technical Abstract: Agricultural production systems and land use change for agriculture and forestry are important sources of anthropogenic greenhouse gas (GHG) emissions. Recent commitments by the European Union, the United States, and China to reduce GHG emissions highlight the need to improve estimates of current emissions and mitigation potentials for the agricultural and forestry sectors of the economy. Because it is not feasible to measure GHG emissions and carbon stock changes for these systems beyond the plot scale, models of varying complexity have been applied at regional and larger scales to estimate current emissions and mitigation potentials for different management change scenarios. The chapters in this volume demonstrate that both simple, empirically based methods and mechanistic, process-based models have strengths and weaknesses depending on the stakeholder interest, scale of application, and other factors. Model outputs often agree closely with observed GHG fluxes and C stock changes associated with management of agricultural soils, forests, and livestock production systems, but uncertainty in model results remains high when they are applied to novel management/landscape combinations due to limitations in model driver data, parametrizations, and representation of key processes. These limitations can be addressed by devoting resources to increasing the quality and quantity of model driver data, particular in developing countries, establishing protocols for modeling methods and making algorithms more transparent, and integrating observations using different approaches (e.g., bottom up chamber based vs. top-down micrometeorological based gas flux measurements) into standardized databases to facilitate rigorous model testing.