Quantifying Greenhouse Gas Emissions from Agricultural and Forest Landscapes for Policy Development and Verification

The emergence of policies to reduce greenhouse gas (GHG) emissions motivated a rapid shift in ecosystem modeling, especially concerning agricultural and forest management effects on nitrous oxide (N2O) emissions and soil carbon (C). First, we review models for estimating GHG fluxes in agricultural and forest landscapes. Second, we investigate the interplay of models with institutions that develop and implement climate change policy. Limited GHG flux observations constrain our understanding of process controls, especially with respect to N2O flux. Even when simulation models explicitly include N2O flux, this capability is infrequently applied or validated. In the policy arena, tools used to estimate aboveground forest biomass C rely on robust inventory data. Such data are scarce outside of the industrialized world. Compared with biomass C, soil C storage in both forest and agricultural systems is difficult to quantify. Widely used estimates depend on critical assumptions such as the extent to which erosion contributes to C loss from fields and the mechanisms by which C is stabilized in soil, both of which are the subject of serious debate. As evidenced by at least one instance we explore, institutions dedicated to policy development and implementation can rapidly achieve a nuanced understanding of these limitations, given sufficient engagement of the research community. Our review illuminates challenges that affect land-based GHG mitigation initiatives as a result of current knowledge gaps, institutional capacity, and decision-making frameworks. We suggest key points that should be communicated to model users and raise questions that could be fruitfully addressed by model developers and consumers of modeled estimates. Abbreviations: AB32, Assembly Bill 32; ACR, American Carbon Registry; APEX, Agricultural Policy/Environmental eXtender; C-AGG, Coalition for Agriculture and Greenhouse Gases; CAR, Climate Action Reserve; CDM, Clean Development Mechanism; CMPP, cropland management project protocol; CO2e, CO2 equivalents; CSA, Climate Smart Agriculture; DAYCENT, Daily CENTURY; DNDC, Denitrification–Decomposition; EF, emission factor; EPIC, Environmental Policy Integrated Climate; EUETS, EU Emissions Trading System; FIA, Forest Inventory and Analysis; FVS, Forest Vegetation Simulator; GHG, greenhouse gas; GPP, gross primary productivity; LCA, life-cycle analysis; LTE, long-term experiment; NI, nitrification inhibitor; NPP, net primary productivity; OM, organic matter; RGGI, Regional Greenhouse Gas Initiative; RZWQM2, Root Zone Water Quality Model 2; SL, slow-release; SOC, soil organic carbon; SOM, soil organic matter; T-AGG, Technical Working Group on Agriculture and Greenhouse Gases; UNFCCC, United Nations Framework Convention on Climate Change; WFPS, water-filled pore spaces. Christina Tonitto, Cornell International Institute for Food, Agriculture and Development, Cornell University, B75 Mann Library, Ithaca, NY 14853. *Corresponding author (ctonitto@cornell.edu). Noel P. Gurwick, Smithsonian Environmental Research Center, 647 Contees Wharf Rd., Edgewater, MD 21037 (noel.gurwick@gmail.com). Peter B. Woodbury, Section of Soil and Crop Sciences, School of Integrative Plant Science, 1017 Bradfield Hall, 306 Tower Rd., Cornell University, Ithaca, NY 14853 (peterwoodbury@cornell.edu). doi:10.2134/advagricsystmodel6.2013. 0007 Published February 8, 2016