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Title:   Author  item
                                            Del Grosso, Stephen

                                                         - *Steve*item
                                            Gollany, Heroitem
                                            Reyes-Fox, Melissa
  Submitted to: American Society of Agronomy
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 9/21/2015
                                    Publication  Date: 2/8/2016
                                    Citation: Del Grosso, S.J., Gollany, H.T., Reyes-Fox, M. 2016. Simulating Soil Organic Carbon Stock Changes in Agro-ecosystems using CQESTR, DayCent, and IPCC Tier 1 Methods. 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. 89-110. doi: 10.2134/advagricsystmodel6.2013.0001.5.Interpretive Summary: Models are often used to quantify how land management impacts soil carbon stocks because carbon is an indicator of soil quality and it is often not feasible to use direct measuring methods. Because models are simplifications of reality, it is essential to compare model outputs with field observations to evaluate model performance. In this chapter we compare the abilities of two simulation models (CQESTR and DayCent) and one empirical model (IPCC Tier 1 methodology) to represent how tillage intensity and nitrogen fertilizer affect soil carbon stocks in a dryland wheat/fallow system in Oregon and an irrigated maize system in Colorado. All three models correctly predicted soil carbon losses in Oregon, but the models tended to under predict the magnitude of losses. In Colorado, all three models under estimated soil carbon losses observed with conventional tillage and no nitrogen fertilizer addition. At both sites, observations showed higher losses (or smaller gains) compared to the models as tillage intensity increased and nitrogen fertilizer decreased. The IPCC Tier 1 method is meant to be used for large scale applications and does not account for all of the processes that influence soil carbon stocks so it is not surprising that this method does not perform particularly well when compared with field level observations. The more complex simulation models performed better than the Tier 1 methodology, but could be improved by better accounting of erosion, the vertical distribution of soil carbon, and the impacts of nitrogen on plant production and carbon allocation.
                                Technical Abstract: Models are often used to quantify how land use change and management impact soil organic carbon (SOC) stocks because it is often not feasible to use direct measuring methods. Because models are simplifications of reality, it is essential to compare model outputs with measured values to evaluate model performance. In this chapter we compare the abilities of two process-based models (CQESTR and DayCent) and one empirical model (IPCC Tier 1 methodology) to represent how tillage intensity and nitrogen (N) fertilizer inputs affect SOC stocks in a dryland wheat/fallow system in Oregon and an irrigated maize system in Colorado. All three models correctly predicted SOC losses in Oregon, but the models (particularly the Tier 1 method) tended to under predict the magnitude of losses. In Colorado, all three models under estimated SOC losses observed with conventional tillage and no N fertilizer addition. At both sites, observations showed higher losses (or smaller gains) compared to the models as tillage intensity increased and N fertilizer additions decreased. The IPCC Tier 1 method is meant to be used for large scale applications and does not account for all of the processes that influence SOC stocks so it is not surprising that this method does not perform particularly well when compared with field level observations. The process-based models performed better than the Tier 1 methodology, but could be improved by better accounting of erosion, the vertical distribution of SOC in the soil profile, and the impacts of N on plant production and carbon allocation.


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