{PAGE NAME} | {WEB AREA NAME}

EM Identity and Description

EM Identification (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
EM Short Name em.detail.shortNameHelp ?	DayCent N2O flux simulation, Ireland	*	*	*	*	*	*	*
EM Full Name em.detail.fullNameHelp ?	DayCent simulation N2O flux and climate change, Ireland	*	*	*	*	*	*	*
EM Source or Collection em.detail.emSourceOrCollectionHelp ?	None	*	*	*	*	*	*	*
EM Source Document ID	358	*	*	*	*	*	*	*
Document Author em.detail.documentAuthorHelp ?	Abdalla, M., Yeluripati, J., Smith, P., Burke, J., Williams, M.	*	*	*	*	*	*	*
Document Year em.detail.documentYearHelp ?	2010	*	*	*	*	*	*	*
Document Title em.detail.sourceIdHelp ?	Testing DayCent and DNDC model simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a humid pasture	*	*	*	*	*	*	*
Document Status em.detail.statusCategoryHelp ?	Peer reviewed and published	*	*	*	*	*	*	*
Comments on Status em.detail.commentsOnStatusHelp ?	Published journal manuscript	*	*	*	*	*	*	*

Software and Access (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
EM Software Access info Exit em.detail.modelAccessInformationHelp ?	Not applicable	*	*	*	*	*	*	*
Contact Name em.detail.contactNameHelp ?	M. Abdalla	*	*	*	*	*	*	*
Contact Address	Dept. of Botany, School of Natural Science, Trinity College Dublin, Dublin2, Ireland	*	*	*	*	*	*	*
Contact Email	abdallm@tcd.ie	*	*	*	*	*	*	*

EM Description (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
Summary Description em.detail.summaryDescriptionHelp ?	Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios.	Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. Testing DayCent and DNDC Model Simulations of N2O Fluxes and Assessing the Impacts of Climate Change on the Gas Flux and Biomass Production from a Humid Pasture \| Request	Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. Testing DayCent and DNDC Model Simulations of N2O Fluxes and Assessing the Impacts of Climate Change on the Gas Flux and Biomass Production from a Humid Pasture \| Request	Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. Testing DayCent and DNDC Model Simulations of N2O Fluxes and Assessing the Impacts of Climate Change on the Gas Flux and Biomass Production from a Humid Pasture \| Request	Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. Testing DayCent and DNDC Model Simulations of N2O Fluxes and Assessing the Impacts of Climate Change on the Gas Flux and Biomass Production from a Humid Pasture \| Request	Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. Testing DayCent and DNDC Model Simulations of N2O Fluxes and Assessing the Impacts of Climate Change on the Gas Flux and Biomass Production from a Humid Pasture \| Request	Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. Testing DayCent and DNDC Model Simulations of N2O Fluxes and Assessing the Impacts of Climate Change on the Gas Flux and Biomass Production from a Humid Pasture \| Request	Simulation models are one of the approaches used to investigate greenhouse gas emissions and potential effects of global warming on terrestrial ecosystems. DayCent which is the daily time-step version of the CENTURY biogeochemical model, and DNDC (the DeNitrification–DeComposition model) were tested against observed nitrous oxide flux data from a field experiment on cut and extensively grazed pasture located at the Teagasc Oak Park Research Centre, Co. Carlow, Ireland. The soil was classified as a free draining sandy clay loam soil with a pH of 7.3 and a mean organic carbon and nitrogen content at 0–20 cm of 38 and 4.4 g kg−1 dry soil, respectively. The aims of this study were to validate DayCent and DNDC models for estimating N2O emissions from fertilized humid pasture, and to investigate the impacts of future climate change on N2O fluxes and biomass production. Measurements of N2O flux were carried out from November 2003 to November 2004 using static chambers. Three climate scenarios, a baseline of measured climatic data from the weather station at Carlow, and high and low temperature sensitivity scenarios predicted by the Community Climate Change Consortium For Ireland (C4I) based on the Hadley Centre Global Climate Model (HadCM3) and the Intergovernment Panel on Climate Change (IPCC) A1B emission scenario were investigated. DayCent predicted cumulative N2O flux and biomass production under fertilized grass with relative deviations of +38% and (−23%) from the measured, respectively. However, DayCent performs poorly under the control plots, with flux relative deviation of (−57%) from the measured. Comparison between simulated and measured flux suggests that both DayCent model’s response to N fertilizer and simulated background flux need to be adjusted. DNDC overestimated the measured flux with relative deviations of +132 and +258% due to overestimation of the effects of SOC. DayCent, though requiring some calibration for Irish conditions, simulated N2O fluxes more consistently than did DNDC. We used DayCent to estimate future fluxes of N2O from this field. No significant differences were found between cumulative N2O flux under climate change and baseline conditions. However, above-ground grass biomass was significantly increased from the baseline of 33 t ha−1 to 45 (+34%) and 50 (+48%) t dry matter ha−1 for the low and high temperature sensitivity scenario respectively. The increase in above-ground grass biomass was mainly due to the overall effects of high precipitation, temperature and CO2 concentration. Our results indicate that because of high N demand by the vigorously growing grass, cumulative N2O flux is not projected to increase significantly under climate change, unless more N is applied. This was observed for both the high and low temperature sensitivity scenarios. Testing DayCent and DNDC Model Simulations of N2O Fluxes and Assessing the Impacts of Climate Change on the Gas Flux and Biomass Production from a Humid Pasture \| Request
Specific Policy or Decision Context Cited em.detail.policyDecisionContextHelp ?	climate change	*	*	*	*	*	*	*
Biophysical Context	Agricultural field, Ann rainfall 824mm, mean air temp 9.4°C	*	*	*	*	*	*	*
EM Scenario Drivers em.detail.scenarioDriverHelp ?	air temperature, precipitation, Atmospheric CO2 concentrations	*	*	*	*	*	*	*

EM Relationship to Other EMs or Applications

EM ID em.detail.idHelp ?	EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
Method Only, Application of Method or Model Run em.detail.methodOrAppHelp ?	Method + Application (multiple runs exist)	Model Run Associated with a Specific EM Application	Model Run Associated with a Specific EM Application	Model Run Associated with a Specific EM Application	Model Run Associated with a Specific EM Application	Model Run Associated with a Specific EM Application	Model Run Associated with a Specific EM Application	Model Run Associated with a Specific EM Application
New or Pre-existing EM? em.detail.newOrExistHelp ?	Application of existing model	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive

Related EMs (for example, other versions or derivations of this EM) described in ESML

EM ID em.detail.idHelp ?	EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
Document ID for related EM em.detail.relatedEmDocumentIdHelp ?	None	None	None	None	None	None	None	None
EM ID for related EM em.detail.relatedEmEmIdHelp ?	EM-598	None	None	None	None	None	None	None

EM Modeling Approach

EM Relationship to Time (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
EM Temporal Extent em.detail.tempExtentHelp ?	1961-1990	*	*	*	*	*	*	*
EM Time Dependence em.detail.timeDependencyHelp ?	time-dependent	*	*	*	*	*	*	*
EM Time Reference (Future/Past) em.detail.futurePastHelp ?	both	*	*	*	*	*	*	*
EM Time Continuity em.detail.continueDiscreteHelp ?	discrete	*	*	*	*	*	*	*
EM Temporal Grain Size Value em.detail.tempGrainSizeHelp ?	1	*	*	*	*	*	*	*
EM Temporal Grain Size Unit em.detail.tempGrainSizeUnitHelp ?	Day	*	*	*	*	*	*	*

EM spatial extent (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
Bounding Type em.detail.boundingTypeHelp ?	Point or points	*	*	*	*	*	*	*
Spatial Extent Name em.detail.extentNameHelp ?	Oak Park Research centre	*	*	*	*	*	*	*
Spatial Extent Area (Magnitude) em.detail.extentAreaHelp ?	1-10 ha	*	*	*	*	*	*	*

Spatial Distribution of Computations (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
EM Spatial Distribution em.detail.distributeLumpHelp ?	spatially lumped (in all cases)	*	*	*	*	*	*	*
Spatial Grain Type em.detail.spGrainTypeHelp ?	Not applicable	*	*	*	*	*	*	*
Spatial Grain Size em.detail.spGrainSizeHelp ?	Not applicable	*	*	*	*	*	*	*

EM Structure and Computation Approach (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
EM Computational Approach em.detail.emComputationalApproachHelp ?	Numeric	*	*	*	*	*	*	*
EM Determinism em.detail.deterStochHelp ?	deterministic	*	*	*	*	*	*	*
Statistical Estimation of EM em.detail.statisticalEstimationHelp ?	Conventional (frequentist) statistics	*	*	*	*	*	*	*

Model Checking Procedures Used (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
Model Calibration Reported? em.detail.calibrationHelp ?	No	Yes	Yes	Yes	*	*	*	*
Model Goodness of Fit Reported? em.detail.goodnessFitHelp ?	Yes ? Comment: for N2O fluxes	*	*	*	*	*	*	*
Goodness of Fit (metric\| value \| unit) em.detail.goodnessFitValuesHelp ?	r squared \| 0.32 \| uniteless	*	*	*	*	*	*	*
Model Operational Validation Reported? em.detail.validationHelp ?	Yes	*	*	*	*	*	*	*
Model Uncertainty Analysis Reported? em.detail.uncertaintyAnalysisHelp ?	No	*	*	*	*	*	*	*
Model Sensitivity Analysis Reported? em.detail.sensAnalysisHelp ?	No	*	*	*	*	*	*	*
Model Sensitivity Analysis Include Interactions? em.detail.interactionConsiderHelp ?	Not applicable	*	*	*	*	*	*	*

EM Locations, Environments, Ecology

Location of EM Application (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])

EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
Europe Ireland	*	*	*	*	*	*	*

Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)

EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
None	*	*	*	*	*	*	*

Centroid Lat/Long (Decimal Degree)

EM ID em.detail.idHelp ?	EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
Centroid Latitude em.detail.ddLatHelp ?	52.86	*	*	*	*	*	*	*
Centroid Longitude em.detail.ddLongHelp ?	6.54	*	*	*	*	*	*	*
Centroid Datum em.detail.datumHelp ?	None provided	*	*	*	*	*	*	*
Centroid Coordinates Status em.detail.coordinateStatusHelp ?	Provided	*	*	*	*	*	*	*

Environments and Scales Modeled (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

EM ID em.detail.idHelp ?	EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
EM Environmental Sub-Class em.detail.emEnvironmentalSubclassHelp ?	Agroecosystems	*	*	*	*	*	*	*
Specific Environment Type em.detail.specificEnvTypeHelp ?	farm pasture	*	*	*	*	*	*	*
EM Ecological Scale em.detail.ecoScaleHelp ?	Ecological scale is finer than that of the Environmental Sub-class	*	*	*	*	*	*	*

Organisms modeled (* Note that run information is shown only where run data differ from the "parent" entry shown at left)

Scale of differentiation of organisms modeled

EM ID em.detail.idHelp ?	EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
EM Organismal Scale em.detail.orgScaleHelp ?	Not applicable	*	*	*	*	*	*	*

Taxonomic level and name of organisms or groups identified

EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
None Available	*	*	*	*	*	*	*

EnviroAtlas URL

EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)	GAP Ecological Systems, Average Annual Precipitation, Agricultural water use (million gallons/day)

EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system

* Note that run information is shown only where run data differ from the "parent" entry shown at left

CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)

EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
[Ecosystem] Regulation & Maintenance Maintenance of physical, chemical, biological conditions Atmospheric composition and climate regulation Global climate regulation by reduction of greenhouse gas concentrations	*	*	*	*	*	*	*

National Ecosystem Services Classification System (NESCS) Plus

(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)

EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
Agroecosystems (22) Flora (5) Green biomass Stock Indicator	*	*	*	*	*	*	*

EM Variable Names (and Units)

* Note that for runs, variable name is displayed only where data for that variable differed by run AND those differences were reported in the source document. Where differences occurred but were not reported, the variable is not listed. Click on variable name to view details.

To view run information for variables, select any variable below or view run data for all variables.

Predictor

EM ID em.detail.idHelp ?	EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
Driving Variables (and Units) em.detail.drivingVariableHelp ?	CO2 concentrations (atmosphere) (ppm) Latitude (DD) Yearly accumulated precipitation (mm) Yearly maximum of average daily temperature (°C) Yearly minimum of average daily temperature (°C)	Atmospheric CO2 concentrations (ppm) N concentration in rainfall (mg NI^-1) Water filled pore space at wilting point (Percent) Yearly accumulated precipitation (mm) Yearly maximum of average daily temperature (°C) Yearly minimum of average daily temperature (°C)	Atmospheric CO2 concentrations (ppm) N concentration in rainfall (mg NI^-1) Water filled pore space at wilting point (Percent) Yearly accumulated precipitation (mm) Yearly maximum of average daily temperature (°C) Yearly minimum of average daily temperature (°C)	Atmospheric CO2 concentrations (ppm) N concentration in rainfall (mg NI^-1) Water filled pore space at wilting point (Percent) Yearly accumulated precipitation (mm) Yearly maximum of average daily temperature (°C) Yearly minimum of average daily temperature (°C)	CO2 concentrations (atmosphere) (ppm) N concentration (rainfall) (mg NI^-1) Water filled pore space (WFPS) at wilting point (Percent) Yearly accumulated precipitation (mm) Yearly maximum of average daily temperature (°C) Yearly minimum of average daily temperature (°C)	CO2 concentrations (atmosphere) (ppm) N concentration (rainfall) (mg NI^-1) Water filled pore space (WFPS) at wilting point (Percent) Yearly accumulated precipitation (mm) Yearly maximum of average daily temperature (°C) Yearly minimum of average daily temperature (°C)	CO2 concentrations (atmosphere) (ppm) N concentration (rainfall) (mg NI^-1) Water filled pore space (WFPS) at wilting point (Percent) Yearly accumulated precipitation (mm) Yearly maximum of average daily temperature (°C) Yearly minimum of average daily temperature (°C)	CO2 concentrations (atmosphere) (ppm) N concentration (rainfall) (mg NI^-1) Water filled pore space (WFPS) at wilting point (Percent) Yearly accumulated precipitation (mm) Yearly maximum of average daily temperature (°C) Yearly minimum of average daily temperature (°C)
Constant or Factor Variables (and Units) em.detail.constantFactorVariableHelp ?	Bulk density (g cm^-3) Clay fraction (%) Depth of water retention layer (cm) Fertilizer Addition (kg N ha^-1 yr^-1) Harvest (green biomass) (Not applicable) Initial organic C content at surface soil (kg C kg^-1) N concentration (rainfall) (mg N l^-1) Slope (%) Soil pH (unitless) Soil texture (sandy clay loam) (Not applicable) Vegetation type (moist pasture) (Not applicable) Water filled pore space (WFPS) at field capacity (%) Water filled pore space (WFPS) at wilting point (%)	Initial organic C content at surface soil (kg C kg^-1) FertilizerAddition (kg N ha^-1 yr^-1) Harvest (Not applicable) Slope (5) Water filled pore space at field capacity (Percent)	Initial organic C content at surface soil (kg C kg^-1) FertilizerAddition (kg N ha^-1 yr^-1) Harvest (Not applicable) Slope (5) Water filled pore space at field capacity (Percent)	Initial organic C content at surface soil (kg C kg^-1) FertilizerAddition (kg N ha^-1 yr^-1) Harvest (Not applicable) Slope (5) Water filled pore space at field capacity (Percent)	Slope (5) Water filled pore space (WFPS) at field capacity (Percent)	Slope (5) Water filled pore space (WFPS) at field capacity (Percent)	Slope (5) Water filled pore space (WFPS) at field capacity (Percent)	Slope (5) Water filled pore space (WFPS) at field capacity (Percent)

Intermediate

EM ID em.detail.idHelp ?	EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
Intermediate (Computed) Variables (and Units) em.detail.intermediateVariableHelp ?	None	*	*	*	*	*	*	*

Response

EM ID em.detail.idHelp ?	EM-593	DaCent Baseline	DaCent High temp sensitive	DaCent Low temp sensitive	Baseline	Baseline - fertilized	Climate- High sensetive	Climate- Low sensetive
Observed Response Variables (and Units) em.detail.observedResponseHelp ?	Annual N2O flux fertilized (kg N2O-N ha^-1 yr^-1) Annual NO2 flux (kg N2O-N ha^-1 yr^-1)	Annual NO2 flux (g kg^-1)	Annual NO2 flux (g kg^-1)	Annual NO2 flux (g kg^-1)	Annual NO2 flux (g kg^-1)	Annual NO2 flux (g kg^-1)	Annual NO2 flux (g kg^-1)	Annual NO2 flux (g kg^-1)
Computed Response Variables (and Units) em.detail.computedResponseHelp ?	Average grass biomass (tons ha^-1 yr^-1) Average soil ammonium (g kg^-1) Average soil nitrate (g kg^-1) Cumulative N2O-N flux (kg N2O-N ha^-1y^-1) Predicted annual N2O-N flux (kg N2O-N ha^-1 yr^-1) Predicted annual N2O-N flux fertilized (kg N2O-N ha^-1 yr^-1)	Average grass biomass (t ha^-1 yr^-1) Average soil ammonium (g kg^-1) Average soil nitrate (g kg^-1) Cumulative N2O-N flux (kg N2O-N ha^-1y^-1)	Average grass biomass (t ha^-1 yr^-1) Average soil ammonium (g kg^-1) Average soil nitrate (g kg^-1) Cumulative N2O-N flux (kg N2O-N ha^-1y^-1)	Average grass biomass (t ha^-1 yr^-1) Average soil ammonium (g kg^-1) Average soil nitrate (g kg^-1) Cumulative N2O-N flux (kg N2O-N ha^-1y^-1)	Average grass biomass (t ha^-1 yr^-1) Average soil ammonium (g kg^-1) Average soil nitrate (g kg^-1) Cumulative N2O-N flux (kg N2O-N ha^-1y^-1) Predicted annual N2O-N flux (kg N2O-N ha^-1 yr^-1)	Average grass biomass (t ha^-1 yr^-1) Average soil ammonium (g kg^-1) Average soil nitrate (g kg^-1) Cumulative N2O-N flux (kg N2O-N ha^-1y^-1)	Average grass biomass (t ha^-1 yr^-1) Average soil ammonium (g kg^-1) Average soil nitrate (g kg^-1) Cumulative N2O-N flux (kg N2O-N ha^-1y^-1)	Average grass biomass (t ha^-1 yr^-1) Average soil ammonium (g kg^-1) Average soil nitrate (g kg^-1) Cumulative N2O-N flux (kg N2O-N ha^-1y^-1)

US EPA

EcoService Models Library (ESML)

View Runs

: DayCent simulation N2O flux and climate change, Ireland (EM-593)