Simulating soil C dynamics with EPIC: Model description and testing against long-term data

Izaurralde, R. C.; Williams, J. R.; McGill, W. B.; Rosenberg, Norman J.; Jakas, M. C. Quiroga

doi:10.1016/j.ecolmodel.2005.07.010

Cited by 438 publications

(269 citation statements)

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“…The NEI fertilizer emissions were removed from the bidirectional case and fertilizer emissions were estimated dynamically within CMAQ using the EPIC agricultural management output. Information regarding inorganic fertilizer application amounts and timing were simulated for bidirectional CMAQ using EPIC (Williams, et al, 2008;Izaurralde et al, 2006) as described above and in Cooter et al (2012). The vegetation apoplastic emission potential, s , was parameterized as a function of the land cover type.…”

Section: Model Simulationmentioning

confidence: 99%

Evaluation of a regional air-quality model with bidirectional NH<sub>3</sub> exchange coupled to an agroecosystem model

et al. 2013

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Abstract. Atmospheric ammonia (NH3) is the primary atmospheric base and an important precursor for inorganic particulate matter and when deposited NH3 contributes to surface water eutrophication, soil acidification and decline in species biodiversity. Flux measurements indicate that the air–surface exchange of NH3 is bidirectional. However, the effects of bidirectional exchange, soil biogeochemistry and human activity are not parameterized in air quality models. The US Environmental Protection Agency's (EPA) Community Multiscale Air-Quality (CMAQ) model with bidirectional NH3 exchange has been coupled with the United States Department of Agriculture's (USDA) Environmental Policy Integrated Climate (EPIC) agroecosystem model. The coupled CMAQ-EPIC model relies on EPIC fertilization timing, rate and composition while CMAQ models the soil ammonium (NH4&plus;) pool by conserving the ammonium mass due to fertilization, evasion, deposition, and nitrification processes. This mechanistically coupled modeling system reduced the biases and error in NHx (NH3 &plus; NH4&plus;) wet deposition and in ambient aerosol concentrations in an annual 2002 Continental US (CONUS) domain simulation when compared to a 2002 annual simulation of CMAQ without bidirectional exchange. Fertilizer emissions estimated in CMAQ 5.0 with bidirectional exchange exhibits markedly different seasonal dynamics than the US EPA's National Emissions Inventory (NEI), with lower emissions in the spring and fall and higher emissions in July.

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Section: Model Simulationmentioning

confidence: 99%

Evaluation of a regional air-quality model with bidirectional NH<sub>3</sub> exchange coupled to an agroecosystem model

et al. 2013

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“…Figure 2 is a schematic representation of the research methodology, which allows a clearer understanding of the research process. Previous research related to soil degradation, cropland organic matter, and agro-hydrological cycles using EPIC-, RUSLE-, and GLEAMS-based agro-environmental variables has produced numerous cases for various time periods [39][40][41][42][43]. Figure 2 is a schematic representation of the research methodology, which allows a clearer understanding of the research process.…”

Section: Model Simulationmentioning

confidence: 99%

Impact of Deforestation on Agro-Environmental Variables in Cropland, North Korea

et al. 2017

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Deforestation in North Korea is becoming the epitome of the environmental change occurring in the Korean Peninsula. This study estimates the agro-environmental variables of North Korea's croplands and analyzes the impact of deforestation using the GEPIC (GIS-based EPIC (Environmental Policy Integrated Climate)) model and time-series land cover maps. To identify the changes in agricultural quality under deforestation, wind erosion, water erosion, organic carbon loss, and runoff were selected as the agro-environmental variables having an impact on cropland stability and productivity. Land cover maps spanning the past three decades showed that 75% of the forests were converted to croplands and that 69% of all converted croplands were originally forests, confirming the significant correlation between deforestation and cropland expansion in North Korea. Despite limitations in the verification data, we conducted qualitative and quantitative validation of the estimated variables and confirmed that our results were reasonable. Over the past 30 years, agro-environmental variables showed no clear time-series changes resulting from climate change, but changes due to spatial differences were seen. Negative changes in organic carbon loss, water erosion, and runoff were observed, regardless of the crop type. On newly-converted agricultural lands, runoff is 1.5 times higher and water-driven erosion and soil organic loss are more than twice as high compared to older croplands. The results showed that the agro-environment affected by deforestation had an impact on cropland stability and productivity.

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“…EPIC is a field-based model designed to simulate crop production based on information about soil, crop rotation and management system. A full description is presented in the model documentation by Izaurralde et al (2006). Among various subroutines, the model considers N and P cycling by flows between inorganic and organic stocks.…”

Section: Modeling With Epicmentioning

confidence: 99%

Sensitive parameters for EPIC model evaluation and validity under soil water and nutrients limited conditions with NERICA cropping in West Africa

Worou¹,

Gaiser²,

Oikeh³

2015

Afr. J. Agric. Res.

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Crop models known to be based on the theory of crop physiology for describing the dynamic process of crop growth are recently explored for their uncertainties in model application under resource limited conditions. The aim of this study was to test Environmental Policy Integrated Climate (EPIC), on upland land rice production by taking into account seasonal variability in Guinean and Guinean-Sudanian zones in Benin and Nigeria (West Africa). A range of data available under farmer or experimental conditions in rainfed agriculture were measured or used from literature. The results show the accuracy of the model to simulate LAI, total above ground biomass and grain yield of upland rice for 2 NERICA rice cultivars. After calibration, the model showed average mean relative error between 0.06 and 0.15 with the model efficiency up to 0.98% in the case of LAI. The assessment of the model performances about sensitivity to N or P fertilizer application is also discussed under Ultisols. Large root mean square (RMSE) in calibration and the validation (>100) process suggested that robustness of the model became restrictive under severe environmental conditions such as in drought or flooding condition. Performance of the model at large scale should be executed of with land marginality classification.

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Simulating soil C dynamics with EPIC: Model description and testing against long-term data

Cited by 438 publications

References 50 publications

Evaluation of a regional air-quality model with bidirectional NH<sub>3</sub> exchange coupled to an agroecosystem model

Evaluation of a regional air-quality model with bidirectional NH<sub>3</sub> exchange coupled to an agroecosystem model

Impact of Deforestation on Agro-Environmental Variables in Cropland, North Korea

Sensitive parameters for EPIC model evaluation and validity under soil water and nutrients limited conditions with NERICA cropping in West Africa

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Simulating soil C dynamics with EPIC: Model description and testing against long-term data

Cited by 438 publications

References 50 publications

Evaluation of a regional air-quality model with bidirectional NH&lt;sub&gt;3&lt;/sub&gt; exchange coupled to an agroecosystem model

Evaluation of a regional air-quality model with bidirectional NH&lt;sub&gt;3&lt;/sub&gt; exchange coupled to an agroecosystem model

Impact of Deforestation on Agro-Environmental Variables in Cropland, North Korea

Sensitive parameters for EPIC model evaluation and validity under soil water and nutrients limited conditions with NERICA cropping in West Africa

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Evaluation of a regional air-quality model with bidirectional NH<sub>3</sub> exchange coupled to an agroecosystem model

Evaluation of a regional air-quality model with bidirectional NH<sub>3</sub> exchange coupled to an agroecosystem model