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Data from: Underestimation of N2O emissions in a comparison of the DayCent, DNDC, and EPIC 1 models

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Process-based models are increasingly used to study mass and energy fluxes from agro-ecosystems, including nitrous oxide (N2O) emissions from agricultural fields. This data set is the output of three process-based models – DayCent, DNDC, and EPIC – which were used to simulate fluxes of N2O from dairy farm soils. The individual models' output and the ensemble mean output were evaluated against field observations from two agricultural research stations in Arlington, WI and Marshfield, WI. These sites utilize cropping systems and nitrogen fertilizer management strategies common to Midwest dairy farms.

The models were calibrated and validated using data collected at Arlington and Marshfield over five years (nine years for crop yield). Calibration and validation used observations of soil temperature (n = 887), volumetric soil water content (VSWC, n = 880), crop yield (n = 67), and soil N2O flux (n = 896). The observed data are presented here with the model output to document model calibration and validation; most of these observed data are also held by Ag Data Commons in separate data sets from field experiments at Arlington and Marshfield (http://dx.doi.org/10.15482/USDA.ADC/1361194, http://dx.doi.org/10.15482/USDA.ADC/1401975, http://dx.doi.org/10.15482/USDA.ADC/1399470). The remaining observed data is described in Osterholz et al. 2014.

Model simulations were run from 2010-2015 for the Arlington site and 2013-2015 for the Marshfield site. The three models were parameterized (i.e. calibrated) for each site using the same climate, initial soil physical and chemical conditions, hydraulic properties, initial soil carbon, and management schedules. Weather data for each site (daily minimum and maximum temperature, precipitation, relative humidity, wind speed, and solar radiation) was reconstructed using the NOAA online climate database (NOAA, 2016). Initial soil physical and chemical properties were constructed from available on-site measurements and supplemented using the Web Soil Survey (Soil Survey Staff, 2016). Soil carbon data was available for each site, and to prioritize model agreement initial soil carbon for the 0-20cm layer was set at 55.7 Mg C ha-1 for Arlington (Sanford et al., 2012), and at 52.6 Mg C ha-1 for Marshfield. Following parameterization of soil C, a 17 year spin-up period (1993-2009) at each site was simulated prior to the years during which data was collected (2010-2015). While DayCent developers typically recommend a spin-up of at least 1000 years, DNDC has been run with spin-up periods as low as 2 years (Zhang et al., 2015). Given that observations of soil C were available, a 17 year spin-up was chosen to reflect the duration between initial soil C sampling (Sanford et al., 2012) and the first measurement of N2O in our data set (Osterholz et al., 2014). Management and input schedules were constructed from on-site data and record-keeping; these are available in the supplementary online data of the primary journal paper. All other initial parameters, such as crop-specific productivity or soil carbon turnover rate, were independently established by each model in calibration.

This work was part of “Climate Change Mitigation and Adaptation in Dairy Production Systems of the Great Lakes Region,” also known as the Dairy Coordinated Agricultural Project (Dairy CAP), funded by the United States Department of Agriculture - National Institute of Food and Agriculture (award number 2013-68002-20525). The main goal of the Dairy CAP was to improve understanding of the magnitudes and controlling factors over greenhouse gas (GHG) emissions from dairy production in the Great Lakes region. Using this knowledge, the Dairy CAP has improved life cycle analysis (LCA) of GHG production by Great Lakes dairy farms, developing farm management tools, and conducting extension, education and outreach activities.

Dataset Info

These fields are compatible with DCAT, an RDF vocabulary designed to facilitate interoperability between data catalogs published on the Web.
Gaillard, Richard K.
Jones, Curtis D.
Ingraham, Pete
Collier, Sarah M.
Izaurralde, Roberto Cesar
Jokela, William
Osterholz, William
Salas, William
Vadas, Peter A.
Ruark, Matthew D.
Product Type
Spatial / Geographical Coverage Location
Arlington Agricultural Research Station, N695 Hopkins Road, Arlington, Wisconsin 53911 United States; M605 Drake Avenue, Stratford, WI 54484 United States
Temporal Coverage
Equipment or Software Used
Intended Use
This work provides an evaluation of simulated N2O flux from three process-based models: DayCent, DNDC, and EPIC. Model estimations of observed soil temperature and water content did not sufficiently explain model underestimations, and significant variation in model estimates of heterotrophic respiration, denitrification, soil NH4+, and soil NO3- were found, which may indicate that additional types of observed data are required to evaluate model performance and possible biases. Results suggested a bias in the model estimation of N2O flux from agro-ecosystems that limits the extension of models beyond calibration and as instruments of policy development. This highlights a growing need for the modeling and measurement communities to collaborate in the collection and analysis of the data necessary to improve models and coordinate future development. Future investigators may use this model output in this data set to inspect other aspects of model performance and inter-comparison, in addition to N2O flux (e.g. for soil nitrogen species, crop yield).
Use Limitations
The model output (i.e. this data set) reflects the specific time period, location and relevant farm management of the observations used to calibrate DayCent, DNDC and EPIC. Comparison with observations or model output from other time periods, locations and farm management may not be appropriate.
Ag Data Commons
Contact Name
Barford, Carol
Contact Email
Public Access Level
Primary Article

Gaillard, R. K., Jones, C. D., Ingraham, P., Collier, S., Izaurralde, R. C., Jokela, W., Osterholz, W., Salas, W., Vadas, P., & Ruark, M. (2018). Underestimation of N2O emissions in a comparison of the DAYCENT, DNDC and EPIC models. Ecological Applications. 28: 694-708.

Related Article

Osterholz, W. R., Kucharik, C. J., Hedtcke, J. L., & Posner, J. L. (2014). Seasonal nitrous oxide and methane fluxes from grain- and forage-based production systems in Wisconsin, USA. J. Environ. Qual. 43(6): 1833–1843.

Methods Citation

Del Grosso, S. J., W. J. Parton, A. R. Mosier, M. K. Walsh, D. S. Ojima, & P. E. Thornton (2006). DAYCENT national-scale simulations of nitrous oxide emissions from cropped soils in the United States. J. Environ. Qual. 35(4): 1451–1460.

AgID: 10113/3819

Parton, W., E. Holland, S. Del Grosso, M. Hartmann, R. Martine, A. Mosier, D. Ojima, & D. Schimel (2001). Generalized model for NOx and N2O emissions from soils. J. Geophys. Res. Atmos. 106(15): 17,403-17,419.

Li, C.S. (2000). Modeling Trace Gas Emissions from Agricultural Ecosystems. Nutrient Cycling in Agroecosystems 58(1-3): 259–276.

AgID: 10113/667588

Li, C. (2007). Quantifying greenhouse gas emissions from soils: Scientific basis and modeling approach. Soil Science and Plant Nutrition 53(4): 344–352.

Izaurralde, R.C., W.B. McGill, & J.R. Williams. (2012). Development and application of the EPIC model for carbon cycle, greenhouse gas mitigation, and biofuel studies. In Liebig, M. A., Franzluebbers, & A. J., Follet, R. F. (eds.), Managing Agricultural Greenhouse Gases: coordinated agricultural research through GRACEnet to address our changing climate (pp. 293–308). London: Academic Press.

Izaurralde, R. C., W. B. McGill, J. R. Williams, C. D. Jones, R. P. Link, D. H. Manowitz, D. E. Schwab, X. Zhang, G. P. Robertson, & N. Millar (2017). Simulating microbial denitrification with EPIC: Model description and evaluation. Ecological Modelling 359: 349–362.

NOAA. 2016. Climate Data Online. Natl. Ocean. Atmos. Adm. Natl. Centers Environ. Inf.Available at https://www.ncdc.noaa.gov/cdo-web/ (verified 8 November 2016).

Soil Survey Staff (2016). Web Soil Survey: Soil Data Mart. Available at http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm.

Sanford, G. R., J. L. Posner, R. D. Jackson, C. J. Kucharik, J. L. Hedtcke, & T.-L. Lin (2012). Soil carbon lost from Mollisols of the North Central U.S.A. with 20 years of agricultural best management practices. Agriculture, Ecosystems, and Environment 162: 68–76.

AgID: 10113/477430
Related Content
Funding Source(s)
National Institute of Food and Agriculture
Dataset DOI (digital object identifier)
Modified Date
Release Date
Ag Data Commons Keywords: 
  • Agroecosystems & Environment
  • Agroecosystems & Environment
  • Soil
  • Agroecosystems & Environment
  • Water
  • Agroecosystems & Environment
  • Plant and animal
  • Agroecosystems & Environment
  • Management
  • Plants & Crops
  • Plants & Crops
  • Crop production
  • Cropping system
  • Plants & Crops
  • Crop production
  • Plants & Crops
  • Life Cycle Assessment
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