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Impact model: DayCent

Sector
Agriculture
Region
global, local, regional

DayCent is a process-based ecosystem model that operates on a daily time step and simulates biogeochemical and water dynamics in croplands, forests, grasslands, and savanna. The model uses daily weather, soil properties, land management, and plant types as primary inputs. Model outputs include crop yield, soil organic carbon, and daily trace gas fluxes (N2O, NOx, NH3, CH4). The vegetation production module was recently improved by using green leaf weight ratio (fraction of green leaf biomass to aboveground biomass) in a function based on growing degree days (GDD) to simulate crop growth and canopy development.

Information for the model DayCent is provided for the simulation rounds shown in the tabs below. Click on the appropriate tab to get the information for the simulation round you are interested in.

Person responsible for model simulations in this simulation round
Yi Yang: yi.yang@colostate.edu, 0000-0002-5548-8811, Natural Resource Ecology Laboratory at Colorado State University (USA)
Additional persons involved: Stephen Ogle
Output Data
Experiments: ssp585_2015soc_default, historical_2015soc_default
Climate Drivers: GFDL-ESM4
Date: 2023-07-13
Basic information
Model Version: DDcentEVI_LAI_Rice_rev423
Model Output License: CC0
Model Homepage: https://www.nrel.colostate.edu/projects/century/index.php
Reference Paper: Main Reference: Yang Y, Ogle S, Del Grosso S, Mueller N, Spencer S, Ray D et al. Regionalizing crop types to enhance global ecosystem modeling of maize production. Environmental Research Letters,17,014013,2021
Reference Paper: Other References:
Resolution
Spatial aggregation: country/region level
Horizontal resolution: 0.5’ x 0.5’
Temporal resolution of input data: climate variables: daily
Temporal resolution of input data: co2: annual
Temporal resolution of input data: land use/land cover: annual
Temporal resolution of input data: soil: constant
Input data
Simulated atmospheric climate data sets used: GFDL-ESM4
Socio-economic data sets used: Historical land transformation
Land use data sets used: Historical, gridded land use
Other human influences data sets used: Nitrogen deposition (ISIMIP3), N-Fertilizer (ISIMIP3b)
Other data sets used: Soil data (hwsd_soil_data_all_land, hwsd_soil_data_on_cropland)
Climate variables: tasmax, tasmin, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: Key state variables were initialized to reach steady-state condition, particularly SOM levels, by simulating native vegetation, historical weather, and soil properties for several thousand years. The steady-state simulations used the weather data between 1901 and 2000 by simulating the same 100 years for several thousand years until the state variables steady-state. The exact number of years for the steady-state simulations varied among the grid cells.
Natural Vegetation
Natural vegetation partition: The native vegetation before cultivation was based on Cramer, W., Kicklighter, D. W., Bondeau, A., Iii, B. M., Churkina, G., Nemry, B., Ruimy, A., Schloss, A. L., & Intercomparison, T. P. O. T. P. N. M. (1999). Comparing global models of terrestrial net primary productivity (NPP): overview and key results. Global Change Biology, 5(S1), 1-15.
Key input and Management
Crops: Yes. Simulated winter and spring wheat. Will simulate rice, maize, soybean in the future
Land cover: Yes. Simulated cropland only.
Planting date decision: No. Planting date data were provided by GGCMI
Planting density: No
Crop cultivars: Yes. Regional and temporal crops cultivars were created by calibrate model crop parameters with measured yield data.
Fertilizer application: Yes. Data were provided by GGCMI.
Irrigation: Yes. Data were provided by GGCMI.
Crop residue: Yes. Data were provided by GGCMI.
Initial soil water: No. Soil water are calculated with weather data and soil properties
Initial soil nitrate and ammonia: Yes. Initial soil nitrate and ammonia were determined by model spin up.
Initial soil c and om: Yes. Initial soil C and OM were determined by model spin up.
Initial crop residue: Yes. Initial crop residue were determined by model spin up.
Key model processes
Leaf area development: No.
Light interception: No.
Light utilization: No.
Yield formation: Yes. Crop yield were calibrated using Bayesian calibration method.
Crop phenology: No.
Root distribution over depth: No.
Stresses involved: No.
Type of water stress: No.
Type of heat stress: No.
Water dynamics: No.
Evapo-transpiration: No.
Soil cn modeling: No.
Co2 effects: No.
Methods for model calibration and validation
Parameters, number and description: Bayesian calibration method. See Yang, Y., Ogle, S., Del Grosso, S., Mueller, N., Spencer, S., & Ray, D. (2021). Regionalizing crop types to enhance global ecosystem modeling of maize production. Environmental Research Letters, 17(1), 014013. for detailed method
Calibrated values: crop yield
Output variable and dataset for calibration validation: grain carbon
Spatial scale of calibration/validation: regional and global
Temporal scale of calibration/validation: decades between 1950 and 2010
Criteria for evaluation (validation): See following paper for detail: Yang, Y., Ogle, S., Del Grosso, S., Mueller, N., Spencer, S., & Ray, D. (2021). Regionalizing crop types to enhance global ecosystem modeling of maize production. Environmental Research Letters, 17(1), 014013.
Person responsible for model simulations in this simulation round
Stephen DelGrosso: delgro@nrel.colostate.edu, Colorado State University (USA)
Tom Hilinski: tom.hilinski@colostate.edu, Colorado State University (USA)
Dennis Ojima: dennis@nrel.colostate.edu, Colorado State University (USA)
William Parton: william.parton@colostate.edu, Colorado State University (USA)
Output Data
Experiments: historical, rcp26, rcp45, rcp60, rcp85
Climate Drivers: None
Date: 2013-12-13
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Key input and Management
Crops: mai, whe(w,s), soy, rice
Land cover: C
Planting date decision: S
Planting density: D
Crop cultivars: GDD
Fertilizer application: N
Irrigation: L
Crop residue: Yes, Crop-specific
Initial soil water: Spin-up
Initial soil nitrate and ammonia: Spin-up
Initial soil c and om: Spin-up
Initial crop residue: Spin-up
Key model processes
Leaf area development: D
Light interception: S
Light utilization: P-R
Yield formation: HI, B
Crop phenology: T & DL
Root distribution over depth: W
Stresses involved: W, N, H, BD
Type of water stress: S
Type of heat stress: V
Water dynamics: R
Evapo-transpiration: P
Soil cn modeling: C/N P(7), B(2)
Co2 effects: TE/NE




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