Impact model: EPIC-Boku

EPIC-Boku is one of the 14 models following the ISIMIP2a protocol which form the base of simulations for the ISIMIP2a agricultural sector outputs; for a full technical description of the ISIMIP2a Simulation Data from Agricultural Sector, see this DOI link: http://doi.org/10.5880/PIK.2017.006

Sector
Agriculture
Region
global
Contact Person
  • Erwin Schmid (erwin.schmid@boku.ac.at), Institute for Sustainable Economic Development, BOKU; University of Natural Resources and Applied Life Sciences, Vienna (Austria)

Information for the model EPIC-Boku 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.

Basic information
Model Version: epic0810
Reference Paper: Main Reference: Kiniry, J. et al. EPIC model parameters for cereal, oilseed, and forage crops in the northern Great Plains region. Canadian Journal of Plant Science,75,679-688,2011
Reference Paper: Other References:
Output Data
Experiments: historical
Climate Drivers: GSWP3, PGMFD v.2 (Princeton), WATCH (WFD), WATCH+WFDEI
Date: 2016-05-04
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Temporal Resolution Of Input Data: Climate Variables: daily
Temporal Resolution Of Input Data: Co2: annual
Temporal Resolution Of Input Data: Land Use/Land Cover: time-constant, unless crop roatations are used; total cropland cover (GLC2000)
Temporal Resolution Of Input Data: Soil: constant
Additional Temporal Resolution Information: Land-use cover time constant unless crop rotations are used. Total cropland cover (GLC2000)
Input data sets used
Observed Atmospheric Climate Data Sets Used: GSWP3, PGMFD v.2 (Princeton), WATCH (WFD), WATCH+WFDEI
Climate Variables: tasmax, tasmin, wind, rhs, rsds, prsn, pr
Spin-up
Was A Spin-Up Performed?: No
Management & Adaptation Measures
Management: The crops are simulated for three management/input systems (AI, AN, and SS): AN: automatic nitrogen fertilization – N-fertilization rates based on crop specific N-stress levels (N-stress free days in 90% of the vegetation period). The upper limit of N application is 200 kg ha-1 a-1. AI: automatic nitrogen fertilization and irrigation – N and irrigation rates are based on crop specific stress levels (N and water stress free days in 90% of the vegetation period. N and irrigation upper limits of 200 kg ha-1 a-1 and 500 mm a-1. SS: subsistence farming – no N fertilizations and irrigation.
Extreme Events & Disturbances
Key Challenges: hail, and rainfall intensity e.g. mm/h
Key input and Management
Crops: mai, whe(s), soy, rice
Land Cover: potential suitable cropland area according to climatic conditions, current harvested areas (data source: IFPRI)
Planting Date Decision: Simulate planting dates according to climatic conditions (fraction of potential heat unit)
Planting Density: Planting density=0.9
Crop Cultivars: Simulate crop Growing Degree Days (GDDs) requirement according to estimated annual GDDs from daily temperature
Fertilizer Application: N automatic application by stress threshold for high input and irrigation systems, P, K (nutrient-stress factor from national stat. IFA), Subsistence farming no fertilization, (Only mineral fertilizer)
Irrigation: AQUA-STAT, Siebert et al. (2007)
Crop Residue: No
Initial Soil Water: Sup(1)
Initial Soil Nitrate And Ammonia: 1 year spin up
Initial Soil C And Om: Soil database, 1-year spin up
Initial Crop Residue: 1 year spin up
Key model processes
Leaf Area Development: Dynamic simulation based on development and growth processes
Light Interception: Simple approach
Light Utilization: Simple (descriptive) Radiation use efficiency approach
Yield Formation: fixed harvest index, partitioning during reproductive stages, total (above-ground) biomass
Crop Phenology: Crop phenology is a function of temperature (heat unit index)
Root Distribution Over Depth: linear partitioning from emergence to maturity
Stresses Involved: Water stress, Nitrogen stress, Oxygen stress, Phosphorus Stress, Heat stress
Type Of Water Stress: ratio of supply to demand of water
Type Of Heat Stress: vegetative (source)
Water Dynamics: soil water capacity approach with 15 soil layers; water table dyn. (runoff, ET, percolation, sub surface flow
Evapo-Transpiration: Penman, Penman-Monteith, Priestley-Taylor, Hargreaves, Baier-Robertson
Soil Cn Modeling: C model, N model, microbial biomass pool, 3 organic matter pools
Co2 Effects: Radiation use efficiency, Transpiration efficiency
Methods for model calibration and validation
Parameters, Number And Description: Default parameters from site-specific analyses of EPIC
Spatial Scale Of Calibration/Validation: Field scale
Output Data
Experiments: historical, rcp26, rcp45, rcp60, rcp85
Climate Drivers: GCM atmospheric climate data (Fast Track)
Date: 2013-12-13