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Impact model: GEPIC-BR

Sector
Agriculture
Region
global

GEPIC-BR is the run of the GEPIC model using the Baier-Robertson estimation method for potential evapotranspiration (PET). GEPIC 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

Information for the model GEPIC-BR 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.

Confirm data for simulation round ISIMIP2a
Person responsible for model simulations in this simulation round
Christian Folberth: folberth@iiasa.ac.at, 0000-0002-6738-5238, International Institute for Applied Systems Analysis (IIASA) (Austria)
Hong Yang: Hong.yang@eawag.ch, Swiss Federal Institute of Aquatic Science and Technology (EAWAG) (Switzerland)
Additional persons involved: Christian Folberth (folberth@iiasa.ac.at), International Institute for Applied Systems Analysis
Output Data
Experiments: historical
Climate Drivers: None
Date: 2020-06-24
Basic information
Reference Paper: Main Reference: Folberth C, Gaiser T, Abbaspour K, Schulin R, Yang H et al. Regionalization of a large-scale crop growth model for sub-Saharan Africa: Model setup, evaluation, and estimation of maize yields. Agriculture, Ecosystems & Environment,151,21-33,2012
Reference Paper: Other References:
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Temporal resolution of input data: climate variables: daily
Temporal resolution of input data: co2: annual
Input data
Observed atmospheric climate data sets used: WATCH-WFDEI
Additional input data sets: N and P fertilizer application rates based on FertiStat (2007)
Spin-up
Was a spin-up performed?: Yes
Spin-up design: Simulations were run for each decade separately with 20 years spin-up
Management & Adaptation Measures
Management: Planting dates were estimated similar to Waha et al. (2012). Time until maturiy was calculated as grid-specific average PHU for the whole simulation period. Hence, maturity in each will depend on the specific growing season temperature. After harvest, 80% of crop residue were removed from the field.
Extreme Events & Disturbances
Key challenges: EPIC does not take floods and any physical damage to plants (e.g. hail or extreme winds) into account. Crops are not killed by extreme drought or temperatures, but only limtied in growth and yield formation.
Key input and Management
Crops: mai, whe(w,s), soy, ric
Land cover: potential suitable cropland area according to climatic conditions; current harvested areas (Portmann et al. 2010)
Planting date decision: Simulate planting dates according to climatic conditions
Planting density: Crop-specific
Crop cultivars: Simulate crop Growing Degree Days (GDDs) requirement according to estimated annual GDDs from daily temperature, 2 cultivars for mai
Fertilizer application: NP, FertiSTAT, dynamic application according to nutrient stress
Irrigation: no restriction on actual water availability, irrigated water applied when water stress, MIRCA 2000 crop specific irrigated area (Portmann et al., 2010)
Crop residue: Crop-specific
Initial soil water: 20 year spin up
Initial soil nitrate and ammonia: ISRIC-WISE, 20 year spin up
Initial soil c and om: ISRIC-WISE, 20 year spin up
Initial crop residue: ISRIC-WISE, 20 year spin up
Key model processes
Leaf area development: prescribed shape of LAI curve as function of phenology
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: Temperature, Heat unit index
Root distribution over depth: exponential, actual water depends on water availability in each soil layer
Stresses involved: Water stress, Nitrogen stress, Oxygen stress, heat stress (phosphorus, bulk density, aluminum (based on pH and base saturation))
Type of water stress: Ratio of supply to demand of water
Type of heat stress: vegetative
Water dynamics: soil water capacity approach with 5 soil layers
Evapo-transpiration: Hargreaves
Soil cn modeling: C model; N model; microbial biomass pool; 6 organic matter pools
Co2 effects: Radiation use efficiency, Transpiration efficiency
Methods for model calibration and validation
Parameters, number and description: Default parameters from EPIC0810 partly adjusted based on prior studies, Potential harvest indext (maize and rice), Fertlizer application rate
Output variable and dataset for calibration validation: Yield (FAO yield statistics)
Spatial scale of calibration/validation: National
Temporal scale of calibration/validation: Average for 1997-2003
Criteria for evaluation (validation): R2




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