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Impact model: GEPIC

Sector
Agriculture
Region
global

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 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
Christian Folberth: folberth@iiasa.ac.at, 0000-0002-6738-5238, International Institute for Applied Systems Analysis (IIASA) (Austria)
Nikolay Khabarov: khabarov@iiasa.ac.at, 0000-0001-5372-4668, International Institute for Applied Systems Analysis (IIASA) (Austria)
Additional persons involved: Christian Folberth (folberth@iiasa.ac.at), International Institute for Applied Systems Analysis (IIASA) (Austria)
Output Data
Experiments: I, II, IIa, III
Climate Drivers: None
Date: 2017-10-16
Basic information
Model Version: Core model based on EPIC0810 with unpublished bug fixes and modifications
Model Output License: CC BY 4.0
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
Temporal resolution of input data: soil: constant
Input data
Simulated atmospheric climate data sets used: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Other human influences data sets used: N-Fertilizer (ISIMIP2)
Additional input data sets: Growing season data were provided by the GGCMI project (Elliott et al., 2015). Soil data are based on HWSD 1.21 (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012)
Climate variables: sfcWind, tasmax, tasmin, rhs, rsds, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: The model was run with a dynamic soil profile for 50 years to per-condition soil nutrients and organic matter. In the actual simulations, soils were re-initialized each year (except for soil humidity and mineral nutrient contents) to avoid mixed effects of prolonged soil degradation and climate change on crop growth.
Key input and Management
Crops: mai, whe(w,s), soy, ric
Land cover: All land grid cells
Planting date decision: Constant based on present day growing seasons as provided by Elliott et al. (2015).
Crop cultivars: Two maize cultivars distributed based on human development index with high-productivity cultivar in countries with HDI>80 and a low-productivity variety in all others. Otherwise, cultivars are defined by their GDD requirement, which is derived from reported growing seasons and historic climate.
Fertilizer application: Split N application with 1/3 at planting and 2/3 40 days after. Annual N rates based on present day reported amounts by crop types as provided by ISI-MIP.
Irrigation: Sufficient irrigation based on plant water requirements with an irrigation threshold of 5% plant water deficit.
Crop residue: 80% of crop residue removed after harvest.
Initial soil water: Based on spin-up cycling the first 8 years of climate data for each respective time period.
Initial soil nitrate and ammonia: Based on spin-up.
Initial soil c and om: Based on spin-up.
Initial crop residue: Based on spin-up.
Key model processes
Leaf area development: prescribed shape of LAI curve as function of phenology
Light interception: Simple approach.
Light utilization: Radiation use efficiency approach
Yield formation: actual harvest index based on potential harvest index and water stress during reproduction stage, partitioning during reproductive stages, total (above-ground) biomass
Crop phenology: Temperature, Heat unit index
Root distribution over depth: exponential, actual water use depends on water availability in each soil layer
Stresses involved: Water stress, Nitrogen stress, Oxygen stress, Heat stress. Other stress included in the model but not applicable here: phosphorus, bulk density, aluminum (based on pH and base saturation)
Type of water stress: vegetative and reproductive (see yield formation). 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)
Calibrated values: Potential HI for maize was set to 0.55 for highly productive and 0.35 for low-yielding variety, lower limit of harvest index to 0.4 and 0.01, respectively.
Output variable and dataset for calibration validation: Parameterization is based on default parameter values and literature. The model has been evaluated based on field trial data and national average yeilds for the time period 1997-2003.
Spatial scale of calibration/validation: National, field trials
Temporal scale of calibration/validation: National: Average for 1997-2003, field trials as provided in the data source
Criteria for evaluation (validation): R2, Nash-Suttcliffe
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)
Fei Lun: rucallen_2008@hotmail.com, Beijing Forestry University (China)
Hong Yang: Hong.yang@eawag.ch, Swiss Federal Institute of Aquatic Science and Technology (EAWAG) (Switzerland)
Shouchun Yi: yisc.bjfu@gmail.com, Beijing Forestry University (China)
Additional persons involved: Christian Folberth (folberth@iiasa.ac.at), International Institute for Applied Systems Analysis
Output Data
Experiments: historical
Climate Drivers: None
Date: 2016-02-24
Basic information
Model Version: EPIC0810; partly modified at EAWAG
Model Output License: CC BY 4.0
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: PGMFD v2.1 (Princeton), WATCH (WFD), WATCH-WFDEI
Additional input data sets: N and P fertilizer application rates based on FertiStat (2007)
Climate variables: tasmax, tasmin, wind, rhs, rsds, prsn, pr
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
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)
Fei Lun: rucallen_2008@hotmail.com, Beijing Forestry University (China)
Hong Yang: Hong.yang@eawag.ch, Swiss Federal Institute of Aquatic Science and Technology (EAWAG) (Switzerland)
Shouchun Yi: yisc.bjfu@gmail.com, Beijing Forestry University (China)
Output Data
Experiments: historical, rcp26, rcp45, rcp60, rcp85
Climate Drivers: None
Date: 2013-12-13




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