Impact model: EPIC-TAMU

EPIC-TAMU 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

ISIMIP2a

Confirm data for simulation round ISIMIP2a

Person responsible for model simulations in this simulation round

Roberto César Izaurralde: cizaurra@umd.edu, 0000-0002-8797-9500, Department of Geographical Sciences, University of Maryland, College Park; Texas AgriLife, Texas A&M University (USA)

Ashwan D. Reddy: areddy01@gmail.com, University of Maryland, Department of Geographical Sciences (USA)

Output Data

Experiments: historical
Climate Drivers: None
Date: 2016-02-11

Basic information

Model Version: EPIC1102

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: Izaurralde R, Williams J, McGill W, Rosenberg N, Jakas M et al. Simulating soil C dynamics with EPIC: Model description and testing against long-term data. Ecological Modelling,192,362-384,2005
Müller, C., Elliott, J., Kelly, D. et al. et al. The Global Gridded Crop Model Intercomparison phase 1 simulation dataset. Sci Data,6,50,2019

Resolution

Spatial aggregation: regular grid

Horizontal resolution: 0.5°x0.5°

Input data

Observed atmospheric climate data sets used: WATCH (WFD), WATCH-WFDEI

Key input and Management

Crops: mai, whe(w,s)

Land cover: potential suitable cropland area according to climatic conditions, current harvested areas

Planting date decision: Simulate planting dates according to climatic conditions, planting delayed until 2 deg above base temp

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: NPK at planting

Irrigation: no restriction on actual water availability, irrigated water applied when water stress

Crop residue: No removal

Initial soil water: ISRIC-WISE, 10 year spin up

Initial soil nitrate and ammonia: ISRIC-WISE, 10 year spin up

Initial soil c and om: ISRIC-WISE, 10 year spin up

Initial crop residue: ISRIC-WISE, 10 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 modified by water stress, 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; soil available water in root zone (a balance of the two based on input of 0-1)

Type of heat stress: vegetative (source)

Water dynamics: soil water capacity approach with 3 soil layers

Evapo-transpiration: Penman-Monteith

Soil cn modeling: C model; N model; 3 organic matter pools; microbial biomass pool

Co2 effects: Radiation use efficiency, Transpiration efficiency

Methods for model calibration and validation

Parameters, number and description: Default EPIC parameters; Potential harvest index (maize)

Calibrated values: harvest index=0.35 or 0.55

Output variable and dataset for calibration validation: Yield (SPAM 2000)

Spatial scale of calibration/validation: 0.5º resolution

Temporal scale of calibration/validation: M3 dataset corresponds to the year 2000 (average for years 1997-2004)