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  3. Impact model: DSSAT-Pythia

Impact model: DSSAT-Pythia

Sector
Agriculture
Region
global

Decision Support System for Agrotechnology Transfer (DSSAT) is software application program that comprises dynamic crop growth simulation models for over 42 crops. DSSAT is supported by a range of utilities and apps for weather, soil, genetic, crop management, and observational experimental data, and includes example data sets for all crop models. The crop simulation models simulate growth, development and yield as a function of the soil-plant-atmosphere dynamics. DSSAT has been applied to address many real-world problems and issues ranging from genetic modeling to on-farm and precision management, regional assessments of the impact of climate variability and climate change, economic and environmental sustainability, and food and nutrition security.

Pythia is a simple framework to assist users in running DSSAT over a spatial area based on grid points.

Information for the model DSSAT-Pythia 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
Thiago Berton Ferreira: t.berton@ufl.edu, 0000-0001-9361-7277, University of Florida (USA)
Gerrit Hoogenboom: gerrit@ufl.edu, 0000-0002-1555-0537, University of Florida (USA)
Additional persons involved: Oscar Castillo, University of Florida (USA)
Output Data
Experiments: ssp126_2015soc_default, ssp585_2015soc_default, historical_2015soc_default, historical_2015soc_2015co2, ssp126_2015soc_2015co2, ssp585_2015soc_2015co2
Climate Drivers: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL
Date: 2021-09-24
Basic information
Model Version: 4.7
Model Output License: CC0
Model Homepage: https://dssat.net
Model License: BSD-3-Clause
Simulation Round Specific Description: * Data in embargo period, not yet publicly available. DSSAT-Pythia is one of the currently 15 models following the ISIMIP3a/b protocol which form the base of simulations for the ISIMIP3a/b agricultural sector outputs.
Resolution
Spatial aggregation: variable grid
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: MRI-ESM2-0, IPSL-CM6A-LR, MPI-ESM1-2-HR, UKESM1-0-LL, GFDL-ESM4
Emissions data sets used: Atmospheric composition (ISIMIP3b)
Other human influences data sets used: Crop calendar, N-Fertilizer (ISIMIP3b)
Other data sets used: Country masks, Soil data (hwsd_soil_data_all_land, hwsd_soil_data_on_cropland), Land-sea mask
Climate variables: tasmax, tasmin, rlds, pr
Spin-up
Was a spin-up performed?: No
Key input and Management
Crops: Wheat, Maize, Rice
Land cover: Yes, GGCMI.
Planting date decision: Yes, GGCMI data.
Planting density: Yes, assumed.
Crop cultivars: Yes, assumed.
Fertilizer application: Yes, GGCMI data.
Irrigation: Yes, GGCMI data
Crop residue: Yes, GGCMI data.
Initial soil water: Yes, assumed.
Initial soil nitrate and ammonia: Yes, GGCMI data.
Initial soil c and om: Yes, assumed.
Initial crop residue: Yes, GGCMI data.




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