Impact model: pDSSAT

Sector
Agriculture
Region
global

pDSSAT is one of the 15 models following the ISIMIP3a/b protocol which form the base of simulations for the ISIMIP3a/b agricultural sector outputs; for a full technical description of the ISIMIP3 Simulation Data from the Agricultural Sector, see this DOI link: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-281/

pDSSAT uses to the pSIMS environment to run the DSSAT crop model in a parallelized global way. In GGCMI/ISIMIP Phase 3a/3b (group 1 and 2) we use DSSAT version 4.6, which is now updated to version 4.8 for the Phase 3a ATTRICI and the Phase 3b group 3 simulations.

Information for the model pDSSAT 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
Jonas Jaegermeyr: jonas.jaegermeyr@columbia.edu, 0000-0002-8368-0018, Columbia University and NASA GISS (USA)
Additional persons involved: Jose Rafael Guarin (jrg2230@columbia.edu), Joshua W. Elliott (jelliott@ci.uchicago.edu)
Output Data
Experiments: historical_2015soc_default, ssp126_2015soc_default, ssp585_2015soc_2015co2, ssp585_2015soc_default, ssp126_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: 4.6 and 4.8
Model Output License: CC0
Model Homepage: https://dssat.net/
Simulation Round Specific Description: * Data in embargo period, not yet publicly available. pDSSAT 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.
Reference Paper: Main Reference: Jägermeyr J, Müller C, et al. et al. Climate change signal in global agriculture emerges earlier in new generation of climate and crop models. Nature Food,None,,2021
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5’ x 0.5’
Temporal resolution of input data: climate variables: daily
Temporal resolution of input data: land use/land cover: annual
Temporal resolution of input data: soil: constant
Input data
Emissions data sets used: Atmospheric composition (ISIMIP3b)
Socio-economic data sets used: Historical land transformation
Land use data sets used: Historical, gridded land use
Other human influences data sets used: Nitrogen deposition (ISIMIP3), Crop calendar, N-Fertilizer (ISIMIP3b)
Additional input data sets: The pDSSAT model uses the Global Soil Data set for Earth system modelling (GSDE) due to difficulties in retrieving all soil parameters from HWSD.
Climate variables: tasmax, tasmin, rlds, rsds, pr
Exceptions to Protocol
Exceptions: GGCMI Phase 3 crop models are using a disaggregated fertilizer application data set that is based on LUH2 but uses additional inputs from Mueller et al. 2012.
Spin-up
Was a spin-up performed?: No
Key input and Management
Planting date decision: prescribed according to GGCMI Phase 3 crop calendar (https://zenodo.org/record/5062513#.YjyskprMI-Q)
Fertilizer application: prescribed according to GGCMI Phase 3 fertilizer application data (https://zenodo.org/record/5176008#.YjyqUZrMI-Q)
Irrigation: full irrigation
Methods for model calibration and validation
Parameters, number and description: The number and name of parameters are different for each crop.
Output variable and dataset for calibration validation: Phenology and yield are calibrated based on observational data, GGCMI crop calendar for phenology and SPAM 2010 yield map for yield.
Spatial scale of calibration/validation: 0.5 x 0.5 deg
Person responsible for model simulations in this simulation round
Jonas Jaegermeyr: jonas.jaegermeyr@columbia.edu, 0000-0002-8368-0018, Columbia University and NASA GISS (USA)
Additional persons involved: Jose Rafael Guarin (jrg2230@columbia.edu), Joshua W. Elliott (jelliott@ci.uchicago.edu)
Output Data
Experiments: obsclim_2015soc_default
Climate Drivers: GSWP3-W5E5
Date: 2022-03-10
Basic information
Model Version: 4.6 and 4.8
Model Output License: CC0
Model Homepage: https://dssat.net/
Simulation Round Specific Description: * Data in embargo period, not yet publicly available. pDSSAT 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; for a full technical description of the ISIMIP3a Simulation Data from Agricultural Sector, see this DOI link: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-281/
Reference Paper: Main Reference: Jägermeyr J, Müller C, et al. et al. Climate change signal in global agriculture emerges earlier in new generation of climate and crop models. Nature Food,None,,2021
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5’ x 0.5’
Temporal resolution of input data: climate variables: daily
Temporal resolution of input data: land use/land cover: annual
Temporal resolution of input data: soil: constant
Input data
Observed atmospheric climate data sets used: W5E5v2.0, W5E5v1.0
Emissions data sets used: Atmospheric composition (ISIMIP3a)
Socio-economic data sets used: Historical land transformation
Land use data sets used: Historical, gridded land use
Other human influences data sets used: Nitrogen deposition (ISIMIP3), Crop calendar, N-Fertilizer (ISIMIP3a)
Additional input data sets: The pDSSAT model uses the Global Soil Data set for Earth system modelling (GSDE) due to difficulties in retrieving all soil parameters from HWSD.
Climate variables: tasmax, tasmin, rlds, rsds, pr
Exceptions to Protocol
Exceptions: GGCMI Phase 3 crop models are using a disaggregated fertilizer application data set that is based on LUH2 but uses additional inputs from Mueller et al. 2012.
Spin-up
Was a spin-up performed?: No
Key input and Management
Planting date decision: prescribed according to GGCMI Phase 3 crop calendar (https://zenodo.org/record/5062513#.YjyskprMI-Q)
Fertilizer application: prescribed according to GGCMI Phase 3 fertilizer application data (https://zenodo.org/record/5176008#.YjyqUZrMI-Q)
Methods for model calibration and validation
Parameters, number and description: The number and name of parameters are different for each crop.
Output variable and dataset for calibration validation: Phenology and yield are calibrated based on observational data, GGCMI crop calendar for phenology and SPAM 2010 yield map for yield.
Person responsible for model simulations in this simulation round
Joshua W. Elliott: jelliott@ci.uchicago.edu, 0000-0003-0258-9886, Computation Institute, University of Chicago (USA)
Michael Glotter: glotter@uchicago.edu, University of Chicago, Department of the Geophysical Sciences (USA)
Output Data
Experiments: historical
Climate Drivers: None
Date: 2016-02-11
Basic information
Model Version: pDSSAT2.0 (DSSAT4.6)
Simulation Round Specific Description: pDSSAT 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
Reference Paper: Other References:
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Input data
Observed atmospheric climate data sets used: WATCH (WFD), WATCH-WFDEI
Additional input data sets: GGCMI harmonized planting, maturity and fertlizer dataset.
Climate variables: tasmax, tasmin, rsds, pr
Spin-up
Was a spin-up performed?: No
Management & Adaptation Measures
Management: Planting window; harvest at maturity.
Extreme Events & Disturbances
Key challenges: Depends on the time scale you're talking about. Do you mean single extreme flood, storm or heatwave events? Or are you talking about extreme drought/hot seasons? Assuming the latter, models are able to capture effects of extremes pretty well. If you mean the former, extreme events are not well represented (especially flood impacts).
Key input and Management
Crops: mai, mil, ric, sor, soy, whe(w,s)
Land cover: potential suitable cropland area according to climatic conditions, current harvested areas (SPAM dataset: Spatial Production Allocation Model dataset - You, L., et al., Spatial Produciton Allocation Model (SPAM) 2000 Version 3 Release 1. http://MapSPAM.info. (Accessed Feb, 2012))
Planting date decision: Simulate planting dates according to climatic conditions within fixed planting window (Sacks et al., 2010), auto day
Planting density: Crop-specific (DSSAT default)
Crop cultivars: For cereal crops, we simulate crop Growing Degree Days (GDDs) requirement according to estimated annual GDDs from daily temperature. For soybean, we choose cultivar based on a range of latitude (which determines daylength), simulating 2-3 cultivars for each cell and taking the best performing variant.
Fertilizer application: GGCMI
Irrigation: no restriction on actual water availability, irrigated water applied when water stress
Crop residue: Crop-specific
Initial soil water: Initial conditions (IC) for soil water are reset to full capacity (FC) each season, ~90 days before planting.
Initial soil nitrate and ammonia: Season
Initial soil c and om: Season
Initial crop residue: 1000kg
Key model processes
Leaf area development: Dynamic simulation based on development and growth processes
Light interception: Cereal crops use simple approach, soy uses detailed approach.
Light utilization: Simple (descriptive) Radiation use efficiency approach / Detailed (explanatory) Gross photosynthesis – respiration, (for more details, see e.g. Adam et al. (2011))
Yield formation: number of grains and grain growth rate
Crop phenology: temperature, photoperiod (day length), other water/nutrient stress effects considered
Root distribution over depth: Exponential
Stresses involved: Water stress, Nitrogen stress, Oxygen stress, heat stress
Type of water stress: ratio of soil available water in the root zone to demand of water
Type of heat stress: vegetative , reporductive organ (sink), number of grain (pod) set during the flowering period
Water dynamics: soil water capacity approach with 4 soil layers
Evapo-transpiration: Penman-Monteith
Soil cn modeling: C model, N model, 3 organic matter pools
Co2 effects: Radiation use efficiency, Leaf-level photosynthesis-rubisco or on QE and Amax, Transpiration efficiency
Methods for model calibration and validation
Parameters, number and description: Default parameters from site-specific analyses of DSSAT
Spatial scale of calibration/validation: Field scale?
Person responsible for model simulations in this simulation round
Joshua W. Elliott: jelliott@ci.uchicago.edu, 0000-0003-0258-9886, Computation Institute, University of Chicago (USA)
Output Data
Experiments: historical, rcp26, rcp45, rcp60, rcp85
Climate Drivers: None
Date: 2013-12-13
Basic information
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: land use/land cover: annual
Temporal resolution of input data: soil: constant
Input data
Simulated atmospheric climate data sets used: GCM atmospheric climate data (Fast Track)
Climate variables: tasmax, tas, tasmin, rsds, pr
Spin-up
Was a spin-up performed?: No
Management & Adaptation Measures
Management: Planting window; harvest at maturity.