Impact model: pDSSAT

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.

ISIMIP3b
ISIMIP3a
ISIMIP2a
ISIMIP Fast Track

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: ssp126_2015soc_default, ssp585_2015soc_default, historical_2015soc_default, 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: 4.6 and 4.8

Model Output License: CC0

Model Homepage: https://dssat.net/

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

Simulated atmospheric climate data sets used: MRI-ESM2-0 (hist-nat, ssp119, ssp245, ssp460, ssp534-over), MPI-ESM1-2-HR (ssp245), IPSL-CM6A-LR (ssp119, ssp245, ssp460), GFDL-ESM4 (ssp245), UKESM1-0-LL (ssp119, ssp245)

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/

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.

Confirm data for simulation round ISIMIP2a

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: Main Reference: The parallel system for integrating impact models and sectors (pSIMS).

Reference Paper: Other References: Development of TREEGRO: A Fruit Tree Model for DSSAT.
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

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

Reference Paper: Main Reference: The parallel system for integrating impact models and sectors (pSIMS).

Reference Paper: Other References: Development of TREEGRO: A Fruit Tree Model for DSSAT.

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.