Impact model: pDSSAT

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

Sector
Agriculture
Region
global
Contact Person

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.

Basic information
Model Version: pDSSAT2.0 (DSSAT4.6)
Reference Paper: Other References:
Output Data
Experiments: historical
Climate Drivers: WATCH (WFD), WATCH+WFDEI
Date: 2016-02-11
Resolution
Spatial Aggregation: regular grid
Spatial Resolution: 0.5°x0.5°
Temporal Resolution Of Input Data: Climate Variables: Daily
Temporal Resolution Of Input Data: Co2: Monthly or annual
Temporal Resolution Of Input Data: Land Use/Land Cover: Various
Temporal Resolution Of Input Data: Soil: Constant
Input data sets used
Observed Atmospheric Climate Data Sets Used: WATCH (WFD), WATCH+WFDEI
Climate Variables: tasmax, tasmin, rsds, pr
Additional Input Data Sets: GGCMI harmonized planting, maturity and fertlizer dataset.
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, whe(w,s), soy, rice
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?