Impact model: pDSSAT-PT

pDSSAT-PT is the run of the pDSSAT model using the Priestley Taylor estimation method for potential evapotranspiration (PET). 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

ISIMIP2a

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)

Basic information

Model Version: pDSSAT2.0 (DSSAT4.6)

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

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, 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

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?