Impact model: pAPSIM

pAPSIM 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

James Chryssanthacopoulos: jchryssanthacopoulos@gmail.com, Climate Systems Research, Columbia University (USA)

Joshua W. Elliott: jelliott@ci.uchicago.edu, 0000-0003-0258-9886, Computation Institute, University of Chicago (USA)

Output Data

Experiments: historical
Climate Drivers: None
Date: 2016-02-11

Basic information

Model Version: pAPSIM1.0 (APSIM V7.5)

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

Reference Paper: Other References: An overview of APSIM, a model designed for farming systems simulation.
APSIM – Evolution towards a new generation of agricultural systems simulation.
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°

Temporal resolution of input data: climate variables: daily

Temporal resolution of input data: co2: annual

Temporal resolution of input data: soil: constant

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

Management & Adaptation Measures

Management: Fixed planting date; harvest at maturity.

Key input and Management

Crops: mai, soy, whe, sor

Planting date decision: fixed planting dates; source of planting date data if applicable

Planting density: Crop specific, default input

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: Yes

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: Simple approach

Light utilization: Simple (descriptive) Radiation use efficiency approach, Detailed (explanatory) Gross photosynthesis – respiration, (for more details, see e.g. Adam et al. (2011)) (pasture only)

Yield formation: number of grains and grain growth rate, partitioning during reproductive stages, harvest index modified by water stress (soy)

Crop phenology: temperature, photoperiod (day length), other water/nutrient stress effects considered, vernalization

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 5 soil layers, Richards approach

Evapo-transpiration: transpiration efficiency

Soil cn modeling: C model, N model, 3 organic matter pools, microbial biomass pool

Co2 effects: Radiation use efficiency, Transpiration efficiency, nutrient use efficiency

Methods for model calibration and validation

Parameters, number and description: Default parameters from site-specific analyses

Spatial scale of calibration/validation: Field scale?