Impact model: CYGMA1p74

Sector
Agriculture
Region
global

CYGMA is a global gridded crop model. The model operates at 0.5° resolution in longitude and latitude and has a daily time step. In the model, crop development is modelled as a fraction of the accumulated growing degree days relative to the crop thermal requirements. For wheat, only spring wheat is considered, because the vernalization process is not currently incorporated into the model. Leaf growth and senescence are calculated according to the fraction of the growing season using the prescribed shape of the leaf area index curve. The yields are computed from the photosynthetically active radiation intercepted by the crop canopy, the radiation-use efficiency (RUE), the effects of CO2 fertilization on the RUE and the fraction of total biomass increments allocated to the harvestable component. The soil water balance sub-model, which is coupled with the snow cover sub-model, is used to calculate the actual evapotranspiration. In the model, crop development is modelled as a fraction of the accumulated growing degree days relative to the crop thermal requirements. For wheat, only spring wheat is considered, because the vernalization process is not currently incorporated into the model. Leaf growth and senescence are calculated according to the fraction of the growing season using the prescribed shape of the leaf area index curve. The yields are computed from the photosynthetically active radiation intercepted by the crop canopy, the radiation-use efficiency (RUE), the effects of CO2 fertilization on the RUE and the fraction of total biomass increments allocated to the harvestable component. The soil water balance sub-model, which is coupled with the snow cover sub-model, is used to calculate the actual evapotranspiration. Five different stress types, i.e., nitrogen (N) deficits, heat, cold, water deficits and water excesses are considered, and the most dominant stress type for a day decreases the daily potential increment in the leaf area for the vegetative growth period and in yield for the reproductive growth period. The growth and yield of soybeans in the model are less sensitive to N deficit stress than are theother crops considered here because the soybean is a legume that fix nitrogen. All of the stress types except N deficits are functions of daily weather, and the tolerance of each crop to these stresses increases as the knowledge stock increases. The knowledge stock is an economic indicator that is calculated as the sum of the public annual agricultural research and development (R&D) expenditures for each country since the year 1961 with a certain obsolescence rate, and it represents the average level of agronomic technology and management among farmers in a country. More details on the modelling are available in Iizumi et al. (2017).

Information for the model CYGMA1p74 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
Toshichika Iizumi: iizumit@affrc.go.jp, 0000-0002-0611-4637, National Institute for Agro-Environmental Sciences (Japan)
Output Data
Experiments: historical_2015soc_default, ssp126_2015soc_default, ssp370_2015soc_2015co2, ssp370_2015soc_default, picontrol_2015soc_default, ssp585_2015soc_default, historical_2015soc_2015co2, ssp585_2015soc_2015co2, 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: 1.74
Model Output License: CC0
Simulation Round Specific Description: * Data in embargo period, not yet publicly available. CYGMA1p74 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: Iizumi T et al. Responses of crop yield growth to global temperature and socioeconomic changes. Scientific Reports,7,7800,2017
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Additional spatial aggregation & resolution information: grid-cell harvested area maps for rainfed and irrigated conditions are necessary when aggregating the model outputs to a larger spatial domain (ex. a country level)
Temporal resolution of input data: climate variables: daily
Temporal resolution of input data: co2: annual
Temporal resolution of input data: soil: constant
Input data
Simulated atmospheric climate data sets used: MRI-ESM2-0, IPSL-CM6A-LR, MPI-ESM1-2-HR, UKESM1-0-LL, GFDL-ESM4
Emissions data sets used: Atmospheric composition (ISIMIP3b)
Other human influences data sets used: Crop calendar, N-Fertilizer (ISIMIP3b)
Climate variables: hurs, sfcWind, tasmax, tas, tasmin, rsds, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: A 120-year (1958–1959 × 60) soil moisture spin-up was applied before the simulation was conducted.
Natural Vegetation
Natural vegetation partition: Not applicable
Natural vegetation dynamics: Not applicable
Natural vegetation cover dataset: Not applicable
Soil layers: The soil water content in root zone in the model is calculated by a single-layer leaky baket model. The depth of the baket is determined by the plant-extractable root-zone soil water capacity estimated from the soil texture, soil organic content and plant root (or soil profile) depth was used. Irrigation within the model is set to increase the soil water content to 90% of the root-zone soil water holding capacity when the root-zone soil water content at the end of a day is below the threshold level.
Management & Adaptation Measures
Management: Sowing date and nitrogen application rate are crop-specific, and these are fed to the model according to the input data.
Extreme Events & Disturbances
Key challenges: This model considers growth stress due to excessive soil water condition and associated lack of aeration which occurs after heavy rainfall events.
Key input and Management
Crops: maize, rice, spring wheat, soybean, millet, and sorghum are simulated.
Land cover: The model simulates all land area regardless wheather crops are currently cultivated or not.
Planting date decision: Planting date decision setting is determined by simulation protocol. When a specific sowing dateinput is designated to use as the input, the model follows it. When considering autonomous adaptation, sowing dates shift according to changes in thermal and moisture regemes for the last 10 years.
Planting density: Average planting density is assumed.
Crop cultivars: Crop's total thermal requirements are set to be location-specific, which represent a part of varietal difference.
Fertilizer application: N fertilizer application follows the input designated in the protocol.
Irrigation: Simulations are performed all land grid cells under each of rainfed and irrigated conditions.
Crop residue: Not applicable.
Initial soil water: A soil moisture spin-up is applied before simulation.
Initial soil nitrate and ammonia: Initial soil total nitrogen content at sowing is determined by N application rate input.
Initial soil c and om: Not explicitly considered.
Initial crop residue: Not explicitly considered.
Key model processes
Leaf area development: Leaf growth and senescence are calculated according to the fraction of the growing season using the prescribed shape of the leaf area index curve.
Yield formation: The yields are computed from the photosynthetically active radiation intercepted by the crop canopy, the radiation-use efficiency (RUE), the effects of CO2 fertilization on the RUE and the fraction of total biomass increments allocated to the harvestable component.
Crop phenology: Crop development is modelled as a fraction of the accumulated growing degree days relative to the crop thermal requirements.
Root distribution over depth: Root growth is not explicitly simulated.
Stresses involved: Five different stress types, i.e., nitrogen deficits, heat, cold, water deficits and water excesses are considered, and the most dominant stress type for a day decreases the daily potential increment in the leaf area and yield.
Type of water stress: The stress due to water deficit is modeled as a function of actual-potential evapotranspiration ratio following Neitsch et al. (2005). The modeling of stress associated with excessive soil water used is a function of soil saturation and follows the method of Wang et al. (2016). Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. Soil and Water Assessment Tool Theoretical Documentation (Version 2005) (USDA, 2005). Wang, R., Bowling, L. C., & Cherkauer, K. A. Estimation of the effects of climate variability on crop yield in the Midwest USA. Agric. For. Meteorol. 216, 141–156 (2016).
Type of heat stress: The stresses associated with low and high temperatures are modeled as a function of daily mean temperature and follow the method of Neitsch et al. (2005). The function of stress due to high temperatures (heat) is tested in Iizumi et al. (2021). Iizumi, T., Ali-Babiker, IE.A., Tsubo, M. et al. Rising temperatures and increasing demand challenge wheat supply in Sudan. Nat. Food 2, 19–27 (2021).
Water dynamics: Vertical root-zone soil water balance is simulated.
Evapo-transpiration: A variant of the Penman-Monteith equation (Neitsch et al. 2005) is used.
Soil cn modeling: Not simulated
Co2 effects: The fertilization effect on radiation-use efficiency and increased water-use efficiency under elevated CO2 concentration are considered.
Methods for model calibration and validation
Parameters, number and description: 35 parameters are set crop by crop (see Table S4 of Iizumi et al. 2017 for details). These determin phenological patterns, base, optimal and upper temperatures for growth, leaf area growth and senescence patterns, maximum canopy height, yield formation patterns, and sensitivity of each stress type.
Output variable and dataset for calibration validation: Crop total thermal requirement is determined based on global crop calendars (Portmann et al. 2010). Parameters related to stress are determined to match model output with actual-potential yield ratio (Mueller et al. 2012). Other parameters follow literature. See for Iizumi et al. (2017) details. Portmann, F. T., Siebert, S. & Döll, P. MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling. Glob. Biogeochem. Cycles 24, GB1011 (2010). Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012). Iizumi, T., Furuya, J., Shen, Z. et al. Responses of crop yield growth to global temperature and socioeconomic changes. Sci Rep 7, 7800 (2017).
Spatial scale of calibration/validation: The data aggregated to a 0.5° resolution are used for the calibration.
Temporal scale of calibration/validation: The data in 2000 are used for the calibration.
Person responsible for model simulations in this simulation round
Toshichika Iizumi: iizumit@affrc.go.jp, 0000-0002-0611-4637, National Institute for Agro-Environmental Sciences (Japan)
Output Data
Experiments: obsclim_2015soc_default
Climate Drivers: GSWP3-W5E5
Date: 2022-03-10
Basic information
Model Output License: CC0
Simulation Round Specific Description: * Data in embargo period, not yet publicly available. CYGMA1p74 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/