Agricultural data for the crop yield variables was taken from the ISIMIP3b agricultural output data. Shown data consists of an ensemble of 11 different agriculture models. Details about each are listed below
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).
CROVER 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 ISIMIP3a Simulation Data from Agricultural Sector, see this DOI link: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-281/
The EPIC-based global gridded crop modelling system “EPIC-IIASA” is used to assess the main global agricultural systems in response to management interventions such as cropping practices, fertilization and irrigation practices or conservation and organic agriculture options, and changing environment, including climate change and soil degradation. Furthermore, EPIC-IIASA is used to compare cropland management systems and their effects on environmental indicators like water availability, nitrogen and phosphorus levels in soil, and greenhouse gas emissions.
Building on the Environmental Policy Integrated Climate model and global or regional datasets on climate, soils, land cover, and land use, EPIC-IIASA can analyze numerous crop types and their management under different weather, topographical, and soil conditions. It investigates the trade-offs between plant growth and yield on the one hand, and environmental impacts and sustainability on the other.
For example, EPIC-IIASA can estimate—based on soil type and prevailing climatic conditions—the extent to which nutrients from fertilizer, such as nitrogen (N) are leaching into nearby river and stream networks. This problem is of growing concern as globally two-fifths of N used in agriculture is lost to ecosystems with harmful environmental effects.
EPIC-IIASA can analyze options of sustainable agriculture including soil erosion control, crop residue management, improving soil organic carbon stock and reducing GHG emissions. Global and regional EPIC-IIASA applications help informing on the potential of agricultural systems to contribute to meeting global climate and food security targets.
Integrated Science Assessment Model (ISAM) is a coupled biogeochemical and biogeophysical model with 0.5° × 0.5° spatial resolution and multiple temporal resolutions ranging from a half-hour to yearly time steps. It simulates C, N, energy, and water budgets for various terrestrial ecosystems through photosynthesis, surface hydrology, radiative transfer, carbon allocation, and ecosystem respiration (Barman et al., 2014a, 2014b; Yang et al., 2009). Moreover, ISAM incorporates crop growth processes for C3 and C4 food crops (maize, soybean, wheat, and rice) and bioenergy grasses (miscanthus, cave-in-rock, and alamo), which are evaluated at site-level, regional, and global scales (Gahlot et al., 2020; Lin et al., 2017; Niyogi et al., 2015; Song et al., 2013, 2015, 2016). Some of the important features, unique to ISAM and critical for crop yield calculations, include (i) dynamic crop-specific phenology and carbon allocation schemes (Song et al., 2013, 2015), accounting for the sensitivity of different crops to extreme environmental conditions; (ii) dynamic vegetation structures, which better capture seasonal variability in leaf area index (LAI), canopy height, and root depth; (iii) dynamic root distribution processes at the depth that improve simulated root-mediated soil water uptake and transpiration.
LandscapeDNDC 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 ISIMIP3a Simulation Data from Agricultural Sector, see this DOI link: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-281/
The model is the crop-enabled version of LPJ-GUESS, described in Lindeskog et al. (2013). It is loosely based on LPJmL as described in Bondeau et al. (2007), but differs in several important aspects, including not being calibrated to observed country-level yields, a new phenology scheme, and a dynamic calculation of the potential heat units (PHU) required for a crop to achieve maturity. Sowing dates are calculated dynamically following Waha et al. (2012). The PHU sum needed for full development of a crop in a particular grid cell is calculated using a 10-year running mean of heat unit sums accumulated from the sowing date to the end of a sampling period (ranging from 190 to 245 days) derived from default sowing and harvest limit dates (Lindeskog et al., 2013). Crops are harvested upon full development. This dynamic variation of PHU to climate effectively assumes a perfect adaptation of crop cultivar to the prevailing climate. N limitation is not explicitly accounted for in this version of the model.
LPJmL is a dynamic global vegetation model that was extended to cover agricultural systems and the terrestrial hydrological cycle. It is capable of transient simulations of different crops, pasture systems and natural vegetation dynamics and can account for different management aspects in crop simulations.
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.
PEPIC is a Python-based Environmental Policy Integrated Climate (EPIC) model.
PROMET is a hydrological land surface process model, which has been extended by a biophysical dynamic vegetation component to model crop growth and yield formation. It uses first order physical and physiological principles to determine net primary production and respiration based on approaches from Farquhar et al. (1980) and Ball et al. (1987), combined with a phenology and a two-layer canopy architecture component of Yin et al (2005). PROMET takes into account the dependency of net primary production and phenology on environmental conditions including meteorology, CO2 concentration for C3 and C4 pathways as well as water and temperature stress. The mass and energy balance of the canopy and underlying soil surface are iteratively closed for each simulation time step. The canopy and phenology component allocates assimilates into the different plant organs of the canopy depending on the phenological stage of development. Assimilates that are accumulated within the fruit fraction during the growing period determine the dry biomass available for yield formation. The simulation is performed on an hourly time step to account for non-linear reactions of crop growth to environmental conditions (mainly light, water, temperature and wind). Conversion of daily climate model data to hourly values is done by the TeddyTool v1.1 (Zabel and Poschlod 2023). Depending on the reaction of the considered crop to meteorological and soil-specific conditions, the crop may either die due to water, heat or cold stress before being harvested or it may not reach maturity. In both cases, this results in total yield loss.
SIMPLACE—a versatile modelling and simulation framework for sustainable crops and agroecosystems hhttps://doi.org/10.1093/insilicoplants/diad006