Impact model: LPJ-GUESS

Sector
Agriculture
Region
global

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.

Bondeau, A. et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glob. Change Biol. 13, 679-706, doi:10.1111/j.1365-2486.2006.01305.x (2007).
Lindeskog, M. et al. Implications of accounting for land use in simulations of carbon cycling in Africa. Earth System Dynamics 4, 385-407, doi:10.5194/esd-4-385-2013 (2013).
Waha, K., et al. Climate-driven simulation of global crop sowing dates. Global Ecology and Biogeography 21, 247-259 (2012).

Information for the model LPJ-GUESS 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
Sam Rabin: samrabin@ucar.edu, 0000-0003-4095-1129, National Center for Atmospheric Research (USA)
Output Data
Experiments: ssp126_2015soc_default, ssp585_2015soc_default, historical_2015soc_default, ssp585_2015soc_2015co2, ssp126_2015soc_2015co2
Climate Drivers: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL
Date: 2023-07-10
Basic information
Model Version: v4.1
Model Output License: CC0
Model Homepage: https://web.nateko.lu.se/lpj-guess/index.html
Model License: Mozilla Public License 2.0
Simulation Round Specific Description: matt_isimip3 branch of the Git repo at https://bitbucket.org/samrabin/lpj-guess_git-svn_20190828/src/matt_isimip3/ Commit SHA 17d32f19d3e0d3f2309452bd7d0853cacc1384c7
Reference Paper: Main Reference: Smith B, Prentice I, Sykes M et al. Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Global Ecology and Biogeography,10,621-637,2003
Reference Paper: Other References:
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: land use/land cover: annual
Temporal resolution of input data: soil: constant
Input data
Exceptions to Protocol
Exceptions: In the agricultural sector, we used the GGCMI (Global Gridded Crop Model Intercomparison) N fertilizer data product, which uses gridded crop-specific fertilizer data from Mueller et al. (2012) scaled in time with the time series of LUH2 v2h (Hurtt et al. 2020).
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 500 years, using the first 99 years of the ISIMIP3a spinclim data. (We could not use all 100 years due to model implementation issues.) Year-1850 CO2 (284.73 ppm).
Natural Vegetation
Natural vegetation partition: Any gridcell with less than 100% cropland or pasture has at least one natural stand. A new natural stand is created any time the area of cropland+pasture in the gridcell decreases.
Natural vegetation dynamics: LPJ-GUESS is a vegetation demographic model in which different plant functional types compete with each other for light, water, and nutrients. Each primary and secondary natural stand had 5 "patches" which realize different vegetation cover based on stochastic establishment, disturbance, and mortality.
Natural vegetation cover dataset: n/a
Soil layers: 15 layers, each 10 mm thick
Management & Adaptation Measures
Management: LPJ-GUESS simulates both cropland (including a number of different crops, synthetic fertilizer and manure application, and irrigation) and pasture (grass-only stands, including removal of biomass via grazing).
Extreme Events & Disturbances
Key challenges: For the agriculture simulations, fire and its impacts are explicitly simulated using the SIMFIRE-BLAZE module. Note that fire does not occur on cropland, but can affect crop simulations via land-use change through its effects on soils. Pests, water logging, and frost damage are not explicitly simulated.
Key input and Management
Crops: Yes. Winter and spring wheat, maize, rice, soybean, bean (CFT TeFb_nlim, faba bean), sorghum, millet
Land cover: Yes, using the standard ISIMIP protocol.
Planting date decision: Using the constant prescribed growing season dataset specified by the protocol.
Crop cultivars: One constant cultivar for each crop in each gridcell (up to two for rice) based on maturity requirement. This is determined based on the the mean growing degree-days accumulated in the growing period (derived from prescribed crop calendars specified in protocol) over 1980–2009 growing seasons.
Fertilizer application: Synthetic fertilizer applied according to protocol: 20% at sowing, 80% once accumulated growing degree-days reaches 25% of maturity requirements. Manure applied 100% at sowing.
Irrigation: As specified by protocol, irrigation is applied to reach soil saturation every day.
Crop residue: At harvest, 90% of harvestable organ is removed, along with 70% of leaves. Remainder of those pools, along with fine root biomass, goes to soil litter pools.
Initial soil water: Determined dynamically
Initial soil nitrate and ammonia: Determined dynamically
Initial soil c and om: Determined dynamically
Initial crop residue: Determined dynamically
Key model processes
Crop phenology: See Olin et al. (2015)
Stresses involved: None explicit, aside from water and temperature effects on photosynthesis
Type of water stress: Through limitation of photosynthesis
Type of heat stress: Through photosynthesis (enzyme optimality curve)
Methods for model calibration and validation
Parameters, number and description: Cultivar GDD requirements determined as described above
Person responsible for model simulations in this simulation round
Almut Arneth: almut.arneth@kit.edu, 0000-0001-6616-0822, Karlsruhe Institute of Technology (Germany)
Stefan Olin: Stefan.Olin@nateko.lu.se, 0000-0002-8621-3300, Lund University, Department for Physical Geography and Ecosystem Scince (Sweden)
Thomas Pugh: t.a.m.pugh@bham.ac.uk, 0000-0002-6242-7371, University of Birmingham (UK)
Output Data
Experiments: historical
Climate Drivers: None
Date: 2016-05-04
Basic information
Model Version: Version 4
Model Output License: CC BY 4.0
Reference Paper: Main Reference: Olin, S. Lindeskog, M. Pugh, T. A.M. Schurgers, G. Wårlind, D. Mishurov, M. Zaehle, S. Stocker, B. D. Smith, B. Arneth, A. et al. Soil carbon management in large-scale Earth system modelling: Implications for crop yields and nitrogen leaching. Earth System Dynamics,6,475-768,2015
Reference Paper: Other References:
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
Input data
Observed atmospheric climate data sets used: GSWP3, WATCH (WFD), WATCH-WFDEI
Additional input data sets: GGCMI harmonized planting and maturity datasets (for a subset of simulations)
Climate variables: tas, rsds, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 30 year spinup, using climate and [CO2] from the first simulation year.
Management & Adaptation Measures
Management: With and without adapting growing periods.
Key input and Management
Crops: bar, ben, cas, mai, mil, nut, pot, rap, ric, rye, sgb, sor, soy, sun, whe(w,s)
Land cover: potential suitable cropland area according to climatic conditions, current harvested areas (Hurtt et al. 2011/Portmann et al., 2010)
Planting date decision: Simulate planting dates according to climatic conditions (Waha et al. 2012) or planting dates fixed at present based on S.
Planting density: planting density=1
Crop cultivars: Simulate crop Growing Degree Days (GDDs) requirement according to estimated annual GDDs from daily temperature. Vernalization requirements computed based on past climate experience (whe, sunfl, rapes); BT (mai); static (others). No differentiation between varieties other than PHU, except for wheat, which automatically selects between spring and winter varieties according to prevailing climate.
Irrigation: No restriction on actual water availability, irrigated water applied whenever plants would otherwise enter water stress due to soil water limitations.
Crop residue: N/A, as does not influence yields in this version of LPJ-GUESS.
Initial soil water: 30 year spin up
Initial soil c and om: Not initialised or spun-up, as they do not influence yields in this version of LPJ-GUESS.
Key model processes
Leaf area development: Dynamic simulation based on development and growth processes
Light interception: Simple approach
Light utilization: Detailed (explanatory) Gross photosynthesis – respiration, (for more details, see e.g. Adam et al. (2011))
Yield formation: harvest index modified by water stress
Crop phenology: temperature, heat unit index
Root distribution over depth: linear
Stresses involved: Water stress
Type of water stress: ratio of supply to demand of water
Water dynamics: soil water capacity approach with 2 soil layers
Evapo-transpiration: Priestley -Taylor
Co2 effects: Leaf-level photosynthesis-rubisco
Person responsible for model simulations in this simulation round
Stefan Olin: Stefan.Olin@nateko.lu.se, 0000-0002-8621-3300, Lund University, Department for Physical Geography and Ecosystem Scince (Sweden)
Thomas Pugh: t.a.m.pugh@bham.ac.uk, 0000-0002-6242-7371, University of Birmingham (UK)
Output Data
Experiments: historical, rcp26, rcp45, rcp60, rcp85
Climate Drivers: None
Date: 2013-12-13
Basic information
Reference Paper: Main Reference: Lindeskog, M. et al. et al. Implications of accounting for land use in simulations of carbon cycling in AfricaEarth System Dynamics,4,385-407,2013
Resolution
Spatial aggregation: regular grid
Horizontal resolution: 0.5°x0.5°
Temporal resolution of input data: climate variables: annual
Temporal resolution of input data: co2: annual
Temporal resolution of input data: soil: constant
Input data
Simulated atmospheric climate data sets used: GCM atmospheric climate data (Fast Track)
Emissions data sets used: CO2 concentration
Climate variables: tas, rsds, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 500 year spinup using 1850 atmospheric CO2 mixing ratio and the first 30 years of detrended climate as input.
Management & Adaptation Measures
Management: Sowing and harvest dates adapting to changes in climate. Number of potential heat units to maturity adapting to changes in climate such that growing season length is maintained with rising temperature.
Key input and Management
Crops: whe(w,s), rice, mai, mill, sub, cass, fpea, soy, sunfl, rapes, gnut, suc, mgr
Land cover: All land mass
Planting date decision: Climate dependent
Planting density: NA
Crop cultivars: Adaptive to local climate
Fertilizer application: NA
Irrigation: Yes, optimal
Crop residue: 90% removed - but no feedback to yields.
Initial soil water: No
Initial soil nitrate and ammonia: NA
Initial soil c and om: No
Initial crop residue: No
Key model processes
Leaf area development: Based on heat sum requirement and allocated carbon.
Light interception: Simple approach
Yield formation: Based on heat sum requirement
Crop phenology: Based on heat sum requirement
Root distribution over depth: Fixed
Stresses involved: Water
Type of water stress: Ratio of supply to demand of water
Water dynamics: soil water capacity with 2 soil layers
Evapo-transpiration: Priestley -Taylor
Soil cn modeling: NA
Co2 effects: Leaf-level photosynthesis-rubisco