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Impact model: LPJmL

Sector
Agriculture
Region
global

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.

Information for the model LPJmL 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
Jonas Jaegermeyr: jonas.jaegermeyr@columbia.edu, 0000-0002-8368-0018, Columbia University and NASA GISS (USA)
Christoph Müller: cmueller@pik-potsdam.de, 0000-0002-9491-3550, Potsdam Institute for Climate Impact Research (PIK) (Germany)
Additional persons involved: Sara Minoli
Output Data
Experiments: ssp370_2015soc_default, ssp126_2015soc_default, ssp370_2015soc_2015co2, ssp585_2015soc_default, historical_2015soc_default, ssp126_2015soc_2015co2, ssp585_2015soc_2015co2, picontrol_2015soc_default
Climate Drivers: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL
Date: 2021-09-24
Basic information
Model Output License: CC0
Model License: AGPLv3
Simulation Round Specific Description: * Data in embargo period, not yet publicly available. LPJmL 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: von Bloh W, Schaphoff S, Müller C, Rolinski S, Waha K, Zaehle S et al. Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0). Geoscientific Model Development,11,2789-2812,2018
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
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)
Socio-economic data sets used: Historical land transformation
Land use data sets used: Historical, gridded land use
Other human influences data sets used: Nitrogen deposition (ISIMIP3), Crop calendar, N-Fertilizer (ISIMIP3b)
Other data sets used: Soil data (hwsd_soil_data_all_land, hwsd_soil_data_on_cropland), Land-sea mask
Additional input data sets: manure input (C and N)
Climate variables: sfcWind, tasmax, tas, tasmin, rlds, rsds, pr
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: first spinup: 8000 years with natural vegetation to bring soil organic matter pools in dynamic equilibrium; recycling first 30 years of climate input data second spinup: 390 years with introducing land use according to input data, using the first year in input data for all years simulated prior to the first year
Natural Vegetation
Natural vegetation dynamics: LPJmL computes natural vegetation dynamics internally, including the distribution of plant functional types (PFTs).
Natural vegetation cover dataset: all area not indicated as cropland or pasture is assumed natural vegetation land
Soil layers: 5 hydrologically active soil layers of 0.2, 0.3, 0.5, 1, and 1m depth
Key input and Management
Crops: yes
Land cover: no; internally computed, land-use prescribed
Planting date decision: presecibed
Planting density: no
Crop cultivars: partially (duration for reaching maturity)
Fertilizer application: yes; according to input
Irrigation: yes; all grid cells are computed with rainfed and fully irrigated systems
Crop residue: yes, internally computed
Initial soil water: spinup
Initial soil nitrate and ammonia: spinup
Initial soil c and om: spinup
Initial crop residue: spinup
Key model processes
Leaf area development: phenology-driven LAI curves, modulated by water and N limitations
Light interception: Lambert-Beer model
Light utilization: photosynthesis
Yield formation: Harvest index
Crop phenology: GDD accumulation
Root distribution over depth: exponential
Stresses involved: water, nitrogen, temperature
Person responsible for model simulations in this simulation round
Jonas Jaegermeyr: jonas.jaegermeyr@columbia.edu, 0000-0002-8368-0018, Columbia University and NASA GISS (USA)
Christoph Müller: cmueller@pik-potsdam.de, 0000-0002-9491-3550, Potsdam Institute for Climate Impact Research (PIK) (Germany)
Additional persons involved: Sara Minoli
Output Data
Experiments: obsclim_2015soc_default
Climate Drivers: GSWP3-W5E5
Date: 2022-03-10
Basic information
Model Output License: CC0
Model Homepage: https://www.pik-potsdam.de/en/institute/departments/activities/biosphere-water-modelling/lpjml
Model License: AGPLv3
Simulation Round Specific Description: * Data in embargo period, not yet publicly available. LPJmL 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/
Reference Paper: Main Reference: von Bloh W, Schaphoff S, Müller C, Rolinski S, Waha K, Zaehle S et al. Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0). Geoscientific Model Development,11,2789-2812,2018
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
Observed atmospheric climate data sets used: W5E5v1.0
Emissions data sets used: Atmospheric composition (ISIMIP3a)
Land use data sets used: Historical, gridded land use
Other human influences data sets used: Nitrogen deposition (ISIMIP3), Crop calendar, N-Fertilizer (ISIMIP3a)
Other data sets used: Soil data (hwsd_soil_data_all_land, hwsd_soil_data_on_cropland), Land-sea mask
Climate variables: sfcWind, tasmax, tas, tasmin, rlds, rsds, pr
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: first spinup: 8000 years with natural vegetation to bring soil organic matter pools in dynamic equilibrium; recycling first 30 years of climate input data second spinup: 390 years with introducing land use according to input data, using the first year in input data for all years simulated prior to the first year
Natural Vegetation
Natural vegetation dynamics: LPJmL computes natural vegetation dynamics internally, including the distribution of plant functional types (PFTs).
Natural vegetation cover dataset: all area not indicated as cropland or pasture is assumed natural vegetation land
Soil layers: 5 hydrologically active soil layers of 0.2, 0.3, 0.5, 1, and 1m depth
Key input and Management
Crops: yes
Land cover: no; internally computed, land-use prescribed
Planting date decision: prescribed
Planting density: no
Crop cultivars: partially (duration for reaching maturity)
Fertilizer application: yes; according to input
Irrigation: yes; all grid cells are computed with rainfed and fully irrigated systems
Crop residue: yes, internally computed
Initial soil water: spinup
Initial soil nitrate and ammonia: spinup
Initial soil c and om: spinup
Initial crop residue: spinup
Key model processes
Leaf area development: phenology-driven LAI curves, modulated by water and N limitations
Light interception: Lambert-Beer model
Light utilization: photosynthesis
Yield formation: Harvest index
Crop phenology: GDD accumulation
Root distribution over depth: exponential
Stresses involved: water, nitrogen, temperature
Person responsible for model simulations in this simulation round
Jonas Jaegermeyr: jonas.jaegermeyr@columbia.edu, 0000-0002-8368-0018, Columbia University and NASA GISS (USA)
Christoph Müller: cmueller@pik-potsdam.de, 0000-0002-9491-3550, Potsdam Institute for Climate Impact Research (PIK) (Germany)
Output Data
Experiments: I, II, IIa, III
Climate Drivers: None
Date: 2017-07-07
Basic information
Model Output License: CC BY 4.0
Simulation Round Specific Description: LPJmL is one of the 3 models following the ISIMIP2b protocol which form the base of simulations for the ISIMIP2b agricultural sector outputs. Simulations are conducted with the same model version as ISIMIP2a simulations and the model has been evaluated in http://dx.doi.org/10.5194/gmd-10-1403-2017
Reference Paper: Main Reference: BONDEAU A, SMITH P, ZAEHLE S, SCHAPHOFF S, LUCHT W, CRAMER W, GERTEN D, LOTZE-CAMPEN H, MÜLLER C, REICHSTEIN M, SMITH B et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Global Change Biology,13,679-706,2007
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: all crops everywhere
Temporal resolution of input data: soil: constant
Input data
Simulated atmospheric climate data sets used: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5
Emissions data sets used: Atmospheric CO2 concentration
Additional input data sets: GGCMI harmonized planting and maturity datasets
Climate variables: tas, rlds, rsds, pr
Additional information about input variables: lwnet derived from tas and rlds
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 200 year spinup for soil temperatures and soil moisture, recycling the first 30 years of the time series
Management & Adaptation Measures
Management: Planting dates are based on the data provided within the global gridded crop model inter-comparison project Elliott et al (2015); total heat units to reach maturity remain constant over time but vary spatially according to reported growing seasons for the recent historic period.
Key input and Management
Crops: cas, mai, mil, nut, pea, rap, ric, sgb, soy, sug, sun, whe(w,s)
Land cover: all land mass
Planting date decision: Planting dates are based on the data provided within the global gridded crop model inter-comparison project Elliott et al (2015)
Planting density: planting density=NA
Crop cultivars: total heat units to reach maturity remain constant over time but vary spatially according to reported growing seasons for the recent historic period
Irrigation: no restriction on actual water availability, irrigated water applied when water stress
Crop residue: Scenario
Initial soil water: 200 year spin up
Key model processes
Leaf area development: prescribed shape of LAI curve as function of phenology, modified by water stress & low productivity
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
Crop phenology: temperature, vernalization
Root distribution over depth: exponential
Stresses involved: Water stress
Type of water stress: ratio of supply to demand of water
Water dynamics: soil water capacity with 5 soil layers
Evapo-transpiration: Priestley -Taylor
Co2 effects: Leaf-level photosynthesis-rubisco or on QE and Amax
Methods for model calibration and validation
Parameters, number and description: 3: maximum LAI under unstressed conditions, harvest index, factor for scaling leaf-level photosynthesis to stand level
Calibrated values: Specific for each crop and country
Output variable and dataset for calibration validation: Yield (FAO yield statistics)
Spatial scale of calibration/validation: National
Temporal scale of calibration/validation: Average for 1998-2003
Criteria for evaluation (validation): Wilmott
Person responsible for model simulations in this simulation round
Christoph Müller: cmueller@pik-potsdam.de, 0000-0002-9491-3550, Potsdam Institute for Climate Impact Research (PIK) (Germany)
Output Data
Experiments: historical
Climate Drivers: None
Date: 2016-05-04
Basic information
Model Output License: CC BY 4.0
Reference Paper: Main Reference: BONDEAU A, SMITH P, ZAEHLE S, SCHAPHOFF S, LUCHT W, CRAMER W, GERTEN D, LOTZE-CAMPEN H, MÜLLER C, REICHSTEIN M, SMITH B et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Global Change Biology,13,679-706,2007
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: soil: constant
Input data
Observed atmospheric climate data sets used: GSWP3, PGMFD v2.1 (Princeton), WATCH (WFD), WATCH-WFDEI
Additional input data sets: GGCMI harmonized planting and maturity datasets (for a subset of simulations)
Climate variables: tas, rlds, rsds, pr
Additional information about input variables: lwnet derived from tas and rlds
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 200 year spinup for soil temperatures and soil moisture, recycling the first 30 years of the time series
Management & Adaptation Measures
Management: crop sowing dates computed internally but fixed after the first simulation year in DEFAULT crop simulations, fixed at prescribed dates for HARMNON crop simulations
Key input and Management
Crops: cas, mai, mgr, mil, nut, pea, rap, ric, sgb, soy, sug, sun, whe(w,s)
Land cover: all land mass
Planting date decision: Simulate planting dates according to climatic conditions (Waha et al. 2012) during spinup and fixed planting dates to those dates after spinup in DEFAULT sims, fixed planting dates in HARMNON sims
Planting density: planting density=1
Crop cultivars: Simulate crop Growing Degree Days (GDDs) requirement according to estimated annual GDDs from daily temperature and vernalization requirements computed based on past climate experience (whe, sunfl, rapes); basetemperature computed based on past climate (mai); static (others)
Irrigation: no restriction on actual water availability, irrigated water applied when water stress
Crop residue: Scenario
Initial soil water: 200 year spin up
Key model processes
Leaf area development: prescribed shape of LAI curve as function of phenology, modified by water stress & low productivity
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, vernalization
Root distribution over depth: exponential
Stresses involved: Water stress
Type of water stress: ratio of supply to demand of water
Water dynamics: soil water capacity with 5 soil layers
Evapo-transpiration: Priestley -Taylor
Co2 effects: Leaf-level photosynthesis-rubisco or on QE and Amax
Methods for model calibration and validation
Parameters, number and description: 3: maximum LAI under unstressed conditions, harvest index, factor for scaling leaf-level photosynthesis to stand level
Calibrated values: Specific for each crop and country
Output variable and dataset for calibration validation: Yield (FAO yield statistics)
Spatial scale of calibration/validation: National
Temporal scale of calibration/validation: Average for 1998-2003
Criteria for evaluation (validation): Wilmott
Person responsible for model simulations in this simulation round
Christoph Müller: cmueller@pik-potsdam.de, 0000-0002-9491-3550, Potsdam Institute for Climate Impact Research (PIK) (Germany)
Output Data
Experiments: historical, rcp26, rcp45, rcp60, rcp85
Climate Drivers: None
Date: 2013-12-13
Basic information
Reference Paper: Main Reference: BONDEAU A, SMITH P, ZAEHLE S, SCHAPHOFF S, LUCHT W, CRAMER W, GERTEN D, LOTZE-CAMPEN H, MÜLLER C, REICHSTEIN M, SMITH B et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Global Change Biology,13,679-706,2007
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: soil: constant
Input data
Simulated atmospheric climate data sets used: GCM atmospheric climate data (Fast Track)
Emissions data sets used: Atmospheric CO2 concentration
Climate variables: tas, rlds, rsds, pr
Spin-up
Was a spin-up performed?: Yes
Spin-up design: 200 year spinup for soil temperatures and soil moisture, recycling the first 30 years of the time series
Management & Adaptation Measures
Management: crop sowing dates computed internally but fixed after the first simulation year
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: Simulate planting dates according to climatic conditions (Waha et al. 2012) during spinup and fixed planting dates to those dates after spinup
Planting density: planting density=1
Crop cultivars: Simulate crop Growing Degree Days (GDDs) requirement according to estimated annual GDDs from daily temperature and vernalization requirements computed based on past climate experience (whe, sunfl, rapes); basetemperature computed based on past climate (mai); static (others)
Irrigation: no restriction on actual water availability, irrigated water applied when water stress
Crop residue: Scenario
Initial soil water: 200 year spin up
Key model processes
Leaf area development: prescribed shape of LAI curve as function of phenology, modified by water stress & low productivity
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
Crop phenology: temperature, vernalization
Root distribution over depth: exponential
Stresses involved: water stress
Type of water stress: ratio of supply to demand of water
Water dynamics: soil water capacity with 5 soil layers
Evapo-transpiration: Priestley -Taylor
Co2 effects: Leaf-level photosynthesis-rubisco or on QE and Amax
Methods for model calibration and validation
Parameters, number and description: 3: maximum LAI under unstressed conditions, harvest index, factor for scaling leaf-level photosynthesis to stand level
Calibrated values: Specific for each crop and country
Output variable and dataset for calibration validation: Yield (FAO yield statistics)
Spatial scale of calibration/validation: National
Temporal scale of calibration/validation: Average for 1998-2003
Criteria for evaluation (validation): Wilmott




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