The LPJmL model is a process-based dynamic vegetation model that simulates plant responses to climate conditions and changes, focusing on agricultural ecosystems and crop yields. It incorporates climate, soil, and land use data and supports simulations for multiple crops under varying management and irrigation practices. Future developments aim to enhance management modules, include additional crops, and address uncertainties related to CO2 fertilization and extreme climate impacts.

1. Global Yield Assessment
Description and data requirements of the global
dynamic vegetation model LPJmL
Katharina Waha
Workshop Beyond Diagnostics: Insights and Recommendations for
Remote Sensing, 14./15. December 2013

2. 1 – LPJmL in Short
LPJmL
Processes
• process‐based dynamic vegetation
model, originates from EPIC and BIOME
models
• simulates plant responses to climate and
climate change in natural and agriculture
ecosystems
Climate
Respiration
NPP
Allocation
• high spatial and temporal resolution
Photosynthesis
Soil water

3. 2 – Main features / modules
Climate, Soil, Land Use
Land use change
• Regular grid (67.420 grid cells 0.5°x 0.5°)
Crop Biomass, Harvest, Water Use
• 13 crops + managed grassland + bioenergy
plants:
‐ wheat, rice, maize, millet, pulses,
sugarbeet, cassava, sunflower, groundnuts,
soybean, rapeseed, sugar cane, other crops

4. 2 – Main features / modules (cont.)
Carbon pools and fluxes
Water balance
• Regular grid (67.420 grid cells 0.5°x 0.5°)
biochemical leaf
photosynthesis model
(Farquhar et al. 1980
/Haxeltine & Prentice 1996)
Daily allocation
driven by phenology,
stress and production
‐ Farquhar, G.D. et al. 1980. A Biochemical
Model of Photosynthetic CO2 Assimilation
in Leaves of C3 Species. Planta. 149, 78‐90.
‐ Haxeltine, A.,Prentice, I.C., 1996. BIOME3:
An equilibrium terrestrial biosphere model
based on ecophysiological constraints,
resource availability, and competition
among plant functional types. Global
Biogeochemical Cycles. 10, 693‐709.

5. 3 – Crop management
•
Management modules (climate‐driven, input‐driven)
+ often more important than climate and soils
‐> Computed internally
+ Planting dates (Waha et al. 2012)
+ Available irrigation water (Biemans et al. 2011)
+ Variety characteristics (Bondeau et al. 2008, van Bussel 2011)
‐> Prescribed
+ Annual land‐use patterns (Fader et al. 2010)
+ Irrigation (yes/no)
+ Intercrops
+ Residue management
+ Management
Intensity (Fader et al. 2010)
Simulated sowing date for maize in 2000 (Waha et al. 2012)

6. 4 – Model Input
•
Soils
+ FAO Harmonized Soil Database (13 soil texture classes ‐> water holding capacity)
•
Climate
+ current and past climate:
monthly: CRU TS 3.21 (1901‐2012)
daily: GPCC (1901‐2007), WATCH (1901‐2001)
+ future climate: climate projections from
GCMs via CMIP5 project
•
Landuse
+ generated from 3 land use data sets
+ rainfed and irrigated cropland in
1700 – 2005 for 13 crops
CRU ‐ Climate Research Unit, University of East Anglia,
GPCC ‐ Global Precipitation Climatology Centre
WATCH ‐ WATCH Forcing Data 20th Century
GCM ‐ General Circulation Model
CMIP5 ‐ Coupled Model Intercomparison Project Phase 5
Compilation procedure of the land‐use dataset for LPJmL
(Fader et al. 2010)

7. 5 – Model Output: Crop Yields
National and grid‐cell yields
Simulated grid‐cell wheat yields (t/ha) in 2000
Simulated mean area‐weighted national wheat yield (t/ha) in 2000
Rainfall
Simulated mean area‐
weighted national
maize yield 1961‐2000
(t/ha) in Burkina Faso
Yield
Interannual variability

8. 5 – Model Output: Crop yields under climate change
With CO2 fertilization
Without CO2 fertilization
Mean climate change impact (%) on (sub‐) national crop yields in 2050 relative to 2000. Climate change impacts are
shown as simulated with LPJmL with climate projections from 5 general circulation models and 3 emission scenarios
(Müller et al. 2009).
Müller, C., Bondeau, A., Popp, A., Waha, K.,Fader, M., 2009. Climate Change Impacts on Agricultural Yields.
Background note to the World Development Report 2010. World Bank, Washington D.C.

9. 6 – Under development and future plans (examples)
• Refine management modules (irrigation, rainwater harvesting and vapor
shift techniques, multiple cropping)
• Add more crops (potato, cotton, date palm, citrus, …)
• Continue development of bioenergy plants
• Add nitrogen cycle
• Understand uncertainty in CO2 fertilization effect (coupled effects from
increased temperatures and CO2)
• Improve grassland management and representation of livestock
• Revise simulated impacts of extreme temperature and precipitation

10. Thank you
http://www.pik‐potsdam.de/research/projects/lpjweb
Dr Katharina Waha
Climate Impacts & Vulnerabilities
tel: +49 331 288 26 27
e‐mail: katharina.waha@pik‐potsdam.de

11. Literature
key model components, LPJmL as LPJ‐DGVM:
•
Collatz, G.J., Ribas‐Carbo, M.,Berry, J.A. 1992. Coupled Photosynthesis‐Stomatal Conductance Model for Leaves of C4 Plants,
pp. 519‐538, Vol. 19.
•
Sitch, S., Smith, B., Prentice, I.C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J.O., Levis, S., Lucht, W., Sykes, M.T., Thonicke,
K.,Venevsky, S., 2003. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic
global vegetation model. Global Change Biology. 9, 161‐185.
agricultural vegetation:
•
Bondeau, A., Smith, P.C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W., Gerten, D., Lotze‐Campen, H., Müller, C., Reichstein,
M.,Smith, B., 2007. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Global Change
Biology. 13, 679‐706.
hydrology, river routing:
•
Biemans, H., Haddeland, I., Kabat, P., Ludwig, F.,Hutjes, R.W.A., 2011. Impact of reservoirs on river discharge and irrigation
water supply during the 20th century. Water Resources Research. 47, W03509.
•
Gerten, D., Schaphoff, S., Haberlandt, U., Lucht, W.,Sitch, S., 2004. Terrestrial vegetation and water balance ‐ hydrological
evaluation of a dynamic global vegetation model. Journal of Hydrology. 286, 249‐270.
water management in agricultural, virtual water, land‐use data set and management intensity:
•
Fader, M., Rost, S., Müller, C., Bondeau, A.,Gerten, D., 2010. Virtual water content of temperate cereals and maize: Present and
potential future patterns. Journal of Hydrology. 384, 218‐231.
•
Rost, S., Gerten, D., Bondeau, A., Lucht, W., Rohwer, J.,Schaphoff, S., 2008. Agricultural green and blue water consumption and
its influence on the global water system. Water Resources Research. 44, W09405 (17pp).
permafrost, soil hydrology update:
•
Schaphoff, S., Heyder, U., Ostberg, S., Gerten, D., Heinke, J.,Lucht, W., 2013. Contribution of permafrost soils to the global
carbon budget. Environmental Research Letters. 8, 014026.
crop phenology, sowing and harvest dates
•
Van Bussel, L.G.J., 2011. From field to globe: upscaling of crop growth modelling., Dissertation, Wageningen University,
Wageningen.
•
Waha, K., van Bussel, L.G.J., Müller, C.,Bondeau, A., 2012. Climate‐driven simulation of global crop sowing dates. Global
Ecology and Biogeography. 21, 247–259.
bioenergy:
•
Beringer, T.I.M., Lucht, W.,Schaphoff, S., 2011. Bioenergy production potential of global biomass plantations under
environmental and agricultural constraints. GCB Bioenergy.