Crops and grass/Data uncertainties limitations: Difference between revisions

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Even though there may be significant differences in susceptibility to climate change, simulations of plausible cropping systems with global coverage are the best available indications of climate change impacts on actual cropping systems.  
Even though there may be significant differences in susceptibility to climate change, simulations of plausible cropping systems with global coverage are the best available indications of climate change impacts on actual cropping systems.  


A major uncertainty in climate change projections is the effectiveness of CO2 fertilisation on crop yields. Crop growth is stimulated under elevated atmospheric CO2 concentrations for many crops (C3 photosynthesis, such as wheat and rice) and water-use efficiency improves for all crops. However, the translation of higher photosynthesis to higher yields is less clear and subject to interacting processes, such as photosynthetic downregulation, increased nutrient limitation, and increased susceptibility to insect damage.
A major uncertainty in climate change projections is the effectiveness of CO<sub>2</sub>fertilisation on crop yields. Crop growth is stimulated under elevated atmospheric CO<sub>2</sub>concentrations for many crops (C3 photosynthesis, such as wheat and rice) and water-use efficiency improves for all crops. However, the translation of higher photosynthesis to higher yields is less clear and subject to interacting processes, such as photosynthetic downregulation, increased nutrient limitation, and increased susceptibility to insect damage.


LPJmL has been shown to be capable of reproducing agricultural water and carbon fluxes and pools for several sites (Bondeau et al., 2007). However, projections of global yield patterns are difficult to evaluate because of the strong management signal that is currently not represented at the process base in the model.  
LPJmL has been shown to be capable of reproducing agricultural water and carbon fluxes and pools for several sites (Bondeau et al., 2007). However, projections of global yield patterns are difficult to evaluate because of the strong management signal that is currently not represented at the process base in the model.  


Initial results from comparison of the global gridded crop models (joint activity of the Agricultural Model Inter-comparison and Improvement Project and the Inter-Sectoral Impact Model Inter-comparison ([[AgMIP and ISI-MIP project]])) indicate that LPJmL results are within the range of other model projections, but are on the optimistic end for effectiveness of CO2 fertilisation ([[Rosenzweig et al., 2013]]).  
Initial results from comparison of the global gridded crop models (joint activity of the Agricultural Model Inter-comparison and Improvement Project and the Inter-Sectoral Impact Model Inter-comparison ([[AgMIP and ISI-MIP project]])) indicate that LPJmL results are within the range of other model projections, but are on the optimistic end for effectiveness of CO<sub>2</sub>fertilisation ([[Rosenzweig et al., 2013]]).  


===Limitations===
===Limitations===

Revision as of 10:24, 1 July 2014

Link to framework components overview

Parts of Crops and grass/Data uncertainties limitations

  1. Introduction page
  2. Model description
  3. Policy issues
  4. Data, uncertainty and limitations
  5. Overview of references
Component is implemented in:
Components:
Related IMAGE components
Projects/Applications
Key publications
References
Crop and grass module of LPJmL, in IMAGE 3.0
Flowchart Crops and grass. See also the Input/Output Table on the introduction page.
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