Crop Modelling (CropM)

Continued pressure on agricultural land, food insecurity and required adaptation to climate change have made integrated assessment and modelling of future agro-ecosystems development increasingly important. Various modelling tools are used to support the decision making and planning in agriculture. An important component in this is crop modelling. Yet, recent reviews revealed that neither crop modelling approaches nor the simulation tools are fully up to the task.

For example, most crop models do not account for crop-specific heat stress impacts around flowering or show other deficiencies in process descriptions. Another issue is that, while most of the crop growth simulation models have been developed and evaluated at field scale, they are usually applied in large area assessments without applying proper scaling methods. These and other deficiencies lead to uncertainties, which are often not quantified.

CropM has the ambition to improve the situation by quantifying the uncertainty in their results and reduce the various model uncertainties through model intercomparison, compiling data as required to fill knowledge gaps, eliminating model deficiencies and improve methods for scaling and uncertainty analysis. Furthermore, cropM intends to further develop crop rotation modelling which will allow FACCE MACSUR as a whole to apply and look at new ways of applying farming systems modelling in risk assessment. This will also require enhanced efforts on linking crop and soil modelling, which also will improve opportunities for exploring linkages between adaptation to and mitigation of climate change.

The key challenges of CropM is to advance crop modelling for improved assessment of climate change impacts on food security. Importantly, the work strategy includes both to stimulate excellent science and to support capacity and network building including demonstration of improved impact assessments with links to and in consultation with decision makers.

The work of CropM can be structured into three phases:

  • Phase I: improve capabilities of crop model application for risk assessment including upscaling, model linking capabilities and better understanding and quantification of model uncertainties
  • Phase II: demonstrate model improvement, advanced methods of upscaling and model linking and uncertainty analysis and strengthen research capacity in these fields
  • Phase III: develop strategies for future research within Europe on crop modelling for improved climatic risk assessment