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Exploring climate model data

Impact of climate change on crop yields, interaction with economical models


  • Assess the impacts of climate change on main agrosystems (forestry, crops and pasture)
  • Assess the impact of agrosystems on main GHG budget
  • Provide information to feed land use models


Agriculture is considered as the most weather-dependent of human activities. Although average levels of crop productivity across the world mostly depend on local non-climatic factors, the temporal variability in crop yields often reflects the variability in weather conditions. In return, land use as an impact on climate thru interaction with hydrologic and energetic budget of the surface and contribution to the GHG budget. In particular the ability of agriculture to mitigate carbon is related to land conversion. The first purpose of this case, initiated in the French AUTREMENT project, is to evaluate impact of climate change on crop yield, animal and wood production. Second purpose is to evaluate impact of climate change and land use change on GHG budget. This requires scenarios from models simulating land use change based on economical constraints. In return, these models needs data on yields that are in general held constant in the future and don’t take into account for impact of climate change on yields. Hence there is an urgent need to couple land use model with yield models to fully account for feedback between climate/yield and land use change. The third purpose of this case is then to couple a yield model to a land use model.


Nicolas Viovy (LSCE, France)



Related use cases


Data needs

  • data type
    main climate drivers (temperature, precipitation, radiation, air humidity, wind speed) period: 1900-2100,
    spatial covering: global data and regional data at higher horizontal resolution for Europe and France Data on management (eg. fertilizer, irrigation, crop rotation, tillage intensity, wood demand, animal load …)
    Land use scenarios
  • data set
    For historical period: combination of reanalysis and climatology (ex. CRUNCEP dataset).
    For future. Scenarios for several models and contrasted climate scenarios + disagragated scenarios for EUROPE
    For historical period: Dataset on forest age distribution, historical data on land management (e.g tillage intensity, fertilizer input, intensity of grassland managment etc..)
    For future period: Wood and production demand, land use scenarios. Soil properties maps.
    For validation: Yield database from FAO at the national scale, district-scale and local scale. Soil map properties. Flux tower measurements

Typical course of events

  • Compilation of data on management and validation data
  • Development and improvement of models
  • Yields simulation in the actual period, comparison with observed yields
  • Yields simulation for the future period based on several contrasted climate scenarios
  • Analyse of trends of future yields
  • Identify vulnerable regions and analyse change in region where each crop can growth
  • Analyse of change in GHG budget
  • Provide yield response curves for land use change model
  • Projected land use change based on economical scenarios and yield response Impact of land use change on GHG budget



  • Crop and land use modelling
  • Method to compare several data-sets (trend, seasonal cycle, inter-annual variability)
  • Multi-model analysis
  • Kendall test for trend significativity evaluation


  • ORCHIDEE (crop modelling tool in the tropics)
  • NEXUS land use & IMACLIM (land use module and economical model
  • Fortran and C programs for data preprocessing and postprocessing
  • CDO, NCO, ferret, for data treatment and data analyses (DTR, indicator..)

File format:

NetCDF, Ascii



Sources of uncertainty:

Several source of uncertainties are considered:
  • Uncertainties on input data that are treated by providing several simulations based on forcing data for climate, land use scenarios etc.
  • Uncertainties on model are trated by considering several model hypotheses



The ENES3 project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 824084.