Tag Archives: MACSUR

input data aggregation

Crop models are often developed and calibrated at the field scale. Using model input data (climate, soil, management, etc.) of coarser resolution may lead to biased results. These so-called aggregation effects are investigated by the MACSUR scaling group. The relevance of aggregating for instance climate, soil or management is estimated for a range of crops, crop models, production situations and variables (yield, biomass, LAI, soil moisture, evapotranspiration, phenology, NPP, NEE, soil organic carbon, N2O-emmissions, NO3-leaching, etc.). These input data domains are adressed at different stages of MACSUR:

  • phase 1: investigates the impact of using spatially aggregated climate data as input for crop models
  • phase 2: investigate the impact of using spatially aggregated climate and soil data as input for crop models
  • phase 3: investigates the interaction of spatial input data aggregation and management rules


Further reading

Variability of aggregation effects of climate data on regional yield simulation by crop models

authors: Holger Hofmann, Gang Zhao, Lenny van Bussel, Andreas Enders, Xenia Specka, Carmen Sosa, Jagadeesh Yeluripati, Fulu Tao, Julie Constantin, Helene Raynal, Edmar Teixeira, Balázs Grosz, Luca Doro, Zhigan Zhao, Enli Wang,  Claas Nendel, Kurt-Christian Kersebaum, Edwin Haas, Ralf Kiese, Steffen Klatt, Henrik Eckersten,  Eline Vanuytrecht, Matthias Kuhnert, Elisabet Lewan,  Reimund Rötter, Pier Paolo Roggero, Daniel Wallach, Davide Cammarano, Senthold Asseng, Gunther Krauss, Stefan Siebert, Thomas Gaiser, Frank Ewert.

abstract: Field-scale crop models can be applied at different spatial resolutions but little is known on the response of models to input data aggregation and why these responses can differ across models.  We therefore evaluated 13 crop models which were supplied with climate input data of different  spatial aggregation. Spatial resolution of climate input data ranged from 1 to 100 km raster and was  used with two crops (winter wheat and silage maize) and three production situations (potential, water  limited  and  nitrogen-water-limited  growth)  to  improve  the  understanding  of  errors  in  model  simulations related to data aggregation and possible interactions with the model structure. The most  important climate variables identified to determining the model-specific input data aggregation on  simulated yields (aggregation effects) were mainly related to changes in radiation (winter wheat) and  temperature (silage maize). Additionally, aggregation effects were systematic since models differed in  the systematic fraction of the aggregation effect, regardless of the extent of the effect (20 to 66 % as  compared to 1.7 % for random effects). Climate input data aggregation changed the mean simulated  yield over the region up to 0.2 t ha -1 , whereas simulated yields from single years and models differed  considerably depending on the data aggregation. This implies that large-scale crop yield simulations
are  robust  on  average  but  can  be systematically  biased  at  higher  temporal or  spatial  resolutions,  depending on the model and its parametrization.

journal: Climate Research


Model and data scales are of concern to nearly all simulations of the environment, including cropping systems. Temporal and spatial resolutions of data and model or between linked models should match. However, data is often scarce and simulations are often conducted for estimating parameters or assessing impacts were data is not available. With an increasing number of simulation studies ranging from field to global scale, scaling models has consequently become top science pick. For an overview on scaling methods with regard to models, please see Ewert et al., 2011. Two main aspects of spatial scales are illustrated in the figure below: spatial data aggregation / resolution and the representation of spatial mean and variability, e.g. via sampling.


Set up of simulation experiments for evaluating deviations from a) high resolution simulations of b) different grid cell sizes and c) reduced numbers of sampling points. Details are described in van Bussel et al. (2015) and Hoffmann et al. (2015). With friendly permission of Ewert et al. (2015).

The scaling research presented on this site has been mainly driven in the past by the AgMIP and MACSUR scaling activities. These activities mainly conduct crop model ensemble studies in order to

  • adress the error which is made by applying field-scale crop models at larger scales
  • identify crop model specific model-data-interactions
  • identify error sources and ways of generalizing these errors
  • estimate the error which is made by choosing a specific spatial sampling strategy
  • develop spatial sampling stragies
  • identify spatial patternsa and corresponding drivers

The following wikis on input data aggregation and spatial sampling  give an overview on the current scaling activities.

If you are a contributing scientist, please register or login to get more details, read or write about the on-going work.

Frank Ewert, Martin K. van Ittersum, Thomas Heckelei, Olivier Therond, Irina Bezlepkina, Erling Andersen, 2011. Scale changes and model linking methods for integrated assessment of agri-environmental systems. Agriculture, Ecosystems & Environment 142, 6 – 17.

Frank Ewert, Lenny G.J. van Bussel, Gang Zhao, Holger Hoffmann, Thomas Gaiser, Xenia Specka, Claas Nendel, Kurt-Christian Kersebaum, Carmen Sosa, Elisabet Lewan,  Jagadeesh Yeluripati,  Matthias Kuhnert, Fulu Tao, Reimund P. Rötter, Julie Constantin, Helene Raynal, Daniel Wallach, Edmar Teixeira, Balasz Grosz, Michaela Bach, Luca Doro,  Pier P. Roggero, Zhigan Zhao, Enli Wang, Ralf Kiese, Edwin Haas, Henrik Eckersten,  Giacomo Trombi, Marco Bindi, Christian Klein, Christian Biernath, Florian Heinlein, Eckart Priesack, Davide Cammarano, Senthold Asseng, Joshua Elliott, Michael Glotter, Bruno Basso, Guillermo A. Baigorria, Consuelo C. Romero, Marco Moriondo, 2015. Uncertainties in Scaling up Crop Models for Large Area Climate-change Impact Assessments. In C. Rosenzweig and D. Hillel, editors. Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP).


This site is about crop modelling  across scales.

The aims are:

  • to give scientists the chance to communicate, contribute and inform
  • to inform about on-going activities in the varying projects
  • to document simulations and data

This site is an initiative of the AgMIP and MACSUR scaling activities.