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Hoffmann, H.; Zhao, G.; van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.; Wang, E.; Nendel, C.; Kersebaum, K.C.; Haas, E.; Kiese, R.; Klatt, S.; Eckersten, H.; Vanuytrecht, E.; Kuhnert, M.; Lewan, E.; Rötter, R.; Roggero, P.P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Title |
Variability of effects of spatial climate data aggregation on regional yield simulation by crop models |
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Journal Article |
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Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
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Volume  |
65 |
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Pages |
53-69 |
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Keywords |
spatial aggregation effects; crop simulation model; input data; scaling; variability; yield simulation; model comparison; input data aggregation; systems simulation; nitrogen dynamics; data resolution; n2o emissions; winter-wheat; scale; water; impact; apsim |
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Abstract |
Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize) and 3 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 in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by 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 against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization. |
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0936-577x 1616-1572 |
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CropM, ft_macsur |
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MA @ admin @ |
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4694 |
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Hoffmann, H.; Zhao, G.; Asseng, S.; Bindi, M.; Biernath, C.; Constantin, J.; Coucheney, E.; Dechow, R.; Doro, L.; Eckersten, H.; Gaiser, T.; Grosz, B.; Heinlein, F.; Kassie, B.T.; Kersebaum, K.-C.; Klein, C.; Kuhnert, M.; Lewan, E.; Moriondo, M.; Nendel, C.; Priesack, E.; Raynal, H.; Roggero, P.P.; Rötter, R.P.; Siebert, S.; Specka, X.; Tao, F.; Teixeira, E.; Trombi, G.; Wallach, D.; Weihermüller, L.; Yeluripati, J.; Ewert, F. |
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Title |
Impact of spatial soil and climate input data aggregation on regional yield simulations |
Type |
Journal Article |
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Year |
2016 |
Publication |
PLoS One |
Abbreviated Journal |
PLoS One |
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Volume |
11 |
Issue |
4 |
Pages |
e0151782 |
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Keywords |
systems simulation; nitrogen dynamics; winter-wheat; crop models; data resolution; scale; water; variability; calibration; weather |
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We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations. |
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1932-6203 |
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CropM, ft_macsur |
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MA @ admin @ |
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4725 |
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Kersebaum, K.-C.; Wallor, E.; Ventrella, D.; Cammarano, D.; Choucheney, E.; Ewert, F.; Ferrise, R.; Gaiser, T.; Garofalo, P.; Giglio, L.; Giola, P.; Hoffmann, M.; Laan, M.; Lewan, E.; Maharjan, G.R.; Moriondo, M.; Mula, L.; Nendel, C.; Pohankova, E.; Roggero, P.P.; Trnka, M.; Trombi, G. |
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Title |
Comparison of site sensitivity of crop models using spatially variable field data from Precision Agriculture |
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Report |
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2017 |
Publication |
FACCE MACSUR Reports |
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10 |
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C1.1-D2 |
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Site conditions and soil properties have a strong influence on impacts of climate change on crop production. Vulnerability of crop production to changing climate conditions is highly determined by the ability of the site to buffer periods of adverse climatic situations like water scarcity or excessive rainfall. Therefore, the capability of models to reflect crop responses and water and nutrient dynamics under different site conditions is essential to assess climate impact even on a regional scale. To test and improve sensitivity of models to various site properties such as soil variability and hydrological boundary conditions, spatial variable data sets from precision farming of two fields in Germany and Italy were provided to modellers. For the German 20 ha field soil and management data for 60 grid points for 3 years (2 years wheat, 1 year triticale) were provided. For the Italian field (12 ha) information for 100 grid points were available for three growing seasons of durum wheat. Modellers were asked to run their models using a) the model specific procedure to estimate soil hydraulic properties from texture using their standard procedure and use in step b) fixed values for field capacity and wilting point derived from soil taxonomy. Only the phenology and crop yield of one grid point provided for a basic calibration. In step c) information for all grid points of the first year (yield, soil water and mineral N content for Germany, yield, biomass and LAI for Italy) were provided. First results of five out of twelve participating models are compared against measured state variables analysing their site specific response and consistency across crop and soil variables. (Main text to be published in a peer-reviewed journal) |
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CropM |
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MA @ admin @ |
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4951 |
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Author |
Kuhnert, M.; Yeluripati, J.; Smith, P.; Hoffmann, H.; Constantin, J.; Coucheney, E.; Dechow, R.; Eckersten, H.; Gaiser, T.; Grosz, B.; Haas, E.; Kersebaum, K.-C.; Kiese, R.; Klatt, S.; Lewan, E.; Nendel, C.; Raynal, H.; Sosa, C.; Specka, X.; Teixeira, E.; Wang, E.; Weihermüller, L.; Zhao, G.; Zhao, Z.; Ewert, F. |
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Title |
Impacts of soil and weather data aggregation in spatial modelling of net primary production of croplands |
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Conference Article |
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Year |
2016 |
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CropM; |
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Belrin (Germany) |
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International Crop Modelling Symposium iCropM 2016, 2016-03-15 to 2016-03-17, Belrin |
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MA @ admin @ |
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2579 |
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Kuhnert, M.; Yeluripati, J.; Smith, P.; Hoffmann, H.; van Oijen, M.; Zhao, G.; Constantin, J.; Raynal, H.; Coucheney, E.; Lewan, E.; Eckersten, H.; Specka, X.; Sosa, C.; Kersebaum, K.-C.; Nendel, C.; Grosz, B.; Dechow, R.; Kiese, R.; Haas, E.; Klatt, S.; Teixeira, E.; Zhao, Z.; Wang, E.; Weihermüller, L.; Gaiser, T.; Ewert, F. |
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Title |
Impact of climate aggregation over different scales on regional NPP modelling |
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Conference Article |
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2016 |
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CropM; |
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Vienna (Austria) |
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European Geosciences Union General Assembly 2016, 2016-04-17 to 2016-04-22, Vienna |
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MA @ admin @ |
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2578 |
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