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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 |
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2016 |
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PLoS One |
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PLoS One |
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11 |
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4 |
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e0151782 |
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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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Hoffmann, H.; Gang, Z.; Van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.; Constantin, J.; Casellas, E.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.; Nendel, C.; Kiese, R.; Raynal, H.; Eckersten, H.; Klatt, S.; Edwin, H.; Wang, E.; Kuhnert, M.; Lewan, E.; Bach, M.; Kersebaum, K.-C.; Rötter, R.; Roggero, P.P.; Wallach, D.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Title |
Sensitivity of crop models to spatial aggregation of soil and climate data |
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Conference Article |
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2014 |
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Annual conference of the German/Austrian Agronomical Society & Max-Eyth-Society IS - |
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MA @ admin @ |
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5041 |
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Hoffmann, H.; Ewert, F. |
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Review on scaling methods for crop models |
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Report |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-C3.1 |
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Agricultural systems cover a range of organisational levels and spatial and temporal scales. To capture multi-scale problems of sustainable management in agricultural systems, Integrated assessment modelling (IAM) including crop models is often applied which require methods of scale changes (scaling methods). Scaling methods, however, are often not well understood and are therefore sources of uncertainty in models. The present report summarizes scaling methods as developed and applied in recent years (e.g. in SEAMLESS-IF and MACSUR) in a classification scheme based on Ewert et al. (2011, 2006). Scale changes refer to different spatial, temporal and functional scales with changes in extent, resolution, and coverage rate. Accordingly, there are a number of different scaling methods that can include data extrapolation, aggregation and disaggregation, sampling and nested simulation. Comparative quantitative analysis of alternative scaling methods are currently under way and covered by other reports in MACSUR and several publications (e.g. Ewert et al., 2014; Hoffmann et al., 2015; Zhao et al., 2015). The following classification of scaling methods assists to structure such analysis. Improved integration of scaling methods in IAM may help to overcome modelling limitations that are related to high data demand, complexity of models and scaling methods considered. No Label |
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MA @ admin @ |
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2094 |
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Hoffmann, H. |
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Title |
Effects of soil and climate input data aggregation on modelling regional crop yields |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-22 |
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Climate and soil data at coarse resolution are often used as input for crop models in order to simulate crop yields at larger scales, e.g. at regional or national level, potentially leading to biased yield estimates. While the response to data resolution differs between crop models, it is unknown how the spatial aggregation of different types of input data interacts and contributes to this so-called aggregation effect. An ensemble of crop models was run with soil and climate input data at different spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. For this purpose, climate time series were averaged spatially and soil data was aggregated by selecting the dominant soil type with a representative soil profile based on a soil map at the scale of 1:50,000. Yields of winter wheat and silage maize were simulated under potential, water-limited and water-nitrogen-limited production conditions. Crop yields from soil and climate aggregation were evaluated separately.Mean of crop yields of the region and over the simulation period were reasonably reproduced by most models regardless of input data resolution, either using aggregated soil or climate as input. However, larger aggregation effects were observed at higher temporal resolution (e.g. annual yields). Models revealed similar spatial patterns in yield. Being distinct for soil and climate aggregation, these patterns indicate a larger impact of soil aggregation on the spatial distribution of simulated crop yield for this region. Additionally, models differed considerably in their susceptibility to input data aggregation. The results reveal the importance of model ensemble assessments and the relevance of data aggregation when short simulation periods are considered. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2137 |
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Haas, E.; R. Kiese; Klatt, S.; Hoffmann, H.; Zhao, G.; Ewert, F.; J. Constantin; Raynal, H.; Coucheney, E.; Lewan, E.; Sosa, C.; Dechow, R.; Grosz, B.; Eckersten, H.; Gaiser, T.; Kuhnert, M.; Smith, P.; Kersebaum, K.C.; C. Nendel; Specka, X.; Wang, E.; Zhao, Z.; Weihermüller, L. |
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Responses of soil nitrous oxide emissions and nitrate leaching on climate, soil and management input data aggregation: a biogeochemistry model ensemble study |
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2016 |
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Berlin (Germany) |
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poster |
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International Crop Modelling Symposium iCROPM 2016, 2016-05-15 to 2016-05-17, Berlin, Germany |
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MA @ admin @ |
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4907 |
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