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Rötter, R.P.; Palosuo, T.; Kersebaum, K.-C.; Angulo, C.; Bindi, M.; Ewert, F.; Ferrise, R.; Hlavinka, P.; Moriondo, M.; Olesen, J.E.; Takáč, J.; Trnka, M. |
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Title |
Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models |
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Journal Article |
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Year |
2012 |
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Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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133 |
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23-36 |
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Climate; Crop growth simulation; Model comparison; Spring barley; Yield variability; Uncertainty; change impacts; nitrogen dynamics; high-temperature; soil-moisture; elevated co2; ceres-wheat; data set; growth; drought; sensitivity |
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Abstract  |
► We compared nine crop simulation models for spring barley at seven sites in Europe. ► Applying crop models with restricted calibration leads to high uncertainties. ► Multi-crop model mean yield estimates were in good agreement with observations. ► The degree of uncertainty for simulated grain yield of barley was similar to winter wheat. ► We need more suitable data enabling us to verify different processes in the models. In this study, the performance of nine widely used and accessible crop growth simulation models (APES-ACE, CROPSYST, DAISY, DSSAT-CERES, FASSET, HERMES, MONICA, STICS and WOFOST) was compared during 44 growing seasons of spring barley (Hordeum vulgare L) at seven sites in Northern and Central Europe. The aims of this model comparison were to examine how different process-based crop models perform at multiple sites across Europe when applied with minimal information for model calibration of spring barley at field scale, whether individual models perform better than the multi-model mean, and what the uncertainty ranges are in simulated grain yields. The reasons for differences among the models and how results for barley compare to winter wheat are discussed. Regarding yield estimation, best performing based on the root mean square error (RMSE) were models HERMES, MONICA and WOFOST with lowest values of 1124, 1282 and 1325 (kg ha(-1)), respectively. Applying the index of agreement (IA), models WOFOST, DAISY and HERMES scored best having highest values (0.632, 0.631 and 0.585, respectively). Most models systematically underestimated yields, whereby CROPSYST showed the highest deviation as indicated by the mean bias error (MBE) (-1159 kg ha(-1)). While the wide range of simulated yields across all sites and years shows the high uncertainties in model estimates with only restricted calibration, mean predictions from the nine models agreed well with observations. Results of this paper also show that models that were more accurate in predicting phenology were not necessarily the ones better estimating grain yields. Total above-ground biomass estimates often did not follow the patterns of grain yield estimates and, thus, harvest indices were also different. Estimates of soil moisture dynamics varied greatly. In comparison, even though the growing cycle for winter wheat is several months longer than for spring barley, using RMSE and IA as indicators, models performed slightly, but not significantly, better in predicting wheat yields. Errors in reproducing crop phenology were similar, which in conjunction with the shorter growth cycle of barley has higher effects on accuracy in yield prediction. |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4592 |
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Hlavinka, P.; Olesen, J.E.; Kersebaum, K.-C.; Trnka, M.; Pohankova, E.; Stella, T.; Ferrise, R.; Moriondo, M.; Hoogenbom, G.; Shelia, V.; Nendel, C.; Wimmerová, M.; Topaj, A.; Medvedev, S.; Ventrella, D.; Ruiz-Ramos, M.; Rodríguez Sánchez, A.; Takáč, J.; Patil, R.H.; Öztürk, I.; Hoffmann, M.; Gobin, A.; Rötter, R.P. |
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Modelling long term effects of cropping and managements systems on soil organic matter, C/N dynamics and crop growth |
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Report |
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2017 |
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FACCE MACSUR Reports |
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10 |
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C1.3-D |
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While simulation of cropping systems over a few years might reflect well the short term effects of management and cultivation, long term effects on soil properties and their consequences for crop growth and matter fluxes are not captured. Especially the effect on soil carbon sequestration/depletion is addressed by this task. Simulations of an ensemble of crop models are performed as transient runs over a period of 120 year using observed weather from three stations in Czech Republic (1961-2010) and transient long time climate change scenarios (2011-2080) from five GCM of the CMIP5 ensemble to assess the effect of different cropping and management systems on carbon sequestration, matter fluxes and crop production in an integrative way. Two cropping systems are regarded comprising two times winter wheat, silage maize, spring barley and oilseed rape. Crop rotations differ regarding their organic input from crop residues, nitrogen fertilization and implementation of catch crops. Models are applied for two soil types with different water holding capacity. Cultivation and nutrient management is adapted using management rules related to weather and soil conditions. Data of phenology and crop yield from the region of the regarded crops were provided to calibrate the models for crops of the rotations. Twelve models were calibrated in this first step. For the transient long term runs results of four models were submitted so far. Outputs are crop yields, nitrogen uptake, soil water and mineral nitrogen contents, as well as water and nitrogen fluxes to the atmosphere and groundwater. Changes in the carbon stocks and the consequences for nitrogen mineralisation, N fertilization and emissions also considered. |
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MA @ admin @ |
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4976 |
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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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Zhao, G.; Hoffmann, H.; Yeluripati, J.; Xenia, S.; Nendel, C.; Coucheney, E.; Kuhnert, M.; Tao, F.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Kiese, R.; Eckersten, H.; Haas, E.; Cammarano, D.; Kassie, B.; Moriondo, M.; Trombi, G.; Bindi, M.; Biernath, C.; Heinlein, F.; Klein, C.; Priesack, E.; Lewan, E.; Kersebaum, K.-C.; Rötter, R.; Roggero, P.P.; Wallach, D.; Asseng, S.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops |
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2016 |
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Environmental Modelling & Software |
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Env. Model. Softw. |
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80 |
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100-112 |
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Crop model; Stratified random sampling; Simple random sampling; Clustering; Up-scaling; Model comparison; Precision gain; species distribution models; systems simulation; weather data; large-scale; design; soil; optimization; growth; apsim; autocorrelation |
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We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known. |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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4724 |
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Webber, H.; Ewert, F.; Olesen, J.E.; Müller, C.; Fronzek, S.; Ruane, A.C.; Bourgault, M.; Martre, P.; Ababaei, B.; Bindi, M.; Ferrise, R.; Finger, R.; Fodor, N.; Gabaldón-Leal, C.; Gaiser, T.; Jabloun, M.; Kersebaum, K.-C.; Lizaso, J.I.; Lorite, I.J.; Manceau, L.; Moriondo, M.; Nendel, C.; Rodríguez, A.; Ruiz-Ramos, M.; Semenov, M.A.; Siebert, S.; Stella, T.; Stratonovitch, P.; Trombi, G.; Wallach, D. |
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Diverging importance of drought stress for maize and winter wheat in Europe |
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2018 |
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Nature Communications |
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Nat. Comm. |
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9 |
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4249 |
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Climate-Change Impacts; Air CO2 Enrichment; Food Security; Heat-Stress; Nitrogen Dynamics; Semiarid Environments; Canopy Temperature; Simulation-Model; Crop Production; Elevated CO2 |
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Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984-2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years. |
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2018-10-25 |
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2041-1723 |
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CropM, ft_macsur |
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MA @ admin @ |
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5211 |
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