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Rötter, P.; Palosuo, T.; Semenov, M.; Ruiz-Ramos, M.; Tao, F.; Fronzek, S.; Pirttioja, K.; Bindi, M.; Carter, R.; Hoffmann, H.; Höhn, J.; Kersebaum, C.; Trnka, M. |
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Title |
Designing new cereal cultivars as an adaptation measure using crop model ensembles |
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Conference Article |
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2014 |
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CropM |
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MACSUR CropM International Symposium and Workshop: Modelling climate change impacts on crop production for food security, Oslo, Norway, 2014-02-10 to 2014-02-12 |
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MA @ admin @ |
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2768 |
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Kollas, C.; Kersebaum, C.; Bindi, M.; Wu, L.; Sharif, B.; Öztürk, I.; Trnka, M.; Hlavinka, P.; Nendel, C.; Palosuo, T.; Müller, C.; Waha, K.; Herrera, C.; Olesen, E.; Eitzinger, J.; Roggero, P.-P.; Conradt, T.; Martre, P.; Ferrise, R.; Moriondo, M.; Ramos, M.; Ventrella, D.; Rötter, P.; Wegehenkel, M.; Eckersten, H.; Torres, I.; Hernandez, C.; Launay, M.; Witt, A.; Hoffmann, H. |
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Title |
Improving yield predictions by crop rotation modelling? a multi-model comparison |
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Conference Article |
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2014 |
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CropM |
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MACSUR CropM International Symposium and Workshop: Modelling climate change impacts on crop production for food security, Oslo, Norway, 2014-02-10 to 2014-02-12 |
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MA @ admin @ |
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2560 |
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Kersebaum, C.; Kollas, C.; Bindi, M.; Nendel, C.; Ferrise, R.; Moriondo, M.; Olesen, J.E.; Sharif, B.; Öztürk, I.; Hoffmann, H.; Launay, M.; Ripoche, D.; Ruget, F.; Bertuzzi, P.; Cortazar, I.G.D.; Beaudoin, N.; Armas-Herrera, C.; Mary, B.; Müller, C.; Waha, K.; Ventrella, D.; Palosuo, T.; Rötter, R.; Trnka, M.; Hlavinka, P.; Wu, L.; Wegehenkel, M.; Mirschel, W.; Conradt, T.; Wechsung, F.; Weigel, H.-J.; Manderscheid, R.; Eitzinger, J. |
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Title |
Modelling complex crop rotations and management across sites in Europe with an ensemble of models |
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Conference Article |
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2014 |
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CropM |
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ASA-CSSA-SSSA Int. Annual Meeting, Long Beach, CA, 2-5 November 2014, 2014-11-02 to 2014-11-05 |
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MA @ admin @ |
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2526 |
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Zhao, G.; Hoffmann, H.; van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.L.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.G.; Nendel, C.; Kiese, R.; Eckersten, H.; Haas, E.; Vanuytrecht, E.; Wang, E.; Kuhnert, M.; Trombi, G.; Moriondo, M.; Bindi, M.; Lewan, E.; Bach, M.; Kersebaum, K.C.; Rotter, R.; Roggero, P.P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Title |
Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables |
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Journal Article |
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Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
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65 |
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141-157 |
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Keywords |
crop model; model comparison; spatial resolution; data aggregation; spatial heterogeneity; scaling; climate-change scenarios; sub-saharan africa; winter-wheat; spatial-resolution; yield response; input data; systems simulation; large-scale; soil data; part i |
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Abstract |
We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area. |
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English |
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0936-577x |
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Notes |
CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4754 |
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Author |
Murat, M.; Malinowska, I.; Hoffmann, H.; Baranowski, P. |
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Title |
Statistical modelling of agrometeorological time series by exponential smoothing |
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Journal Article |
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Year |
2016 |
Publication |
International Agrophysics |
Abbreviated Journal |
International Agrophysics |
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30 |
Issue |
1 |
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57-65 |
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Keywords |
exponential smoothing; meteorological time series; statistical forecasting; daily temperature records; weighted moving averages; climate-change; prediction; forecasts; state; weather |
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Abstract |
Meteorological time series are used in modelling agrophysical processes of the soil-plant-atmosphere system which determine plant growth and yield. Additionally, longterm meteorological series are used in climate change scenarios. Such studies often require forecasting or projection of meteorological variables, eg the projection of occurrence of the extreme events. The aim of the article was to determine the most suitable exponential smoothing models to generate forecast using data on air temperature, wind speed, and precipitation time series in Jokioinen (Finland), Dikopshof (Germany), Lleida (Spain), and Lublin (Poland). These series exhibit regular additive seasonality or non-seasonality without any trend, which is confirmed by their autocorrelation functions and partial autocorrelation functions. The most suitable models were indicated by the smallest mean absolute error and the smallest root mean squared error. |
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0236-8722 |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4728 |
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