| Records |
| Author |
Graux, A.-I.; Bellocchi, G.; Lardy, R.; Soussana, J.-F. |
| Title |
Ensemble modelling of climate change risks and opportunities for managed grasslands in France |
Type |
Journal Article |
| Year |
2013 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
| Volume |
170 |
Issue |
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Pages |
114-131 |
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Edition |
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| ISSN |
0168-1923 |
ISBN |
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| Notes |
CropM, LiveM, ftnotmacsur, IPCC-AR5 |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4926 |
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| Author |
Rosenzweig, C.; Jones, J.W.; Hatfield, J.L.; Ruane, A.C.; Boote, K.J.; Thorburn, P.; Antle, J.M.; Nelson, G.C.; Porter, C.; Janssen, S.; Asseng, S.; Basso, B.; Ewert, F.; Wallach, D.; Baigorria, G.; Winter, J.M. |
| Title |
The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies |
Type |
Journal Article |
| Year |
2013 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
| Volume |
170 |
Issue |
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Pages |
166-182 |
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| ISSN |
0168-1923 |
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| Notes |
CropM, ftnotmacsur, IPCC-AR5 |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4927 |
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| Author |
Teixeira, E.I.; Fischer, G.; van Velthuizen, H.; Walter, C.; Ewert, F. |
| Title |
Global hot-spots of heat stress on agricultural crops due to climate change |
Type |
Journal Article |
| Year |
2013 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
| Volume |
170 |
Issue |
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Pages |
206-215 |
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| ISSN |
0168-1923 |
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Conference |
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| Notes |
CropM, ftnotmacsur, IPCC-AR5 |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4929 |
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| Author |
Watson, J.; Challinor, A. |
| Title |
The relative importance of rainfall, temperature and yield data for a regional-scale crop model |
Type |
Journal Article |
| Year |
2013 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
| Volume |
170 |
Issue |
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Pages |
47-57 |
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| ISSN |
0168-1923 |
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| Notes |
CropM, ftnotmacsur, IPCC-AR5 |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4930 |
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| Author |
Zhao, G.; Siebert, S.; Enders, A.; Rezaei, E.E.; Yan, C.; Ewert, F. |
| Title |
Demand for multi-scale weather data for regional crop modeling |
Type |
Journal Article |
| Year |
2015 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
| Volume |
200 |
Issue |
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Pages |
156-171 |
| Keywords |
multi-scale; spatial heterogeneity; spatial resolution; crop model; climate variability; climate-change scenarios; integrated assessment; large-scale; phenological development; agricultural systems; spatial-resolution; data aggregation; european-union; winter-wheat; input data |
| Abstract |
A spatial resolution needs to be determined prior to using models to simulate crop yields at a regional scale, but a dilemma exists in compromising between different demands. A fine spatial resolution demands extensive computation load for input data assembly, model runs, and output analysis. A coarse spatial resolution could result in loss of spatial detail in variability. This paper studied the impact of spatial resolution, data aggregation and spatial heterogeneity of weather data on simulations of crop yields, thus providing guidelines for choosing a proper spatial resolution for simulations of crop yields at regional scale. Using a process-based crop model SIMPLACE (LINTUL2) and daily weather data at 1 km resolution we simulated a continuous rainfed winter wheat cropping system at the national scale of Germany. Then we aggregated the weather data to four resolutions from 10 to 100 km, repeated the simulation, compared them with the 1 km results, and correlated the difference with the intra-pixel heterogeneity quantified by an ensemble of four semivariogram models. Aggregation of weather data had small effects over regions with a flat terrain located in northern Germany, but large effects over southern regions with a complex topography. The spatial distribution of yield bias at different spatial resolutions was consistent with the intra-pixel spatial heterogeneity of the terrain and a log-log linear relationship between them was established. By using this relationship we demonstrated the way to optimize the model resolution to minimize both the number of simulation runs and the expected loss of spatial detail in variability due to aggregation effects. We concluded that a high spatial resolution is desired for regions with high spatial environmental heterogeneity, and vice versa. This calls for the development of multi-scale approaches in regional and global crop modeling. The obtained results require substantiation for other production situations, crops, output variables and for different crop models. (C) 2014 Elsevier B.V. All rights reserved. |
| Address |
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| Language |
English |
Summary Language |
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Edition |
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| ISSN |
0168-1923 |
ISBN |
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Medium |
Article |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4753 |
| Permanent link to this record |
