| Records |
| Author |
Höglind, M.; Persson, T.; van Oijen, M. |
Title  |
Breeding forage grasses: simulation modelling as a tool to identify important cultivar characteristics for winter survival and yield under future climate conditions in Norway |
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Conference Article |
| Year |
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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2487 |
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| Author |
Höglind, M.; Persson, T.; van Oijen, M. |
| Title |
Breeding forage grasses: simulation modelling as a tool to identify important cultivar characteristics for winter survival and yield under future climate conditions in Norway |
Type |
Conference Article |
| Year |
2014 |
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CropM |
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Conference on Genetic Resources for Food and Agriculture in a Changing Climate, Lillehammer, Norway., 2014-01-27 to 2014-01-29 |
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no |
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MA @ admin @ |
Serial |
2488 |
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| Author |
Van Oijen, M.; Cameron, D.; Levy, P.E.; Preston, R. |
| Title |
Correcting errors from spatial upscaling of nonlinear greenhouse gas flux models |
Type |
Journal Article |
| Year |
2017 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Environmental Modelling & Software |
| Volume |
94 |
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Pages |
157-165 |
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1364-8152 |
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article |
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CropM, ft_macsur |
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no |
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MA @ admin @ |
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4945 |
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| Author |
Höglind, M.; Persson, T.; van Oijen, M. |
| Title |
Identifying target traits for forage grass breeding under a changing climate in Norway using the BASGRA model |
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Conference Article |
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2013 |
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CropM |
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Proceedings of the 22nd International Grasslands Congress, Sydney, Australia, 2013-09-15 to 2013-09-19 |
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MA @ admin @ |
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2483 |
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Kuhnert, M.; Yeluripati, J.; Smith, P.; Hoffmann, H.; van Oijen, M.; 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.; Ogle, S.; Ewert, F. |
| Title |
Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands |
Type |
Journal Article |
| Year |
2016 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
| Volume |
88 |
Issue |
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Pages |
41-52 |
| Keywords |
Net primary production; NPP; Scaling; Extreme events; Crop modelling; Climate Data; aggregation |
| Abstract |
For spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km2), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1–100 km grid cells show changes of 0.4–7.8% and 0.0–4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9–4.2 g C m−2 d−1 and 2.7–6.1 g C m−2 d−1for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data. |
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2016-09-13 |
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English |
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Newsletter July |
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1161-0301 |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4775 |
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