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Author |
Nendel, C.; Kersebaum, K.C.; Mirschel, W.; Wenkel, K.O. |
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Title  |
Testing farm management options as climate change adaptation strategies using the MONICA model |
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Journal Article |
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Year |
2014 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
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Volume |
52 |
Issue |
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Pages |
47-56 |
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Keywords |
simulation model; climate change; crop management; adaptation strategies; nitrogen dynamics; carbon sequestration; crop productivity; simulation-model; change impacts; land-use; agriculture; scenarios; growth; yield |
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Abstract |
Adaptation of agriculture to climate change will be driven at the farm level in first place. The MONICA model was employed in four different modelling exercises for demonstration and testing different management options for farmers in Germany to adjust their production system. 30-Year simulations were run for the periods 1996-2025 and 2056-2085 using future climate data generated by a statistical method on the basis of measured data from 1961 to 2000 and the A1B scenario of the IPCC (2007a). Crop rotation designs that are expected to become possible in the future due to a prolonged vegetation period and at the same time shortened cereal growth period were tested for their likely success. The model suggested that a spring barley succeeding a winter barley may be successfully grown in the second half of the century, allowing for a larger yields by intensification of the cropping cycle. Growing a winter wheat after a sugar beet may lead to future problems as late sowing makes the winter wheat grow into periods prone to drought. Irrigation is projected to considerably improve and stabilise the yields of late cereals and of shallow rooting crops (maize and pea) on sandy soils in the continental climate part of Germany, but not in the humid West. Nitrogen fertiliser management needs to be adjusted to increasing or decreasing yield expectations and for decreasing soil moisture. On soils containing sufficient amounts of Moisture and soil organic matter, enhanced mineralisation is expected to compensate for a greater N demand. (C) 2012 Elsevier B.V. All rights reserved. |
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1161-0301 |
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CropM |
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MA @ admin @ |
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4631 |
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Author |
Sánchez, B.; Rasmussen, A.; Porter, J.R. |
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Title |
Temperatures and the growth and development of maize and rice: a review |
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Journal Article |
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Year |
2014 |
Publication |
Global Change Biology |
Abbreviated Journal |
Glob. Chang. Biol. |
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Volume |
20 |
Issue |
2 |
Pages |
408-417 |
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Keywords |
Climate Change; Oryza/*growth & development; Temperature; Zea mays/*growth & development; cardinal temperatures; climatic change impacts; development; growth; lethal temperatures; maize; rice |
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Abstract |
Because of global land surface warming, extreme temperature events are expected to occur more often and more intensely, affecting the growth and development of the major cereal crops in several ways, thus affecting the production component of food security. In this study, we have identified rice and maize crop responses to temperature in different, but consistent, phenological phases and development stages. A literature review and data compilation of around 140 scientific articles have determined the key temperature thresholds and response to extreme temperature effects for rice and maize, complementing an earlier study on wheat. Lethal temperatures and cardinal temperatures, together with error estimates, have been identified for phenological phases and development stages. Following the methodology of previous work, we have collected and statistically analysed temperature thresholds of the three crops for the key physiological processes such as leaf initiation, shoot growth and root growth and for the most susceptible phenological phases such as sowing to emergence, anthesis and grain filling. Our summary shows that cardinal temperatures are conservative between studies and are seemingly well defined in all three crops. Anthesis and ripening are the most sensitive temperature stages in rice as well as in wheat and maize. We call for further experimental studies of the effects of transgressing threshold temperatures so such responses can be included into crop impact and adaptation models. |
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1354-1013 |
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CropM, ftnotmacsur, IPCC-AR5 |
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MA @ admin @ |
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4693 |
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Pirttioja, N.; Carter, T.R.; Fronzek, S.; Bindi, M.; Hoffmann, H.; Palosuo, T.; Ruiz-Ramos, M.; Tao, F.; Trnka, M.; Acutis, M.; Asseng, S.; Baranowski, P.; Basso, B.; Bodin, P.; Buis, S.; Cammarano, D.; Deligios, P.; Destain, M.F.; Dumont, B.; Ewert, F.; Ferrise, R.; François, L.; Gaiser, T.; Hlavinka, P.; Jacquemin, I.; Kersebaum, K.C.; Kollas, C.; Krzyszczak, J.; Lorite, I.J.; Minet, J.; Minguez, M.I.; Montesino-San Martin, M.; Moriondo, M.; Müller, C.; Nendel, C.; Öztürk, I.; Perego, A.; Rodríguez, A.; Ruane, A.C.; Ruget, F.; Sanna, M.; Semenov, M.A.; Slawinski, C.; Stratonovitch, P.; Supit, I.; Waha, K.; Wang, E.; Wu, L.; Zhao, Z.; Rötter, R.P. |
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Title |
Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces |
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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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87-105 |
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Keywords |
climate; crop model; impact response surface; IRS; sensitivity analysis; wheat; yield; climate-change impacts; uncertainty; 21st-century; projections; simulation; growth; region |
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Abstract |
This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to +9°C) and precipitation (-50 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development. |
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0936-577x 1616-1572 |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
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4662 |
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Author |
van Bussel, L.G.J.; Ewert, F.; Zhao, G.; Hoffmann, H.; Enders, A.; Wallach, D.; Asseng, S.; Baigorria, G.A.; Basso, B.; Biernath, C.; Cammarano, D.; Chryssanthacopoulos, J.; Constantin, J.; Elliott, J.; Glotter, M.; Heinlein, F.; Kersebaum, K.-C.; Klein, C.; Nendel, C.; Priesack, E.; Raynal, H.; Romero, C.C.; Rötter, R.P.; Specka, X.; Tao, F. |
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Title |
Spatial sampling of weather data for regional crop yield simulations |
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Journal Article |
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Year |
2016 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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Volume |
220 |
Issue |
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Pages |
101-115 |
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Keywords |
Regional crop simulations; Winter wheat; Upscaling; Stratified sampling; Yield estimates; climate-change scenarios; water availability; growth simulation; potential impact; food-production; winter-wheat; model; resolution; systems; soil |
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Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio-temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982-2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50,100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated. The results showed differences in simulated yields among crop models but all models reproduced well the pattern of the stratification. Importantly, the regional mean of simulated yields based on full coverage could already be reproduced by a small sample of 10 points. This was also true for reproducing the temporal variability in simulated yields but more sampling points (about 100) were required to accurately reproduce spatial yield variability. The number of sampling points can be smaller when a stratified sampling is applied as compared to a random sampling. However, differences between crop models were observed including some interaction between the effect of sampling on simulated yields and the model used. We concluded that stratified sampling can considerably reduce the number of required simulations. But, differences between crop models must be considered as the choice for a specific model can have larger effects on simulated yields than the sampling strategy. Assessing the impact of sampling soil and crop management data for regional simulations of crop yields is still needed. |
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0168-1923 |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4673 |
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Author |
Rötter, R.P.; Palosuo, T.; Kersebaum, K.C.; Angulo, C.; Bindi, M.; Ewert, F.; Ferrise, R.; Hlavinka, P.; Moriondo, M.; Nendel, C.; Olesen, J.E.; Patil, R.H.; Ruget, F.; Takác, J.; Trnka, M. |
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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 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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Volume |
133 |
Issue |
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Pages |
23-36 |
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Keywords |
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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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. (C) 2012 Elsevier B.V. All rights reserved. |
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Address |
2016-10-31 |
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English |
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ISSN |
0378-4290 |
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CropM, ftnotmacsur |
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Call Number |
MA @ admin @ |
Serial |
4803 |
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