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Lizaso, J.I.; Ruiz-Rarnos, M.; Rodriguez, L.; Gabaldon-Leal, C.; Oliveira, J.A.; Lorite, I.J.; Sanchez, D.; Garcia, E.; Rodriguez, A. |
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Title |
Impact of high temperatures in maize: Phenology and yield components |
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Journal Article |
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Year |
2018 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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Volume |
216 |
Issue |
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Pages |
129-140 |
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Keywords |
Heat stress; Maize; Kernel number; Anthesis, Beta function; Vapor-Pressure Deficit; Heat-Stress; Transpiration Response; Pollen; Viability; Leaf Appearance; Climate-Change; Kernel Number; Grain-Yield; Growth; Plants |
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Heat stress is a main threat to current and future global maize production. Adaptation of maize to future warmer conditions requires improving our understanding of crop responses to elevated temperatures. For this purpose, the same short-season (FAO 300) maize hybrid PR37N01 was grown over three years of field experiments on three contrasting Spanish locations in terms of temperature regime. The information complemented three years of greenhouse experiments with the same hybrid, applying heat treatments at various critical moments of the crop cycle. Crop phenology, growth, grain yield, and yield components were monitored. An optimized beta function improved the calculation of thermal time compared to the linear-cutoff estimator with base and optimum temperatures of 8 and 34 degrees C, respectively. Our results showed that warmer temperatures accelerate development rate resulting in shorter vegetative and reproductive phases (ca. 30 days for the whole cycle). Heat stress did not cause silking delay in relation to anthesis (extended anthesis-silking interval), at least in the range of temperatures (maximum temperature up to 42.9 degrees C in the field and up to 52.5 degrees C in the greenhouse) considered in this study. Our results indicated that maize grain yield is reduced under heat stress mainly via pollen viability that in turn determines kernel number, although a smaller but significant effect of the female component has been also detected. |
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2018-02-19 |
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0378-4290 |
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CropM, ft_macsur |
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MA @ admin @ |
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5190 |
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Maiorano, A.; Martre, P.; Asseng, S.; Ewert, F.; Müller, C.; Rötter, R.P.; Ruane, A.C.; Semenov, M.A.; Wallach, D.; Wang, E.; Alderman, P.D.; Kassie, B.T.; Biernath, C.; Basso, B.; Cammarano, D.; Challinor, A.J.; Doltra, J.; Dumont, B.; Rezaei, E.E.; Gayler, S.; Kersebaum, K.C.; Kimball, B.A.; Koehler, A.-K.; Liu, B.; O’Leary, G.J.; Olesen, J.E.; Ottman, M.J.; Priesack, E.; Reynolds, M.; Stratonovitch, P.; Streck, T.; Thorburn, P.J.; Waha, K.; Wall, G.W.; White, J.W.; Zhao, Z.; Zhu, Y. |
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Title |
Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles |
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Journal Article |
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Year |
2016 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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Volume |
202 |
Issue |
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Pages |
5-20 |
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Keywords |
Impact uncertainty; High temperature; Model improvement; Multi-model ensemble; Wheat crop model |
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To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT world-wide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures >24 °C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively. |
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2016-09-13 |
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Newsletter July 2016 |
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0378-4290 |
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CropM |
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CropMwp;wos; ft=macsur; wsnot_yet; |
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MA @ admin @ |
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4776 |
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Mäkinen, H.; Kaseva, J.; Trnka, M.; Balek, J.; Kersebaum, K.C.; Nendel, C.; Gobin, A.; Olesen, J.E.; Bindi, M.; Ferrise, R.; Moriondo, M.; Rodriguez, A.; Ruiz-Ramos, M.; Takáč, J.; Bezák, P.; Ventrella, D.; Ruget, F.; Capellades, G.; Kahiluoto, H. |
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Title |
Sensitivity of European wheat to extreme weather |
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Journal Article |
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Year |
2018 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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Volume |
222 |
Issue |
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Pages |
209-217 |
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Keywords |
European wheat; Cultivar; Weather; Extreme; Climate change; Yield response; High-Temperature; Heat-Stress; Use Efficiency; Growth-Stages; Winter-Wheat; Yield; Crop; Barley; Tolerance |
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The frequency and intensity of extreme weather is increasing concomitant with changes in the global climate change. Although wheat is the most important food crop in Europe, there is currently no comprehensive empirical information available regarding the sensitivity of European wheat to extreme weather. In this study, we assessed the sensitivity of European wheat yields to extreme weather related to phenology (sowing, heading) in cultivar trials across Europe (latitudes 37.21 degrees to 61.34 degrees and longitudes- 6.02 degrees to 26.24 degrees) during the period 1991-2014. All the observed agro-climatic extremes (>= 31 degrees C, >= 35 degrees C, or drought around heading; >= 35 degrees C from heading to maturity; excessive rainfall; heavy rainfall and low global radiation) led to marked yield penalties in a selected set of European cultivars, whereas few cultivars were found to with no yield penalty in such conditions. There were no European wheat cultivars that responded positively (+ 10%) to drought after sowing, or frost during winter (- 15 degrees C and – 20 degrees C). Positive responses to extremes were often shown by cultivars associated with specific regions, such as good performance under high temperatures by southern-origin cultivars. Consequently, a major future breeding challenge will be to evaluate the potential of combining such cultivar properties with other properties required under different growing conditions with, for example, long day conditions at higher latitudes, when the intensity and frequency of extremes rapidly increase. |
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2018-06-05 |
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0378-4290 |
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CropM, ft_macsur |
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MA @ admin @ |
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5200 |
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Nendel, C.; Wieland, R.; Mirschel, W.; Specka, X.; Guddat, C.; Kersebaum, K.C. |
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Title |
Simulating regional winter wheat yields using input data of different spatial resolution |
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Journal Article |
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Year |
2013 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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Volume |
145 |
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67-77 |
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Keywords |
monica; agro-ecosystem model; dynamic modelling; scaling; input data; climate-change; crop yield; nitrogen dynamics; food security; mineral nitrogen; soil-moisture; scaling-up; model; maize; water |
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The success of using agro-ecosystem models for the high-resolution simulation of agricultural yields for larger areas is often hampered by a lack of input data. We investigated the effect of different spatially resolved soil and weather data used as input for the MONICA model on its ability to reproduce winter wheat yields in the Federal State of Thuringia, Germany (16,172 km(2)). The combination of one representative soil and one weather station was insufficient to reproduce the observed mean yield of 6.66 +/- 0.87 t ha(-1) for the federal state. Use of a 100 m x 100 m grid of soil and relief information combined with just one representative weather station yielded a good estimator (7.01 +/- 1.47 t ha(-1)). The soil and relief data grid used in combination with weather information from 14 weather stations in a nearest neighbour approach produced even better results (6.60 +/- 1.37 t ha(-1)); the same grid used with 39 additional rain gauges and an interpolation algorithm that included an altitude correction of temperature data slightly overpredicted the observed mean (7.36 +/- 1.17 t ha(-1)). It was concluded that the apparent success of the first two high-resolution approaches over the latter was based on two effects that cancelled each other out: the calibration of MONICA to match high-yield experimental data and the growth-defining and -limiting effect of weather data that is not representative for large parts of the region. At the county and farm level the MONICA model failed to reproduce the 1992-2010 time series of yields, which is partly explained by the fact that many growth-reducing factors were not considered in the model. (C) 2013 Elsevier B.V. All rights reserved. |
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0378-4290 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4498 |
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Persson, T.; Höglind, M.; Gustavsson, A.-M.; Halling, M.; Jauhiainen, L.; Niemeläinen, O.; Thorvaldsson, G.; Virkajärvi, P. |
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Title |
Evaluation of the LINGRA timothy model under Nordic conditions |
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Journal Article |
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Year |
2014 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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161 |
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87-97 |
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Keywords |
crop model; forage grass; perennial ley; simulation model; nutritive-value; climate-change; systems simulation; growth; dynamics; crop; performance; regrowth; calibration; pastures |
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Simulation models are frequently applied to determine the production potential of forage grasses under various scenarios, including climate change. Thorough calibrations and evaluations of forage grass models can help improve their applicability. This study evaluated the ability of the Light Interception and Utilization Simulator-GRAss (LINGRA) model to predict biomass yield of timothy (Phleum pratense L. cv. Grindstad) in the Nordic countries. Variety trial data for the first and second year after establishment were obtained for seven locations: Jokioinen, Finland (60 degrees 48 ‘ N; 23 degrees 29 ‘ E), Maaninka, Finland (63 degrees 09 ‘ N; 27 degrees 18 ‘ E), Korpa, Iceland (64 degrees 09 ‘ N; 21 degrees 45 ‘ W), Srheim, Norway (58 degrees 41 ‘ N; 5 degrees 39 ‘ E), Lillerud, Sweden (59 degrees 24’ N; 13 degrees 16 ‘ E), Ostersund, Sweden (63 degrees 15 ‘ N; 14 degrees 34 ‘ E) and Ulna Sweden (63 degrees 49 ‘ N; 20 degrees 13 ‘ E) from 1992 to 2012. Two calibrations of the LINGRA model were carried out using Bayesian techniques. In the first of these (SRrheim calibration), data on biomass yield and underlying variables obtained from independent field trials at Srheim were used. In the second (Nordic calibration), biomass data from the other locations were used as well. The model was validated against the remaining set of biomass yields from all locations not included in the Nordic calibration. The observed total seasonal yield the first and second year after establishment was 913 and 991 g DM m(-2) respectively on average across the locations. The corresponding average simulated yield after the Srheim calibration was 1044 (root mean square error (RMSE) 258) and 1112 g DM m(-2) (RMSE 312), respectively. After the Nordic calibration, the simulated average total seasonal yield was 863 (RMSE 242) the first year and 927 g DM m(-2) (RMSE 271) the second year after establishment. The differences between the observed and simulated first cut yield followed the same patterns, whereas the prediction accuracy for second cut yield did not differ substantially between the calibration approaches.Using the parameter set from the Nordic region decreased the model predictability at Srheim compared with only using model parameters derived from this location. These results show that using biomass data from several locations, instead of only one specific location, in the calibration of the LINGRA model improved the overall prediction accuracy of first cut dry matter yield and total seasonal dry matter yield across an environmentally heterogeneous region. To further analyse the usefulness of including multi-site data in forage grass model calibrations, other forage grass models could be evaluated against the same dataset. |
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0378-4290 |
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CropM, ft_macsur |
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MA @ admin @ |
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4634 |
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