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Webber, H.; Martre, P.; Asseng, S.; Kimball, B.; White, J.; Ottman, M.; Wall, G.W.; De Sanctis, G.; Doltra, J.; Grant, R.; Kassie, B.; Maiorano, A.; Olesen, J.E.; Ripoche, D.; Rezaei, E.E.; Semenov, M.A.; Stratonovitch, P.; Ewert, F. |
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Title |
Canopy temperature for simulation of heat stress in irrigated wheat in a semi-arid environment: A multi-model comparison |
Type |
Journal Article |
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Year |
2017 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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Volume  |
202 |
Issue |
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Pages |
21-35 |
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Keywords |
Crop model comparison; Canopy temperature; Heat stress; Wheat |
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Abstract |
Even brief periods of high temperatures occurring around flowering and during grain filling can severely reduce grain yield in cereals. Recently, ecophysiological and crop models have begun to represent such phenomena. Most models use air temperature (Tair) in their heat stress responses despite evidence that crop canopy temperature (Tc) better explains grain yield losses. Tc can deviate significantly from Tair based on climatic factors and the crop water status. The broad objective of this study was to evaluate whether simulation of Tc improves the ability of crop models to simulate heat stress impacts on wheat under irrigated conditions. Nine process-based models, each using one of three broad approaches (empirical, EMP; energy balance assuming neutral atmospheric stability, EBN; and energy balance correcting for the atmospheric stability conditions, EBSC) to simulate Tc, simulated grain yield under a range of temperature conditions. The models varied widely in their ability to reproduce the measured Tc with the commonly used EBN models performing much worse than either EMP or EBSC. Use of Tc to account for heat stress effects did improve simulations compared to using only Tair to a relatively minor extent, but the models that additionally use Tc on various other processes as well did not have better yield simulations. Models that simulated yield well under heat stress had varying skill in simulating Tc. For example, the EBN models had very poor simulations of Tc but performed very well in simulating grain yield. These results highlight the need to more systematically understand and model heat stress events in wheat. |
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2016-10-31 |
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English |
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0378-4290 |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
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4824 |
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Author |
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 |
Type |
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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Abstract |
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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CropMwp;wos; ft=macsur; wsnot_yet; |
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MA @ admin @ |
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4776 |
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Montesino-San Martín, M.; Olesen, J.E.; Porter, J.R. |
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Title |
Can crop-climate models be accurate and precise? A case study for wheat production in Denmark |
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Journal Article |
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Year |
2015 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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202 |
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51-60 |
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Keywords |
Uncertainty; Model intercomparison; Bayesian approach; Climate change; Wheat; Denmark; uncertainty analysis; simulation-models; bayesian-approach; change; impact; yields; variability; projections; scale; calibration; framework |
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Crop models, used to make projections of climate change impacts, differ greatly in structural detail. Complexity of model structure has generic effects on uncertainty and error propagation in climate change impact assessments. We applied Bayesian calibration to three distinctly different empirical and mechanistic wheat models to assess how differences in the extent of process understanding in models affects uncertainties in projected impact. Predictive power of the models was tested via both accuracy (bias) and precision (or tightness of grouping) of yield projections for extrapolated weather conditions. Yields predicted by the mechanistic model were generally more accurate than the empirical models for extrapolated conditions. This trend does not hold for all extrapolations; mechanistic and empirical models responded differently due to their sensitivities to distinct weather features. However, higher accuracy comes at the cost of precision of the mechanistic model to embrace all observations within given boundaries. The approaches showed complementarity in sensitivity to weather variables and in accuracy for different extrapolation domains. Their differences in model precision and accuracy make them suitable for generic model ensembles for near-term agricultural impact assessments of climate change. |
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0168-1923 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4572 |
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Raymundo, R.; Asseng, S.; Prassad, R.; Kleinwechter, U.; Concha, J.; Condori, B.; Bowen, W.; Wolf, J.; Olesen, J.E.; Dong, Q.; Zotarelli, L.; Gastelo, M.; Alva, A.; Travasso, M.; Quiroz, R.; Arora, V.; Graham, W.; Porter, C. |
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Title |
Performance of the SUBSTOR-potato model across contrasting growing conditions |
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Journal Article |
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Year |
2017 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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Volume |
202 |
Issue |
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Pages |
57-76 |
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0378-4290 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4967 |
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Author |
Zhao, G.; Siebert, S.; Enders, A.; Rezaei, E.E.; Yan, C.; Ewert, F. |
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Title |
Demand for multi-scale weather data for regional crop modeling |
Type |
Journal Article |
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Year |
2015 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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Volume |
200 |
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156-171 |
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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 |
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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. |
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0168-1923 |
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CropM, ft_macsur |
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MA @ admin @ |
Serial |
4753 |
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