Rötter, R. P., Tao, F., Höhn, J. G., & Palosuo, T. (2015). Use of crop simulation modelling to aid ideotype design of future cereal cultivars. J. Experim. Bot., 66(12), 3463–3476.
Abstract: A major challenge of the 21st century is to achieve food supply security under a changing climate and roughly a doubling in food demand by 2050 compared to present, the majority of which needs to be met by the cereals wheat, rice, maize, and barley. Future harvests are expected to be especially threatened through increased frequency and severity of extreme events, such as heat waves and drought, that pose particular challenges to plant breeders and crop scientists. Process-based crop models developed for simulating interactions between genotype, environment, and management are widely applied to assess impacts of environmental change on crop yield potentials, phenology, water use, etc. During the last decades, crop simulation has become important for supporting plant breeding, in particular in designing ideotypes, i.e. ‘model plants’, for different crops and cultivation environments. In this review we (i) examine the main limitations of crop simulation modelling for supporting ideotype breeding, (ii) describe developments in cultivar traits in response to climate variations, and (iii) present examples of how crop simulation has supported evaluation and design of cereal cultivars for future conditions. An early success story for rice demonstrates the potential of crop simulation modelling for ideotype breeding. Combining conventional crop simulation with new breeding methods and genetic modelling holds promise to accelerate delivery of future cereal cultivars for different environments. Robustness of model-aided ideotype design can further be enhanced through continued improvements of simulation models to better capture effects of extremes and the use of multi-model ensembles.
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Wallach, D., Mearns, L. O., Ruane, A. C., Rötter, R. P., & Asseng, S. (2016). Lessons from climate modeling on the design and use of ensembles for crop modeling. Clim. Change, .
Abstract: Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.
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Lana, M., Kersebaum, K. C., Kollas, C., Yin, X., Nendel, C., Manevski, K., et al. (2016). Effect of different levels of calibration in rotation schemes simulated in five European sites in a multi-model approach.. Berlin (Germany).
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Rötter, R. P. A. U. -, J.H., Kassie, B. T., Paff, K., Palosuo, T. A. U. -, C.Y., Tao, F., Ewert, F., et al. (2016). Analysis of crop yield variability and yield gaps for maize and wheat in diverse climatic zones.. Berlin (Germany).
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de Wit, A., Boogaard, H., van Diepen, K., van Kraalingen, D., Rötter, R., Supit, I., et al. (2015). WOFOST developer’s response to article by Stella et al., Environmental Modelling & Software 59 (2014): 44–58. Env. Model. Softw., 73, 57–59.
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