Abstract: The frequency and intensity of extreme high temperature events are expected to increase with climate change. Higher temperatures near anthesis have a large negative effect on maize (Zea mays, L.) grain yield. While crop growth models are commonly used to assess climate change impacts on maize and other crops, it is only recently that they have accounted for such heat stress effects, despite limited field data availability for model evaluation. There is also increasing awareness but limited testing of the importance of canopy temperature as compared to air temperature for heat stress impact simulations. In this study, four independent irrigated field trials with controlled heating imposed using polyethylene shelters were used to develop and evaluate a heat stress response function in the crop modeling framework SIMPLACE, in which the Lintul5 crop model was combined with a canopy temperature model. A dataset from Argentina with the temperate hybrid Nidera AX 842 MG (RM 119) was used to develop a yield reduction function based on accumulated hourly stress thermal time above a critical temperature of 34 degrees C. A second dataset from Spain with a FAO 700 cultivar was used to evaluate the model with daily weather inputs in two sets of simulations. The first was used to calibrate SIMPLACE for conditions with no heat stress, and the second was used to evaluate SIMPLACE under conditions of heat stress using the reduction factor obtained with the Argentine dataset. Both sets of simulations were conducted twice; with the heat stress function alternatively driven with air and simulated canopy temperature. Grain yield simulated under heat stress conditions improved when canopy temperature was used instead of air temperature (RMSE equal to 175 and 309 g m(-2), respectively). For the irrigated and high radiative conditions, raising the critical threshold temperature for heat stress to 39 degrees C improved yield simulation using air temperature (RMSE: 221 gm(-2)) without the need to simulate canopy temperature (RMSE: 175 gm(-2)). However, this approach of adjusting thresholds is only likely to work in environments where climatic variables and the level of soil water deficit are constant, such as irrigated conditions and are not appropriate for rainfed production conditions. (C) 2016 Elsevier B.V. All rights reserved.

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.