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.

Abstract: Adaptation of crops to climate change has to be addressed locally due to the variability of soil, climate and the specific socio-economic settings influencing farm management decisions. Adaptation of rainfed cropping systems in the Mediterranean is especially challenging due to the projected decline in precipitation in the coming decades, which will increase the risk of droughts. Methods that can help explore uncertainties in climate projections and crop modelling, such as impact response surfaces (IRSs) and ensemble modelling, can then be valuable for identifying effective adaptations. Here, an ensemble of 17 crop models was used to simulate a total of 54 adaptation options for rainfed winter wheat (Triticum aestivum) at Lleida (NE Spain). To support the ensemble building, an ex post quality check of model simulations based on several criteria was performed. Those criteria were based on the “According to Our Current Knowledge” (AOCK) concept, which has been formalized here. Adaptations were based on changes in cultivars and management regarding phenology, vernalization, sowing date and irrigation. The effects of adaptation options under changed precipitation (P), temperature (T.),[CO2] and soil type were analysed by constructing response surfaces, which we termed, in accordance with their specific purpose, adaptation response surfaces (ARSs). These were created to assess the effect of adaptations through a range of plausible P, T and [CO2] perturbations. The results indicated that impacts of altered climate were predominantly negative. No single adaptation was capable of overcoming the detrimental effect of the complex interactions imposed by the P, T and [CO2] perturbations except for supplementary irrigation (sI), which reduced the potential impacts under most of the perturbations. Yet, a combination of adaptations for dealing with climate change demonstrated that effective adaptation is possible at Lleida. Combinations based on a cultivar without vernalization requirements showed good and wide adaptation potential. Few combined adaptation options performed well under rainfed conditions. However, a single sI was sufficient to develop a high adaptation potential, including options mainly based on spring wheat, current cycle duration and early sowing date. Depending on local environment (e.g. soil type), many of these adaptations can maintain current yield levels under moderate changes in T and P, and some also under strong changes. We conclude that ARSs can offer a useful tool for supporting planning of field level adaptation under conditions of high uncertainty. (C) 2017 Elsevier Ltd. All rights reserved.