The physiological and genetic basis of combined drought and heat tolerance in wheat

The physiological and genetic basis of combined drought and heat tolerance in wheat Abstract Drought and heat stress cause losses in wheat productivity in major growing regions worldwide, and both the occurrence and the severity of these events are likely to increase with global climate change. Water deficits and high temperatures frequently occur simultaneously at sensitive growth stages, reducing wheat yields by reducing grain number or weight. Although genetic variation and underlying quantitative trait loci for either individual stress are known, the combination of the two stresses has rarely been studied. Complex and often antagonistic physiology means that genetic loci underlying tolerance to the combined stress are likely to differ from those for drought or heat stress tolerance alone. Here, we review what is known of the physiological traits and genetic control of drought and heat tolerance in wheat and discuss potential physiological traits to study for combined tolerance. We further place this knowledge in the context of breeding for new, more tolerant varieties and discuss opportunities and constraints. We conclude that a fine control of water relations across the growing cycle will be beneficial for combined tolerance and might be achieved through fine management of spatial and temporal gas exchange. Cereal, climate, stress, temperature, water, yield Introduction Wheat is the major food for numerous regions around the world, providing approximately 20% of daily calories and protein for 4.5 billion people (Shiferaw et al., 2013). Wheat ranks first in terms of harvested area (223.67 million hectares in 2016) and is the second most produced crop with a global production of 735.3 million tons in 2016 (USDA, 2017). A recent study predicted that wheat yields will decline by 4.1% to 6.4% for each global increase of 1 °C due to climate change (Liu et al., 2016) while wheat consumption is expected to increase by over 30% in the next 40 years (Weigand, 2011). Wheat production would need to reach 858 million tons by 2050 in order to match the predicted global food demand (Alexandratos and Bruinsma, 2012). Drought and heat are two major abiotic stresses constraining wheat productivity worldwide, causing yield losses of up to 86% and 69%, respectively (Fischer and Maurer, 1978; Prasad et al., 2011). Both stresses are more likely to occur simultaneously rather than separately in semi-arid and hot growing regions in North Africa, Argentina, Mexico, Australia, South Africa, and the Mediterranean countries, and in high latitude, semi-arid growing regions of central and eastern Asia, Canada, the USA, and Kazakhstan (Mooney and Di Castri, 1973; Araus et al., 2002; Pradhan et al., 2012; Tricker et al., 2016). Yield penalty is associated with long periods of drought coinciding with heat waves above 32 °C during heading and grain filling stages (Wardlaw and Wrigley, 1994). In the Australian wheat belt, average daily maximum temperatures and numbers of days over 30 °C during the period of grain filling have been steadily increasing over the past three decades, and further rises are projected with climate change (ABS, 2012). The major decrease in wheat production across central Europe in the exceptionally hot summer of 2003 was likely to be due to short, but severe, heat waves during reproductive development (Wheeler, 2012). Stress tolerance is particularly critical in growing regions where the gap between attained yields and maximum yields is highest, and may have more consequence globally than where differences are lower (Tester and Langridge, 2010). Hence, producing wheat varieties with high and stable yield under these environmental stresses is one of the most important aims of breeding (Gavuzzi et al., 1997; Tilman et al., 2011). Whereas responses to either drought or heat stress have been studied extensively in wheat, the combination of both environmental stresses has only recently become a matter for research. When irrigated, and with saturated atmospheric humidity (low vapour pressure deficit; VPD) at high temperatures, Australian modern wheat varieties did not show symptoms of heat stress: plants were lush and produced up to 6.8 t ha−1 (Parent et al., 2017). This example and others demonstrate that wheat is heat tolerant when water is available. To improve wheat for dual tolerance, plants must be studied under the combination of stresses. Overall, the combination of both high temperature and drought has a negative, additive impact on plant phenology and physiology, i.e. growth, chlorophyll content, leaf photosynthesis, grain number, spikelet fertility, grain filling duration, and grain yield (Altenbach et al., 2003; Shah and Paulsen, 2003; Prasad et al., 2011; Pradhan et al., 2012; Perdomo et al., 2015, 2017). Although responses to the two stresses share some common mechanisms, other physiological processes are antagonistic (Machado and Paulsen, 2001). For instance, combined drought and heat stress decreases leaf chlorophyll content by 49% while drought or heat alone reduce it by 9% or 27%, respectively (Pradhan et al., 2012; Awasthi et al., 2014). This early senescence of green tissues affects the total amount of carbohydrates transported to the grains and final grain weight. Delayed senescence, a stay-green phenotype, has been associated with drought tolerance (e.g. Pinto et al., 2010) and with heat tolerance in experiments using irrigation (e.g. Shirdelmoghanloo et al., 2016) where water reserves are available and accessible in deep soils for continued water use and transport of assimilates to grains post-anthesis (Reynolds et al., 2005; Christopher et al., 2008). In contrast, a stay-green phenotype is unlikely to contribute to combined drought and heat tolerance where no water reserves are available for continuous water use and might exacerbate the combined stress. Although plants’ responses to the combination of drought and heat have been described (reviewed in Zandalinas et al. 2018), few models or explanations are proposed for the physiological traits underlying combined tolerance (Pinto and Reynolds, 2015), and very little is known about genes and loci underlying these physiological mechanisms in wheat. Quantitative trait loci (QTLs) for drought and heat tolerance have, to date, mostly been reported for low-yield field environments where stress is present (such as the mega-environments 1 and 4 defined by CIMMYT, http://wheatatlas.org/), but not controlled and often not measured (Table 1). Complex interactions between QTLs and environments exist that may limit the usefulness of a particular allele. For example, using multi-environment analysis, Bonneau et al. (2013) showed that alternative parental alleles of a major QTL for yield in dry and hot environments (qDHY.3B) were positive, depending on the severity of the water deficit, soil depth, and co-occurrence with high temperatures. Table 1. QTL identified in wheat under combined dry and hot conditions, drought or heat stress Trait Chromosome References Combined dry and hot conditions Grain yield 1AL, 1B, 1D, 2A, 2BL, 3A, 3B, 4AL, 4B, 5A, 6A, 6B, 7A, 7B, 7D Kirigwi et al. (2007),a, Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Merchuk-Ovnat et al. (2016),a, Tahmasebi et al. (2017)a Thousand grain weight 1D, 2B, 3A, 3B, 4A, 6A, 7A, 7B, 7D Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Tahmasebi et al. (2017)a Kernel weight index (large grains−all grains) 1A, 2B, 6A Pinto et al. (2010)a Grain weight spike−1 5B, 6A, 7B Golabadi et al. (2011)a Grain number m−2 1B, 2A, 3B, 3D, 4AL, 6B, 7A Kirigwi et al. (2007),a, Pinto et al. (2010),a, Bennett et al. (2012)a Grain number spike−1 2B, 7B Golabadi et al. (2011),a, Tahmasebi et al. (2017)a Harvest index 1B, 2A, 2B, 3B, 4A, 5A, 5B, 6A, 6B, 7B Peleg et al. (2009),d, Golabadi et al. (2011)a Spike weight 1B, 2A, 4A, 6A, 7A, 7B Peleg et al. (2009),d, Golabadi et al. (2011)a Spike number m−2 2B, 4AL, 5B Kirigwi et al. (2007),a, Golabadi et al. (2011)a Spike harvest index 2B, 3B Golabadi et al. (2011)a Spikelet number spike−1 5A Tahmasebi et al. (2017)a Biomass 2BS, 4AL, 4B, 5A, 7AS Kirigwi et al. (2007),a, Peleg et al. (2009),d, Merchuk-Ovnat et al. (2016)a Plant height 1A, 1B, 2BL, 3AL, 3BS, 4A, 4B, 5A, 7AS Maccaferri et al. (2008),ab, Pinto et al. (2010),a, Tahmasebi et al. (2017)a Shoot length 2B, 3B, 4A, 4B, 6B, 7A, 7B Peleg et al. (2009)d Peduncle length 3A, 3B Bennett et al. (2012)a Flag leaf width 2B, 3B Bennett et al. (2012)a Days to heading 1A, 1B, 1D, 2AS, 2BS, 2BL, 3A, 3B, 4AL, 4B, 4D, 5A, 6A, 7AS, 7BS, 7D Kirigwi et al. (2007),a, Maccaferri et al. (2008)a,b, Peleg et al. (2009),d, Pinto et al. (2010),a, Merchuk-Ovnat et al. (2016),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Days to maturity 1A, 1D,5A, 7B, 7D Pinto et al. (2010),a, Tahmasebi et al. (2017)a Days from heading to maturity 1B, 2B, 4A, 4B, 5A, 5B, 7A, 7B Peleg et al. (2009)d NDVI at the vegetative stage 1B, 3B, 4A, 7A Pinto et al. (2010),a, Bennett et al. (2012)a NDVI at the grain filling stage 1B, 1D, 2A, 2B, 4A, 4B, 5A, 6A, 6B, 7A, 7B Pinto et al. (2010)a Stem WSC 1A, 1B, 3A, 3B, 4A, 6D Pinto et al. (2010),a, Bennett et al. (2012)a Grain fill rate 4AL Kirigwi et al. (2007)a Grain fill duration 4AL Kirigwi et al. (2007)a Canopy temperature at the vegetative stage 1B, 2B, 3B, 4A, 4B, 6B, 7A Pinto et al. (2010),a, Tahmasebi et al. (2017)a Canopy temperature at the grain filling stage 1A, 1B, 2B, 3B, 4A, 5A, 6B, 7A Pinto et al. (2010)a Canopy temperature depression 1A, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Flag leaf rolling 1A, 2A, 2B, 4B, 5A, 5B, 6B, 7A, 7D Peleg et al. (2009),d, Tahmasebi et al. (2017)a Early vigour 2B, 2D, 3B, 4A Bennett et al. (2012)a Early ground cover 6AS Mondal et al. (2017)a Chlorophyll content 1A, 1B, 3A, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A Diab et al. (2008),a, Peleg et al. (2009),d, Bennett et al. (2012)a Chlorophyll fluorescence 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Carbon isotope discrimination 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6B, 7A, 7B Diab et al. (2008),a, Peleg et al. (2009)d Photosynthetically active radiation 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Stomatal density 4AS, 5AS, 7AL Shahinnia et al. (2016)a Stomatal index 2BL, 7BL Shahinnia et al. (2016)a Stomatal aperture area 7AL Shahinnia et al. (2016)a Stomatal aperture length 2BS, 2BL, 7AL Shahinnia et al. (2016)a Guard cell length 1AS, 3BL, 7AL Shahinnia et al. (2016)a Guard cell area 1BL, 4BL, 5AL, 5DL Shahinnia et al. (2016)a Transpiration efficiency 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Leaf relative water content 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B Diab et al. (2008)a Water index 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Leaf osmotic potential 2A, 2B, 3A, 3B, 4B, 5A, 5B, 6B Peleg et al. (2009)d Osmotic adjustment 1A, 3A, 3B, 4A, 7A Diab et al. (2008)a Metabolites (mQTL) 2B, 4A, 5A, 7A, 7D Hill et al. (2015)a Expression of stress-related genes (eQTL) 6BL Aprile et al. (2013)c Drought stress Grain yield 2D, 3D, 3DL, 4AL, 4BS, 4DL, 5A, 5B, 5DL, 6B, 6D, 7AL, 7BL, 7D Quarrie et al. (2005),a, Czyczyło-Mysza et al. (2011),d, Kadam et al. (2012),c, Tahmasebi et al. (2017)a Grain weight spike−1 1B, 1D Xu et al. (2017)a Thousand grain weight 1B, 1D, 2A, 2B, 3A, 3D, 4A, 4D, 5A, 6A, 6D, 7A, 7B Quarrie et al. (2005),a, Dashti et al. (2007),c, Yang et al. (2007),a, Tahmasebi et al. (2017),a, Xu et al. (2017)a Grain number m−2 1B, 5B, 7D Tahmasebi et al. (2017)a Grain number spike−1 1A, 2A, 2B, 2D, 3A, 3B, 4A, 4B, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B Quarrie et al. (2005),a, Czyczyło-Mysza et al. (2011),d, Xu et al. (2017)a Harvest index 1B, 2D, 4BS, 5A Kadam et al. (2012),c, Xu et al. (2017)a Spike number plant−1 1A, 2A, 2B, 2D, 4B, 5A, 7B Quarrie et al. (2005),a, Xu et al. (2017)a Spikelet compactness 6A, 7A Xu et al. (2017)a Spikelet number spike−1 1A, 7D Tahmasebi et al. (2017),a, Xu et al. (2017)a Sterile spikelet number spike−1 7A Xu et al. (2017)a Fertile spikelet spike−1 2A Xu et al. (2017)a Biomass 1B Xu et al. (2017)a Shoot biomass 4B Kadam et al. (2012)c Root biomass 2D, 4BS Kadam et al. (2012)c Plant height 1B, 4B, 7D Tahmasebi et al. (2017),a, Xu et al. (2017)a Peduncle length 3B Dashti et al. (2007)c Coleoptile length 6AS Spielmeyer et al. (2007)c Spike length 2B, 7A, 7B Xu et al. (2017)a Root length 2D, 4B, 5D, 6B Kadam et al. (2012)c Growth rate 5BL Parent et al. (2015)c Relative growth rate 4AL Parent et al. (2015)c Inflexion point in growth curves 7DS Parent et al. (2015)c Leaf expansion rate 5BL Parent et al. (2015)c Inflexion point in leaf expansion curves 5BL Parent et al. (2015)c Days to heading 1D, 4B, 7D Tahmasebi et al. (2017)a Days to flowering 2D Kadam et al. (2012)c Stem WSC at the flowering stage 1A, 1D, 2D, 4A, 4B, 7B Yang et al. (2007)a Stem WSC at the grain filling stage 4A Yang et al. (2007)a Stem WSC at the maturity stage 6B Yang et al. (2007)a Accumulation efficiency of stem WSC 1A, 2A, 5A, 7B Yang et al. (2007)a Remobilization efficiency of stem WSC 7A Yang et al. (2007)a Grain filling efficiency 2A, 4B, 5A, Yang et al. (2007)a Flag leaf rolling 4B, 5A Tahmasebi et al. (2017)a Chlorophyll content 1B, 2B, 5B, 7A, 7B Ilyas et al. (2014),c, Tahmasebi et al. (2017),a, Xu et al. (2017)a Flag leaf persistence 2D, 3B, 4B, 5A, 6A Verma et al. (2004)a Net photosynthetic rate 6B Xu et al. (2017)a Chlorophyll fluorescence 1B, 2A, 2D, 3A, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A, 7B, 7D Czyczyło-Mysza et al. (2011)d Stomatal conductance 5A Xu et al. (2017)a Stomatal density 5BS Shahinnia et al. (2016)c Stomatal index 5BS, 6DL Shahinnia et al. (2016)c Stomatal aperture length 2BL, 4BS, 7AS, 7DL Shahinnia et al. (2016)c Guard cell area 1BL, 5BS Shahinnia et al. (2016)c Guard cell length 1BL, 4BS, 7AS Shahinnia et al. (2016)c Transpiration rate 3Al, 4BL, 6D Parent et al. (2015),c, Xu et al. (2017)a Water use efficiency 2AL, 4D Parent et al. (2015),c, Xu et al. (2017)a Heat stress Grain yield 1A, 1BL, 1D, 2BS, 3A, 3BS, 3BL, 3D, 4A, 4B, 4DL, 5A, 5B, 6A, 6B, 6D, 7AS, 7AL, 7BS, 7BL Quarrie et al. (2005),a, Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Paliwal et al. (2012),a, Merchuk-Ovnat et al. (2016),a, Ogbonnaya et al. (2017)a Grain weight spike−1 3A, 3BS, 6A, 7A, 7B Golabadi et al. (2011),a, Shirdelmoghanloo et al. (2016),c, Ogbonnaya et al. (2017)a Thousand grain weight 1A, 2A, 2B, 2D, 3A, 3BS, 3D, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7D Quarrie et al. (2005),a, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Single grain weight 2D, 3BS, 5B, 6A Shirdelmoghanloo et al. (2016)c Kernel weight index (large grains−all grains) 1A, 1D, 2B, 3B, 4B, 5A, 5B, 6A, 6B, 6D Pinto et al. (2010)a Grain number m−2 1A, 1B, 1D, 3BS, 3BL, 3D, 4A, 4B, 4D, 5B, 6A, 6B, 6D, 7A Pinto et al. (2010),a, Bennett et al. (2012)a Grain number spike−1 1A, 1B, 2A, 3B, 4B, 4D, 5D, 6A, 7B, 7D Quarrie et al. (2005),a, Golabadi et al. (2011),a, Ogbonnaya et al. (2017),a, Tahmasebi et al (2017)a Threshing index 1A, 1B, 5B Ogbonnaya et al. (2017)a Harvest index 1B, 2B, 3B, 4A, 5A, 5B, 6A, 6B, 7B Peleg et al. (2009)d Spike number m−2 1A, 1B, 3A, 3B, 4B, 5A, 5B, 7B, 7D Golabadi et al. (2011),a, Ogbonnaya et al. (2017)a Spike number plant−1 3A Quarrie et al. (2005)a Spike weight 1B, 2B, 2D, 3D, 4A, 5D, 6A, 7B Peleg et al. (2009),d, Golabadi et al. (2011),a, Ogbonnaya et al. (2017)a Spike harvest index 2B, 5B, 7A, 7B Golabadi et al. (2011)a Spikelet compactness 1A Tahmasebi et al. (2017)a Spikelet number spike−1 1B, 1D, 2B, 4A, 5B, 6A, 6B Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Number of productive tiller 1B Sharma et al. (2016)a Biomass 1BL, 2BS, 7AS, 7BS Merchuk-Ovnat et al. (2016)a Shoot biomass 3BS, 4A, 6B Shirdelmoghanloo et al. (2016)c Plant height 1A, 1B, 2A, 2B, 2D, 3A, 3B, 3D, 4A, 4B, 5A, 5B, 6A, 6D, 7A, 7B, 7D Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Shoot length 1B, 2B, 3A, 3B, 4A, 4B, 5D, 7A, 7B Peleg et al. (2009),d, Ogbonnaya et al. (2017)a Peduncle length 1A, 1B, 2B, 3A, 3B, 5B, 7A Ogbonnaya et al. (2017)a Flag leaf length 3B, 5B Mason et al. (2010)c Flag leaf width 1D, 2B, 3BL, 7A, 3BL Mason et al. (2010),c, Bennett et al. (2012)a Wax score 1B, 2A, 2B, 2D, 3A, 3B, 5A, 6A, 6B, 7B Mason et al. (2010),c, Ogbonnaya et al. (2017)a Days to heading 1BL, 1D, 2A, 2BS, 3B, 3A, 4A, 4B, 4D, 5A, 6A, 7AS, 7BS, 7D Maccaferri et al. (2008)a,b, Peleg et al. (2009),d, Pinto et al. (2010),a, Merchuk- Ovnat et al. (2016),a, Ogbonnaya et al. (2017)a Days to flowering 1B, 1D, 4A, 4B, 4D, 5B Mason et al. (2010),c, Pinto et al. (2010)a Days to maturity 1B, 1D, 2A, 2B, 3B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B, 7DS Pinto et al. (2010),a, Bennett et al. (2012),a, Paliwal et al. (2012),a, Ogbonnaya et al. (2017)a NDVI at the vegetative stage 1B, 1D, 2B, 2D, 3A, 3B, 4A, 4D, 5A, 6A, 6B, 6D, 7A Pinto et al. (2010),a, Bennett et al. (2012)a NDVI at the grain filling stage 1A, 1B, 3A, 4A, 4B, 5A, 5B, 6A, 7B Pinto et al. (2010)a Stem WSC 1A, 1B, 2D, 3A, 3BL, 5A, 5B, 6A Pinto et al. (2010),a, Bennett et al. (2012)a Grain filling duration 1B, 1D, 2A, 2B, 2D, 3BS, 5A, 6A, 6B, 6D Mason et al. (2010),c, Shirdelmoghanloo et al. (2016),c, Ogbonnaya et al. (2017)a Canopy temperature at the vegetative stage 1A, 1B, 1D, 2B, 3A, 3BL, 4A, 4B, 5B, 6B, 7A Pinto et al. (2010),a, Bennett et al. (2012)a Canopy temperature at the grain filling stage 1A, 1B, 1D, 2B, 3BS, 3BL, 4A, 4D, 5A, 5D, 7A, 7B Pinto et al. (2010),a, Bennett et al. (2012)a Canopy temperature depression 7BL Paliwal et al. (2012)a Flag leaf rolling 1A, 2A, 2B, 2D, 3D, 4B, 5A, 5B, 6A, 6B, 7A, 7B Peleg et al. (2009),d, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Early vigour 2B, 2D, 3BL Bennett et al. (2012)a Chlorophyll content 1A, 1B, 1D, 2B, 3A, 3BS, 4A, 4D, 5A, 5B, 6A, 6D, 7A, 7B, 7D Peleg et al. (2009),d, Pinto et al. (2010),a, Bennett et al. (2012),a, Tahmasebi et al. (2017)a Flag leaf persistence 1B, 1D, 2A, 3A, 3BS, 6A, 6B, 7A, Vijayalakshmi et al. (2010),c, Talukder et al. (2014),c, Shirdelmoghanloo et al. (2016)c Chlorophyll loss rate 3BS, 6BL Shirdelmoghanloo et al. (2016)c Chlorophyll fluorescence 7A Vijayalakshmi et al. (2010)c Carbon isotope discrimination 1A, 2A, 4A, 5B, 6A, 6B, 7B Peleg et al. (2009)d Leaf osmotic potential 2A, 3A, 3B, 5A, 5B, 6A, 6B Peleg et al. (2009) Plasma membrane damage 1D, 2B, 7A Talukder et al. (2014)c Thylakoid membrane damage 1D, 6A, 7A Talukder et al. (2014)c Trait Chromosome References Combined dry and hot conditions Grain yield 1AL, 1B, 1D, 2A, 2BL, 3A, 3B, 4AL, 4B, 5A, 6A, 6B, 7A, 7B, 7D Kirigwi et al. (2007),a, Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Merchuk-Ovnat et al. (2016),a, Tahmasebi et al. (2017)a Thousand grain weight 1D, 2B, 3A, 3B, 4A, 6A, 7A, 7B, 7D Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Tahmasebi et al. (2017)a Kernel weight index (large grains−all grains) 1A, 2B, 6A Pinto et al. (2010)a Grain weight spike−1 5B, 6A, 7B Golabadi et al. (2011)a Grain number m−2 1B, 2A, 3B, 3D, 4AL, 6B, 7A Kirigwi et al. (2007),a, Pinto et al. (2010),a, Bennett et al. (2012)a Grain number spike−1 2B, 7B Golabadi et al. (2011),a, Tahmasebi et al. (2017)a Harvest index 1B, 2A, 2B, 3B, 4A, 5A, 5B, 6A, 6B, 7B Peleg et al. (2009),d, Golabadi et al. (2011)a Spike weight 1B, 2A, 4A, 6A, 7A, 7B Peleg et al. (2009),d, Golabadi et al. (2011)a Spike number m−2 2B, 4AL, 5B Kirigwi et al. (2007),a, Golabadi et al. (2011)a Spike harvest index 2B, 3B Golabadi et al. (2011)a Spikelet number spike−1 5A Tahmasebi et al. (2017)a Biomass 2BS, 4AL, 4B, 5A, 7AS Kirigwi et al. (2007),a, Peleg et al. (2009),d, Merchuk-Ovnat et al. (2016)a Plant height 1A, 1B, 2BL, 3AL, 3BS, 4A, 4B, 5A, 7AS Maccaferri et al. (2008),ab, Pinto et al. (2010),a, Tahmasebi et al. (2017)a Shoot length 2B, 3B, 4A, 4B, 6B, 7A, 7B Peleg et al. (2009)d Peduncle length 3A, 3B Bennett et al. (2012)a Flag leaf width 2B, 3B Bennett et al. (2012)a Days to heading 1A, 1B, 1D, 2AS, 2BS, 2BL, 3A, 3B, 4AL, 4B, 4D, 5A, 6A, 7AS, 7BS, 7D Kirigwi et al. (2007),a, Maccaferri et al. (2008)a,b, Peleg et al. (2009),d, Pinto et al. (2010),a, Merchuk-Ovnat et al. (2016),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Days to maturity 1A, 1D,5A, 7B, 7D Pinto et al. (2010),a, Tahmasebi et al. (2017)a Days from heading to maturity 1B, 2B, 4A, 4B, 5A, 5B, 7A, 7B Peleg et al. (2009)d NDVI at the vegetative stage 1B, 3B, 4A, 7A Pinto et al. (2010),a, Bennett et al. (2012)a NDVI at the grain filling stage 1B, 1D, 2A, 2B, 4A, 4B, 5A, 6A, 6B, 7A, 7B Pinto et al. (2010)a Stem WSC 1A, 1B, 3A, 3B, 4A, 6D Pinto et al. (2010),a, Bennett et al. (2012)a Grain fill rate 4AL Kirigwi et al. (2007)a Grain fill duration 4AL Kirigwi et al. (2007)a Canopy temperature at the vegetative stage 1B, 2B, 3B, 4A, 4B, 6B, 7A Pinto et al. (2010),a, Tahmasebi et al. (2017)a Canopy temperature at the grain filling stage 1A, 1B, 2B, 3B, 4A, 5A, 6B, 7A Pinto et al. (2010)a Canopy temperature depression 1A, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Flag leaf rolling 1A, 2A, 2B, 4B, 5A, 5B, 6B, 7A, 7D Peleg et al. (2009),d, Tahmasebi et al. (2017)a Early vigour 2B, 2D, 3B, 4A Bennett et al. (2012)a Early ground cover 6AS Mondal et al. (2017)a Chlorophyll content 1A, 1B, 3A, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A Diab et al. (2008),a, Peleg et al. (2009),d, Bennett et al. (2012)a Chlorophyll fluorescence 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Carbon isotope discrimination 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6B, 7A, 7B Diab et al. (2008),a, Peleg et al. (2009)d Photosynthetically active radiation 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Stomatal density 4AS, 5AS, 7AL Shahinnia et al. (2016)a Stomatal index 2BL, 7BL Shahinnia et al. (2016)a Stomatal aperture area 7AL Shahinnia et al. (2016)a Stomatal aperture length 2BS, 2BL, 7AL Shahinnia et al. (2016)a Guard cell length 1AS, 3BL, 7AL Shahinnia et al. (2016)a Guard cell area 1BL, 4BL, 5AL, 5DL Shahinnia et al. (2016)a Transpiration efficiency 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Leaf relative water content 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B Diab et al. (2008)a Water index 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Leaf osmotic potential 2A, 2B, 3A, 3B, 4B, 5A, 5B, 6B Peleg et al. (2009)d Osmotic adjustment 1A, 3A, 3B, 4A, 7A Diab et al. (2008)a Metabolites (mQTL) 2B, 4A, 5A, 7A, 7D Hill et al. (2015)a Expression of stress-related genes (eQTL) 6BL Aprile et al. (2013)c Drought stress Grain yield 2D, 3D, 3DL, 4AL, 4BS, 4DL, 5A, 5B, 5DL, 6B, 6D, 7AL, 7BL, 7D Quarrie et al. (2005),a, Czyczyło-Mysza et al. (2011),d, Kadam et al. (2012),c, Tahmasebi et al. (2017)a Grain weight spike−1 1B, 1D Xu et al. (2017)a Thousand grain weight 1B, 1D, 2A, 2B, 3A, 3D, 4A, 4D, 5A, 6A, 6D, 7A, 7B Quarrie et al. (2005),a, Dashti et al. (2007),c, Yang et al. (2007),a, Tahmasebi et al. (2017),a, Xu et al. (2017)a Grain number m−2 1B, 5B, 7D Tahmasebi et al. (2017)a Grain number spike−1 1A, 2A, 2B, 2D, 3A, 3B, 4A, 4B, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B Quarrie et al. (2005),a, Czyczyło-Mysza et al. (2011),d, Xu et al. (2017)a Harvest index 1B, 2D, 4BS, 5A Kadam et al. (2012),c, Xu et al. (2017)a Spike number plant−1 1A, 2A, 2B, 2D, 4B, 5A, 7B Quarrie et al. (2005),a, Xu et al. (2017)a Spikelet compactness 6A, 7A Xu et al. (2017)a Spikelet number spike−1 1A, 7D Tahmasebi et al. (2017),a, Xu et al. (2017)a Sterile spikelet number spike−1 7A Xu et al. (2017)a Fertile spikelet spike−1 2A Xu et al. (2017)a Biomass 1B Xu et al. (2017)a Shoot biomass 4B Kadam et al. (2012)c Root biomass 2D, 4BS Kadam et al. (2012)c Plant height 1B, 4B, 7D Tahmasebi et al. (2017),a, Xu et al. (2017)a Peduncle length 3B Dashti et al. (2007)c Coleoptile length 6AS Spielmeyer et al. (2007)c Spike length 2B, 7A, 7B Xu et al. (2017)a Root length 2D, 4B, 5D, 6B Kadam et al. (2012)c Growth rate 5BL Parent et al. (2015)c Relative growth rate 4AL Parent et al. (2015)c Inflexion point in growth curves 7DS Parent et al. (2015)c Leaf expansion rate 5BL Parent et al. (2015)c Inflexion point in leaf expansion curves 5BL Parent et al. (2015)c Days to heading 1D, 4B, 7D Tahmasebi et al. (2017)a Days to flowering 2D Kadam et al. (2012)c Stem WSC at the flowering stage 1A, 1D, 2D, 4A, 4B, 7B Yang et al. (2007)a Stem WSC at the grain filling stage 4A Yang et al. (2007)a Stem WSC at the maturity stage 6B Yang et al. (2007)a Accumulation efficiency of stem WSC 1A, 2A, 5A, 7B Yang et al. (2007)a Remobilization efficiency of stem WSC 7A Yang et al. (2007)a Grain filling efficiency 2A, 4B, 5A, Yang et al. (2007)a Flag leaf rolling 4B, 5A Tahmasebi et al. (2017)a Chlorophyll content 1B, 2B, 5B, 7A, 7B Ilyas et al. (2014),c, Tahmasebi et al. (2017),a, Xu et al. (2017)a Flag leaf persistence 2D, 3B, 4B, 5A, 6A Verma et al. (2004)a Net photosynthetic rate 6B Xu et al. (2017)a Chlorophyll fluorescence 1B, 2A, 2D, 3A, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A, 7B, 7D Czyczyło-Mysza et al. (2011)d Stomatal conductance 5A Xu et al. (2017)a Stomatal density 5BS Shahinnia et al. (2016)c Stomatal index 5BS, 6DL Shahinnia et al. (2016)c Stomatal aperture length 2BL, 4BS, 7AS, 7DL Shahinnia et al. (2016)c Guard cell area 1BL, 5BS Shahinnia et al. (2016)c Guard cell length 1BL, 4BS, 7AS Shahinnia et al. (2016)c Transpiration rate 3Al, 4BL, 6D Parent et al. (2015),c, Xu et al. (2017)a Water use efficiency 2AL, 4D Parent et al. (2015),c, Xu et al. (2017)a Heat stress Grain yield 1A, 1BL, 1D, 2BS, 3A, 3BS, 3BL, 3D, 4A, 4B, 4DL, 5A, 5B, 6A, 6B, 6D, 7AS, 7AL, 7BS, 7BL Quarrie et al. (2005),a, Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Paliwal et al. (2012),a, Merchuk-Ovnat et al. (2016),a, Ogbonnaya et al. (2017)a Grain weight spike−1 3A, 3BS, 6A, 7A, 7B Golabadi et al. (2011),a, Shirdelmoghanloo et al. (2016),c, Ogbonnaya et al. (2017)a Thousand grain weight 1A, 2A, 2B, 2D, 3A, 3BS, 3D, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7D Quarrie et al. (2005),a, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Single grain weight 2D, 3BS, 5B, 6A Shirdelmoghanloo et al. (2016)c Kernel weight index (large grains−all grains) 1A, 1D, 2B, 3B, 4B, 5A, 5B, 6A, 6B, 6D Pinto et al. (2010)a Grain number m−2 1A, 1B, 1D, 3BS, 3BL, 3D, 4A, 4B, 4D, 5B, 6A, 6B, 6D, 7A Pinto et al. (2010),a, Bennett et al. (2012)a Grain number spike−1 1A, 1B, 2A, 3B, 4B, 4D, 5D, 6A, 7B, 7D Quarrie et al. (2005),a, Golabadi et al. (2011),a, Ogbonnaya et al. (2017),a, Tahmasebi et al (2017)a Threshing index 1A, 1B, 5B Ogbonnaya et al. (2017)a Harvest index 1B, 2B, 3B, 4A, 5A, 5B, 6A, 6B, 7B Peleg et al. (2009)d Spike number m−2 1A, 1B, 3A, 3B, 4B, 5A, 5B, 7B, 7D Golabadi et al. (2011),a, Ogbonnaya et al. (2017)a Spike number plant−1 3A Quarrie et al. (2005)a Spike weight 1B, 2B, 2D, 3D, 4A, 5D, 6A, 7B Peleg et al. (2009),d, Golabadi et al. (2011),a, Ogbonnaya et al. (2017)a Spike harvest index 2B, 5B, 7A, 7B Golabadi et al. (2011)a Spikelet compactness 1A Tahmasebi et al. (2017)a Spikelet number spike−1 1B, 1D, 2B, 4A, 5B, 6A, 6B Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Number of productive tiller 1B Sharma et al. (2016)a Biomass 1BL, 2BS, 7AS, 7BS Merchuk-Ovnat et al. (2016)a Shoot biomass 3BS, 4A, 6B Shirdelmoghanloo et al. (2016)c Plant height 1A, 1B, 2A, 2B, 2D, 3A, 3B, 3D, 4A, 4B, 5A, 5B, 6A, 6D, 7A, 7B, 7D Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Shoot length 1B, 2B, 3A, 3B, 4A, 4B, 5D, 7A, 7B Peleg et al. (2009),d, Ogbonnaya et al. (2017)a Peduncle length 1A, 1B, 2B, 3A, 3B, 5B, 7A Ogbonnaya et al. (2017)a Flag leaf length 3B, 5B Mason et al. (2010)c Flag leaf width 1D, 2B, 3BL, 7A, 3BL Mason et al. (2010),c, Bennett et al. (2012)a Wax score 1B, 2A, 2B, 2D, 3A, 3B, 5A, 6A, 6B, 7B Mason et al. (2010),c, Ogbonnaya et al. (2017)a Days to heading 1BL, 1D, 2A, 2BS, 3B, 3A, 4A, 4B, 4D, 5A, 6A, 7AS, 7BS, 7D Maccaferri et al. (2008)a,b, Peleg et al. (2009),d, Pinto et al. (2010),a, Merchuk- Ovnat et al. (2016),a, Ogbonnaya et al. (2017)a Days to flowering 1B, 1D, 4A, 4B, 4D, 5B Mason et al. (2010),c, Pinto et al. (2010)a Days to maturity 1B, 1D, 2A, 2B, 3B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B, 7DS Pinto et al. (2010),a, Bennett et al. (2012),a, Paliwal et al. (2012),a, Ogbonnaya et al. (2017)a NDVI at the vegetative stage 1B, 1D, 2B, 2D, 3A, 3B, 4A, 4D, 5A, 6A, 6B, 6D, 7A Pinto et al. (2010),a, Bennett et al. (2012)a NDVI at the grain filling stage 1A, 1B, 3A, 4A, 4B, 5A, 5B, 6A, 7B Pinto et al. (2010)a Stem WSC 1A, 1B, 2D, 3A, 3BL, 5A, 5B, 6A Pinto et al. (2010),a, Bennett et al. (2012)a Grain filling duration 1B, 1D, 2A, 2B, 2D, 3BS, 5A, 6A, 6B, 6D Mason et al. (2010),c, Shirdelmoghanloo et al. (2016),c, Ogbonnaya et al. (2017)a Canopy temperature at the vegetative stage 1A, 1B, 1D, 2B, 3A, 3BL, 4A, 4B, 5B, 6B, 7A Pinto et al. (2010),a, Bennett et al. (2012)a Canopy temperature at the grain filling stage 1A, 1B, 1D, 2B, 3BS, 3BL, 4A, 4D, 5A, 5D, 7A, 7B Pinto et al. (2010),a, Bennett et al. (2012)a Canopy temperature depression 7BL Paliwal et al. (2012)a Flag leaf rolling 1A, 2A, 2B, 2D, 3D, 4B, 5A, 5B, 6A, 6B, 7A, 7B Peleg et al. (2009),d, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Early vigour 2B, 2D, 3BL Bennett et al. (2012)a Chlorophyll content 1A, 1B, 1D, 2B, 3A, 3BS, 4A, 4D, 5A, 5B, 6A, 6D, 7A, 7B, 7D Peleg et al. (2009),d, Pinto et al. (2010),a, Bennett et al. (2012),a, Tahmasebi et al. (2017)a Flag leaf persistence 1B, 1D, 2A, 3A, 3BS, 6A, 6B, 7A, Vijayalakshmi et al. (2010),c, Talukder et al. (2014),c, Shirdelmoghanloo et al. (2016)c Chlorophyll loss rate 3BS, 6BL Shirdelmoghanloo et al. (2016)c Chlorophyll fluorescence 7A Vijayalakshmi et al. (2010)c Carbon isotope discrimination 1A, 2A, 4A, 5B, 6A, 6B, 7B Peleg et al. (2009)d Leaf osmotic potential 2A, 3A, 3B, 5A, 5B, 6A, 6B Peleg et al. (2009) Plasma membrane damage 1D, 2B, 7A Talukder et al. (2014)c Thylakoid membrane damage 1D, 6A, 7A Talukder et al. (2014)c Dry and hot field conditions are defined using the CIMMYT mega-environments 1 and 4 (Rajaram et al., 1994). NDVI, near differential vegetative index; WSC, water-soluble carbohydrates a Field conditions. b Trials in Italy, Tunisia and Morocco with maximum temperature at grain filling ≤26.1 °C. c Controlled conditions. d Semi-controlled conditions. View Large Table 1. QTL identified in wheat under combined dry and hot conditions, drought or heat stress Trait Chromosome References Combined dry and hot conditions Grain yield 1AL, 1B, 1D, 2A, 2BL, 3A, 3B, 4AL, 4B, 5A, 6A, 6B, 7A, 7B, 7D Kirigwi et al. (2007),a, Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Merchuk-Ovnat et al. (2016),a, Tahmasebi et al. (2017)a Thousand grain weight 1D, 2B, 3A, 3B, 4A, 6A, 7A, 7B, 7D Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Tahmasebi et al. (2017)a Kernel weight index (large grains−all grains) 1A, 2B, 6A Pinto et al. (2010)a Grain weight spike−1 5B, 6A, 7B Golabadi et al. (2011)a Grain number m−2 1B, 2A, 3B, 3D, 4AL, 6B, 7A Kirigwi et al. (2007),a, Pinto et al. (2010),a, Bennett et al. (2012)a Grain number spike−1 2B, 7B Golabadi et al. (2011),a, Tahmasebi et al. (2017)a Harvest index 1B, 2A, 2B, 3B, 4A, 5A, 5B, 6A, 6B, 7B Peleg et al. (2009),d, Golabadi et al. (2011)a Spike weight 1B, 2A, 4A, 6A, 7A, 7B Peleg et al. (2009),d, Golabadi et al. (2011)a Spike number m−2 2B, 4AL, 5B Kirigwi et al. (2007),a, Golabadi et al. (2011)a Spike harvest index 2B, 3B Golabadi et al. (2011)a Spikelet number spike−1 5A Tahmasebi et al. (2017)a Biomass 2BS, 4AL, 4B, 5A, 7AS Kirigwi et al. (2007),a, Peleg et al. (2009),d, Merchuk-Ovnat et al. (2016)a Plant height 1A, 1B, 2BL, 3AL, 3BS, 4A, 4B, 5A, 7AS Maccaferri et al. (2008),ab, Pinto et al. (2010),a, Tahmasebi et al. (2017)a Shoot length 2B, 3B, 4A, 4B, 6B, 7A, 7B Peleg et al. (2009)d Peduncle length 3A, 3B Bennett et al. (2012)a Flag leaf width 2B, 3B Bennett et al. (2012)a Days to heading 1A, 1B, 1D, 2AS, 2BS, 2BL, 3A, 3B, 4AL, 4B, 4D, 5A, 6A, 7AS, 7BS, 7D Kirigwi et al. (2007),a, Maccaferri et al. (2008)a,b, Peleg et al. (2009),d, Pinto et al. (2010),a, Merchuk-Ovnat et al. (2016),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Days to maturity 1A, 1D,5A, 7B, 7D Pinto et al. (2010),a, Tahmasebi et al. (2017)a Days from heading to maturity 1B, 2B, 4A, 4B, 5A, 5B, 7A, 7B Peleg et al. (2009)d NDVI at the vegetative stage 1B, 3B, 4A, 7A Pinto et al. (2010),a, Bennett et al. (2012)a NDVI at the grain filling stage 1B, 1D, 2A, 2B, 4A, 4B, 5A, 6A, 6B, 7A, 7B Pinto et al. (2010)a Stem WSC 1A, 1B, 3A, 3B, 4A, 6D Pinto et al. (2010),a, Bennett et al. (2012)a Grain fill rate 4AL Kirigwi et al. (2007)a Grain fill duration 4AL Kirigwi et al. (2007)a Canopy temperature at the vegetative stage 1B, 2B, 3B, 4A, 4B, 6B, 7A Pinto et al. (2010),a, Tahmasebi et al. (2017)a Canopy temperature at the grain filling stage 1A, 1B, 2B, 3B, 4A, 5A, 6B, 7A Pinto et al. (2010)a Canopy temperature depression 1A, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Flag leaf rolling 1A, 2A, 2B, 4B, 5A, 5B, 6B, 7A, 7D Peleg et al. (2009),d, Tahmasebi et al. (2017)a Early vigour 2B, 2D, 3B, 4A Bennett et al. (2012)a Early ground cover 6AS Mondal et al. (2017)a Chlorophyll content 1A, 1B, 3A, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A Diab et al. (2008),a, Peleg et al. (2009),d, Bennett et al. (2012)a Chlorophyll fluorescence 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Carbon isotope discrimination 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6B, 7A, 7B Diab et al. (2008),a, Peleg et al. (2009)d Photosynthetically active radiation 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Stomatal density 4AS, 5AS, 7AL Shahinnia et al. (2016)a Stomatal index 2BL, 7BL Shahinnia et al. (2016)a Stomatal aperture area 7AL Shahinnia et al. (2016)a Stomatal aperture length 2BS, 2BL, 7AL Shahinnia et al. (2016)a Guard cell length 1AS, 3BL, 7AL Shahinnia et al. (2016)a Guard cell area 1BL, 4BL, 5AL, 5DL Shahinnia et al. (2016)a Transpiration efficiency 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Leaf relative water content 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B Diab et al. (2008)a Water index 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Leaf osmotic potential 2A, 2B, 3A, 3B, 4B, 5A, 5B, 6B Peleg et al. (2009)d Osmotic adjustment 1A, 3A, 3B, 4A, 7A Diab et al. (2008)a Metabolites (mQTL) 2B, 4A, 5A, 7A, 7D Hill et al. (2015)a Expression of stress-related genes (eQTL) 6BL Aprile et al. (2013)c Drought stress Grain yield 2D, 3D, 3DL, 4AL, 4BS, 4DL, 5A, 5B, 5DL, 6B, 6D, 7AL, 7BL, 7D Quarrie et al. (2005),a, Czyczyło-Mysza et al. (2011),d, Kadam et al. (2012),c, Tahmasebi et al. (2017)a Grain weight spike−1 1B, 1D Xu et al. (2017)a Thousand grain weight 1B, 1D, 2A, 2B, 3A, 3D, 4A, 4D, 5A, 6A, 6D, 7A, 7B Quarrie et al. (2005),a, Dashti et al. (2007),c, Yang et al. (2007),a, Tahmasebi et al. (2017),a, Xu et al. (2017)a Grain number m−2 1B, 5B, 7D Tahmasebi et al. (2017)a Grain number spike−1 1A, 2A, 2B, 2D, 3A, 3B, 4A, 4B, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B Quarrie et al. (2005),a, Czyczyło-Mysza et al. (2011),d, Xu et al. (2017)a Harvest index 1B, 2D, 4BS, 5A Kadam et al. (2012),c, Xu et al. (2017)a Spike number plant−1 1A, 2A, 2B, 2D, 4B, 5A, 7B Quarrie et al. (2005),a, Xu et al. (2017)a Spikelet compactness 6A, 7A Xu et al. (2017)a Spikelet number spike−1 1A, 7D Tahmasebi et al. (2017),a, Xu et al. (2017)a Sterile spikelet number spike−1 7A Xu et al. (2017)a Fertile spikelet spike−1 2A Xu et al. (2017)a Biomass 1B Xu et al. (2017)a Shoot biomass 4B Kadam et al. (2012)c Root biomass 2D, 4BS Kadam et al. (2012)c Plant height 1B, 4B, 7D Tahmasebi et al. (2017),a, Xu et al. (2017)a Peduncle length 3B Dashti et al. (2007)c Coleoptile length 6AS Spielmeyer et al. (2007)c Spike length 2B, 7A, 7B Xu et al. (2017)a Root length 2D, 4B, 5D, 6B Kadam et al. (2012)c Growth rate 5BL Parent et al. (2015)c Relative growth rate 4AL Parent et al. (2015)c Inflexion point in growth curves 7DS Parent et al. (2015)c Leaf expansion rate 5BL Parent et al. (2015)c Inflexion point in leaf expansion curves 5BL Parent et al. (2015)c Days to heading 1D, 4B, 7D Tahmasebi et al. (2017)a Days to flowering 2D Kadam et al. (2012)c Stem WSC at the flowering stage 1A, 1D, 2D, 4A, 4B, 7B Yang et al. (2007)a Stem WSC at the grain filling stage 4A Yang et al. (2007)a Stem WSC at the maturity stage 6B Yang et al. (2007)a Accumulation efficiency of stem WSC 1A, 2A, 5A, 7B Yang et al. (2007)a Remobilization efficiency of stem WSC 7A Yang et al. (2007)a Grain filling efficiency 2A, 4B, 5A, Yang et al. (2007)a Flag leaf rolling 4B, 5A Tahmasebi et al. (2017)a Chlorophyll content 1B, 2B, 5B, 7A, 7B Ilyas et al. (2014),c, Tahmasebi et al. (2017),a, Xu et al. (2017)a Flag leaf persistence 2D, 3B, 4B, 5A, 6A Verma et al. (2004)a Net photosynthetic rate 6B Xu et al. (2017)a Chlorophyll fluorescence 1B, 2A, 2D, 3A, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A, 7B, 7D Czyczyło-Mysza et al. (2011)d Stomatal conductance 5A Xu et al. (2017)a Stomatal density 5BS Shahinnia et al. (2016)c Stomatal index 5BS, 6DL Shahinnia et al. (2016)c Stomatal aperture length 2BL, 4BS, 7AS, 7DL Shahinnia et al. (2016)c Guard cell area 1BL, 5BS Shahinnia et al. (2016)c Guard cell length 1BL, 4BS, 7AS Shahinnia et al. (2016)c Transpiration rate 3Al, 4BL, 6D Parent et al. (2015),c, Xu et al. (2017)a Water use efficiency 2AL, 4D Parent et al. (2015),c, Xu et al. (2017)a Heat stress Grain yield 1A, 1BL, 1D, 2BS, 3A, 3BS, 3BL, 3D, 4A, 4B, 4DL, 5A, 5B, 6A, 6B, 6D, 7AS, 7AL, 7BS, 7BL Quarrie et al. (2005),a, Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Paliwal et al. (2012),a, Merchuk-Ovnat et al. (2016),a, Ogbonnaya et al. (2017)a Grain weight spike−1 3A, 3BS, 6A, 7A, 7B Golabadi et al. (2011),a, Shirdelmoghanloo et al. (2016),c, Ogbonnaya et al. (2017)a Thousand grain weight 1A, 2A, 2B, 2D, 3A, 3BS, 3D, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7D Quarrie et al. (2005),a, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Single grain weight 2D, 3BS, 5B, 6A Shirdelmoghanloo et al. (2016)c Kernel weight index (large grains−all grains) 1A, 1D, 2B, 3B, 4B, 5A, 5B, 6A, 6B, 6D Pinto et al. (2010)a Grain number m−2 1A, 1B, 1D, 3BS, 3BL, 3D, 4A, 4B, 4D, 5B, 6A, 6B, 6D, 7A Pinto et al. (2010),a, Bennett et al. (2012)a Grain number spike−1 1A, 1B, 2A, 3B, 4B, 4D, 5D, 6A, 7B, 7D Quarrie et al. (2005),a, Golabadi et al. (2011),a, Ogbonnaya et al. (2017),a, Tahmasebi et al (2017)a Threshing index 1A, 1B, 5B Ogbonnaya et al. (2017)a Harvest index 1B, 2B, 3B, 4A, 5A, 5B, 6A, 6B, 7B Peleg et al. (2009)d Spike number m−2 1A, 1B, 3A, 3B, 4B, 5A, 5B, 7B, 7D Golabadi et al. (2011),a, Ogbonnaya et al. (2017)a Spike number plant−1 3A Quarrie et al. (2005)a Spike weight 1B, 2B, 2D, 3D, 4A, 5D, 6A, 7B Peleg et al. (2009),d, Golabadi et al. (2011),a, Ogbonnaya et al. (2017)a Spike harvest index 2B, 5B, 7A, 7B Golabadi et al. (2011)a Spikelet compactness 1A Tahmasebi et al. (2017)a Spikelet number spike−1 1B, 1D, 2B, 4A, 5B, 6A, 6B Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Number of productive tiller 1B Sharma et al. (2016)a Biomass 1BL, 2BS, 7AS, 7BS Merchuk-Ovnat et al. (2016)a Shoot biomass 3BS, 4A, 6B Shirdelmoghanloo et al. (2016)c Plant height 1A, 1B, 2A, 2B, 2D, 3A, 3B, 3D, 4A, 4B, 5A, 5B, 6A, 6D, 7A, 7B, 7D Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Shoot length 1B, 2B, 3A, 3B, 4A, 4B, 5D, 7A, 7B Peleg et al. (2009),d, Ogbonnaya et al. (2017)a Peduncle length 1A, 1B, 2B, 3A, 3B, 5B, 7A Ogbonnaya et al. (2017)a Flag leaf length 3B, 5B Mason et al. (2010)c Flag leaf width 1D, 2B, 3BL, 7A, 3BL Mason et al. (2010),c, Bennett et al. (2012)a Wax score 1B, 2A, 2B, 2D, 3A, 3B, 5A, 6A, 6B, 7B Mason et al. (2010),c, Ogbonnaya et al. (2017)a Days to heading 1BL, 1D, 2A, 2BS, 3B, 3A, 4A, 4B, 4D, 5A, 6A, 7AS, 7BS, 7D Maccaferri et al. (2008)a,b, Peleg et al. (2009),d, Pinto et al. (2010),a, Merchuk- Ovnat et al. (2016),a, Ogbonnaya et al. (2017)a Days to flowering 1B, 1D, 4A, 4B, 4D, 5B Mason et al. (2010),c, Pinto et al. (2010)a Days to maturity 1B, 1D, 2A, 2B, 3B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B, 7DS Pinto et al. (2010),a, Bennett et al. (2012),a, Paliwal et al. (2012),a, Ogbonnaya et al. (2017)a NDVI at the vegetative stage 1B, 1D, 2B, 2D, 3A, 3B, 4A, 4D, 5A, 6A, 6B, 6D, 7A Pinto et al. (2010),a, Bennett et al. (2012)a NDVI at the grain filling stage 1A, 1B, 3A, 4A, 4B, 5A, 5B, 6A, 7B Pinto et al. (2010)a Stem WSC 1A, 1B, 2D, 3A, 3BL, 5A, 5B, 6A Pinto et al. (2010),a, Bennett et al. (2012)a Grain filling duration 1B, 1D, 2A, 2B, 2D, 3BS, 5A, 6A, 6B, 6D Mason et al. (2010),c, Shirdelmoghanloo et al. (2016),c, Ogbonnaya et al. (2017)a Canopy temperature at the vegetative stage 1A, 1B, 1D, 2B, 3A, 3BL, 4A, 4B, 5B, 6B, 7A Pinto et al. (2010),a, Bennett et al. (2012)a Canopy temperature at the grain filling stage 1A, 1B, 1D, 2B, 3BS, 3BL, 4A, 4D, 5A, 5D, 7A, 7B Pinto et al. (2010),a, Bennett et al. (2012)a Canopy temperature depression 7BL Paliwal et al. (2012)a Flag leaf rolling 1A, 2A, 2B, 2D, 3D, 4B, 5A, 5B, 6A, 6B, 7A, 7B Peleg et al. (2009),d, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Early vigour 2B, 2D, 3BL Bennett et al. (2012)a Chlorophyll content 1A, 1B, 1D, 2B, 3A, 3BS, 4A, 4D, 5A, 5B, 6A, 6D, 7A, 7B, 7D Peleg et al. (2009),d, Pinto et al. (2010),a, Bennett et al. (2012),a, Tahmasebi et al. (2017)a Flag leaf persistence 1B, 1D, 2A, 3A, 3BS, 6A, 6B, 7A, Vijayalakshmi et al. (2010),c, Talukder et al. (2014),c, Shirdelmoghanloo et al. (2016)c Chlorophyll loss rate 3BS, 6BL Shirdelmoghanloo et al. (2016)c Chlorophyll fluorescence 7A Vijayalakshmi et al. (2010)c Carbon isotope discrimination 1A, 2A, 4A, 5B, 6A, 6B, 7B Peleg et al. (2009)d Leaf osmotic potential 2A, 3A, 3B, 5A, 5B, 6A, 6B Peleg et al. (2009) Plasma membrane damage 1D, 2B, 7A Talukder et al. (2014)c Thylakoid membrane damage 1D, 6A, 7A Talukder et al. (2014)c Trait Chromosome References Combined dry and hot conditions Grain yield 1AL, 1B, 1D, 2A, 2BL, 3A, 3B, 4AL, 4B, 5A, 6A, 6B, 7A, 7B, 7D Kirigwi et al. (2007),a, Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Merchuk-Ovnat et al. (2016),a, Tahmasebi et al. (2017)a Thousand grain weight 1D, 2B, 3A, 3B, 4A, 6A, 7A, 7B, 7D Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Tahmasebi et al. (2017)a Kernel weight index (large grains−all grains) 1A, 2B, 6A Pinto et al. (2010)a Grain weight spike−1 5B, 6A, 7B Golabadi et al. (2011)a Grain number m−2 1B, 2A, 3B, 3D, 4AL, 6B, 7A Kirigwi et al. (2007),a, Pinto et al. (2010),a, Bennett et al. (2012)a Grain number spike−1 2B, 7B Golabadi et al. (2011),a, Tahmasebi et al. (2017)a Harvest index 1B, 2A, 2B, 3B, 4A, 5A, 5B, 6A, 6B, 7B Peleg et al. (2009),d, Golabadi et al. (2011)a Spike weight 1B, 2A, 4A, 6A, 7A, 7B Peleg et al. (2009),d, Golabadi et al. (2011)a Spike number m−2 2B, 4AL, 5B Kirigwi et al. (2007),a, Golabadi et al. (2011)a Spike harvest index 2B, 3B Golabadi et al. (2011)a Spikelet number spike−1 5A Tahmasebi et al. (2017)a Biomass 2BS, 4AL, 4B, 5A, 7AS Kirigwi et al. (2007),a, Peleg et al. (2009),d, Merchuk-Ovnat et al. (2016)a Plant height 1A, 1B, 2BL, 3AL, 3BS, 4A, 4B, 5A, 7AS Maccaferri et al. (2008),ab, Pinto et al. (2010),a, Tahmasebi et al. (2017)a Shoot length 2B, 3B, 4A, 4B, 6B, 7A, 7B Peleg et al. (2009)d Peduncle length 3A, 3B Bennett et al. (2012)a Flag leaf width 2B, 3B Bennett et al. (2012)a Days to heading 1A, 1B, 1D, 2AS, 2BS, 2BL, 3A, 3B, 4AL, 4B, 4D, 5A, 6A, 7AS, 7BS, 7D Kirigwi et al. (2007),a, Maccaferri et al. (2008)a,b, Peleg et al. (2009),d, Pinto et al. (2010),a, Merchuk-Ovnat et al. (2016),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Days to maturity 1A, 1D,5A, 7B, 7D Pinto et al. (2010),a, Tahmasebi et al. (2017)a Days from heading to maturity 1B, 2B, 4A, 4B, 5A, 5B, 7A, 7B Peleg et al. (2009)d NDVI at the vegetative stage 1B, 3B, 4A, 7A Pinto et al. (2010),a, Bennett et al. (2012)a NDVI at the grain filling stage 1B, 1D, 2A, 2B, 4A, 4B, 5A, 6A, 6B, 7A, 7B Pinto et al. (2010)a Stem WSC 1A, 1B, 3A, 3B, 4A, 6D Pinto et al. (2010),a, Bennett et al. (2012)a Grain fill rate 4AL Kirigwi et al. (2007)a Grain fill duration 4AL Kirigwi et al. (2007)a Canopy temperature at the vegetative stage 1B, 2B, 3B, 4A, 4B, 6B, 7A Pinto et al. (2010),a, Tahmasebi et al. (2017)a Canopy temperature at the grain filling stage 1A, 1B, 2B, 3B, 4A, 5A, 6B, 7A Pinto et al. (2010)a Canopy temperature depression 1A, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Flag leaf rolling 1A, 2A, 2B, 4B, 5A, 5B, 6B, 7A, 7D Peleg et al. (2009),d, Tahmasebi et al. (2017)a Early vigour 2B, 2D, 3B, 4A Bennett et al. (2012)a Early ground cover 6AS Mondal et al. (2017)a Chlorophyll content 1A, 1B, 3A, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A Diab et al. (2008),a, Peleg et al. (2009),d, Bennett et al. (2012)a Chlorophyll fluorescence 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Carbon isotope discrimination 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6B, 7A, 7B Diab et al. (2008),a, Peleg et al. (2009)d Photosynthetically active radiation 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Stomatal density 4AS, 5AS, 7AL Shahinnia et al. (2016)a Stomatal index 2BL, 7BL Shahinnia et al. (2016)a Stomatal aperture area 7AL Shahinnia et al. (2016)a Stomatal aperture length 2BS, 2BL, 7AL Shahinnia et al. (2016)a Guard cell length 1AS, 3BL, 7AL Shahinnia et al. (2016)a Guard cell area 1BL, 4BL, 5AL, 5DL Shahinnia et al. (2016)a Transpiration efficiency 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Leaf relative water content 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B Diab et al. (2008)a Water index 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Leaf osmotic potential 2A, 2B, 3A, 3B, 4B, 5A, 5B, 6B Peleg et al. (2009)d Osmotic adjustment 1A, 3A, 3B, 4A, 7A Diab et al. (2008)a Metabolites (mQTL) 2B, 4A, 5A, 7A, 7D Hill et al. (2015)a Expression of stress-related genes (eQTL) 6BL Aprile et al. (2013)c Drought stress Grain yield 2D, 3D, 3DL, 4AL, 4BS, 4DL, 5A, 5B, 5DL, 6B, 6D, 7AL, 7BL, 7D Quarrie et al. (2005),a, Czyczyło-Mysza et al. (2011),d, Kadam et al. (2012),c, Tahmasebi et al. (2017)a Grain weight spike−1 1B, 1D Xu et al. (2017)a Thousand grain weight 1B, 1D, 2A, 2B, 3A, 3D, 4A, 4D, 5A, 6A, 6D, 7A, 7B Quarrie et al. (2005),a, Dashti et al. (2007),c, Yang et al. (2007),a, Tahmasebi et al. (2017),a, Xu et al. (2017)a Grain number m−2 1B, 5B, 7D Tahmasebi et al. (2017)a Grain number spike−1 1A, 2A, 2B, 2D, 3A, 3B, 4A, 4B, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B Quarrie et al. (2005),a, Czyczyło-Mysza et al. (2011),d, Xu et al. (2017)a Harvest index 1B, 2D, 4BS, 5A Kadam et al. (2012),c, Xu et al. (2017)a Spike number plant−1 1A, 2A, 2B, 2D, 4B, 5A, 7B Quarrie et al. (2005),a, Xu et al. (2017)a Spikelet compactness 6A, 7A Xu et al. (2017)a Spikelet number spike−1 1A, 7D Tahmasebi et al. (2017),a, Xu et al. (2017)a Sterile spikelet number spike−1 7A Xu et al. (2017)a Fertile spikelet spike−1 2A Xu et al. (2017)a Biomass 1B Xu et al. (2017)a Shoot biomass 4B Kadam et al. (2012)c Root biomass 2D, 4BS Kadam et al. (2012)c Plant height 1B, 4B, 7D Tahmasebi et al. (2017),a, Xu et al. (2017)a Peduncle length 3B Dashti et al. (2007)c Coleoptile length 6AS Spielmeyer et al. (2007)c Spike length 2B, 7A, 7B Xu et al. (2017)a Root length 2D, 4B, 5D, 6B Kadam et al. (2012)c Growth rate 5BL Parent et al. (2015)c Relative growth rate 4AL Parent et al. (2015)c Inflexion point in growth curves 7DS Parent et al. (2015)c Leaf expansion rate 5BL Parent et al. (2015)c Inflexion point in leaf expansion curves 5BL Parent et al. (2015)c Days to heading 1D, 4B, 7D Tahmasebi et al. (2017)a Days to flowering 2D Kadam et al. (2012)c Stem WSC at the flowering stage 1A, 1D, 2D, 4A, 4B, 7B Yang et al. (2007)a Stem WSC at the grain filling stage 4A Yang et al. (2007)a Stem WSC at the maturity stage 6B Yang et al. (2007)a Accumulation efficiency of stem WSC 1A, 2A, 5A, 7B Yang et al. (2007)a Remobilization efficiency of stem WSC 7A Yang et al. (2007)a Grain filling efficiency 2A, 4B, 5A, Yang et al. (2007)a Flag leaf rolling 4B, 5A Tahmasebi et al. (2017)a Chlorophyll content 1B, 2B, 5B, 7A, 7B Ilyas et al. (2014),c, Tahmasebi et al. (2017),a, Xu et al. (2017)a Flag leaf persistence 2D, 3B, 4B, 5A, 6A Verma et al. (2004)a Net photosynthetic rate 6B Xu et al. (2017)a Chlorophyll fluorescence 1B, 2A, 2D, 3A, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A, 7B, 7D Czyczyło-Mysza et al. (2011)d Stomatal conductance 5A Xu et al. (2017)a Stomatal density 5BS Shahinnia et al. (2016)c Stomatal index 5BS, 6DL Shahinnia et al. (2016)c Stomatal aperture length 2BL, 4BS, 7AS, 7DL Shahinnia et al. (2016)c Guard cell area 1BL, 5BS Shahinnia et al. (2016)c Guard cell length 1BL, 4BS, 7AS Shahinnia et al. (2016)c Transpiration rate 3Al, 4BL, 6D Parent et al. (2015),c, Xu et al. (2017)a Water use efficiency 2AL, 4D Parent et al. (2015),c, Xu et al. (2017)a Heat stress Grain yield 1A, 1BL, 1D, 2BS, 3A, 3BS, 3BL, 3D, 4A, 4B, 4DL, 5A, 5B, 6A, 6B, 6D, 7AS, 7AL, 7BS, 7BL Quarrie et al. (2005),a, Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Paliwal et al. (2012),a, Merchuk-Ovnat et al. (2016),a, Ogbonnaya et al. (2017)a Grain weight spike−1 3A, 3BS, 6A, 7A, 7B Golabadi et al. (2011),a, Shirdelmoghanloo et al. (2016),c, Ogbonnaya et al. (2017)a Thousand grain weight 1A, 2A, 2B, 2D, 3A, 3BS, 3D, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7D Quarrie et al. (2005),a, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Single grain weight 2D, 3BS, 5B, 6A Shirdelmoghanloo et al. (2016)c Kernel weight index (large grains−all grains) 1A, 1D, 2B, 3B, 4B, 5A, 5B, 6A, 6B, 6D Pinto et al. (2010)a Grain number m−2 1A, 1B, 1D, 3BS, 3BL, 3D, 4A, 4B, 4D, 5B, 6A, 6B, 6D, 7A Pinto et al. (2010),a, Bennett et al. (2012)a Grain number spike−1 1A, 1B, 2A, 3B, 4B, 4D, 5D, 6A, 7B, 7D Quarrie et al. (2005),a, Golabadi et al. (2011),a, Ogbonnaya et al. (2017),a, Tahmasebi et al (2017)a Threshing index 1A, 1B, 5B Ogbonnaya et al. (2017)a Harvest index 1B, 2B, 3B, 4A, 5A, 5B, 6A, 6B, 7B Peleg et al. (2009)d Spike number m−2 1A, 1B, 3A, 3B, 4B, 5A, 5B, 7B, 7D Golabadi et al. (2011),a, Ogbonnaya et al. (2017)a Spike number plant−1 3A Quarrie et al. (2005)a Spike weight 1B, 2B, 2D, 3D, 4A, 5D, 6A, 7B Peleg et al. (2009),d, Golabadi et al. (2011),a, Ogbonnaya et al. (2017)a Spike harvest index 2B, 5B, 7A, 7B Golabadi et al. (2011)a Spikelet compactness 1A Tahmasebi et al. (2017)a Spikelet number spike−1 1B, 1D, 2B, 4A, 5B, 6A, 6B Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Number of productive tiller 1B Sharma et al. (2016)a Biomass 1BL, 2BS, 7AS, 7BS Merchuk-Ovnat et al. (2016)a Shoot biomass 3BS, 4A, 6B Shirdelmoghanloo et al. (2016)c Plant height 1A, 1B, 2A, 2B, 2D, 3A, 3B, 3D, 4A, 4B, 5A, 5B, 6A, 6D, 7A, 7B, 7D Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Shoot length 1B, 2B, 3A, 3B, 4A, 4B, 5D, 7A, 7B Peleg et al. (2009),d, Ogbonnaya et al. (2017)a Peduncle length 1A, 1B, 2B, 3A, 3B, 5B, 7A Ogbonnaya et al. (2017)a Flag leaf length 3B, 5B Mason et al. (2010)c Flag leaf width 1D, 2B, 3BL, 7A, 3BL Mason et al. (2010),c, Bennett et al. (2012)a Wax score 1B, 2A, 2B, 2D, 3A, 3B, 5A, 6A, 6B, 7B Mason et al. (2010),c, Ogbonnaya et al. (2017)a Days to heading 1BL, 1D, 2A, 2BS, 3B, 3A, 4A, 4B, 4D, 5A, 6A, 7AS, 7BS, 7D Maccaferri et al. (2008)a,b, Peleg et al. (2009),d, Pinto et al. (2010),a, Merchuk- Ovnat et al. (2016),a, Ogbonnaya et al. (2017)a Days to flowering 1B, 1D, 4A, 4B, 4D, 5B Mason et al. (2010),c, Pinto et al. (2010)a Days to maturity 1B, 1D, 2A, 2B, 3B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B, 7DS Pinto et al. (2010),a, Bennett et al. (2012),a, Paliwal et al. (2012),a, Ogbonnaya et al. (2017)a NDVI at the vegetative stage 1B, 1D, 2B, 2D, 3A, 3B, 4A, 4D, 5A, 6A, 6B, 6D, 7A Pinto et al. (2010),a, Bennett et al. (2012)a NDVI at the grain filling stage 1A, 1B, 3A, 4A, 4B, 5A, 5B, 6A, 7B Pinto et al. (2010)a Stem WSC 1A, 1B, 2D, 3A, 3BL, 5A, 5B, 6A Pinto et al. (2010),a, Bennett et al. (2012)a Grain filling duration 1B, 1D, 2A, 2B, 2D, 3BS, 5A, 6A, 6B, 6D Mason et al. (2010),c, Shirdelmoghanloo et al. (2016),c, Ogbonnaya et al. (2017)a Canopy temperature at the vegetative stage 1A, 1B, 1D, 2B, 3A, 3BL, 4A, 4B, 5B, 6B, 7A Pinto et al. (2010),a, Bennett et al. (2012)a Canopy temperature at the grain filling stage 1A, 1B, 1D, 2B, 3BS, 3BL, 4A, 4D, 5A, 5D, 7A, 7B Pinto et al. (2010),a, Bennett et al. (2012)a Canopy temperature depression 7BL Paliwal et al. (2012)a Flag leaf rolling 1A, 2A, 2B, 2D, 3D, 4B, 5A, 5B, 6A, 6B, 7A, 7B Peleg et al. (2009),d, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Early vigour 2B, 2D, 3BL Bennett et al. (2012)a Chlorophyll content 1A, 1B, 1D, 2B, 3A, 3BS, 4A, 4D, 5A, 5B, 6A, 6D, 7A, 7B, 7D Peleg et al. (2009),d, Pinto et al. (2010),a, Bennett et al. (2012),a, Tahmasebi et al. (2017)a Flag leaf persistence 1B, 1D, 2A, 3A, 3BS, 6A, 6B, 7A, Vijayalakshmi et al. (2010),c, Talukder et al. (2014),c, Shirdelmoghanloo et al. (2016)c Chlorophyll loss rate 3BS, 6BL Shirdelmoghanloo et al. (2016)c Chlorophyll fluorescence 7A Vijayalakshmi et al. (2010)c Carbon isotope discrimination 1A, 2A, 4A, 5B, 6A, 6B, 7B Peleg et al. (2009)d Leaf osmotic potential 2A, 3A, 3B, 5A, 5B, 6A, 6B Peleg et al. (2009) Plasma membrane damage 1D, 2B, 7A Talukder et al. (2014)c Thylakoid membrane damage 1D, 6A, 7A Talukder et al. (2014)c Dry and hot field conditions are defined using the CIMMYT mega-environments 1 and 4 (Rajaram et al., 1994). NDVI, near differential vegetative index; WSC, water-soluble carbohydrates a Field conditions. b Trials in Italy, Tunisia and Morocco with maximum temperature at grain filling ≤26.1 °C. c Controlled conditions. d Semi-controlled conditions. View Large A greater understanding of the physiology underlying combined drought and heat tolerance should enable researchers and breeders to discriminate between traits and loci useful for improvement. With improving genomic resources and high-throughput phenotyping methods, it becomes possible to identify loci and genes for tolerance and incorporate favourable alleles into breeding programmes. In this review, we outline what is known in wheat of the physiology and genetic variation underlying drought and heat tolerance – defined here as the ability to maintain yield under stress. We propose traits to measure in genetic mapping populations that are likely to prove beneficial for combined tolerance (Fig. 1) and discuss opportunities and constraints for incorporating alleles into breeding for tolerant wheat. Fig. 1. View largeDownload slide Beneficial traits for combined drought and heat tolerance in wheat. Fig. 1. View largeDownload slide Beneficial traits for combined drought and heat tolerance in wheat. Wheat growth, architecture and biomass partitioning under drought and heat Water deficit and high temperature affect every aspect of wheat growth from germination to maturity. The impact on yield components depends on the duration and the severity of the stress as well as the stage of plant development when stress occurs (Salter and Goode, 1967; Barnabás et al., 2008; Parent et al., 2017). As water stress reduces plant growth through reduced tillering and leaf expansion (Acevedo et al., 1971), and high temperature accelerates plant growth and shortens developmental stages (Parent and Tardieu, 2012), under combined stress plants flower earlier and produce less biomass than under single stress. Reproductive organs are especially sensitive to drought and heat stress (Stone and Nicolas, 1995; Saini and Lalonde, 1997). Episodes of drought and heat stress around anthesis severely reduce the final number of grains per spike by more than either individual stress due to an increased abortion of ovules (Asana and Williams, 1965; Hochman, 1982; Saini and Aspinall, 1982; Pradhan et al., 2012; Weldearegay et al., 2012). During grain filling, combined drought and high temperature, as frequently occur in major growing regions, reduce the size and weight of individual grains by reducing the division rate of endosperm cells and shortening the duration of grain filling (Jenner, 1994; Barnabás et al., 2008; Prasad et al., 2011; Pradhan et al., 2012). Complex source–sink interactions underlie tolerance to drought and heat stress, and remobilization of stored assimilates to grain filling following stress at sensitive periods is dependent on sink strength. In maize, grain size, determining sink strength for grain filling, is determined by expansive plant growth, which is the increase in volume due to water entry into growing cells (Tardieu et al., 2014). There is limited evidence for differences in carbon metabolism or status in ovules under stress, but many studies demonstrate reductions in organ elongation rates at sensitive periods with either drought or heat stress. In maize, silk growth and leaf elongation rate are highly correlated (Parent and Tardieu, 2012; Tardieu et al., 2014). When the PLASTOCHRON1 (ZmPLA1) gene was expressed in maize, increasing the length of the cell division zone, the duration of cell division, the duration of leaf elongation, kernel number, and size were increased in field experiments under mild drought (Sun et al., 2017). QTLs for organ size and growth and expansion rates have been identified in wheat under drought (Table 1) but have not been studied under combined drought and heat stress, and no studies of genetic variation for the expansive growth trait have yet been carried out. Theoretically, increased expansive growth will be beneficial for combined drought and heat tolerance where loss of grain number is due to reduction in spike growth and development. Expansive growth will increase sink strength and be beneficial for remobilization of assimilates to the grain during filling. Traits that increase overall assimilation should increase drought and heat tolerance when partitioned beneficially to the grain. Several QTLs for harvest index (HI) have been reported (Table 1). Meta-analysis of reported QTLs for drought or heat stress revealed meta-QTLs for spike weight/density and plant height were significantly (at P<0.1) associated with meta-QTL regions for yield under drought or heat in wheat (Acuña-Galindo et al. 2015). Major clusters were located at the Rht-B1 and Rht-D1 dwarfing loci. Plant height restriction due to the Rht-B1 allele increases HI and is due to gibberellin insensitivity (Peng et al., 1999). In barley, exogenous gibberellin application increases sensitivity to high temperature stress (Vettakkorumakankav et al., 1999), so it is possible that widely used dwarfing alleles in modern, semi-dwarf wheat varieties already contribute to heat tolerance through the gibberellin pathway. Modern, semi-dwarf phenotypes are already widely used to prevent undesirable lodging, but there are alleles that appear more or less beneficial in particular environments. For example, Wang et al. (2014b) suggested that the Rht13 or combination of Rht13 + Rht8 alleles could be favourable in water-limited environments. Thus, there is scope to study and improve wheat drought and heat tolerance through the deployment of new combinations of dwarfing alleles, identification of genes controlling the gibberellin pathway, and optimization of expansive growth (Fig. 1). Breeding for canopy temperature and evapotranspiration under drought and heat The main mechanism wheat plants use to decrease their internal temperatures under heat stress is evaporative cooling, driven by transpiration. Under drought, plants close their stomata to avoid excessive water loss; this reduces transpiration and evaporative cooling and, as a result, drought-stressed plants display higher leaf and canopy temperatures than well-watered plants (Reynolds et al., 2009). Cool canopies were always associated with better yield performance (Pinto and Reynolds, 2015). Several QTLs have been reported for canopy temperature depression under drought and heat in wheat grown in deep soils of northern Mexico (Pinto et al., 2010; Pinto and Reynolds, 2015). The major QTLs on chromosome 2B were shown to be associated with root distribution, with cool canopy genotypes able to extract more water at depth under water stress due to a greater proportion of deeper roots (Pinto and Reynolds, 2015). The deep root trait was not recapitulated under heat stress alone (with irrigation) (Pinto and Reynolds, 2015). This suggested that the beneficial physiological trait conferred by the 2B QTL was not a different root system architecture or distribution per se, but the ability to optimize root distribution to capture water for continued cooling dependent on water distribution in the soil. Transpiration efficiency is a ratio between biomass and transpiration, while water use efficiency (WUE) is the biomass produced per unit of water used, at the whole plant level or whole plot in the field. Carbon isotope discrimination (12C/13C ratio) in dry matter is negatively correlated to transpiration efficiency in wheat and a surrogate for this trait (Condon et al., 1990). It has been successfully used for breeding water use efficient wheat for dry regions in Australia (Condon et al., 1990, 2002). Increased transpiration efficiency alone might not improve tolerance. The equation for grain yield in water-limited environments includes harvest index (HI) and water use (WU) as well as WUE (Passioura, 1977; Passioura, 1996): GY=HI×WU×WUE. The theoretical physiology underlying this relationship has been extensively explained and reviewed (Ehrler et al., 1978; Araus et al., 2002; Blum, 2005; Reynolds et al., 2007; Fischer, 2011; Vadez et al., 2014). It has been argued that, if transpiration efficiency is increased by a reduction in the transpiration term of the equation, a low intrinsic stomatal conductance and transpiration reduces growth, biomass accumulation and light interception. Therefore, selecting plants with high transpiration efficiency might select for smaller plants (Blum, 2009). When small plants are selected, sink strength is lost and fewer assimilates are mobilized to the grain. Under the combination of drought and heat, low intrinsic transpiration could, additionally, penalize evaporative cooling. Reynolds et al. (2007) found that carbon isotope discrimination, together with canopy temperature linked to water uptake, was associated with improved performance in drought-stressed environments. Diab et al. (2008) found QTLs associated with tolerance in wheat for canopy temperature depression, transpiration efficiency, water index, and grain carbon isotope discrimination in dry and hot field conditions (Table 1). Evaporative demand, or VPD, which depends on the amount of moisture in the air and the air temperature, also plays a critical role in transpiration and transpiration efficiency. Different sensitivities of transpiration to high VPD have been found amongst wheats and its genetic control described in the Australian wheat population RAC875/Kukri (Schoppach et al., 2016). Six QTLs were identified for transpiration response to VPD, with one QTL on chromosome 5A individually explaining 25.4% of the genetic variance (Schoppach et al., 2016). A study of 23 Australian wheat varieties released from 1890 to 2008 showed that whole-plant transpiration rate in response to VPD was limited at VPD above a breakpoint of about 2 kPa (Schoppach et al., 2016). The breakpoint and transpiration response at VPD>2 kPa were correlated with the year of release indicating that breeders, by selecting for yield in the hot and dry climate of southern Australia, selected lines with limited whole-plant transpiration rate. Transpiration rate might also be moderated by patchy stomatal closure and the threshold for closure might differ in sensitivity between VPD and soil moisture deficit (Vadez et al., 2014). In maize, the relationship between expansive growth (leaf expansion rate; LER) and stomatal conductance was rapid and linear in contrast to the relationship between LER and transpiration rate (Caldeira et al., 2014b). Tardieu et al. (2014) suggest that this is because increases in biomass and in expansive growth in volume are under different genetic controls and that, under water deficit, they are uncoupled over time. Because of the dependence of transpiration efficiency on both the biomass term and VPD, transpiration response traits should be evaluated in QTL studies. To keep an optimal balance between evaporative cooling and water saving, plants with fine adjustment of transpiration should have an advantage under combined drought and heat (Fig. 1). Temporal regulation of gas exchange Vadez et al. (2014) have argued that the total plant water use over the growing season and WUE for yield depend on available water and use at critical stages. Plants can increase effective use of water by timely modifications of water uptake at critical stages. Timely modifications in stomatal conductance, transpiration, and water use might include different patterns of stomatal opening with developmental stage, time of the day, time of season, and microclimate VPD driven by differences in plant architecture. High stomatal densities and conductance are associated with increased yield potential in both well-watered and water-limited environments (reviewed in Roche, 2015). High stomatal density could give more flexibility to the plant to adjust stomatal opening depending on the local environmental conditions and ensure continued water uptake and use under favourable conditions. For example, the Australian line RAC875, which is drought and heat tolerant, has many small stomata by contrast with the susceptible Australian variety Kukri with fewer large stomata (Shahinnia et al., 2016). QTLs for stomatal size and density have been identified in dry and hot field conditions in wheat (Table 1). While no correlation was found between yield and stomatal traits in the RAC875/Kukri population, we found a locus for stomatal density and size on chromosome 7A that overlaps with QTLs for grain number per spike, normalized difference vegetation index, harvest index, and yield in the same population (Shahinnia et al., 2016). When heat stress is severe, leaf stomata will open to allow evaporative cooling despite water limitation. At very high temperatures, the photosynthetic machinery is damaged (Berry and Bjorkman, 1980) and leaf or other vegetative tissues may be sacrificed (Lohraseb et al., 2017). Under combined drought and heat stress, this balance between open stomata and damaged photosynthetic machinery can become critical to allow continued assimilation and can depend on the fine spatiotemporal regulation of gas exchange. That is, continued assimilation in periods of lower stress, as temperatures rise and cool diurnally, may make a plant more tolerant (Richards et al., 1986). Diurnal regulation of gas exchange will make a difference during stress exposure and circadian use of water and regulation of transpiration may both alleviate combined drought and heat stress and be a source of tolerance. A shift in transpiration to cooler times of the day could confer tolerance. Nocturnal water use, particularly night-time transpiration, is of increasing interest for its role in sustaining sugars export at night (Marks and Lechowicz, 2007) and its potential role in drought tolerance in wheat (Schoppach et al., 2014; Resco de Dios et al., 2016; Sadok, 2016). Genotypic variation for night-time transpiration and its sensitivity to VPD has been documented in wheat and influences the next day’s gas exchange under normal conditions and drought (Schoppach and Sadok, 2013; Schoppach et al., 2014; Claverie et al., 2017). Night-time transpiration rate in response to VPD varied consistently with the sensitivity of the genotypes to drought and increased under soil water deficit (Claverie et al., 2017). The effect of night-time temperature was also significant, with an increase in transpiration with increasing temperature observed, as well as genotypic variation. Despite the importance of nocturnal water use for potential drought and heat stress tolerance, no genetic studies have yet been carried out in wheat and no QTLs are known. The interplay between night-time export of assimilates and day-time gas exchange is also yet to be explored. Supply and demand ratios are likely to play a role in determining assimilation and export and, as yet, no studies of circadian regulation in wheat have been carried out in plants during grain filling when grains determine sink strength. With the development of non-destructive phenotyping methods, it will become possible to collect plant data over time and examine the kinematics of plant physiology. Optimal hydraulic conductance for drought and heat tolerance Hydraulic conductance is a measure of the flow induced by a pressure or water potential gradient normalized to the plant/organ geometry. Caldeira et al. (2014b) proposed that circadian oscillations of hydraulic conductance accounted for fluctuating growth (leaf elongation rates) in Arabidopsis. The degree of oscillation was highly dependent on evaporative demand and water stress. High root hydraulic conductance oscillation under water deficit likely led to the ability to control water uptake in response to available soil water when needed. Soil water status regulates the root hydraulic conductance of maize (Caldeira et al., 2014a) adjusting growth to water availability. Maintenance of high hydraulic conductance in spikes of long-awned cultivars of wheat significantly reduces spike temperature during grain filling (Maydup et al., 2014). The end of grain filling correlates with a loss of hydraulic conductance at the rachis-xylem conduit (Neghliz et al., 2016). Thus, we hypothesize that by maintaining optimal hydraulic conductance in the different tissues under drought and heat stress (Fig. 1), wheat plants could extend grain filling duration, cool down grain and spike, and optimize water uptake for expansive growth. In grapevine, soil–leaf differences in water potential among genotypes were shown to be less related to sensitivity of transpiration to soil water deficit than to change in soil–leaf hydraulic conductance, likely due to rapid changes in water transport within the plant (Scharwies and Tyerman, 2017). The ability to partition and channel water between stem, leaf, tillers, and spikes determines both expansive growth in these tissues and remobilization of assimilates following stress. Differences in hydraulic resistances in different tissues influence water transport capacity and drought and heat tolerance (Coupel-Ledru et al., 2014; Bramley et al., 2015). Hydraulic resistance may be determined by differences in structure and architecture of stems, peduncles, and rachis, and differences in xylem vessel diameter and leaf venation (Scharwies and Tyerman, 2017). Vessel structure has an important role in the control of water conductivity in plants in water-limited environments (Tixier et al., 2013; Caringella et al., 2015; Kadam et al., 2015). In wheat, Barlow et al. (1980) demonstrated that a xylem discontinuity at the base of the peduncle permitted the isolation of spike hydraulics from the rest of the plant, and that this anatomical feature was crucial during water scarcity, resulting in the independence of water relations in the spike from the rest of the plant. The xylem in wheat is also discontinuous between rachis and grains, isolating grains and, potentially, preventing water loss during stress (Zee and O’brien, 1970). Photoperiod response (Ppd loci) genes have pleiotropic effects on plant growth and development (Cockram et al., 2007) that can modify plant hydraulics. The photoperiod sensitive allele Ppd-D1 increases daytime and night-time transpiration while decreasing whole-plant leaf area in response to VPD increase in wheat (Schoppach et al., 2016). This suggests that whole-plant hydraulics are developmentally controlled. Deciphering the relationship between vessel structure and plant hydraulics and the genetic control of plant development in wheat will provide a better understanding of the involvement of these physiological mechanisms in tolerance to combined drought and heat stress and their potential for breeding tolerant varieties. Competition for assimilates under drought and heat stress Redox balance is crucial for the normal function of many cellular processes. Its fine control is essential for a proper integration of environmental and developmental stimuli and signal transduction (Choudhury et al., 2017). Recent studies demonstrated the important role of photorespiration in maintaining redox homeostasis (Scheibe and Dietz, 2012), mitigating oxidative stress and protecting the photosynthetic apparatus from photoinhibition (Rivero et al., 2009; Peterhansel and Maurino, 2011; Voss et al., 2013). With either drought or heat stress, net photosynthesis is reduced and photorespiration increased (Long and Ort, 2010), but the relative contributions of photorespiration and mitochondrial respiration to combined drought and heat stress tolerance in wheat are unknown and genetic variation for this ratio has not been explored. Heat stress affects membrane stability and the quantum efficiency of photosystem II, which can be measured, respectively, as cell viability and chlorophyll fluorescence (Blum, 1988; Mohammed and Tarpley, 2009). Drought stress also affects chlorophyll fluorescence with a dramatic decrease of Fv/Fm ratio in susceptible wheat compared with tolerant lines (Izanloo et al., 2008). QTLs have been reported for chlorophyll fluorescence in drought- or heat-stressed wheat (Table 1), but studies in other species suggest that responses to combined drought and heat stress are unique in comparison with either individual stress (Mittler, 2006). At the ecosystem level, drought may actually reduce heat-driven increases in plant respiration due to reduction in carbon substrates available (Schauberger et al., 2017). However, if stored carbohydrates are used for respiration and less available for remobilization following heat stress, drought may exacerbate the effect of heat stress-induced increases in respiration. The rate of grain filling from stem reserves is increased with increasing temperature, reducing grain filling duration (Blum et al., 1994). Tolerance to drought and heat stress will then depend on both the initial concentration of remobilizable carbohydrates and the use of these reserves for respiration. Genetic variation for stem water-soluble carbohydrate content has been explored with known QTLs in drought or heat stress and in combined drought and heat stress (Table 1). Yang et al. (2007) also investigated genotype × environment (G×E) interactions for QTLs for stem water-soluble carbohydrate content and remobilization efficiency under water stress in wheat and found significant interactions for all traits. They showed that not all reserves were translocated to grain following water stress and suggested that losses due to respiration could be significant. Zhang et al. (2014) explicitly investigated water-soluble carbohydrate QTLs under drought, heat, and combined drought and heat stress and were able to identify additive effects and combinations of favourable alleles for both content and remobilization, suggesting that the genetic mechanisms underlying tolerance will not depend purely on accumulation of stored carbohydrates. QTLs for respiration are now being studied in wheat for the first time under the International Wheat Yield Partnership umbrella (http://iwyp.org/wp-content/; accessed 5 February 2018). Under prolonged stress exposure, photosynthetic activity is further inhibited by excessive accumulation of reactive oxygen species (ROS), causing damage to the membranes, proteins, and chlorophyll molecules of the photosynthetic apparatus (Price and Hendry, 1991; Jiang and Huang, 2001; Allakhverdiev et al., 2008; Silva et al., 2010; Redondo-Gómez, 2013; Awasthi et al., 2014; Das et al., 2016). Plants use a complex antioxidant system to regulate ROS levels and avoid toxicity, but changes in redox status are also perceived by plants as a signature of a specific stress that will result in a corresponding acclimation response (Foyer and Noctor, 2005; Choudhury et al., 2017). The balance between accumulation of ROS in response to stress and their signalling role under stress is yet to be defined. ROS scavenging is generally induced under drought and heat stress, and higher antioxidant capacity is generally correlated with tolerance to stress (Koussevitzky et al., 2008; Suzuki et al., 2014; Wang et al., 2014a). In some wheat genotypes, tolerance to drought or heat stress was associated with increased antioxidant capacity and reduced oxidative damage (Sairam and Saxena, 2000; Sairam et al., 2000; Lascano et al., 2001; Almeselmani et al., 2006; Sečenji et al., 2010; Lu et al., 2017; Zang et al., 2017; Zhang et al., 2017). The effects of combined drought and heat on the ROS system in wheat are unknown, but recent studies highlight the importance of modulation of ROS scavenging, some pathways being specifically induced by combined stress (Rizhsky et al., 2002; Koussevitzky et al., 2008; Demirevska et al., 2010; Zandalinas et al., 2017). The alleles that regulate photorespiration, membrane stability and antioxidant capacity under drought and heat are yet to be discovered in wheat. As genomics and phenomics advance, the ability to analyse differences in physiological traits in empirical experiments has improved. Important advances in phenotyping with imaging or other equipment mean that it is possible to, for example, measure senescence or canopy temperature in real time in fields (Araus and Cairns, 2014). Further advances that allow, for example, field-scale simultaneous measurements of gas exchange, or non-destructive measurements of water-soluble carbohydrate movement can be anticipated. For researchers, these will provide a wealth of previously unquantifiable data for physiological traits. Breeding for stability, plasticity, and G×E interaction under drought and heat In past breeding of tolerant varieties, efforts have been concentrated on the search for stable QTLs that show the same allelic effect across environments to produce generalist, high-yielding varieties (Eberhart and Russell, 1966). Acuña-Galindo et al. (2015) conducted a meta-QTL analysis of 24 genetic studies where QTLs had been mapped for drought, heat, or combined stress in wheat. Co-localization with meta-QTLs for yield was only significant (at P<0.1) for the maturity/date of anthesis, spike weight/density, plant height, and canopy temperature depression QTLs. This analysis underscored the pleiotropic effects of phenology and dwarfing alleles on wheat stress response. These generalist QTLs are already bred for with Ppd and Vrn alleles routinely used in marker-assisted selection. Other stress tolerance QTLs are not generalist and have strong G×E interaction. In wheat, directional selection (Chapman et al., 2012) has been used to breed varieties that respond consistently to the target environment and management practice. Whilst this approach has been successful in achieving yield gains in some tested environments, strong G×E interactions mean that it is difficult to identify genotypes responding consistently positively in a range of stressful environments, even for a single physiological trait (Reynolds et al., 2009; Lopes et al., 2012). When testing lines bred in high- and low-moisture and reciprocal environments at different sites, Kirigwi et al. (2004) found significant environment × selection regime interactions. In this study, development in alternating high-to-low or low-to-high-moisture regimes facilitated the selection of lines that performed well for yield in both, whereas lines selected in either continuous high- or continuous low-moisture regimes had lower yields in these respective environments. The authors suggested that selection under these alternating environmental conditions favoured retention of both high yield under stress and high responsiveness to water input. In a changing environment, trait plasticity is theoretically beneficial (Bradshaw, 1965; Aspinwall et al., 2015). Plasticity can be defined as the variance in genotypic response across an environmental gradient – that is the slope of its reaction to change, with a steeper slope indicating higher plasticity (Nicotra et al., 2010). Plasticity can be measured as phenotype versus an environmental range for any trait and considered as a trait in itself (Sadras and Slafer, 2012), i.e. it has its own genetic variation and underlying QTLs. Phenotypic plasticity should be advantageous for fitness in variable environments and neutral in stable environments (Bradshaw, 1965; Nicotra et al., 2010). It can be argued that selection for plasticity QTLs, against the background of the increased pace of climate change, will prove beneficial for maintaining or improving agricultural yields (Aspinwall et al., 2015). However, plasticity is particular to the trait. For example, Sadras et al. (2009) found that high yield plasticity in wheat was disadvantageous in low-yield environments when it was associated with low plasticity of post-anthesis development. Breeding for plasticity in grain yield components coupled with plasticity for the length of the grain-filling phase will be useful but is limited due to a trade-off between low plasticity in grain size and high plasticity in grain number during this stage. Many QTLs have been found for grain production in dry and hot climates (Table 1), but very few (possibly none) are used in breeding programs. The main limiting factor to the deployment of these QTLs in breeding is the inconsistency in performances of the introgressed lines due to the strong QTL×E interaction. Although only field experiments are relevant for evaluating crop tolerance to stress as performance in an agricultural system, most studies fail to explain why a QTL is significant in one environment and not in another. Field trials are usually considered as a qualitative factor, which enables detection of G×E interactions but not its measurement (Acuña-Galindo et al., 2015). Recent development in phenomics and sensors means that we can now continuously measure soil water potential and air temperature across the crop cycle in field conditions. But how can we use these data to understand G×E? Uncoupling responsive and adaptive physiological traits is often complex and disentangling the effect of a specific environmental condition is not simple in experiments and often requires complex analysis and modelling (reviewed by Parent and Tardieu, 2014). Parent et al. (2017) described new models that exploit such data and measure a plant’s response to quantitative variations in drought and heat stress. Applied to lines that segregated for specific yield QTLs, such models revealed, in Australian wheats, that a QTL on chromosome 1B was constitutively expressed under various combinations of soil water potential and high temperature, while a QTL on chromosome 3B was heat responsive with a positive effect of the drought-tolerant parental line RAC875 when temperature was above 23 °C around flowering stage (Parent et al., 2017). This information is highly valuable as it enables us to understand a QTL’s function and use it in appropriate environments. By equipping national variety trials with sensors to measure soil moisture and air temperature, such models could also predict the level of tolerance of new varieties to quantified drought and heat. Combined with whole genome genotyping, this would provide information on the effects of haplotypes on yield in response to specific environmental conditions. Find the drought and heat tolerance genes and design the genome Another obstacle in using yield QTLs in breeding programmes is the small effect of a single QTL and the need to introgress several QTLs to gain a significant increment in yield improvement. As breeders can only recombine as many loci as the size of their breeding programmes allows, they prioritize those with strong and stable effects, such as phenology, plant height, and disease resistance, and select for yield under dry and hot environment empirically or, more recently, by genomic selection (GS). So, were the efforts in finding QTLs for drought and heat tolerance wasted? The answer is probably yes, unless we carry on the positional cloning of these QTLs and find the genes controlling combined drought and heat tolerance. Gene-level knowledge of the control of drought and heat tolerance will enable the identification and creation of new sequence variants. Although many QTLs have been found for drought or heat tolerance (Table 1), little is known about the genes underlying these effects in wheat. The molecular network of drought and heat stress response in model species includes heat shock proteins (HSPs, chaperone proteins that protect the cell machinery), a number of drought stress response or heat stress transcription factors (DSF, HSF), and signal transduction proteins (Mittler et al., 2012). A study in adult durum plants identified genes that respond specifically to combined drought and heat including a chaperone homologous to a putative t-complex protein 1 theta chain (Rizhsky et al., 2002, 2004; Rampino et al., 2012). Two classes of heat shock factors, A6 and C2, have been shown recently to enhance heat tolerance in transgenic wheat (Xue et al., 2014; Hu et al., 2018). Over-expression of TaHsfC2a-B in transgenics up-regulated a cascade of HSP genes in grains during grain filling under heat and also in leaves under drought stress. Combining positive alleles of HSF and DSF such as dehydration-responsive element-binding (DREB) proteins (Morran et al., 2011) might be a way to enhance wheat tolerance to simultaneous stress, but the positive effects will need to be tested in the field in dry and hot climates and redundancy and interactions measured. The forward genetics approach starting with a locus with a demonstrated yield effect is attractive but, to date, none of the QTLs for drought and heat tolerance (Table 1) has been cloned in wheat. While GS is an efficient tool to quickly identify the best haplotypes, it still requires the incorporation of new alleles into the breeding programme New alleles can also be found in wild relatives of wheat and landraces well adapted to local environments (Lopes et al., 2015), including hot and arid environments. Natural diversity encompasses adaptive mechanisms that wheat plants developed to cope with harsh conditions (Huang and Han, 2014). Emmer wheat and cultivated wheat’s wild relatives are sources of tolerance to high temperature or water limitation that could be used to overcome the bottleneck in genetic diversity within the cultivated wheat genepool (Feuillet et al., 2008). The usefulness of a wider germplasm is illustrated by the QTLs deriving from wild emmer wheat for drought (Peleg et al, 2005; 2009) and QTLs for salinity tolerance from Triticum monococcum (Munns et al., 2012). This is a rare example of successful introgression of a locus (Nax2) for abiotic stress tolerance in wheat, following both physiological characterization (James et al., 2006) and positional cloning of the causative gene (TmHKT1;5-A) and demonstrates the power of this approach. New alleles of known genes can also be created by deliberate mutagenesis or genome design (E. Buckler, Plant and Animal Genome conference XXVI, 2018). The ability to efficiently screen for mutations by sequencing (TILLING (Targeting Induced Local Lesions IN Genomes) by sequencing) is quite recent in wheat (Tsai et al., 2011) and is based on both an increased understanding of genomics and advances in next generation sequencing and analysis. Using this approach, Simmonds et al. (2016) were able to rapidly identify the causative mutation for the locus TaGW2-A1 and cross the mutant allele into durum and bread wheat to develop isogenic lines with increased grain weight. The advantage of a mutant collection over wild germplasm is that the new alleles are in agronomically relevant backgrounds where their effect can be readily measured. As the current sequenced collections are in English and US genetic backgrounds, namely Kronos and Cadenza (Tsai et al., 2011), the sequencing of new TILLING collections in varieties that are locally relevant to hot and dry climates is urgently needed. An alternative method is to specifically edit genes for drought and heat tolerance in a modern, relevant variety. The ability to specifically edit the wheat genome using CRISPR-cas9 ribonucleoproteins has been demonstrated in bread wheat (Liang et al., 2017). This technique promises transgene-free modification of the genome to enhance traits of agronomic interest including abiotic stress tolerance. The use of this technique, however, depends on a detailed knowledge of the sequences underlying tolerance and is likely to require cassettes of sequence edits in the case of editing for combined drought and heat tolerance for wheat. With three highly similar sub-genomes, the majority of wheat gene sequences have homeologues and the contributions of these homeologues to copy number variation and dosage-dependent expression as well as functional redundancy are often unknown in wheat but will influence the success of gene editing approaches. In some cases, a gene/QTL effect could be increased if we were to combine the positive alleles of the three homeologous copies. On a whole genome level, pan-genome data are now being used to understand and mark structural variation of this kind, for instance in maize (Lu et al., 2015). The coming together of advances in genome editing and pan-genomics in wheat should facilitate editing for the future. Conclusions Because wheat is heat tolerant when water is available (Parent et al., 2017), to improve wheat for dual tolerance, plants must be studied under the combination of stresses. Results from experiments with heat treatments and well-watered conditions are unlikely to be relevant when water is limiting in the field. There is a large body of evidence showing that water use is essential for either drought or heat tolerance and that, for tolerance of the combined stress, fine control of water relations across the growing cycle will be beneficial. This might be achieved through fine management of spatial and temporal gas exchange. For a wheat plant to be drought and heat tolerant, beneficial traits likely include the following: finely regulated transpiration through small, dense stomata, able to respond to the micro-environment (shade, water, VPD, radiation); maintenance of optimal hydraulic conductance in different tissues; a root system able to grow fast in response to water availability; water-adjustable circadian regulation of plant growth; ability to retain water in essential organs to avoid tissue dehydration; efficient HSPs to protect enzymes and membranes against high temperature; efficient carbohydrate synthesis, export, and remobilization; and an efficient ROS scavenging system (Fig. 1). The rationale for identifying and deploying alleles for combined drought and heat tolerance in wheat breeding is compelling. Improvements in phenotyping of physiological traits and genomic information are particularly encouraging as we seek to discover and incorporate, possibly, rare, novel tolerance alleles in breeding programmes. Improvement of methods capturing plant and environmental data over time will enable us to phenotype genetic populations for kinematic traits, and this will help us unravel the genetic basis of complex biological processes. Although wheat physiology under drought and heat stress is complex, this complexity and plasticity in itself provides sources of tolerance and hope. Modifying a single trait might not have a significant effect on yield under stress as some of these traits are co-dependent and would be effective only in combination. Rather than improving a single trait at a time, we might need to combine them in order to increase yield. With underscoring genetic resources and a clear picture of valuable physiological traits, combined drought and heat tolerance in wheat can now be realized in research for use in breeding programmes. Abbreviations Abbreviations G×E genotype by environment GS genomic selection HI harvest index HSF heat shock factor HSP heat shock protein QTL quantitative trait locus ROS reactive oxygen species VPD vapour pressure deficit WU water use WUE water use efficiency. Acknowledgements The authors’ research is supported by the Australian Research Council Industrial Transformation Research Hub for Genetic Diversity and Molecular Breeding for Wheat in a Hot and Dry Climate (project number IH130200027). References ABS (Australian Bureau of Statistics) . 2012 . Year Book Australia . Canberra : Australian Bureau of Statistics . Acevedo E , Hsiao TC , Henderson DW . 1971 . Immediate and subsequent growth responses of maize leaves to changes in water status . Plant Physiology 48 , 631 – 636 . Google Scholar CrossRef Search ADS Acuña-Galindo MA , Mason RE , Subramanian NK , Hays DB . 2015 . Meta-analysis of wheat QTL regions associated with adaptation to drought and heat stress . Crop Science 55 , 477 – 492 . Google Scholar CrossRef Search ADS Alexandratos N , Bruinsma J . 2012 . World agriculture towards 2030/2050: the 2012 revision . ESA Working paper No. 12-03. Rome : Food and Agriculture Organization of the United Nations . Allakhverdiev SI , Kreslavski VD , Klimov VV , Los DA , Carpentier R , Mohanty P . 2008 . Heat stress: an overview of molecular responses in photosynthesis . Photosynthesis Research 98 , 541 – 550 . Google Scholar CrossRef Search ADS Almeselmani M , Deshmukh PS , Sairam RK , Kushwaha SR , Singh TP . 2006 . Protective role of antioxidant enzymes under high temperature stress . Plant Science 171 , 382 – 388 . Google Scholar CrossRef Search ADS Altenbach SB , DuPont FM , Kothari KM , Chan R , Johnson EL , Lieu D . 2003 . Temperature, water and fertilizer influence the timing of key events during grain development in a US spring wheat . Journal of Cereal Science 37 , 9 – 20 . Google Scholar CrossRef Search ADS Aprile A , Havlickova L , Panna R et al. 2013 . Different stress responsive strategies to drought and heat in two durum wheat cultivars with contrasting water use efficiency . BMC Genomics 14 , 821 . Google Scholar CrossRef Search ADS Araus JL , Cairns JE . 2014 . Field high-throughput phenotyping: the new crop breeding frontier . Trends in Plant Science 19 , 52 – 61 . Google Scholar CrossRef Search ADS Araus JL , Slafer GA , Reynolds MP , Royo C . 2002 . Plant breeding and drought in C3 cereals: what should we breed for ? Annals of Botany 89 Spec No , 925 – 940 . Google Scholar CrossRef Search ADS Asana R , Williams R . 1965 . The effect of temperature stress on grain development in wheat . Australian Journal of Agricultural Research 16 , 1 – 13 . Google Scholar CrossRef Search ADS Aspinwall MJ , Loik ME , Resco de Dios V , Tjoelker MG , Payton PR , Tissue DT . 2015 . Utilizing intraspecific variation in phenotypic plasticity to bolster agricultural and forest productivity under climate change . Plant, Cell & Environment 38 , 1752 – 1764 . Google Scholar CrossRef Search ADS Awasthi R , Kaushal N , Vadez V , Turner NC , Berger J , Siddique KHM , Nayyar H . 2014 . Individual and combined effects of transient drought and heat stress on carbon assimilation and seed filling in chickpea . Functional Plant Biology 41 , 1148 – 1167 . Google Scholar CrossRef Search ADS Barlow E , Lee J , Munns R , Smart M . 1980 . Water relations of the developing wheat grain . Functional Plant Biology 7 , 519 – 525 . Barnabás B , Jäger K , Fehér A . 2008 . The effect of drought and heat stress on reproductive processes in cereals . Plant, Cell & Environment 31 , 11 – 38 . Bennett D , Reynolds M , Mullan D , Izanloo A , Kuchel H , Langridge P , Schnurbusch T . 2012 . Detection of two major grain yield QTL in bread wheat (Triticum aestivum L.) under heat, drought and high yield potential environments . Theoretical and Applied Genetics 125 , 1473 – 1485 . Google Scholar CrossRef Search ADS Berry J , Bjorkman O . 1980 . Photosynthetic response and adaptation to temperature in higher plants . Annual Review of Plant Physiology 31 , 491 – 543 . Google Scholar CrossRef Search ADS Blum A . 1988 . Plant breeding for stress environments . Boca Raton, FL, USA : CRC Press . Blum A . 2005 . Drought resistance, water-use efficiency, and yield potential are they compatible, dissonant, or mutually exclusive ? Australian Journal of Agricultural Research 56 , 1159 – 1168 . Google Scholar CrossRef Search ADS Blum A . 2009 . Effective use of water (EUW) and not water-use efficiency (WUE) is the target of crop yield improvement under drought stress . Field Crops Research 112 , 119 – 123 . Google Scholar CrossRef Search ADS Blum A , Sinmena B , Mayer J , Golan G , Shpiler L . 1994 . Stem reserve mobilisation supports wheat-grain filling under heat stress . Functional Plant Biology 21 , 771 – 781 . Bonneau J , Taylor J , Parent B , Bennett D , Reynolds M , Feuillet C , Langridge P , Mather D . 2013 . Multi-environment analysis and improved mapping of a yield-related QTL on chromosome 3B of wheat . Theoretical and Applied Genetics 126 , 747 – 761 . Google Scholar CrossRef Search ADS Bradshaw AD . 1965 . Evolutionary significance of phenotypic plasticity in plants . Advances in Genetics 13 , 115 – 155 . Google Scholar CrossRef Search ADS Bramley H , Bitter R , Zimmermann G , Zimmermann U . 2015 . Simultaneous recording of diurnal changes in leaf turgor pressure and stem water status of bread wheat reveal variation in hydraulic mechanisms in response to drought . Functional Plant Biology 42 , 1001 – 1009 . Google Scholar CrossRef Search ADS Caldeira CF , Bosio M , Parent B , Jeanguenin L , Chaumont F , Tardieu F . 2014a. A hydraulic model is compatible with rapid changes in leaf elongation under fluctuating evaporative demand and soil water status . Plant Physiology 164 , 1718 – 1730 . Google Scholar CrossRef Search ADS Caldeira CF , Jeanguenin L , Chaumont F , Tardieu F . 2014b. Circadian rhythms of hydraulic conductance and growth are enhanced by drought and improve plant performance . Nature Communications 5 , 5365 . Google Scholar CrossRef Search ADS Caringella MA , Bongers FJ , Sack L . 2015 . Leaf hydraulic conductance varies with vein anatomy across Arabidopsis thaliana wild-type and leaf vein mutants . Plant, Cell & Environment 38 , 2735 – 2746 . Google Scholar CrossRef Search ADS Chapman SC , Chakraborty S , Dreccer MF , Howden SM . 2012 . Plant adaptation to climate change—opportunities and priorities in breeding . Crop and Pasture Science 63 , 251 – 268 . Google Scholar CrossRef Search ADS Choudhury FK , Rivero RM , Blumwald E , Mittler R . 2017 . Reactive oxygen species, abiotic stress and stress combination . The Plant Journal 90 , 856 – 867 . Google Scholar CrossRef Search ADS Christopher JT , Manschadi AM , Hammer GL , Borrell AK . 2008 . Developmental and physiological traits associated with high yield and stay-green phenotype in wheat . Australian Journal of Agricultural Research 59 , 354 – 364 . Google Scholar CrossRef Search ADS Claverie E , Meunier F , Javaux M , Sadok W . 2017 . Increased contribution of wheat nocturnal transpiration to daily water use under drought . Physiologia Plantarum 162 , 290 – 300 . Google Scholar CrossRef Search ADS Cockram J , Jones H , Leigh FJ , O’Sullivan D , Powell W , Laurie DA , Greenland AJ . 2007 . Control of flowering time in temperate cereals: genes, domestication, and sustainable productivity . Journal of Experimental Botany 58 , 1231 – 1244 . Google Scholar CrossRef Search ADS Condon A , Farquhar G , Richards R . 1990 . Genotypic variation in carbon isotope discrimination and transpiration efficiency in wheat. Leaf gas exchange and whole plant studies . Functional Plant Biology 17 , 9 – 22 . Condon AG , Richards RA , Rebetzke GJ , Farquhar GD . 2002 . Improving intrinsic water-use efficiency and crop yield . Crop Science 42 , 122 – 131 . Google Scholar CrossRef Search ADS Coupel-Ledru A , Lebon É , Christophe A , Doligez A , Cabrera-Bosquet L , Péchier P , Hamard P , This P , Simonneau T . 2014 . Genetic variation in a grapevine progeny (Vitis vinifera L. cvs Grenache×Syrah) reveals inconsistencies between maintenance of daytime leaf water potential and response of transpiration rate under drought . Journal of Experimental Botany 65 , 6205 – 6218 . Google Scholar CrossRef Search ADS Czyczyło-Mysza I , Marcińska I , Skrzypek E et al. 2011 . Mapping QTLs for yield components and chlorophyll a fluorescence parameters in wheat under three levels of water availability . Plant Genetic Resources 9 , 291 – 295 . Google Scholar CrossRef Search ADS Das A , Eldakak M , Paudel B , Kim DW , Hemmati H , Basu C , Rohila JS . 2016 . Leaf proteome analysis reveals prospective drought and heat stress response mechanisms in soybean . BioMed Research International 2016 , 6021047 . Google Scholar CrossRef Search ADS Dashti H , Yazdisamadi B , Bihamta Naghavi MR , Quarrie S . 2007 . QTL analysis for drought resistance in wheat using doubled haploid lines . International Journal of Agriculture and Biology 9 , 98 – 101 . Demirevska K , Simova-Stoilova L , Fedina I , Georgieva K , Kunert K . 2010 . Response of oryzacystatin I transformed tobacco plants to drought, heat and light stress . Journal of Agronomy and Crop Science 196 , 90 – 99 . Google Scholar CrossRef Search ADS Diab AA , Kantety RV , Ozturk NZ , Benscher D , Nachit MM , Sorrells ME . 2008 . Drought-inducible genes and differentially expressed sequence tags associated with components of drought tolerance in durum wheat . Scientific Research and Essays 3 , 009 – 026 . Eberhart SA , Russell WA . 1966 . Stability parameters for comparing varieties . Crop Science 6 , 36 – 40 . Google Scholar CrossRef Search ADS Ehrler WL , Idso SB , Jackson RD , Reginato RJ . 1978 . Wheat canopy temperature: relation to plant water potential . Agronomy Journal 70 , 251 – 256 . Google Scholar CrossRef Search ADS Feuillet C , Langridge P , Waugh R . 2008 . Cereal breeding takes a walk on the wild side . Trends in Genetics 24 , 24 – 32 . Google Scholar CrossRef Search ADS Fischer RA . 2011 . Wheat physiology: a review of recent developments . Crop and Pasture Science 62 , 95 – 114 . Google Scholar CrossRef Search ADS Fischer RA , Maurer R . 1978 . Drought resistance in spring wheat cultivars. I. Grain yield responses . Australian Journal of Agricultural Research 29 , 897 – 912 . Google Scholar CrossRef Search ADS Foyer CH , Noctor G . 2005 . Oxidant and antioxidant signalling in plants: a re-evaluation of the concept of oxidative stress in a physiological context . Plant, Cell & Environment 28 , 1056 – 1071 . Google Scholar CrossRef Search ADS Gavuzzi P , Rizza F , Palumbo M , Campanile RG , Ricciardi GL , Borghi B . 1997 . Evaluation of field and laboratory predictors of drought and heat tolerance in winter cereals . Canadian Journal of Plant Science 77 , 523 – 531 . Google Scholar CrossRef Search ADS Golabadi M , Arzani A , Mirmohammadi Maibody SAM , Sayed Tabatabaei BE , Mohammadi SA . 2011 . Identification of microsatellite markers linked with yield components under drought stress at terminal growth stages in durum wheat . Euphytica 177 , 207 – 221 . Google Scholar CrossRef Search ADS Hill CB , Taylor JD , Edwards J , Mather D , Langridge P , Bacic A , Roessner U . 2015 . Detection of QTL for metabolic and agronomic traits in wheat with adjustments for variation at genetic loci that affect plant phenology . Plant Science 233 , 143 – 154 . Google Scholar CrossRef Search ADS Hochman Z . 1982 . Effect of water stress with phasic development on yield of wheat grown in a semi-arid environment . Field Crops Research 5 , 55 – 67 . Google Scholar CrossRef Search ADS Hu X-J , Chen D , Lynne Mclntyre C , Fernanda Dreccer M , Zhang Z-B , Drenth J , Kalaipandian S , Chang H , Xue G-P . 2018 . Heat shock factor C2a serves as a proactive mechanism for heat protection in developing grains in wheat via an ABA-mediated regulatory pathway . Plant, Cell & Environment 41 , 79 – 98 . Google Scholar CrossRef Search ADS Huang X , Han B . 2014 . Natural variations and genome-wide association studies in crop plants . Annual Review of Plant Biology 65 , 531 – 551 . Google Scholar CrossRef Search ADS Ilyas M , Ilyas N , Arshad M , Kazi AG . 2014 . QTL mapping of wheat doubled haploids for chlorophyll content and chlorophyll fluorescence kinetics under drought stress imposed at anthesis stage . Pakistan Journal of Botany 46 , 1889 – 1897 . Izanloo A , Condon AG , Langridge P , Tester M , Schnurbusch T . 2008 . Different mechanisms of adaptation to cyclic water stress in two South Australian bread wheat cultivars . Journal of Experimental Botany 59 , 3327 – 3346 . Google Scholar CrossRef Search ADS James RA , Davenport RJ , Munns R . 2006 . Physiological characterization of two genes for Na+ exclusion in durum wheat, Nax1 and Nax2 . Plant Physiology 142 , 1537 – 1547 . Google Scholar CrossRef Search ADS Jenner C . 1994 . Starch synthesis in the kernel of wheat under high temperature conditions . Functional Plant Biology 21 , 791 – 806 . Jiang Y , Huang B . 2001 . Drought and heat stress injury to two cool-season turfgrasses in relation to antioxidant metabolism and lipid peroxidation . Crop Science 41 , 436 – 442 . Google Scholar CrossRef Search ADS Kadam NN , Yin X , Bindraban PS , Struik PC , Jagadish KS . 2015 . Does morphological and anatomical plasticity during the vegetative stage make wheat more tolerant of water deficit stress than rice ? Plant Physiology 167 , 1389 – 1401 . Google Scholar CrossRef Search ADS Kadam S , Singh K , Shukla S , Goel S , Vikram P , Pawar V , Gaikwad K , Khanna-Chopra R , Singh N . 2012 . Genomic associations for drought tolerance on the short arm of wheat chromosome 4B . Functional & Integrative Genomics 12 , 447 – 464 . Google Scholar CrossRef Search ADS Kirigwi FM , van Ginkel M , Trethowan R , Sears RG , Rajaram S , Paulsen GM . 2004 . Evaluation of selection strategies for wheat adaptation across water regimes . Euphytica 135 , 361 – 371 . Google Scholar CrossRef Search ADS Kirigwi FM , Van Ginkel M , Brown-Guedira G , Gill BS , Paulsen GM , Fritz AK . 2007 . Markers associated with a QTL for grain yield in wheat under drought . Molecular Breeding 20 , 401 – 413 . Google Scholar CrossRef Search ADS Koussevitzky S , Suzuki N , Huntington S , Armijo L , Sha W , Cortes D , Shulaev V , Mittler R . 2008 . Ascorbate peroxidase 1 plays a key role in the response of Arabidopsis thaliana to stress combination . The Journal of Biological Chemistry 283 , 34197 – 34203 . Google Scholar CrossRef Search ADS Lascano HR , Antonicelli GE , Luna CM , Melchiorre MN , Gómez LD , Racca RW , Trippi VS , Casano LM . 2001 . Antioxidant system response of different wheat cultivars under drought: field and in vitro studies . Functional Plant Biology 28 , 1095 – 1102 . Google Scholar CrossRef Search ADS Liang Z , Chen K , Li T et al. 2017 . Efficient DNA-free genome editing of bread wheat using CRISPR/Cas9 ribonucleoprotein complexes . Nature Communications 8 , 14261 . Google Scholar CrossRef Search ADS Liu B , Asseng S , Muller C et al. 2016 . Similar estimates of temperature impacts on global wheat yield by three independent methods . Nature Climate Change 6 , 1130 – 1136 . Google Scholar CrossRef Search ADS Lohraseb I , Collins NC , Parent B . 2017 . Diverging temperature responses of CO2 assimilation and plant development explain the overall effect of temperature on biomass accumulation in wheat leaves and grains . AoB Plants 9 , plw092 . Long SP , Ort DR . 2010 . More than taking the heat: crops and global change . Current Opinion in Plant Biology 13 , 241 – 248 . Google Scholar CrossRef Search ADS Lopes MS , El-Basyoni I , Baenziger PS et al. 2015 . Exploiting genetic diversity from landraces in wheat breeding for adaptation to climate change . Journal of Experimental Botany 66 , 3477 – 3486 . Google Scholar CrossRef Search ADS Lopes MS , Reynolds MP , Jalal-Kamali MR et al. 2012 . The yield correlations of selectable physiological traits in a population of advanced spring wheat lines grown in warm and drought environments . Field Crops Research 128 , 129 – 136 . Google Scholar CrossRef Search ADS Lu F , Romay MC , Glaubitz JC et al. 2015 . High-resolution genetic mapping of maize pan-genome sequence anchors . Nature Communications 6 , 6914 . Google Scholar CrossRef Search ADS Lu Y , Li R , Wang R , Wang X , Zheng W , Sun Q , Tong S , Dai S , Xu S . 2017 . Comparative proteomic analysis of flag leaves reveals new insight into wheat heat adaptation . Frontiers in Plant Science 8 , 1086 . Google Scholar CrossRef Search ADS Maccaferri M , Sanguineti MC , Corneti S et al. 2008 . Quantitative trait loci for grain yield and adaptation of durum wheat (Triticum durum Desf.) across a wide range of water availability . Genetics 178 , 489 – 511 . Google Scholar CrossRef Search ADS Machado S , Paulsen GM . 2001 . Combined effects of drought and high temperature on water relations of wheat and sorghum . Plant and Soil 233 , 179 – 187 . Google Scholar CrossRef Search ADS Marks CO , Lechowicz MJ . 2007 . The ecological and functional correlates of nocturnal transpiration . Tree Physiology 27 , 577 – 584 . Google Scholar CrossRef Search ADS Mason RE , Mondal S , Beecher FW , Pacheco A , Jampala B , Ibrahim AMH , Hays DB . 2010 . QTL associated with heat susceptibility index in wheat (Triticum aestivum L.) under short-term reproductive stage heat stress . Euphytica 174 , 423 – 436 . Google Scholar CrossRef Search ADS Maydup ML , Antonietta M , Graciano C , Guiamet JJ , Tambussi EA . 2014 . The contribution of the awns of bread wheat (Triticum aestivum L.) to grain filling: Responses to water deficit and the effects of awns on ear temperature and hydraulic conductance . Field Crops Research 167 , 102 – 111 . Google Scholar CrossRef Search ADS Merchuk-Ovnat L , Fahima T , Krugman T , Saranga Y . 2016 . Ancestral QTL alleles from wild emmer wheat improve grain yield, biomass and photosynthesis across environments in modern wheat . Plant Science 251 , 23 – 34 . Google Scholar CrossRef Search ADS Mittler R . 2006 . Abiotic stress, the field environment and stress combination . Trends in Plant Science 11 , 15 – 19 . Google Scholar CrossRef Search ADS Mittler R , Finka A , Goloubinoff P . 2012 . How do plants feel the heat ? Trends in Biochemical Sciences 37 , 118 – 125 . Google Scholar CrossRef Search ADS Mohammed A-R , Tarpley L . 2009 . Impact of high nighttime temperature on respiration, membrane stability, antioxidant capacity, and yield of rice plants . Crop Science 49 , 313 – 322 . Google Scholar CrossRef Search ADS Mondal B , Singh A , Yadav A , Singh Tomar RS , Vinod Singh GP , Prabhu KV . 2017 . QTL mapping for early ground cover in wheat (Triticum aestivum L.) under drought stress . Current Science 112 , 1266 – 1271 . Google Scholar CrossRef Search ADS Mooney HA , Di Castri F . 1973 . Mediterranean type ecosystems: origin and structure . Berlin, Heidelberg : Springer-Verlag . Google Scholar CrossRef Search ADS Morran S , Eini O , Pyvovarenko T et al. 2011 . Improvement of stress tolerance of wheat and barley by modulation of expression of DREB/CBF factors . Plant Biotechnology Journal 9 , 230 – 249 . Google Scholar CrossRef Search ADS Munns R , James RA , Xu B et al. 2012 . Wheat grain yield on saline soils is improved by an ancestral Na⁺ transporter gene . Nature Biotechnology 30 , 360 – 364 . Google Scholar CrossRef Search ADS Neghliz H , Cochard H , Brunel N , Martre P . 2016 . Ear rachis xylem occlusion and associated loss in hydraulic conductance coincide with the end of grain filling for wheat . Frontiers in Plant Science 7 , 920 . Google Scholar CrossRef Search ADS Nicotra AB , Atkin OK , Bonser SP et al. 2010 . Plant phenotypic plasticity in a changing climate . Trends in Plant Science 15 , 684 – 692 . Google Scholar CrossRef Search ADS Ogbonnaya FC , Rasheed A , Okechukwu EC , Jighly A , Makdis F , Wuletaw T , Hagras A , Uguru MI , Agbo CU . 2017 . Genome-wide association study for agronomic and physiological traits in spring wheat evaluated in a range of heat prone environments . Theoretical and Applied Genetics 130 , 1819 – 1835 . Google Scholar CrossRef Search ADS Paliwal R , Röder MS , Kumar U , Srivastava JP , Joshi AK . 2012 . QTL mapping of terminal heat tolerance in hexaploid wheat (T. aestivum L.) . Theoretical and Applied Genetics 125 , 561 – 575 . Google Scholar CrossRef Search ADS Parent B , Bonneau J , Maphosa L , Kovalchuk A , Langridge P , Fleury D . 2017 . Quantifying wheat sensitivities to environmental constraints to dissect Genotype × Environment interactions in the field . Plant Physiology 174 , 1669 – 1682 . Google Scholar CrossRef Search ADS Parent B , Shahinnia F , Maphosa L , Berger B , Rabie H , Chalmers K , Kovalchuk A , Langridge P , Fleury D . 2015 . Combining field performance with controlled environment plant imaging to identify the genetic control of growth and transpiration underlying yield response to water-deficit stress in wheat . Journal of Experimental Botany 66 , 5481 – 5492 . Google Scholar CrossRef Search ADS Parent B , Tardieu F . 2012 . Temperature responses of developmental processes have not been affected by breeding in different ecological areas for 17 crop species . New Phytologist 194 , 760 – 774 . Google Scholar CrossRef Search ADS Parent B , Tardieu F . 2014 . Can current crop models be used in the phenotyping era for predicting the genetic variability of yield of plants subjected to drought or high temperature ? Journal of Experimental Botany 65 , 6179 – 6189 . Google Scholar CrossRef Search ADS Passioura JB . 1977 . Grain yield, harvest index, and water use of wheat . Journal of the Australian Institute of Agricultural Sciences 43 , 117 – 120 . Passioura JB . 1996 . Drought and drought tolerance . Plant Growth Regulation 20 , 79 – 83 . Google Scholar CrossRef Search ADS Peleg Z , Fahima T , Abbo S , Krugman T , Nevo E , Yakir D , Saranga Y . 2005 . Genetic diversity for drought resistance in wild emmer wheat and its ecogeographical associations . Plant, Cell & Environment 28 , 176 – 191 . Google Scholar CrossRef Search ADS Peleg Z , Fahima T , Krugman T , Abbo S , Yakir D , Korol AB , Saranga Y . 2009 . Genomic dissection of drought resistance in durum wheat × wild emmer wheat recombinant inbreed line population . Plant, Cell & Environment 32 , 758 – 779 . Google Scholar CrossRef Search ADS Peng J , Richards DE , Hartley NM et al. 1999 . ‘Green revolution’ genes encode mutant gibberellin response modulators . Nature 400 , 256 – 261 . Google Scholar CrossRef Search ADS Perdomo JA , Capó-Bauçà S , Carmo-Silva E , Galmés J . 2017 . Rubisco and Rubisco activase play an important role in the biochemical limitations of photosynthesis in rice, wheat, and maize under high temperature and water deficit . Frontiers in Plant Science 8 , 490 . Google Scholar CrossRef Search ADS Perdomo JA , Conesa MÀ , Medrano H , Ribas-Carbó M , Galmés J . 2015 . Effects of long-term individual and combined water and temperature stress on the growth of rice, wheat and maize: relationship with morphological and physiological acclimation . Physiologia Plantarum 155 , 149 – 165 . Google Scholar CrossRef Search ADS Peterhansel C , Maurino VG . 2011 . Photorespiration redesigned . Plant Physiology 155 , 49 – 55 . Google Scholar CrossRef Search ADS Pinto RS , Reynolds MP . 2015 . Common genetic basis for canopy temperature depression under heat and drought stress associated with optimized root distribution in bread wheat . Theoretical and Applied Genetics 128 , 575 – 585 . Google Scholar CrossRef Search ADS Pinto RS , Reynolds MP , Mathews KL , McIntyre CL , Olivares-Villegas JJ , Chapman SC . 2010 . Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects . Theoretical and Applied Genetics 121 , 1001 – 1021 . Google Scholar CrossRef Search ADS Pradhan GP , Prasad PVV , Fritz AK , Kirkham MB , Gill BS . 2012 . Effects of drought and high temperature stress on synthetic hexaploid wheat . Functional Plant Biology 39 , 190 – 198 . Google Scholar CrossRef Search ADS Prasad PVV , Pisipati SR , Momčilović I , Ristic Z . 2011 . Independent and combined effects of high temperature and drought stress during grain filling on plant yield and chloroplast EF-Tu expression in spring wheat . Journal of Agronomy and Crop Science 197 , 430 – 441 . Google Scholar CrossRef Search ADS Price AH , Hendry GAF . 1991 . Iron-catalysed oxygen radical formation and its possible contribution to drought damage in nine native grasses and three cereals . Plant, Cell & Environment 14 , 477 – 484 . Google Scholar CrossRef Search ADS Quarrie SA , Steed A , Calestani C et al. 2005 . A high-density genetic map of hexaploid wheat (Triticum aestivum L.) from the cross Chinese Spring × SQ1 and its use to compare QTLs for grain yield across a range of environments . Theoretical and Applied Genetics 110 , 865 – 880 . Google Scholar CrossRef Search ADS Rajaram S , van Ginkel M , Fischer RA . 1994 . CIMMYT’s wheat breeding mega-environments (ME) . In: Proceedings of the 8th International Wheat Genetics Symposium, Beijing, 20–25 July 1993 . Beijing : Institute of Genetics, Chinese Academy of Sciences , 1101 – 1106 . Rampino P , Mita G , Fasano P , Borrelli GM , Aprile A , Dalessandro G , De Bellis L , Perrotta C . 2012 . Novel durum wheat genes up-regulated in response to a combination of heat and drought stress . Plant Physiology and Biochemistry 56 , 72 – 78 . Google Scholar CrossRef Search ADS Redondo-Gómez S . 2013 . Abiotic and biotic stress tolerance in plants . In: Rout GR , Das AB , eds. Molecular stress physiology of plants . New Delhi : Springer India , 1 – 20 . Google Scholar CrossRef Search ADS Resco de Dios V , Loik ME , Smith R , Aspinwall MJ , Tissue DT . 2016 . Genetic variation in circadian regulation of nocturnal stomatal conductance enhances carbon assimilation and growth . Plant, Cell & Environment 39 , 3 – 11 . Google Scholar CrossRef Search ADS Reynolds M , Foulkes MJ , Slafer GA , Berry P , Parry MA , Snape JW , Angus WJ . 2009 . Raising yield potential in wheat . Journal of Experimental Botany 60 , 1899 – 1918 . Google Scholar CrossRef Search ADS Reynolds MP , Pellegrineschi A , Skovmand B . 2005 . Sink-limitation to yield and biomass: a summary of some investigations in spring wheat . Annals of Applied Biology 146 , 39 – 49 . Google Scholar CrossRef Search ADS Reynolds MP , Pierre CS , Saad ASI , Vargas M , Condon AG . 2007 . Evaluating potential genetic gains in wheat associated with stress-adaptive trait expression in elite genetic resources under drought and heat stress . Crop Science 47 , S172 – S189 . Google Scholar CrossRef Search ADS Richards RA , Rawson HM , Johnson DA . 1986 . Glaucousness in wheat: Its development and effect on water-use efficiency, gas exchange and photosynthetic tissue temperatures . Functional Plant Biology 13 , 465 – 473 . Rivero RM , Shulaev V , Blumwald E . 2009 . Cytokinin-dependent photorespiration and the protection of photosynthesis during water deficit . Plant Physiology 150 , 1530 – 1540 . Google Scholar CrossRef Search ADS Rizhsky L , Liang H , Mittler R . 2002 . The combined effect of drought stress and heat shock on gene expression in tobacco . Plant Physiology 130 , 1143 – 1151 . Google Scholar CrossRef Search ADS Rizhsky L , Liang H , Shuman J , Shulaev V , Davletova S , Mittler R . 2004 . When defense pathways collide. The response of Arabidopsis to a combination of drought and heat stress . Plant Physiology 134 , 1683 – 1696 . Google Scholar CrossRef Search ADS Roche D . 2015 . Stomatal conductance is essential for higher yield potential of C3 crops . Critical Reviews in Plant Sciences 34 , 429 – 453 . Google Scholar CrossRef Search ADS Sadok W . 2016 . The circadian life of nocturnal water use: when late-night decisions help improve your day . Plant, Cell & Environment 39 , 1 – 2 . Google Scholar CrossRef Search ADS Sadras VO , Reynolds MP , de la Vega AJ , Petrie PR , Robinson R . 2009 . Phenotypic plasticity of yield and phenology in wheat, sunflower and grapevine . Field Crops Research 110 , 242 – 250 . Google Scholar CrossRef Search ADS Sadras VO , Slafer GA . 2012 . Environmental modulation of yield components in cereals: Heritabilities reveal a hierarchy of phenotypic plasticities . Field Crops Research 127 , 215 – 224 . Google Scholar CrossRef Search ADS Saini HS , Aspinall D . 1982 . Abnormal sporogenesis in wheat (Triticum aestivum L.) induced by short periods of high temperature . Annals of Botany 49 , 835 – 846 . Google Scholar CrossRef Search ADS Saini HS , Lalonde S . 1997 . Injuries to reproductive development under water stress, and their consequences for crop productivity . Journal of Crop Production 1 , 223 – 248 . Google Scholar CrossRef Search ADS Sairam RK , Saxena DC . 2000 . Oxidative stress and antioxidants in wheat genotypes: possible mechanism of water stress tolerance . Journal of Agronomy and Crop Science 184 , 55 – 61 . Google Scholar CrossRef Search ADS Sairam RK , Srivastava GC , Saxena DC . 2000 . Increased antioxidant activity under elevated temperatures: a mechanism of heat stress tolerance in wheat genotypes . Biologia Plantarum 43 , 245 – 251 . Google Scholar CrossRef Search ADS Salter PJ , Goode JE . 1967 . Crop responses to water at different stages of growth . Farnham Royal, Bucks, England : Commonwealth Agricultural Bureaux . Scharwies JD , Tyerman SD . 2017 . Comparison of isohydric and anisohydric Vitis vinifera L. cultivars reveals a fine balance between hydraulic resistances, driving forces and transpiration in ripening berries . Functional Plant Biology 44 , 324 – 338 . Google Scholar CrossRef Search ADS Schauberger B , Archontoulis S , Arneth A et al. 2017 . Consistent negative response of US crops to high temperatures in observations and crop models . Nature Communications 8 , 13931 . Google Scholar CrossRef Search ADS Scheibe R , Dietz KJ . 2012 . Reduction-oxidation network for flexible adjustment of cellular metabolism in photoautotrophic cells . Plant, Cell & Environment 35 , 202 – 216 . Google Scholar CrossRef Search ADS Schoppach R , Claverie E , Sadok W . 2014 . Genotype-dependent influence of night-time vapour pressure deficit on night-time transpiration and daytime gas exchange in wheat . Functional Plant Biology 41 , 963 – 971 . Google Scholar CrossRef Search ADS Schoppach R , Sadok W . 2013 . Transpiration sensitivities to evaporative demand and leaf areas vary with night and day warming regimes among wheat genotypes . Functional Plant Biology 40 , 708 – 718 . Google Scholar CrossRef Search ADS Schoppach R , Taylor JD , Majerus E , Claverie E , Baumann U , Suchecki R , Fleury D , Sadok W . 2016 . High resolution mapping of traits related to whole-plant transpiration under increasing evaporative demand in wheat . Journal of Experimental Botany 67 , 2847 – 2860 . Google Scholar CrossRef Search ADS Sečenji M , Hideg É , Bebes A , Györgyey J . 2010 . Transcriptional differences in gene families of the ascorbate–glutathione cycle in wheat during mild water deficit . Plant Cell Reports 29 , 37 – 50 . Google Scholar CrossRef Search ADS Shah NH , Paulsen GM . 2003 . Interaction of drought and high temperature on photosynthesis and grain-filling of wheat . Plant and Soil 257 , 219 – 226 . Google Scholar CrossRef Search ADS Shahinnia F , Le Roy J , Laborde B , Sznajder B , Kalambettu P , Mahjourimajd S , Tilbrook J , Fleury D . 2016 . Genetic association of stomatal traits and yield in wheat grown in low rainfall environments . BMC Plant Biology 16 , 150 . Google Scholar CrossRef Search ADS Sharma D , Singh R , Rane J , Gupta VK , Mamrutha HM , Tiwari R . 2016 . Mapping quantitative trait loci associated with grain filling duration and grain number under terminal heat stress in bread wheat (Triticum aestivum L.) . Plant Breeding 135 , 538 – 545 . Google Scholar CrossRef Search ADS Shiferaw B , Smale M , Braun H-J , Duveiller E , Reynolds M , Muricho G . 2013 . Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security . Food Security 5 , 291 – 317 . Google Scholar CrossRef Search ADS Shirdelmoghanloo H , Taylor JD , Lohraseb I et al. 2016 . A QTL on the short arm of wheat (Triticum aestivum L.) chromosome 3B affects the stability of grain weight in plants exposed to a brief heat shock early in grain filling . BMC Plant Biology 16 , 100 . Google Scholar CrossRef Search ADS Silva EN , Ferreira-Silva SL , Fontenele Ade V , Ribeiro RV , Viégas RA , Silveira JA . 2010 . Photosynthetic changes and protective mechanisms against oxidative damage subjected to isolated and combined drought and heat stresses in Jatropha curcas plants . Journal of Plant Physiology 167 , 1157 – 1164 . Google Scholar CrossRef Search ADS Simmonds J , Scott P , Brinton J , Mestre TC , Bush M , Del Blanco A , Dubcovsky J , Uauy C . 2016 . A splice acceptor site mutation in TaGW2-A1 increases thousand grain weight in tetraploid and hexaploid wheat through wider and longer grains . Theoretical and Applied Genetics 129 , 1099 – 1112 . Google Scholar CrossRef Search ADS Spielmeyer W , Hyles J , Joaquim P , Azanza F , Bonnett D , Ellis ME , Moore C , Richards RA . 2007 . A QTL on chromosome 6A in bread wheat (Triticum aestivum) is associated with longer coleoptiles, greater seedling vigour and final plant height . Theoretical and Applied Genetics 115 , 59 – 66 . Google Scholar CrossRef Search ADS Stone P , Nicolas M . 1995 . A survey of the effects of high temperature during grain filling on yield and quality of 75 wheat cultivars . Australian Journal of Agricultural Research 46 , 475 – 492 . Google Scholar CrossRef Search ADS Sun X , Cahill J , Van Hautegem T et al. 2017 . Altered expression of maize PLASTOCHRON1 enhances biomass and seed yield by extending cell division duration . Nature Communications 8 , 14752 . Google Scholar CrossRef Search ADS Suzuki N , Rivero RM , Shulaev V , Blumwald E , Mittler R . 2014 . Abiotic and biotic stress combinations . New Phytologist 203 , 32 – 43 . Google Scholar CrossRef Search ADS Tahmasebi S , Heidari B , Pakniyat H , McIntyre CL . 2017 . Mapping QTLs associated with agronomic and physiological traits under terminal drought and heat stress conditions in wheat (Triticum aestivum L.) . Genome 60 , 26 – 45 . Google Scholar CrossRef Search ADS Talukder SK , Babar MA , Vijayalakshmi K , Poland J , Prasad PV , Bowden R , Fritz A . 2014 . Mapping QTL for the traits associated with heat tolerance in wheat (Triticum aestivum L.) . BMC Genetics 15 , 97 . Google Scholar CrossRef Search ADS Tardieu F , Parent B , Caldeira CF , Welcker C . 2014 . Genetic and physiological controls of growth under water deficit . Plant Physiology 164 , 1628 – 1635 . Google Scholar CrossRef Search ADS Tester M , Langridge P . 2010 . Breeding technologies to increase crop production in a changing world . Science 327 , 818 – 822 . Google Scholar CrossRef Search ADS Tilman D , Balzer C , Hill J , Befort BL . 2011 . Global food demand and the sustainable intensification of agriculture . Proceedings of the National Academy of Sciences, USA 108 , 20260 – 20264 . Google Scholar CrossRef Search ADS Tixier A , Cochard H , Badel E , Dusotoit-Coucaud A , Jansen S , Herbette S . 2013 . Arabidopsis thaliana as a model species for xylem hydraulics: does size matter ? Journal of Experimental Botany 64 , 2295 – 2305 . Google Scholar CrossRef Search ADS Tricker PJ , Haefele SM , Okamoto M . 2016 . The interaction of drought and nutrient stress in wheat . In: Ahmad P , ed. Water stress and crop plants: A sustainable approach . Chichester : John Wiley & Sons, Ltd , 695 – 710 . Google Scholar CrossRef Search ADS Tsai H , Howell T , Nitcher R et al. 2011 . Discovery of rare mutations in populations: TILLING by sequencing . Plant Physiology 156 , 1257 – 1268 . Google Scholar CrossRef Search ADS USDA . 2017 . World agricultural production . Washington, DC, USA : United States Department of Agriculture Foreign Agricultural Service . Vadez V , Kholova J , Medina S , Kakkera A , Anderberg H . 2014 . Transpiration efficiency: new insights into an old story . Journal of Experimental Botany 65 , 6141 – 6153 . Google Scholar CrossRef Search ADS Verma V , Foulkes MJ , Worland AJ , Sylvester-Bradley R , Caligari PDS , Snape JW . 2004 . Mapping quantitative trait loci for flag leaf senescence as a yield determinant in winter wheat under optimal and drought-stressed environments . Euphytica 135 , 255 – 263 . Google Scholar CrossRef Search ADS Vettakkorumakankav NN , Falk D , Saxena P , Fletcher RA . 1999 . A crucial role for gibberellins in stress protection of plants . Plant and Cell Physiology 40 , 542 – 548 . Google Scholar CrossRef Search ADS Vijayalakshmi K , Fritz AK , Paulsen GM , Bai G , Pandravada S , Gill BS . 2010 . Modeling and mapping QTL for senescence-related traits in winter wheat under high temperature . Molecular Breeding 26 , 163 – 175 . Google Scholar CrossRef Search ADS Voss I , Sunil B , Scheibe R , Raghavendra AS . 2013 . Emerging concept for the role of photorespiration as an important part of abiotic stress response . Plant Biology 15 , 713 – 722 . Google Scholar CrossRef Search ADS Wang X , Cai J , Liu F , Dai T , Cao W , Wollenweber B , Jiang D . 2014a. Multiple heat priming enhances thermo-tolerance to a later high temperature stress via improving subcellular antioxidant activities in wheat seedlings . Plant Physiology and Biochemistry 74 , 185 – 192 . Google Scholar CrossRef Search ADS Wang Y , Chen L , Du Y , Yang Z , Condon AG , Hu Y-G . 2014b. Genetic effect of dwarfing gene Rht13 compared with Rht-D1b on plant height and some agronomic traits in common wheat (Triticum aestivum L.) . Field Crops Research 162 , 39 – 47 . Google Scholar CrossRef Search ADS Wardlaw I , Wrigley C . 1994 . Heat tolerance in temperate cereals: an overview . Functional Plant Biology 21 , 695 – 703 . Weigand C . 2011 . Wheat import projections towards 2050 . Arlington, VA, USA : US Wheat Associates . Weldearegay DF , Yan F , Jiang D , Liu F . 2012 . Independent and combined effects of soil warming and drought stress during anthesis on seed set and grain yield in two spring wheat varieties . Journal of Agronomy and Crop Science 198 , 245 – 253 . Google Scholar CrossRef Search ADS Wheeler T . 2012 . Agriculture: Wheat crops feel the heat . Nature Climate Change 2 , 152 – 153 . Google Scholar CrossRef Search ADS Xu Y-F , Li S-S , Li L-H , Ma F-F , Fu X-Y , Shi Z-L , Xu H-X , Ma P-T , An D-G . 2017 . QTL mapping for yield and photosynthetic related traits under different water regimes in wheat . Molecular Breeding 37 , 34 . Google Scholar CrossRef Search ADS Xue GP , Sadat S , Drenth J , McIntyre CL . 2014 . The heat shock factor family from Triticum aestivum in response to heat and other major abiotic stresses and their role in regulation of heat shock protein genes . Journal of Experimental Botany 65 , 539 – 557 . Google Scholar CrossRef Search ADS Yang DL , Jing RL , Chang XP , Li W . 2007 . Identification of quantitative trait loci and environmental interactions for accumulation and remobilization of water-soluble carbohydrates in wheat (Triticum aestivum L.) stems . Genetics 176 , 571 – 584 . Google Scholar CrossRef Search ADS Zandalinas SI , Balfagón D , Arbona V , Gómez-Cadenas A . 2017 . Modulation of antioxidant defense system is associated with combined drought and heat stress tolerance in Citrus . Frontiers in Plant Science 8 , 953 . Google Scholar CrossRef Search ADS Zandalinas SI , Mittler R , Balfagón D , Arbona V , Gómez-Cadenas A . 2018 . Plant adaptations to the combination of drought and high temperatures . Physiologia Plantarum 162 , 2 – 12 . Google Scholar CrossRef Search ADS Zang X , Geng X , Wang F et al. 2017 . Overexpression of wheat ferritin gene TaFER-5B enhances tolerance to heat stress and other abiotic stresses associated with the ROS scavenging . BMC Plant Biology 17 , 14 . Google Scholar CrossRef Search ADS Zee S , O’brien T . 1970 . A special type of tracheary element associated with “xylem discontinuity” in the floral axis of wheat . Australian Journal of Biological Sciences 23 , 783 – 792 . Google Scholar CrossRef Search ADS Zhang B , Li W , Chang X , Li R , Jing R . 2014 . Effects of favorable alleles for water-soluble carbohydrates at grain filling on grain weight under drought and heat stresses in wheat . PLoS One 9 , e102917 . Google Scholar CrossRef Search ADS Zhang G , Zhang M , Zhao Z , Ren Y , Li Q , Wang W . 2017 . Wheat TaPUB1 modulates plant drought stress resistance by improving antioxidant capability . Scientific Reports 7 , 7549 . Google Scholar CrossRef Search ADS © The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Experimental Botany Oxford University Press

The physiological and genetic basis of combined drought and heat tolerance in wheat

Loading next page...
 
/lp/ou_press/the-physiological-and-genetic-basis-of-combined-drought-and-heat-EwEEOLdF4H
Publisher
Oxford University Press
Copyright
© The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com
ISSN
0022-0957
eISSN
1460-2431
D.O.I.
10.1093/jxb/ery081
Publisher site
See Article on Publisher Site

Abstract

Abstract Drought and heat stress cause losses in wheat productivity in major growing regions worldwide, and both the occurrence and the severity of these events are likely to increase with global climate change. Water deficits and high temperatures frequently occur simultaneously at sensitive growth stages, reducing wheat yields by reducing grain number or weight. Although genetic variation and underlying quantitative trait loci for either individual stress are known, the combination of the two stresses has rarely been studied. Complex and often antagonistic physiology means that genetic loci underlying tolerance to the combined stress are likely to differ from those for drought or heat stress tolerance alone. Here, we review what is known of the physiological traits and genetic control of drought and heat tolerance in wheat and discuss potential physiological traits to study for combined tolerance. We further place this knowledge in the context of breeding for new, more tolerant varieties and discuss opportunities and constraints. We conclude that a fine control of water relations across the growing cycle will be beneficial for combined tolerance and might be achieved through fine management of spatial and temporal gas exchange. Cereal, climate, stress, temperature, water, yield Introduction Wheat is the major food for numerous regions around the world, providing approximately 20% of daily calories and protein for 4.5 billion people (Shiferaw et al., 2013). Wheat ranks first in terms of harvested area (223.67 million hectares in 2016) and is the second most produced crop with a global production of 735.3 million tons in 2016 (USDA, 2017). A recent study predicted that wheat yields will decline by 4.1% to 6.4% for each global increase of 1 °C due to climate change (Liu et al., 2016) while wheat consumption is expected to increase by over 30% in the next 40 years (Weigand, 2011). Wheat production would need to reach 858 million tons by 2050 in order to match the predicted global food demand (Alexandratos and Bruinsma, 2012). Drought and heat are two major abiotic stresses constraining wheat productivity worldwide, causing yield losses of up to 86% and 69%, respectively (Fischer and Maurer, 1978; Prasad et al., 2011). Both stresses are more likely to occur simultaneously rather than separately in semi-arid and hot growing regions in North Africa, Argentina, Mexico, Australia, South Africa, and the Mediterranean countries, and in high latitude, semi-arid growing regions of central and eastern Asia, Canada, the USA, and Kazakhstan (Mooney and Di Castri, 1973; Araus et al., 2002; Pradhan et al., 2012; Tricker et al., 2016). Yield penalty is associated with long periods of drought coinciding with heat waves above 32 °C during heading and grain filling stages (Wardlaw and Wrigley, 1994). In the Australian wheat belt, average daily maximum temperatures and numbers of days over 30 °C during the period of grain filling have been steadily increasing over the past three decades, and further rises are projected with climate change (ABS, 2012). The major decrease in wheat production across central Europe in the exceptionally hot summer of 2003 was likely to be due to short, but severe, heat waves during reproductive development (Wheeler, 2012). Stress tolerance is particularly critical in growing regions where the gap between attained yields and maximum yields is highest, and may have more consequence globally than where differences are lower (Tester and Langridge, 2010). Hence, producing wheat varieties with high and stable yield under these environmental stresses is one of the most important aims of breeding (Gavuzzi et al., 1997; Tilman et al., 2011). Whereas responses to either drought or heat stress have been studied extensively in wheat, the combination of both environmental stresses has only recently become a matter for research. When irrigated, and with saturated atmospheric humidity (low vapour pressure deficit; VPD) at high temperatures, Australian modern wheat varieties did not show symptoms of heat stress: plants were lush and produced up to 6.8 t ha−1 (Parent et al., 2017). This example and others demonstrate that wheat is heat tolerant when water is available. To improve wheat for dual tolerance, plants must be studied under the combination of stresses. Overall, the combination of both high temperature and drought has a negative, additive impact on plant phenology and physiology, i.e. growth, chlorophyll content, leaf photosynthesis, grain number, spikelet fertility, grain filling duration, and grain yield (Altenbach et al., 2003; Shah and Paulsen, 2003; Prasad et al., 2011; Pradhan et al., 2012; Perdomo et al., 2015, 2017). Although responses to the two stresses share some common mechanisms, other physiological processes are antagonistic (Machado and Paulsen, 2001). For instance, combined drought and heat stress decreases leaf chlorophyll content by 49% while drought or heat alone reduce it by 9% or 27%, respectively (Pradhan et al., 2012; Awasthi et al., 2014). This early senescence of green tissues affects the total amount of carbohydrates transported to the grains and final grain weight. Delayed senescence, a stay-green phenotype, has been associated with drought tolerance (e.g. Pinto et al., 2010) and with heat tolerance in experiments using irrigation (e.g. Shirdelmoghanloo et al., 2016) where water reserves are available and accessible in deep soils for continued water use and transport of assimilates to grains post-anthesis (Reynolds et al., 2005; Christopher et al., 2008). In contrast, a stay-green phenotype is unlikely to contribute to combined drought and heat tolerance where no water reserves are available for continuous water use and might exacerbate the combined stress. Although plants’ responses to the combination of drought and heat have been described (reviewed in Zandalinas et al. 2018), few models or explanations are proposed for the physiological traits underlying combined tolerance (Pinto and Reynolds, 2015), and very little is known about genes and loci underlying these physiological mechanisms in wheat. Quantitative trait loci (QTLs) for drought and heat tolerance have, to date, mostly been reported for low-yield field environments where stress is present (such as the mega-environments 1 and 4 defined by CIMMYT, http://wheatatlas.org/), but not controlled and often not measured (Table 1). Complex interactions between QTLs and environments exist that may limit the usefulness of a particular allele. For example, using multi-environment analysis, Bonneau et al. (2013) showed that alternative parental alleles of a major QTL for yield in dry and hot environments (qDHY.3B) were positive, depending on the severity of the water deficit, soil depth, and co-occurrence with high temperatures. Table 1. QTL identified in wheat under combined dry and hot conditions, drought or heat stress Trait Chromosome References Combined dry and hot conditions Grain yield 1AL, 1B, 1D, 2A, 2BL, 3A, 3B, 4AL, 4B, 5A, 6A, 6B, 7A, 7B, 7D Kirigwi et al. (2007),a, Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Merchuk-Ovnat et al. (2016),a, Tahmasebi et al. (2017)a Thousand grain weight 1D, 2B, 3A, 3B, 4A, 6A, 7A, 7B, 7D Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Tahmasebi et al. (2017)a Kernel weight index (large grains−all grains) 1A, 2B, 6A Pinto et al. (2010)a Grain weight spike−1 5B, 6A, 7B Golabadi et al. (2011)a Grain number m−2 1B, 2A, 3B, 3D, 4AL, 6B, 7A Kirigwi et al. (2007),a, Pinto et al. (2010),a, Bennett et al. (2012)a Grain number spike−1 2B, 7B Golabadi et al. (2011),a, Tahmasebi et al. (2017)a Harvest index 1B, 2A, 2B, 3B, 4A, 5A, 5B, 6A, 6B, 7B Peleg et al. (2009),d, Golabadi et al. (2011)a Spike weight 1B, 2A, 4A, 6A, 7A, 7B Peleg et al. (2009),d, Golabadi et al. (2011)a Spike number m−2 2B, 4AL, 5B Kirigwi et al. (2007),a, Golabadi et al. (2011)a Spike harvest index 2B, 3B Golabadi et al. (2011)a Spikelet number spike−1 5A Tahmasebi et al. (2017)a Biomass 2BS, 4AL, 4B, 5A, 7AS Kirigwi et al. (2007),a, Peleg et al. (2009),d, Merchuk-Ovnat et al. (2016)a Plant height 1A, 1B, 2BL, 3AL, 3BS, 4A, 4B, 5A, 7AS Maccaferri et al. (2008),ab, Pinto et al. (2010),a, Tahmasebi et al. (2017)a Shoot length 2B, 3B, 4A, 4B, 6B, 7A, 7B Peleg et al. (2009)d Peduncle length 3A, 3B Bennett et al. (2012)a Flag leaf width 2B, 3B Bennett et al. (2012)a Days to heading 1A, 1B, 1D, 2AS, 2BS, 2BL, 3A, 3B, 4AL, 4B, 4D, 5A, 6A, 7AS, 7BS, 7D Kirigwi et al. (2007),a, Maccaferri et al. (2008)a,b, Peleg et al. (2009),d, Pinto et al. (2010),a, Merchuk-Ovnat et al. (2016),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Days to maturity 1A, 1D,5A, 7B, 7D Pinto et al. (2010),a, Tahmasebi et al. (2017)a Days from heading to maturity 1B, 2B, 4A, 4B, 5A, 5B, 7A, 7B Peleg et al. (2009)d NDVI at the vegetative stage 1B, 3B, 4A, 7A Pinto et al. (2010),a, Bennett et al. (2012)a NDVI at the grain filling stage 1B, 1D, 2A, 2B, 4A, 4B, 5A, 6A, 6B, 7A, 7B Pinto et al. (2010)a Stem WSC 1A, 1B, 3A, 3B, 4A, 6D Pinto et al. (2010),a, Bennett et al. (2012)a Grain fill rate 4AL Kirigwi et al. (2007)a Grain fill duration 4AL Kirigwi et al. (2007)a Canopy temperature at the vegetative stage 1B, 2B, 3B, 4A, 4B, 6B, 7A Pinto et al. (2010),a, Tahmasebi et al. (2017)a Canopy temperature at the grain filling stage 1A, 1B, 2B, 3B, 4A, 5A, 6B, 7A Pinto et al. (2010)a Canopy temperature depression 1A, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Flag leaf rolling 1A, 2A, 2B, 4B, 5A, 5B, 6B, 7A, 7D Peleg et al. (2009),d, Tahmasebi et al. (2017)a Early vigour 2B, 2D, 3B, 4A Bennett et al. (2012)a Early ground cover 6AS Mondal et al. (2017)a Chlorophyll content 1A, 1B, 3A, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A Diab et al. (2008),a, Peleg et al. (2009),d, Bennett et al. (2012)a Chlorophyll fluorescence 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Carbon isotope discrimination 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6B, 7A, 7B Diab et al. (2008),a, Peleg et al. (2009)d Photosynthetically active radiation 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Stomatal density 4AS, 5AS, 7AL Shahinnia et al. (2016)a Stomatal index 2BL, 7BL Shahinnia et al. (2016)a Stomatal aperture area 7AL Shahinnia et al. (2016)a Stomatal aperture length 2BS, 2BL, 7AL Shahinnia et al. (2016)a Guard cell length 1AS, 3BL, 7AL Shahinnia et al. (2016)a Guard cell area 1BL, 4BL, 5AL, 5DL Shahinnia et al. (2016)a Transpiration efficiency 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Leaf relative water content 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B Diab et al. (2008)a Water index 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Leaf osmotic potential 2A, 2B, 3A, 3B, 4B, 5A, 5B, 6B Peleg et al. (2009)d Osmotic adjustment 1A, 3A, 3B, 4A, 7A Diab et al. (2008)a Metabolites (mQTL) 2B, 4A, 5A, 7A, 7D Hill et al. (2015)a Expression of stress-related genes (eQTL) 6BL Aprile et al. (2013)c Drought stress Grain yield 2D, 3D, 3DL, 4AL, 4BS, 4DL, 5A, 5B, 5DL, 6B, 6D, 7AL, 7BL, 7D Quarrie et al. (2005),a, Czyczyło-Mysza et al. (2011),d, Kadam et al. (2012),c, Tahmasebi et al. (2017)a Grain weight spike−1 1B, 1D Xu et al. (2017)a Thousand grain weight 1B, 1D, 2A, 2B, 3A, 3D, 4A, 4D, 5A, 6A, 6D, 7A, 7B Quarrie et al. (2005),a, Dashti et al. (2007),c, Yang et al. (2007),a, Tahmasebi et al. (2017),a, Xu et al. (2017)a Grain number m−2 1B, 5B, 7D Tahmasebi et al. (2017)a Grain number spike−1 1A, 2A, 2B, 2D, 3A, 3B, 4A, 4B, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B Quarrie et al. (2005),a, Czyczyło-Mysza et al. (2011),d, Xu et al. (2017)a Harvest index 1B, 2D, 4BS, 5A Kadam et al. (2012),c, Xu et al. (2017)a Spike number plant−1 1A, 2A, 2B, 2D, 4B, 5A, 7B Quarrie et al. (2005),a, Xu et al. (2017)a Spikelet compactness 6A, 7A Xu et al. (2017)a Spikelet number spike−1 1A, 7D Tahmasebi et al. (2017),a, Xu et al. (2017)a Sterile spikelet number spike−1 7A Xu et al. (2017)a Fertile spikelet spike−1 2A Xu et al. (2017)a Biomass 1B Xu et al. (2017)a Shoot biomass 4B Kadam et al. (2012)c Root biomass 2D, 4BS Kadam et al. (2012)c Plant height 1B, 4B, 7D Tahmasebi et al. (2017),a, Xu et al. (2017)a Peduncle length 3B Dashti et al. (2007)c Coleoptile length 6AS Spielmeyer et al. (2007)c Spike length 2B, 7A, 7B Xu et al. (2017)a Root length 2D, 4B, 5D, 6B Kadam et al. (2012)c Growth rate 5BL Parent et al. (2015)c Relative growth rate 4AL Parent et al. (2015)c Inflexion point in growth curves 7DS Parent et al. (2015)c Leaf expansion rate 5BL Parent et al. (2015)c Inflexion point in leaf expansion curves 5BL Parent et al. (2015)c Days to heading 1D, 4B, 7D Tahmasebi et al. (2017)a Days to flowering 2D Kadam et al. (2012)c Stem WSC at the flowering stage 1A, 1D, 2D, 4A, 4B, 7B Yang et al. (2007)a Stem WSC at the grain filling stage 4A Yang et al. (2007)a Stem WSC at the maturity stage 6B Yang et al. (2007)a Accumulation efficiency of stem WSC 1A, 2A, 5A, 7B Yang et al. (2007)a Remobilization efficiency of stem WSC 7A Yang et al. (2007)a Grain filling efficiency 2A, 4B, 5A, Yang et al. (2007)a Flag leaf rolling 4B, 5A Tahmasebi et al. (2017)a Chlorophyll content 1B, 2B, 5B, 7A, 7B Ilyas et al. (2014),c, Tahmasebi et al. (2017),a, Xu et al. (2017)a Flag leaf persistence 2D, 3B, 4B, 5A, 6A Verma et al. (2004)a Net photosynthetic rate 6B Xu et al. (2017)a Chlorophyll fluorescence 1B, 2A, 2D, 3A, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A, 7B, 7D Czyczyło-Mysza et al. (2011)d Stomatal conductance 5A Xu et al. (2017)a Stomatal density 5BS Shahinnia et al. (2016)c Stomatal index 5BS, 6DL Shahinnia et al. (2016)c Stomatal aperture length 2BL, 4BS, 7AS, 7DL Shahinnia et al. (2016)c Guard cell area 1BL, 5BS Shahinnia et al. (2016)c Guard cell length 1BL, 4BS, 7AS Shahinnia et al. (2016)c Transpiration rate 3Al, 4BL, 6D Parent et al. (2015),c, Xu et al. (2017)a Water use efficiency 2AL, 4D Parent et al. (2015),c, Xu et al. (2017)a Heat stress Grain yield 1A, 1BL, 1D, 2BS, 3A, 3BS, 3BL, 3D, 4A, 4B, 4DL, 5A, 5B, 6A, 6B, 6D, 7AS, 7AL, 7BS, 7BL Quarrie et al. (2005),a, Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Paliwal et al. (2012),a, Merchuk-Ovnat et al. (2016),a, Ogbonnaya et al. (2017)a Grain weight spike−1 3A, 3BS, 6A, 7A, 7B Golabadi et al. (2011),a, Shirdelmoghanloo et al. (2016),c, Ogbonnaya et al. (2017)a Thousand grain weight 1A, 2A, 2B, 2D, 3A, 3BS, 3D, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7D Quarrie et al. (2005),a, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Single grain weight 2D, 3BS, 5B, 6A Shirdelmoghanloo et al. (2016)c Kernel weight index (large grains−all grains) 1A, 1D, 2B, 3B, 4B, 5A, 5B, 6A, 6B, 6D Pinto et al. (2010)a Grain number m−2 1A, 1B, 1D, 3BS, 3BL, 3D, 4A, 4B, 4D, 5B, 6A, 6B, 6D, 7A Pinto et al. (2010),a, Bennett et al. (2012)a Grain number spike−1 1A, 1B, 2A, 3B, 4B, 4D, 5D, 6A, 7B, 7D Quarrie et al. (2005),a, Golabadi et al. (2011),a, Ogbonnaya et al. (2017),a, Tahmasebi et al (2017)a Threshing index 1A, 1B, 5B Ogbonnaya et al. (2017)a Harvest index 1B, 2B, 3B, 4A, 5A, 5B, 6A, 6B, 7B Peleg et al. (2009)d Spike number m−2 1A, 1B, 3A, 3B, 4B, 5A, 5B, 7B, 7D Golabadi et al. (2011),a, Ogbonnaya et al. (2017)a Spike number plant−1 3A Quarrie et al. (2005)a Spike weight 1B, 2B, 2D, 3D, 4A, 5D, 6A, 7B Peleg et al. (2009),d, Golabadi et al. (2011),a, Ogbonnaya et al. (2017)a Spike harvest index 2B, 5B, 7A, 7B Golabadi et al. (2011)a Spikelet compactness 1A Tahmasebi et al. (2017)a Spikelet number spike−1 1B, 1D, 2B, 4A, 5B, 6A, 6B Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Number of productive tiller 1B Sharma et al. (2016)a Biomass 1BL, 2BS, 7AS, 7BS Merchuk-Ovnat et al. (2016)a Shoot biomass 3BS, 4A, 6B Shirdelmoghanloo et al. (2016)c Plant height 1A, 1B, 2A, 2B, 2D, 3A, 3B, 3D, 4A, 4B, 5A, 5B, 6A, 6D, 7A, 7B, 7D Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Shoot length 1B, 2B, 3A, 3B, 4A, 4B, 5D, 7A, 7B Peleg et al. (2009),d, Ogbonnaya et al. (2017)a Peduncle length 1A, 1B, 2B, 3A, 3B, 5B, 7A Ogbonnaya et al. (2017)a Flag leaf length 3B, 5B Mason et al. (2010)c Flag leaf width 1D, 2B, 3BL, 7A, 3BL Mason et al. (2010),c, Bennett et al. (2012)a Wax score 1B, 2A, 2B, 2D, 3A, 3B, 5A, 6A, 6B, 7B Mason et al. (2010),c, Ogbonnaya et al. (2017)a Days to heading 1BL, 1D, 2A, 2BS, 3B, 3A, 4A, 4B, 4D, 5A, 6A, 7AS, 7BS, 7D Maccaferri et al. (2008)a,b, Peleg et al. (2009),d, Pinto et al. (2010),a, Merchuk- Ovnat et al. (2016),a, Ogbonnaya et al. (2017)a Days to flowering 1B, 1D, 4A, 4B, 4D, 5B Mason et al. (2010),c, Pinto et al. (2010)a Days to maturity 1B, 1D, 2A, 2B, 3B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B, 7DS Pinto et al. (2010),a, Bennett et al. (2012),a, Paliwal et al. (2012),a, Ogbonnaya et al. (2017)a NDVI at the vegetative stage 1B, 1D, 2B, 2D, 3A, 3B, 4A, 4D, 5A, 6A, 6B, 6D, 7A Pinto et al. (2010),a, Bennett et al. (2012)a NDVI at the grain filling stage 1A, 1B, 3A, 4A, 4B, 5A, 5B, 6A, 7B Pinto et al. (2010)a Stem WSC 1A, 1B, 2D, 3A, 3BL, 5A, 5B, 6A Pinto et al. (2010),a, Bennett et al. (2012)a Grain filling duration 1B, 1D, 2A, 2B, 2D, 3BS, 5A, 6A, 6B, 6D Mason et al. (2010),c, Shirdelmoghanloo et al. (2016),c, Ogbonnaya et al. (2017)a Canopy temperature at the vegetative stage 1A, 1B, 1D, 2B, 3A, 3BL, 4A, 4B, 5B, 6B, 7A Pinto et al. (2010),a, Bennett et al. (2012)a Canopy temperature at the grain filling stage 1A, 1B, 1D, 2B, 3BS, 3BL, 4A, 4D, 5A, 5D, 7A, 7B Pinto et al. (2010),a, Bennett et al. (2012)a Canopy temperature depression 7BL Paliwal et al. (2012)a Flag leaf rolling 1A, 2A, 2B, 2D, 3D, 4B, 5A, 5B, 6A, 6B, 7A, 7B Peleg et al. (2009),d, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Early vigour 2B, 2D, 3BL Bennett et al. (2012)a Chlorophyll content 1A, 1B, 1D, 2B, 3A, 3BS, 4A, 4D, 5A, 5B, 6A, 6D, 7A, 7B, 7D Peleg et al. (2009),d, Pinto et al. (2010),a, Bennett et al. (2012),a, Tahmasebi et al. (2017)a Flag leaf persistence 1B, 1D, 2A, 3A, 3BS, 6A, 6B, 7A, Vijayalakshmi et al. (2010),c, Talukder et al. (2014),c, Shirdelmoghanloo et al. (2016)c Chlorophyll loss rate 3BS, 6BL Shirdelmoghanloo et al. (2016)c Chlorophyll fluorescence 7A Vijayalakshmi et al. (2010)c Carbon isotope discrimination 1A, 2A, 4A, 5B, 6A, 6B, 7B Peleg et al. (2009)d Leaf osmotic potential 2A, 3A, 3B, 5A, 5B, 6A, 6B Peleg et al. (2009) Plasma membrane damage 1D, 2B, 7A Talukder et al. (2014)c Thylakoid membrane damage 1D, 6A, 7A Talukder et al. (2014)c Trait Chromosome References Combined dry and hot conditions Grain yield 1AL, 1B, 1D, 2A, 2BL, 3A, 3B, 4AL, 4B, 5A, 6A, 6B, 7A, 7B, 7D Kirigwi et al. (2007),a, Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Merchuk-Ovnat et al. (2016),a, Tahmasebi et al. (2017)a Thousand grain weight 1D, 2B, 3A, 3B, 4A, 6A, 7A, 7B, 7D Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Tahmasebi et al. (2017)a Kernel weight index (large grains−all grains) 1A, 2B, 6A Pinto et al. (2010)a Grain weight spike−1 5B, 6A, 7B Golabadi et al. (2011)a Grain number m−2 1B, 2A, 3B, 3D, 4AL, 6B, 7A Kirigwi et al. (2007),a, Pinto et al. (2010),a, Bennett et al. (2012)a Grain number spike−1 2B, 7B Golabadi et al. (2011),a, Tahmasebi et al. (2017)a Harvest index 1B, 2A, 2B, 3B, 4A, 5A, 5B, 6A, 6B, 7B Peleg et al. (2009),d, Golabadi et al. (2011)a Spike weight 1B, 2A, 4A, 6A, 7A, 7B Peleg et al. (2009),d, Golabadi et al. (2011)a Spike number m−2 2B, 4AL, 5B Kirigwi et al. (2007),a, Golabadi et al. (2011)a Spike harvest index 2B, 3B Golabadi et al. (2011)a Spikelet number spike−1 5A Tahmasebi et al. (2017)a Biomass 2BS, 4AL, 4B, 5A, 7AS Kirigwi et al. (2007),a, Peleg et al. (2009),d, Merchuk-Ovnat et al. (2016)a Plant height 1A, 1B, 2BL, 3AL, 3BS, 4A, 4B, 5A, 7AS Maccaferri et al. (2008),ab, Pinto et al. (2010),a, Tahmasebi et al. (2017)a Shoot length 2B, 3B, 4A, 4B, 6B, 7A, 7B Peleg et al. (2009)d Peduncle length 3A, 3B Bennett et al. (2012)a Flag leaf width 2B, 3B Bennett et al. (2012)a Days to heading 1A, 1B, 1D, 2AS, 2BS, 2BL, 3A, 3B, 4AL, 4B, 4D, 5A, 6A, 7AS, 7BS, 7D Kirigwi et al. (2007),a, Maccaferri et al. (2008)a,b, Peleg et al. (2009),d, Pinto et al. (2010),a, Merchuk-Ovnat et al. (2016),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Days to maturity 1A, 1D,5A, 7B, 7D Pinto et al. (2010),a, Tahmasebi et al. (2017)a Days from heading to maturity 1B, 2B, 4A, 4B, 5A, 5B, 7A, 7B Peleg et al. (2009)d NDVI at the vegetative stage 1B, 3B, 4A, 7A Pinto et al. (2010),a, Bennett et al. (2012)a NDVI at the grain filling stage 1B, 1D, 2A, 2B, 4A, 4B, 5A, 6A, 6B, 7A, 7B Pinto et al. (2010)a Stem WSC 1A, 1B, 3A, 3B, 4A, 6D Pinto et al. (2010),a, Bennett et al. (2012)a Grain fill rate 4AL Kirigwi et al. (2007)a Grain fill duration 4AL Kirigwi et al. (2007)a Canopy temperature at the vegetative stage 1B, 2B, 3B, 4A, 4B, 6B, 7A Pinto et al. (2010),a, Tahmasebi et al. (2017)a Canopy temperature at the grain filling stage 1A, 1B, 2B, 3B, 4A, 5A, 6B, 7A Pinto et al. (2010)a Canopy temperature depression 1A, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Flag leaf rolling 1A, 2A, 2B, 4B, 5A, 5B, 6B, 7A, 7D Peleg et al. (2009),d, Tahmasebi et al. (2017)a Early vigour 2B, 2D, 3B, 4A Bennett et al. (2012)a Early ground cover 6AS Mondal et al. (2017)a Chlorophyll content 1A, 1B, 3A, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A Diab et al. (2008),a, Peleg et al. (2009),d, Bennett et al. (2012)a Chlorophyll fluorescence 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Carbon isotope discrimination 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6B, 7A, 7B Diab et al. (2008),a, Peleg et al. (2009)d Photosynthetically active radiation 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Stomatal density 4AS, 5AS, 7AL Shahinnia et al. (2016)a Stomatal index 2BL, 7BL Shahinnia et al. (2016)a Stomatal aperture area 7AL Shahinnia et al. (2016)a Stomatal aperture length 2BS, 2BL, 7AL Shahinnia et al. (2016)a Guard cell length 1AS, 3BL, 7AL Shahinnia et al. (2016)a Guard cell area 1BL, 4BL, 5AL, 5DL Shahinnia et al. (2016)a Transpiration efficiency 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Leaf relative water content 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B Diab et al. (2008)a Water index 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Leaf osmotic potential 2A, 2B, 3A, 3B, 4B, 5A, 5B, 6B Peleg et al. (2009)d Osmotic adjustment 1A, 3A, 3B, 4A, 7A Diab et al. (2008)a Metabolites (mQTL) 2B, 4A, 5A, 7A, 7D Hill et al. (2015)a Expression of stress-related genes (eQTL) 6BL Aprile et al. (2013)c Drought stress Grain yield 2D, 3D, 3DL, 4AL, 4BS, 4DL, 5A, 5B, 5DL, 6B, 6D, 7AL, 7BL, 7D Quarrie et al. (2005),a, Czyczyło-Mysza et al. (2011),d, Kadam et al. (2012),c, Tahmasebi et al. (2017)a Grain weight spike−1 1B, 1D Xu et al. (2017)a Thousand grain weight 1B, 1D, 2A, 2B, 3A, 3D, 4A, 4D, 5A, 6A, 6D, 7A, 7B Quarrie et al. (2005),a, Dashti et al. (2007),c, Yang et al. (2007),a, Tahmasebi et al. (2017),a, Xu et al. (2017)a Grain number m−2 1B, 5B, 7D Tahmasebi et al. (2017)a Grain number spike−1 1A, 2A, 2B, 2D, 3A, 3B, 4A, 4B, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B Quarrie et al. (2005),a, Czyczyło-Mysza et al. (2011),d, Xu et al. (2017)a Harvest index 1B, 2D, 4BS, 5A Kadam et al. (2012),c, Xu et al. (2017)a Spike number plant−1 1A, 2A, 2B, 2D, 4B, 5A, 7B Quarrie et al. (2005),a, Xu et al. (2017)a Spikelet compactness 6A, 7A Xu et al. (2017)a Spikelet number spike−1 1A, 7D Tahmasebi et al. (2017),a, Xu et al. (2017)a Sterile spikelet number spike−1 7A Xu et al. (2017)a Fertile spikelet spike−1 2A Xu et al. (2017)a Biomass 1B Xu et al. (2017)a Shoot biomass 4B Kadam et al. (2012)c Root biomass 2D, 4BS Kadam et al. (2012)c Plant height 1B, 4B, 7D Tahmasebi et al. (2017),a, Xu et al. (2017)a Peduncle length 3B Dashti et al. (2007)c Coleoptile length 6AS Spielmeyer et al. (2007)c Spike length 2B, 7A, 7B Xu et al. (2017)a Root length 2D, 4B, 5D, 6B Kadam et al. (2012)c Growth rate 5BL Parent et al. (2015)c Relative growth rate 4AL Parent et al. (2015)c Inflexion point in growth curves 7DS Parent et al. (2015)c Leaf expansion rate 5BL Parent et al. (2015)c Inflexion point in leaf expansion curves 5BL Parent et al. (2015)c Days to heading 1D, 4B, 7D Tahmasebi et al. (2017)a Days to flowering 2D Kadam et al. (2012)c Stem WSC at the flowering stage 1A, 1D, 2D, 4A, 4B, 7B Yang et al. (2007)a Stem WSC at the grain filling stage 4A Yang et al. (2007)a Stem WSC at the maturity stage 6B Yang et al. (2007)a Accumulation efficiency of stem WSC 1A, 2A, 5A, 7B Yang et al. (2007)a Remobilization efficiency of stem WSC 7A Yang et al. (2007)a Grain filling efficiency 2A, 4B, 5A, Yang et al. (2007)a Flag leaf rolling 4B, 5A Tahmasebi et al. (2017)a Chlorophyll content 1B, 2B, 5B, 7A, 7B Ilyas et al. (2014),c, Tahmasebi et al. (2017),a, Xu et al. (2017)a Flag leaf persistence 2D, 3B, 4B, 5A, 6A Verma et al. (2004)a Net photosynthetic rate 6B Xu et al. (2017)a Chlorophyll fluorescence 1B, 2A, 2D, 3A, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A, 7B, 7D Czyczyło-Mysza et al. (2011)d Stomatal conductance 5A Xu et al. (2017)a Stomatal density 5BS Shahinnia et al. (2016)c Stomatal index 5BS, 6DL Shahinnia et al. (2016)c Stomatal aperture length 2BL, 4BS, 7AS, 7DL Shahinnia et al. (2016)c Guard cell area 1BL, 5BS Shahinnia et al. (2016)c Guard cell length 1BL, 4BS, 7AS Shahinnia et al. (2016)c Transpiration rate 3Al, 4BL, 6D Parent et al. (2015),c, Xu et al. (2017)a Water use efficiency 2AL, 4D Parent et al. (2015),c, Xu et al. (2017)a Heat stress Grain yield 1A, 1BL, 1D, 2BS, 3A, 3BS, 3BL, 3D, 4A, 4B, 4DL, 5A, 5B, 6A, 6B, 6D, 7AS, 7AL, 7BS, 7BL Quarrie et al. (2005),a, Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Paliwal et al. (2012),a, Merchuk-Ovnat et al. (2016),a, Ogbonnaya et al. (2017)a Grain weight spike−1 3A, 3BS, 6A, 7A, 7B Golabadi et al. (2011),a, Shirdelmoghanloo et al. (2016),c, Ogbonnaya et al. (2017)a Thousand grain weight 1A, 2A, 2B, 2D, 3A, 3BS, 3D, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7D Quarrie et al. (2005),a, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Single grain weight 2D, 3BS, 5B, 6A Shirdelmoghanloo et al. (2016)c Kernel weight index (large grains−all grains) 1A, 1D, 2B, 3B, 4B, 5A, 5B, 6A, 6B, 6D Pinto et al. (2010)a Grain number m−2 1A, 1B, 1D, 3BS, 3BL, 3D, 4A, 4B, 4D, 5B, 6A, 6B, 6D, 7A Pinto et al. (2010),a, Bennett et al. (2012)a Grain number spike−1 1A, 1B, 2A, 3B, 4B, 4D, 5D, 6A, 7B, 7D Quarrie et al. (2005),a, Golabadi et al. (2011),a, Ogbonnaya et al. (2017),a, Tahmasebi et al (2017)a Threshing index 1A, 1B, 5B Ogbonnaya et al. (2017)a Harvest index 1B, 2B, 3B, 4A, 5A, 5B, 6A, 6B, 7B Peleg et al. (2009)d Spike number m−2 1A, 1B, 3A, 3B, 4B, 5A, 5B, 7B, 7D Golabadi et al. (2011),a, Ogbonnaya et al. (2017)a Spike number plant−1 3A Quarrie et al. (2005)a Spike weight 1B, 2B, 2D, 3D, 4A, 5D, 6A, 7B Peleg et al. (2009),d, Golabadi et al. (2011),a, Ogbonnaya et al. (2017)a Spike harvest index 2B, 5B, 7A, 7B Golabadi et al. (2011)a Spikelet compactness 1A Tahmasebi et al. (2017)a Spikelet number spike−1 1B, 1D, 2B, 4A, 5B, 6A, 6B Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Number of productive tiller 1B Sharma et al. (2016)a Biomass 1BL, 2BS, 7AS, 7BS Merchuk-Ovnat et al. (2016)a Shoot biomass 3BS, 4A, 6B Shirdelmoghanloo et al. (2016)c Plant height 1A, 1B, 2A, 2B, 2D, 3A, 3B, 3D, 4A, 4B, 5A, 5B, 6A, 6D, 7A, 7B, 7D Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Shoot length 1B, 2B, 3A, 3B, 4A, 4B, 5D, 7A, 7B Peleg et al. (2009),d, Ogbonnaya et al. (2017)a Peduncle length 1A, 1B, 2B, 3A, 3B, 5B, 7A Ogbonnaya et al. (2017)a Flag leaf length 3B, 5B Mason et al. (2010)c Flag leaf width 1D, 2B, 3BL, 7A, 3BL Mason et al. (2010),c, Bennett et al. (2012)a Wax score 1B, 2A, 2B, 2D, 3A, 3B, 5A, 6A, 6B, 7B Mason et al. (2010),c, Ogbonnaya et al. (2017)a Days to heading 1BL, 1D, 2A, 2BS, 3B, 3A, 4A, 4B, 4D, 5A, 6A, 7AS, 7BS, 7D Maccaferri et al. (2008)a,b, Peleg et al. (2009),d, Pinto et al. (2010),a, Merchuk- Ovnat et al. (2016),a, Ogbonnaya et al. (2017)a Days to flowering 1B, 1D, 4A, 4B, 4D, 5B Mason et al. (2010),c, Pinto et al. (2010)a Days to maturity 1B, 1D, 2A, 2B, 3B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B, 7DS Pinto et al. (2010),a, Bennett et al. (2012),a, Paliwal et al. (2012),a, Ogbonnaya et al. (2017)a NDVI at the vegetative stage 1B, 1D, 2B, 2D, 3A, 3B, 4A, 4D, 5A, 6A, 6B, 6D, 7A Pinto et al. (2010),a, Bennett et al. (2012)a NDVI at the grain filling stage 1A, 1B, 3A, 4A, 4B, 5A, 5B, 6A, 7B Pinto et al. (2010)a Stem WSC 1A, 1B, 2D, 3A, 3BL, 5A, 5B, 6A Pinto et al. (2010),a, Bennett et al. (2012)a Grain filling duration 1B, 1D, 2A, 2B, 2D, 3BS, 5A, 6A, 6B, 6D Mason et al. (2010),c, Shirdelmoghanloo et al. (2016),c, Ogbonnaya et al. (2017)a Canopy temperature at the vegetative stage 1A, 1B, 1D, 2B, 3A, 3BL, 4A, 4B, 5B, 6B, 7A Pinto et al. (2010),a, Bennett et al. (2012)a Canopy temperature at the grain filling stage 1A, 1B, 1D, 2B, 3BS, 3BL, 4A, 4D, 5A, 5D, 7A, 7B Pinto et al. (2010),a, Bennett et al. (2012)a Canopy temperature depression 7BL Paliwal et al. (2012)a Flag leaf rolling 1A, 2A, 2B, 2D, 3D, 4B, 5A, 5B, 6A, 6B, 7A, 7B Peleg et al. (2009),d, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Early vigour 2B, 2D, 3BL Bennett et al. (2012)a Chlorophyll content 1A, 1B, 1D, 2B, 3A, 3BS, 4A, 4D, 5A, 5B, 6A, 6D, 7A, 7B, 7D Peleg et al. (2009),d, Pinto et al. (2010),a, Bennett et al. (2012),a, Tahmasebi et al. (2017)a Flag leaf persistence 1B, 1D, 2A, 3A, 3BS, 6A, 6B, 7A, Vijayalakshmi et al. (2010),c, Talukder et al. (2014),c, Shirdelmoghanloo et al. (2016)c Chlorophyll loss rate 3BS, 6BL Shirdelmoghanloo et al. (2016)c Chlorophyll fluorescence 7A Vijayalakshmi et al. (2010)c Carbon isotope discrimination 1A, 2A, 4A, 5B, 6A, 6B, 7B Peleg et al. (2009)d Leaf osmotic potential 2A, 3A, 3B, 5A, 5B, 6A, 6B Peleg et al. (2009) Plasma membrane damage 1D, 2B, 7A Talukder et al. (2014)c Thylakoid membrane damage 1D, 6A, 7A Talukder et al. (2014)c Dry and hot field conditions are defined using the CIMMYT mega-environments 1 and 4 (Rajaram et al., 1994). NDVI, near differential vegetative index; WSC, water-soluble carbohydrates a Field conditions. b Trials in Italy, Tunisia and Morocco with maximum temperature at grain filling ≤26.1 °C. c Controlled conditions. d Semi-controlled conditions. View Large Table 1. QTL identified in wheat under combined dry and hot conditions, drought or heat stress Trait Chromosome References Combined dry and hot conditions Grain yield 1AL, 1B, 1D, 2A, 2BL, 3A, 3B, 4AL, 4B, 5A, 6A, 6B, 7A, 7B, 7D Kirigwi et al. (2007),a, Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Merchuk-Ovnat et al. (2016),a, Tahmasebi et al. (2017)a Thousand grain weight 1D, 2B, 3A, 3B, 4A, 6A, 7A, 7B, 7D Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Tahmasebi et al. (2017)a Kernel weight index (large grains−all grains) 1A, 2B, 6A Pinto et al. (2010)a Grain weight spike−1 5B, 6A, 7B Golabadi et al. (2011)a Grain number m−2 1B, 2A, 3B, 3D, 4AL, 6B, 7A Kirigwi et al. (2007),a, Pinto et al. (2010),a, Bennett et al. (2012)a Grain number spike−1 2B, 7B Golabadi et al. (2011),a, Tahmasebi et al. (2017)a Harvest index 1B, 2A, 2B, 3B, 4A, 5A, 5B, 6A, 6B, 7B Peleg et al. (2009),d, Golabadi et al. (2011)a Spike weight 1B, 2A, 4A, 6A, 7A, 7B Peleg et al. (2009),d, Golabadi et al. (2011)a Spike number m−2 2B, 4AL, 5B Kirigwi et al. (2007),a, Golabadi et al. (2011)a Spike harvest index 2B, 3B Golabadi et al. (2011)a Spikelet number spike−1 5A Tahmasebi et al. (2017)a Biomass 2BS, 4AL, 4B, 5A, 7AS Kirigwi et al. (2007),a, Peleg et al. (2009),d, Merchuk-Ovnat et al. (2016)a Plant height 1A, 1B, 2BL, 3AL, 3BS, 4A, 4B, 5A, 7AS Maccaferri et al. (2008),ab, Pinto et al. (2010),a, Tahmasebi et al. (2017)a Shoot length 2B, 3B, 4A, 4B, 6B, 7A, 7B Peleg et al. (2009)d Peduncle length 3A, 3B Bennett et al. (2012)a Flag leaf width 2B, 3B Bennett et al. (2012)a Days to heading 1A, 1B, 1D, 2AS, 2BS, 2BL, 3A, 3B, 4AL, 4B, 4D, 5A, 6A, 7AS, 7BS, 7D Kirigwi et al. (2007),a, Maccaferri et al. (2008)a,b, Peleg et al. (2009),d, Pinto et al. (2010),a, Merchuk-Ovnat et al. (2016),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Days to maturity 1A, 1D,5A, 7B, 7D Pinto et al. (2010),a, Tahmasebi et al. (2017)a Days from heading to maturity 1B, 2B, 4A, 4B, 5A, 5B, 7A, 7B Peleg et al. (2009)d NDVI at the vegetative stage 1B, 3B, 4A, 7A Pinto et al. (2010),a, Bennett et al. (2012)a NDVI at the grain filling stage 1B, 1D, 2A, 2B, 4A, 4B, 5A, 6A, 6B, 7A, 7B Pinto et al. (2010)a Stem WSC 1A, 1B, 3A, 3B, 4A, 6D Pinto et al. (2010),a, Bennett et al. (2012)a Grain fill rate 4AL Kirigwi et al. (2007)a Grain fill duration 4AL Kirigwi et al. (2007)a Canopy temperature at the vegetative stage 1B, 2B, 3B, 4A, 4B, 6B, 7A Pinto et al. (2010),a, Tahmasebi et al. (2017)a Canopy temperature at the grain filling stage 1A, 1B, 2B, 3B, 4A, 5A, 6B, 7A Pinto et al. (2010)a Canopy temperature depression 1A, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Flag leaf rolling 1A, 2A, 2B, 4B, 5A, 5B, 6B, 7A, 7D Peleg et al. (2009),d, Tahmasebi et al. (2017)a Early vigour 2B, 2D, 3B, 4A Bennett et al. (2012)a Early ground cover 6AS Mondal et al. (2017)a Chlorophyll content 1A, 1B, 3A, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A Diab et al. (2008),a, Peleg et al. (2009),d, Bennett et al. (2012)a Chlorophyll fluorescence 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Carbon isotope discrimination 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6B, 7A, 7B Diab et al. (2008),a, Peleg et al. (2009)d Photosynthetically active radiation 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Stomatal density 4AS, 5AS, 7AL Shahinnia et al. (2016)a Stomatal index 2BL, 7BL Shahinnia et al. (2016)a Stomatal aperture area 7AL Shahinnia et al. (2016)a Stomatal aperture length 2BS, 2BL, 7AL Shahinnia et al. (2016)a Guard cell length 1AS, 3BL, 7AL Shahinnia et al. (2016)a Guard cell area 1BL, 4BL, 5AL, 5DL Shahinnia et al. (2016)a Transpiration efficiency 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Leaf relative water content 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B Diab et al. (2008)a Water index 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Leaf osmotic potential 2A, 2B, 3A, 3B, 4B, 5A, 5B, 6B Peleg et al. (2009)d Osmotic adjustment 1A, 3A, 3B, 4A, 7A Diab et al. (2008)a Metabolites (mQTL) 2B, 4A, 5A, 7A, 7D Hill et al. (2015)a Expression of stress-related genes (eQTL) 6BL Aprile et al. (2013)c Drought stress Grain yield 2D, 3D, 3DL, 4AL, 4BS, 4DL, 5A, 5B, 5DL, 6B, 6D, 7AL, 7BL, 7D Quarrie et al. (2005),a, Czyczyło-Mysza et al. (2011),d, Kadam et al. (2012),c, Tahmasebi et al. (2017)a Grain weight spike−1 1B, 1D Xu et al. (2017)a Thousand grain weight 1B, 1D, 2A, 2B, 3A, 3D, 4A, 4D, 5A, 6A, 6D, 7A, 7B Quarrie et al. (2005),a, Dashti et al. (2007),c, Yang et al. (2007),a, Tahmasebi et al. (2017),a, Xu et al. (2017)a Grain number m−2 1B, 5B, 7D Tahmasebi et al. (2017)a Grain number spike−1 1A, 2A, 2B, 2D, 3A, 3B, 4A, 4B, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B Quarrie et al. (2005),a, Czyczyło-Mysza et al. (2011),d, Xu et al. (2017)a Harvest index 1B, 2D, 4BS, 5A Kadam et al. (2012),c, Xu et al. (2017)a Spike number plant−1 1A, 2A, 2B, 2D, 4B, 5A, 7B Quarrie et al. (2005),a, Xu et al. (2017)a Spikelet compactness 6A, 7A Xu et al. (2017)a Spikelet number spike−1 1A, 7D Tahmasebi et al. (2017),a, Xu et al. (2017)a Sterile spikelet number spike−1 7A Xu et al. (2017)a Fertile spikelet spike−1 2A Xu et al. (2017)a Biomass 1B Xu et al. (2017)a Shoot biomass 4B Kadam et al. (2012)c Root biomass 2D, 4BS Kadam et al. (2012)c Plant height 1B, 4B, 7D Tahmasebi et al. (2017),a, Xu et al. (2017)a Peduncle length 3B Dashti et al. (2007)c Coleoptile length 6AS Spielmeyer et al. (2007)c Spike length 2B, 7A, 7B Xu et al. (2017)a Root length 2D, 4B, 5D, 6B Kadam et al. (2012)c Growth rate 5BL Parent et al. (2015)c Relative growth rate 4AL Parent et al. (2015)c Inflexion point in growth curves 7DS Parent et al. (2015)c Leaf expansion rate 5BL Parent et al. (2015)c Inflexion point in leaf expansion curves 5BL Parent et al. (2015)c Days to heading 1D, 4B, 7D Tahmasebi et al. (2017)a Days to flowering 2D Kadam et al. (2012)c Stem WSC at the flowering stage 1A, 1D, 2D, 4A, 4B, 7B Yang et al. (2007)a Stem WSC at the grain filling stage 4A Yang et al. (2007)a Stem WSC at the maturity stage 6B Yang et al. (2007)a Accumulation efficiency of stem WSC 1A, 2A, 5A, 7B Yang et al. (2007)a Remobilization efficiency of stem WSC 7A Yang et al. (2007)a Grain filling efficiency 2A, 4B, 5A, Yang et al. (2007)a Flag leaf rolling 4B, 5A Tahmasebi et al. (2017)a Chlorophyll content 1B, 2B, 5B, 7A, 7B Ilyas et al. (2014),c, Tahmasebi et al. (2017),a, Xu et al. (2017)a Flag leaf persistence 2D, 3B, 4B, 5A, 6A Verma et al. (2004)a Net photosynthetic rate 6B Xu et al. (2017)a Chlorophyll fluorescence 1B, 2A, 2D, 3A, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A, 7B, 7D Czyczyło-Mysza et al. (2011)d Stomatal conductance 5A Xu et al. (2017)a Stomatal density 5BS Shahinnia et al. (2016)c Stomatal index 5BS, 6DL Shahinnia et al. (2016)c Stomatal aperture length 2BL, 4BS, 7AS, 7DL Shahinnia et al. (2016)c Guard cell area 1BL, 5BS Shahinnia et al. (2016)c Guard cell length 1BL, 4BS, 7AS Shahinnia et al. (2016)c Transpiration rate 3Al, 4BL, 6D Parent et al. (2015),c, Xu et al. (2017)a Water use efficiency 2AL, 4D Parent et al. (2015),c, Xu et al. (2017)a Heat stress Grain yield 1A, 1BL, 1D, 2BS, 3A, 3BS, 3BL, 3D, 4A, 4B, 4DL, 5A, 5B, 6A, 6B, 6D, 7AS, 7AL, 7BS, 7BL Quarrie et al. (2005),a, Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Paliwal et al. (2012),a, Merchuk-Ovnat et al. (2016),a, Ogbonnaya et al. (2017)a Grain weight spike−1 3A, 3BS, 6A, 7A, 7B Golabadi et al. (2011),a, Shirdelmoghanloo et al. (2016),c, Ogbonnaya et al. (2017)a Thousand grain weight 1A, 2A, 2B, 2D, 3A, 3BS, 3D, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7D Quarrie et al. (2005),a, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Single grain weight 2D, 3BS, 5B, 6A Shirdelmoghanloo et al. (2016)c Kernel weight index (large grains−all grains) 1A, 1D, 2B, 3B, 4B, 5A, 5B, 6A, 6B, 6D Pinto et al. (2010)a Grain number m−2 1A, 1B, 1D, 3BS, 3BL, 3D, 4A, 4B, 4D, 5B, 6A, 6B, 6D, 7A Pinto et al. (2010),a, Bennett et al. (2012)a Grain number spike−1 1A, 1B, 2A, 3B, 4B, 4D, 5D, 6A, 7B, 7D Quarrie et al. (2005),a, Golabadi et al. (2011),a, Ogbonnaya et al. (2017),a, Tahmasebi et al (2017)a Threshing index 1A, 1B, 5B Ogbonnaya et al. (2017)a Harvest index 1B, 2B, 3B, 4A, 5A, 5B, 6A, 6B, 7B Peleg et al. (2009)d Spike number m−2 1A, 1B, 3A, 3B, 4B, 5A, 5B, 7B, 7D Golabadi et al. (2011),a, Ogbonnaya et al. (2017)a Spike number plant−1 3A Quarrie et al. (2005)a Spike weight 1B, 2B, 2D, 3D, 4A, 5D, 6A, 7B Peleg et al. (2009),d, Golabadi et al. (2011),a, Ogbonnaya et al. (2017)a Spike harvest index 2B, 5B, 7A, 7B Golabadi et al. (2011)a Spikelet compactness 1A Tahmasebi et al. (2017)a Spikelet number spike−1 1B, 1D, 2B, 4A, 5B, 6A, 6B Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Number of productive tiller 1B Sharma et al. (2016)a Biomass 1BL, 2BS, 7AS, 7BS Merchuk-Ovnat et al. (2016)a Shoot biomass 3BS, 4A, 6B Shirdelmoghanloo et al. (2016)c Plant height 1A, 1B, 2A, 2B, 2D, 3A, 3B, 3D, 4A, 4B, 5A, 5B, 6A, 6D, 7A, 7B, 7D Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Shoot length 1B, 2B, 3A, 3B, 4A, 4B, 5D, 7A, 7B Peleg et al. (2009),d, Ogbonnaya et al. (2017)a Peduncle length 1A, 1B, 2B, 3A, 3B, 5B, 7A Ogbonnaya et al. (2017)a Flag leaf length 3B, 5B Mason et al. (2010)c Flag leaf width 1D, 2B, 3BL, 7A, 3BL Mason et al. (2010),c, Bennett et al. (2012)a Wax score 1B, 2A, 2B, 2D, 3A, 3B, 5A, 6A, 6B, 7B Mason et al. (2010),c, Ogbonnaya et al. (2017)a Days to heading 1BL, 1D, 2A, 2BS, 3B, 3A, 4A, 4B, 4D, 5A, 6A, 7AS, 7BS, 7D Maccaferri et al. (2008)a,b, Peleg et al. (2009),d, Pinto et al. (2010),a, Merchuk- Ovnat et al. (2016),a, Ogbonnaya et al. (2017)a Days to flowering 1B, 1D, 4A, 4B, 4D, 5B Mason et al. (2010),c, Pinto et al. (2010)a Days to maturity 1B, 1D, 2A, 2B, 3B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B, 7DS Pinto et al. (2010),a, Bennett et al. (2012),a, Paliwal et al. (2012),a, Ogbonnaya et al. (2017)a NDVI at the vegetative stage 1B, 1D, 2B, 2D, 3A, 3B, 4A, 4D, 5A, 6A, 6B, 6D, 7A Pinto et al. (2010),a, Bennett et al. (2012)a NDVI at the grain filling stage 1A, 1B, 3A, 4A, 4B, 5A, 5B, 6A, 7B Pinto et al. (2010)a Stem WSC 1A, 1B, 2D, 3A, 3BL, 5A, 5B, 6A Pinto et al. (2010),a, Bennett et al. (2012)a Grain filling duration 1B, 1D, 2A, 2B, 2D, 3BS, 5A, 6A, 6B, 6D Mason et al. (2010),c, Shirdelmoghanloo et al. (2016),c, Ogbonnaya et al. (2017)a Canopy temperature at the vegetative stage 1A, 1B, 1D, 2B, 3A, 3BL, 4A, 4B, 5B, 6B, 7A Pinto et al. (2010),a, Bennett et al. (2012)a Canopy temperature at the grain filling stage 1A, 1B, 1D, 2B, 3BS, 3BL, 4A, 4D, 5A, 5D, 7A, 7B Pinto et al. (2010),a, Bennett et al. (2012)a Canopy temperature depression 7BL Paliwal et al. (2012)a Flag leaf rolling 1A, 2A, 2B, 2D, 3D, 4B, 5A, 5B, 6A, 6B, 7A, 7B Peleg et al. (2009),d, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Early vigour 2B, 2D, 3BL Bennett et al. (2012)a Chlorophyll content 1A, 1B, 1D, 2B, 3A, 3BS, 4A, 4D, 5A, 5B, 6A, 6D, 7A, 7B, 7D Peleg et al. (2009),d, Pinto et al. (2010),a, Bennett et al. (2012),a, Tahmasebi et al. (2017)a Flag leaf persistence 1B, 1D, 2A, 3A, 3BS, 6A, 6B, 7A, Vijayalakshmi et al. (2010),c, Talukder et al. (2014),c, Shirdelmoghanloo et al. (2016)c Chlorophyll loss rate 3BS, 6BL Shirdelmoghanloo et al. (2016)c Chlorophyll fluorescence 7A Vijayalakshmi et al. (2010)c Carbon isotope discrimination 1A, 2A, 4A, 5B, 6A, 6B, 7B Peleg et al. (2009)d Leaf osmotic potential 2A, 3A, 3B, 5A, 5B, 6A, 6B Peleg et al. (2009) Plasma membrane damage 1D, 2B, 7A Talukder et al. (2014)c Thylakoid membrane damage 1D, 6A, 7A Talukder et al. (2014)c Trait Chromosome References Combined dry and hot conditions Grain yield 1AL, 1B, 1D, 2A, 2BL, 3A, 3B, 4AL, 4B, 5A, 6A, 6B, 7A, 7B, 7D Kirigwi et al. (2007),a, Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Merchuk-Ovnat et al. (2016),a, Tahmasebi et al. (2017)a Thousand grain weight 1D, 2B, 3A, 3B, 4A, 6A, 7A, 7B, 7D Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Tahmasebi et al. (2017)a Kernel weight index (large grains−all grains) 1A, 2B, 6A Pinto et al. (2010)a Grain weight spike−1 5B, 6A, 7B Golabadi et al. (2011)a Grain number m−2 1B, 2A, 3B, 3D, 4AL, 6B, 7A Kirigwi et al. (2007),a, Pinto et al. (2010),a, Bennett et al. (2012)a Grain number spike−1 2B, 7B Golabadi et al. (2011),a, Tahmasebi et al. (2017)a Harvest index 1B, 2A, 2B, 3B, 4A, 5A, 5B, 6A, 6B, 7B Peleg et al. (2009),d, Golabadi et al. (2011)a Spike weight 1B, 2A, 4A, 6A, 7A, 7B Peleg et al. (2009),d, Golabadi et al. (2011)a Spike number m−2 2B, 4AL, 5B Kirigwi et al. (2007),a, Golabadi et al. (2011)a Spike harvest index 2B, 3B Golabadi et al. (2011)a Spikelet number spike−1 5A Tahmasebi et al. (2017)a Biomass 2BS, 4AL, 4B, 5A, 7AS Kirigwi et al. (2007),a, Peleg et al. (2009),d, Merchuk-Ovnat et al. (2016)a Plant height 1A, 1B, 2BL, 3AL, 3BS, 4A, 4B, 5A, 7AS Maccaferri et al. (2008),ab, Pinto et al. (2010),a, Tahmasebi et al. (2017)a Shoot length 2B, 3B, 4A, 4B, 6B, 7A, 7B Peleg et al. (2009)d Peduncle length 3A, 3B Bennett et al. (2012)a Flag leaf width 2B, 3B Bennett et al. (2012)a Days to heading 1A, 1B, 1D, 2AS, 2BS, 2BL, 3A, 3B, 4AL, 4B, 4D, 5A, 6A, 7AS, 7BS, 7D Kirigwi et al. (2007),a, Maccaferri et al. (2008)a,b, Peleg et al. (2009),d, Pinto et al. (2010),a, Merchuk-Ovnat et al. (2016),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Days to maturity 1A, 1D,5A, 7B, 7D Pinto et al. (2010),a, Tahmasebi et al. (2017)a Days from heading to maturity 1B, 2B, 4A, 4B, 5A, 5B, 7A, 7B Peleg et al. (2009)d NDVI at the vegetative stage 1B, 3B, 4A, 7A Pinto et al. (2010),a, Bennett et al. (2012)a NDVI at the grain filling stage 1B, 1D, 2A, 2B, 4A, 4B, 5A, 6A, 6B, 7A, 7B Pinto et al. (2010)a Stem WSC 1A, 1B, 3A, 3B, 4A, 6D Pinto et al. (2010),a, Bennett et al. (2012)a Grain fill rate 4AL Kirigwi et al. (2007)a Grain fill duration 4AL Kirigwi et al. (2007)a Canopy temperature at the vegetative stage 1B, 2B, 3B, 4A, 4B, 6B, 7A Pinto et al. (2010),a, Tahmasebi et al. (2017)a Canopy temperature at the grain filling stage 1A, 1B, 2B, 3B, 4A, 5A, 6B, 7A Pinto et al. (2010)a Canopy temperature depression 1A, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Flag leaf rolling 1A, 2A, 2B, 4B, 5A, 5B, 6B, 7A, 7D Peleg et al. (2009),d, Tahmasebi et al. (2017)a Early vigour 2B, 2D, 3B, 4A Bennett et al. (2012)a Early ground cover 6AS Mondal et al. (2017)a Chlorophyll content 1A, 1B, 3A, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A Diab et al. (2008),a, Peleg et al. (2009),d, Bennett et al. (2012)a Chlorophyll fluorescence 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Carbon isotope discrimination 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6B, 7A, 7B Diab et al. (2008),a, Peleg et al. (2009)d Photosynthetically active radiation 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Stomatal density 4AS, 5AS, 7AL Shahinnia et al. (2016)a Stomatal index 2BL, 7BL Shahinnia et al. (2016)a Stomatal aperture area 7AL Shahinnia et al. (2016)a Stomatal aperture length 2BS, 2BL, 7AL Shahinnia et al. (2016)a Guard cell length 1AS, 3BL, 7AL Shahinnia et al. (2016)a Guard cell area 1BL, 4BL, 5AL, 5DL Shahinnia et al. (2016)a Transpiration efficiency 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Leaf relative water content 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B Diab et al. (2008)a Water index 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6A, 6B, 7A, 7B Diab et al. (2008)a Leaf osmotic potential 2A, 2B, 3A, 3B, 4B, 5A, 5B, 6B Peleg et al. (2009)d Osmotic adjustment 1A, 3A, 3B, 4A, 7A Diab et al. (2008)a Metabolites (mQTL) 2B, 4A, 5A, 7A, 7D Hill et al. (2015)a Expression of stress-related genes (eQTL) 6BL Aprile et al. (2013)c Drought stress Grain yield 2D, 3D, 3DL, 4AL, 4BS, 4DL, 5A, 5B, 5DL, 6B, 6D, 7AL, 7BL, 7D Quarrie et al. (2005),a, Czyczyło-Mysza et al. (2011),d, Kadam et al. (2012),c, Tahmasebi et al. (2017)a Grain weight spike−1 1B, 1D Xu et al. (2017)a Thousand grain weight 1B, 1D, 2A, 2B, 3A, 3D, 4A, 4D, 5A, 6A, 6D, 7A, 7B Quarrie et al. (2005),a, Dashti et al. (2007),c, Yang et al. (2007),a, Tahmasebi et al. (2017),a, Xu et al. (2017)a Grain number m−2 1B, 5B, 7D Tahmasebi et al. (2017)a Grain number spike−1 1A, 2A, 2B, 2D, 3A, 3B, 4A, 4B, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B Quarrie et al. (2005),a, Czyczyło-Mysza et al. (2011),d, Xu et al. (2017)a Harvest index 1B, 2D, 4BS, 5A Kadam et al. (2012),c, Xu et al. (2017)a Spike number plant−1 1A, 2A, 2B, 2D, 4B, 5A, 7B Quarrie et al. (2005),a, Xu et al. (2017)a Spikelet compactness 6A, 7A Xu et al. (2017)a Spikelet number spike−1 1A, 7D Tahmasebi et al. (2017),a, Xu et al. (2017)a Sterile spikelet number spike−1 7A Xu et al. (2017)a Fertile spikelet spike−1 2A Xu et al. (2017)a Biomass 1B Xu et al. (2017)a Shoot biomass 4B Kadam et al. (2012)c Root biomass 2D, 4BS Kadam et al. (2012)c Plant height 1B, 4B, 7D Tahmasebi et al. (2017),a, Xu et al. (2017)a Peduncle length 3B Dashti et al. (2007)c Coleoptile length 6AS Spielmeyer et al. (2007)c Spike length 2B, 7A, 7B Xu et al. (2017)a Root length 2D, 4B, 5D, 6B Kadam et al. (2012)c Growth rate 5BL Parent et al. (2015)c Relative growth rate 4AL Parent et al. (2015)c Inflexion point in growth curves 7DS Parent et al. (2015)c Leaf expansion rate 5BL Parent et al. (2015)c Inflexion point in leaf expansion curves 5BL Parent et al. (2015)c Days to heading 1D, 4B, 7D Tahmasebi et al. (2017)a Days to flowering 2D Kadam et al. (2012)c Stem WSC at the flowering stage 1A, 1D, 2D, 4A, 4B, 7B Yang et al. (2007)a Stem WSC at the grain filling stage 4A Yang et al. (2007)a Stem WSC at the maturity stage 6B Yang et al. (2007)a Accumulation efficiency of stem WSC 1A, 2A, 5A, 7B Yang et al. (2007)a Remobilization efficiency of stem WSC 7A Yang et al. (2007)a Grain filling efficiency 2A, 4B, 5A, Yang et al. (2007)a Flag leaf rolling 4B, 5A Tahmasebi et al. (2017)a Chlorophyll content 1B, 2B, 5B, 7A, 7B Ilyas et al. (2014),c, Tahmasebi et al. (2017),a, Xu et al. (2017)a Flag leaf persistence 2D, 3B, 4B, 5A, 6A Verma et al. (2004)a Net photosynthetic rate 6B Xu et al. (2017)a Chlorophyll fluorescence 1B, 2A, 2D, 3A, 3B, 3D, 4A, 4B, 4D, 5A, 5B, 6A, 6B, 7A, 7B, 7D Czyczyło-Mysza et al. (2011)d Stomatal conductance 5A Xu et al. (2017)a Stomatal density 5BS Shahinnia et al. (2016)c Stomatal index 5BS, 6DL Shahinnia et al. (2016)c Stomatal aperture length 2BL, 4BS, 7AS, 7DL Shahinnia et al. (2016)c Guard cell area 1BL, 5BS Shahinnia et al. (2016)c Guard cell length 1BL, 4BS, 7AS Shahinnia et al. (2016)c Transpiration rate 3Al, 4BL, 6D Parent et al. (2015),c, Xu et al. (2017)a Water use efficiency 2AL, 4D Parent et al. (2015),c, Xu et al. (2017)a Heat stress Grain yield 1A, 1BL, 1D, 2BS, 3A, 3BS, 3BL, 3D, 4A, 4B, 4DL, 5A, 5B, 6A, 6B, 6D, 7AS, 7AL, 7BS, 7BL Quarrie et al. (2005),a, Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Paliwal et al. (2012),a, Merchuk-Ovnat et al. (2016),a, Ogbonnaya et al. (2017)a Grain weight spike−1 3A, 3BS, 6A, 7A, 7B Golabadi et al. (2011),a, Shirdelmoghanloo et al. (2016),c, Ogbonnaya et al. (2017)a Thousand grain weight 1A, 2A, 2B, 2D, 3A, 3BS, 3D, 4A, 4B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7D Quarrie et al. (2005),a, Pinto et al. (2010),a, Golabadi et al. (2011),a, Bennett et al. (2012),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Single grain weight 2D, 3BS, 5B, 6A Shirdelmoghanloo et al. (2016)c Kernel weight index (large grains−all grains) 1A, 1D, 2B, 3B, 4B, 5A, 5B, 6A, 6B, 6D Pinto et al. (2010)a Grain number m−2 1A, 1B, 1D, 3BS, 3BL, 3D, 4A, 4B, 4D, 5B, 6A, 6B, 6D, 7A Pinto et al. (2010),a, Bennett et al. (2012)a Grain number spike−1 1A, 1B, 2A, 3B, 4B, 4D, 5D, 6A, 7B, 7D Quarrie et al. (2005),a, Golabadi et al. (2011),a, Ogbonnaya et al. (2017),a, Tahmasebi et al (2017)a Threshing index 1A, 1B, 5B Ogbonnaya et al. (2017)a Harvest index 1B, 2B, 3B, 4A, 5A, 5B, 6A, 6B, 7B Peleg et al. (2009)d Spike number m−2 1A, 1B, 3A, 3B, 4B, 5A, 5B, 7B, 7D Golabadi et al. (2011),a, Ogbonnaya et al. (2017)a Spike number plant−1 3A Quarrie et al. (2005)a Spike weight 1B, 2B, 2D, 3D, 4A, 5D, 6A, 7B Peleg et al. (2009),d, Golabadi et al. (2011),a, Ogbonnaya et al. (2017)a Spike harvest index 2B, 5B, 7A, 7B Golabadi et al. (2011)a Spikelet compactness 1A Tahmasebi et al. (2017)a Spikelet number spike−1 1B, 1D, 2B, 4A, 5B, 6A, 6B Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Number of productive tiller 1B Sharma et al. (2016)a Biomass 1BL, 2BS, 7AS, 7BS Merchuk-Ovnat et al. (2016)a Shoot biomass 3BS, 4A, 6B Shirdelmoghanloo et al. (2016)c Plant height 1A, 1B, 2A, 2B, 2D, 3A, 3B, 3D, 4A, 4B, 5A, 5B, 6A, 6D, 7A, 7B, 7D Maccaferri et al. (2008)a,b, Pinto et al. (2010),a, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Shoot length 1B, 2B, 3A, 3B, 4A, 4B, 5D, 7A, 7B Peleg et al. (2009),d, Ogbonnaya et al. (2017)a Peduncle length 1A, 1B, 2B, 3A, 3B, 5B, 7A Ogbonnaya et al. (2017)a Flag leaf length 3B, 5B Mason et al. (2010)c Flag leaf width 1D, 2B, 3BL, 7A, 3BL Mason et al. (2010),c, Bennett et al. (2012)a Wax score 1B, 2A, 2B, 2D, 3A, 3B, 5A, 6A, 6B, 7B Mason et al. (2010),c, Ogbonnaya et al. (2017)a Days to heading 1BL, 1D, 2A, 2BS, 3B, 3A, 4A, 4B, 4D, 5A, 6A, 7AS, 7BS, 7D Maccaferri et al. (2008)a,b, Peleg et al. (2009),d, Pinto et al. (2010),a, Merchuk- Ovnat et al. (2016),a, Ogbonnaya et al. (2017)a Days to flowering 1B, 1D, 4A, 4B, 4D, 5B Mason et al. (2010),c, Pinto et al. (2010)a Days to maturity 1B, 1D, 2A, 2B, 3B, 4D, 5A, 5B, 5D, 6A, 6B, 6D, 7A, 7B, 7DS Pinto et al. (2010),a, Bennett et al. (2012),a, Paliwal et al. (2012),a, Ogbonnaya et al. (2017)a NDVI at the vegetative stage 1B, 1D, 2B, 2D, 3A, 3B, 4A, 4D, 5A, 6A, 6B, 6D, 7A Pinto et al. (2010),a, Bennett et al. (2012)a NDVI at the grain filling stage 1A, 1B, 3A, 4A, 4B, 5A, 5B, 6A, 7B Pinto et al. (2010)a Stem WSC 1A, 1B, 2D, 3A, 3BL, 5A, 5B, 6A Pinto et al. (2010),a, Bennett et al. (2012)a Grain filling duration 1B, 1D, 2A, 2B, 2D, 3BS, 5A, 6A, 6B, 6D Mason et al. (2010),c, Shirdelmoghanloo et al. (2016),c, Ogbonnaya et al. (2017)a Canopy temperature at the vegetative stage 1A, 1B, 1D, 2B, 3A, 3BL, 4A, 4B, 5B, 6B, 7A Pinto et al. (2010),a, Bennett et al. (2012)a Canopy temperature at the grain filling stage 1A, 1B, 1D, 2B, 3BS, 3BL, 4A, 4D, 5A, 5D, 7A, 7B Pinto et al. (2010),a, Bennett et al. (2012)a Canopy temperature depression 7BL Paliwal et al. (2012)a Flag leaf rolling 1A, 2A, 2B, 2D, 3D, 4B, 5A, 5B, 6A, 6B, 7A, 7B Peleg et al. (2009),d, Ogbonnaya et al. (2017),a, Tahmasebi et al. (2017)a Early vigour 2B, 2D, 3BL Bennett et al. (2012)a Chlorophyll content 1A, 1B, 1D, 2B, 3A, 3BS, 4A, 4D, 5A, 5B, 6A, 6D, 7A, 7B, 7D Peleg et al. (2009),d, Pinto et al. (2010),a, Bennett et al. (2012),a, Tahmasebi et al. (2017)a Flag leaf persistence 1B, 1D, 2A, 3A, 3BS, 6A, 6B, 7A, Vijayalakshmi et al. (2010),c, Talukder et al. (2014),c, Shirdelmoghanloo et al. (2016)c Chlorophyll loss rate 3BS, 6BL Shirdelmoghanloo et al. (2016)c Chlorophyll fluorescence 7A Vijayalakshmi et al. (2010)c Carbon isotope discrimination 1A, 2A, 4A, 5B, 6A, 6B, 7B Peleg et al. (2009)d Leaf osmotic potential 2A, 3A, 3B, 5A, 5B, 6A, 6B Peleg et al. (2009) Plasma membrane damage 1D, 2B, 7A Talukder et al. (2014)c Thylakoid membrane damage 1D, 6A, 7A Talukder et al. (2014)c Dry and hot field conditions are defined using the CIMMYT mega-environments 1 and 4 (Rajaram et al., 1994). NDVI, near differential vegetative index; WSC, water-soluble carbohydrates a Field conditions. b Trials in Italy, Tunisia and Morocco with maximum temperature at grain filling ≤26.1 °C. c Controlled conditions. d Semi-controlled conditions. View Large A greater understanding of the physiology underlying combined drought and heat tolerance should enable researchers and breeders to discriminate between traits and loci useful for improvement. With improving genomic resources and high-throughput phenotyping methods, it becomes possible to identify loci and genes for tolerance and incorporate favourable alleles into breeding programmes. In this review, we outline what is known in wheat of the physiology and genetic variation underlying drought and heat tolerance – defined here as the ability to maintain yield under stress. We propose traits to measure in genetic mapping populations that are likely to prove beneficial for combined tolerance (Fig. 1) and discuss opportunities and constraints for incorporating alleles into breeding for tolerant wheat. Fig. 1. View largeDownload slide Beneficial traits for combined drought and heat tolerance in wheat. Fig. 1. View largeDownload slide Beneficial traits for combined drought and heat tolerance in wheat. Wheat growth, architecture and biomass partitioning under drought and heat Water deficit and high temperature affect every aspect of wheat growth from germination to maturity. The impact on yield components depends on the duration and the severity of the stress as well as the stage of plant development when stress occurs (Salter and Goode, 1967; Barnabás et al., 2008; Parent et al., 2017). As water stress reduces plant growth through reduced tillering and leaf expansion (Acevedo et al., 1971), and high temperature accelerates plant growth and shortens developmental stages (Parent and Tardieu, 2012), under combined stress plants flower earlier and produce less biomass than under single stress. Reproductive organs are especially sensitive to drought and heat stress (Stone and Nicolas, 1995; Saini and Lalonde, 1997). Episodes of drought and heat stress around anthesis severely reduce the final number of grains per spike by more than either individual stress due to an increased abortion of ovules (Asana and Williams, 1965; Hochman, 1982; Saini and Aspinall, 1982; Pradhan et al., 2012; Weldearegay et al., 2012). During grain filling, combined drought and high temperature, as frequently occur in major growing regions, reduce the size and weight of individual grains by reducing the division rate of endosperm cells and shortening the duration of grain filling (Jenner, 1994; Barnabás et al., 2008; Prasad et al., 2011; Pradhan et al., 2012). Complex source–sink interactions underlie tolerance to drought and heat stress, and remobilization of stored assimilates to grain filling following stress at sensitive periods is dependent on sink strength. In maize, grain size, determining sink strength for grain filling, is determined by expansive plant growth, which is the increase in volume due to water entry into growing cells (Tardieu et al., 2014). There is limited evidence for differences in carbon metabolism or status in ovules under stress, but many studies demonstrate reductions in organ elongation rates at sensitive periods with either drought or heat stress. In maize, silk growth and leaf elongation rate are highly correlated (Parent and Tardieu, 2012; Tardieu et al., 2014). When the PLASTOCHRON1 (ZmPLA1) gene was expressed in maize, increasing the length of the cell division zone, the duration of cell division, the duration of leaf elongation, kernel number, and size were increased in field experiments under mild drought (Sun et al., 2017). QTLs for organ size and growth and expansion rates have been identified in wheat under drought (Table 1) but have not been studied under combined drought and heat stress, and no studies of genetic variation for the expansive growth trait have yet been carried out. Theoretically, increased expansive growth will be beneficial for combined drought and heat tolerance where loss of grain number is due to reduction in spike growth and development. Expansive growth will increase sink strength and be beneficial for remobilization of assimilates to the grain during filling. Traits that increase overall assimilation should increase drought and heat tolerance when partitioned beneficially to the grain. Several QTLs for harvest index (HI) have been reported (Table 1). Meta-analysis of reported QTLs for drought or heat stress revealed meta-QTLs for spike weight/density and plant height were significantly (at P<0.1) associated with meta-QTL regions for yield under drought or heat in wheat (Acuña-Galindo et al. 2015). Major clusters were located at the Rht-B1 and Rht-D1 dwarfing loci. Plant height restriction due to the Rht-B1 allele increases HI and is due to gibberellin insensitivity (Peng et al., 1999). In barley, exogenous gibberellin application increases sensitivity to high temperature stress (Vettakkorumakankav et al., 1999), so it is possible that widely used dwarfing alleles in modern, semi-dwarf wheat varieties already contribute to heat tolerance through the gibberellin pathway. Modern, semi-dwarf phenotypes are already widely used to prevent undesirable lodging, but there are alleles that appear more or less beneficial in particular environments. For example, Wang et al. (2014b) suggested that the Rht13 or combination of Rht13 + Rht8 alleles could be favourable in water-limited environments. Thus, there is scope to study and improve wheat drought and heat tolerance through the deployment of new combinations of dwarfing alleles, identification of genes controlling the gibberellin pathway, and optimization of expansive growth (Fig. 1). Breeding for canopy temperature and evapotranspiration under drought and heat The main mechanism wheat plants use to decrease their internal temperatures under heat stress is evaporative cooling, driven by transpiration. Under drought, plants close their stomata to avoid excessive water loss; this reduces transpiration and evaporative cooling and, as a result, drought-stressed plants display higher leaf and canopy temperatures than well-watered plants (Reynolds et al., 2009). Cool canopies were always associated with better yield performance (Pinto and Reynolds, 2015). Several QTLs have been reported for canopy temperature depression under drought and heat in wheat grown in deep soils of northern Mexico (Pinto et al., 2010; Pinto and Reynolds, 2015). The major QTLs on chromosome 2B were shown to be associated with root distribution, with cool canopy genotypes able to extract more water at depth under water stress due to a greater proportion of deeper roots (Pinto and Reynolds, 2015). The deep root trait was not recapitulated under heat stress alone (with irrigation) (Pinto and Reynolds, 2015). This suggested that the beneficial physiological trait conferred by the 2B QTL was not a different root system architecture or distribution per se, but the ability to optimize root distribution to capture water for continued cooling dependent on water distribution in the soil. Transpiration efficiency is a ratio between biomass and transpiration, while water use efficiency (WUE) is the biomass produced per unit of water used, at the whole plant level or whole plot in the field. Carbon isotope discrimination (12C/13C ratio) in dry matter is negatively correlated to transpiration efficiency in wheat and a surrogate for this trait (Condon et al., 1990). It has been successfully used for breeding water use efficient wheat for dry regions in Australia (Condon et al., 1990, 2002). Increased transpiration efficiency alone might not improve tolerance. The equation for grain yield in water-limited environments includes harvest index (HI) and water use (WU) as well as WUE (Passioura, 1977; Passioura, 1996): GY=HI×WU×WUE. The theoretical physiology underlying this relationship has been extensively explained and reviewed (Ehrler et al., 1978; Araus et al., 2002; Blum, 2005; Reynolds et al., 2007; Fischer, 2011; Vadez et al., 2014). It has been argued that, if transpiration efficiency is increased by a reduction in the transpiration term of the equation, a low intrinsic stomatal conductance and transpiration reduces growth, biomass accumulation and light interception. Therefore, selecting plants with high transpiration efficiency might select for smaller plants (Blum, 2009). When small plants are selected, sink strength is lost and fewer assimilates are mobilized to the grain. Under the combination of drought and heat, low intrinsic transpiration could, additionally, penalize evaporative cooling. Reynolds et al. (2007) found that carbon isotope discrimination, together with canopy temperature linked to water uptake, was associated with improved performance in drought-stressed environments. Diab et al. (2008) found QTLs associated with tolerance in wheat for canopy temperature depression, transpiration efficiency, water index, and grain carbon isotope discrimination in dry and hot field conditions (Table 1). Evaporative demand, or VPD, which depends on the amount of moisture in the air and the air temperature, also plays a critical role in transpiration and transpiration efficiency. Different sensitivities of transpiration to high VPD have been found amongst wheats and its genetic control described in the Australian wheat population RAC875/Kukri (Schoppach et al., 2016). Six QTLs were identified for transpiration response to VPD, with one QTL on chromosome 5A individually explaining 25.4% of the genetic variance (Schoppach et al., 2016). A study of 23 Australian wheat varieties released from 1890 to 2008 showed that whole-plant transpiration rate in response to VPD was limited at VPD above a breakpoint of about 2 kPa (Schoppach et al., 2016). The breakpoint and transpiration response at VPD>2 kPa were correlated with the year of release indicating that breeders, by selecting for yield in the hot and dry climate of southern Australia, selected lines with limited whole-plant transpiration rate. Transpiration rate might also be moderated by patchy stomatal closure and the threshold for closure might differ in sensitivity between VPD and soil moisture deficit (Vadez et al., 2014). In maize, the relationship between expansive growth (leaf expansion rate; LER) and stomatal conductance was rapid and linear in contrast to the relationship between LER and transpiration rate (Caldeira et al., 2014b). Tardieu et al. (2014) suggest that this is because increases in biomass and in expansive growth in volume are under different genetic controls and that, under water deficit, they are uncoupled over time. Because of the dependence of transpiration efficiency on both the biomass term and VPD, transpiration response traits should be evaluated in QTL studies. To keep an optimal balance between evaporative cooling and water saving, plants with fine adjustment of transpiration should have an advantage under combined drought and heat (Fig. 1). Temporal regulation of gas exchange Vadez et al. (2014) have argued that the total plant water use over the growing season and WUE for yield depend on available water and use at critical stages. Plants can increase effective use of water by timely modifications of water uptake at critical stages. Timely modifications in stomatal conductance, transpiration, and water use might include different patterns of stomatal opening with developmental stage, time of the day, time of season, and microclimate VPD driven by differences in plant architecture. High stomatal densities and conductance are associated with increased yield potential in both well-watered and water-limited environments (reviewed in Roche, 2015). High stomatal density could give more flexibility to the plant to adjust stomatal opening depending on the local environmental conditions and ensure continued water uptake and use under favourable conditions. For example, the Australian line RAC875, which is drought and heat tolerant, has many small stomata by contrast with the susceptible Australian variety Kukri with fewer large stomata (Shahinnia et al., 2016). QTLs for stomatal size and density have been identified in dry and hot field conditions in wheat (Table 1). While no correlation was found between yield and stomatal traits in the RAC875/Kukri population, we found a locus for stomatal density and size on chromosome 7A that overlaps with QTLs for grain number per spike, normalized difference vegetation index, harvest index, and yield in the same population (Shahinnia et al., 2016). When heat stress is severe, leaf stomata will open to allow evaporative cooling despite water limitation. At very high temperatures, the photosynthetic machinery is damaged (Berry and Bjorkman, 1980) and leaf or other vegetative tissues may be sacrificed (Lohraseb et al., 2017). Under combined drought and heat stress, this balance between open stomata and damaged photosynthetic machinery can become critical to allow continued assimilation and can depend on the fine spatiotemporal regulation of gas exchange. That is, continued assimilation in periods of lower stress, as temperatures rise and cool diurnally, may make a plant more tolerant (Richards et al., 1986). Diurnal regulation of gas exchange will make a difference during stress exposure and circadian use of water and regulation of transpiration may both alleviate combined drought and heat stress and be a source of tolerance. A shift in transpiration to cooler times of the day could confer tolerance. Nocturnal water use, particularly night-time transpiration, is of increasing interest for its role in sustaining sugars export at night (Marks and Lechowicz, 2007) and its potential role in drought tolerance in wheat (Schoppach et al., 2014; Resco de Dios et al., 2016; Sadok, 2016). Genotypic variation for night-time transpiration and its sensitivity to VPD has been documented in wheat and influences the next day’s gas exchange under normal conditions and drought (Schoppach and Sadok, 2013; Schoppach et al., 2014; Claverie et al., 2017). Night-time transpiration rate in response to VPD varied consistently with the sensitivity of the genotypes to drought and increased under soil water deficit (Claverie et al., 2017). The effect of night-time temperature was also significant, with an increase in transpiration with increasing temperature observed, as well as genotypic variation. Despite the importance of nocturnal water use for potential drought and heat stress tolerance, no genetic studies have yet been carried out in wheat and no QTLs are known. The interplay between night-time export of assimilates and day-time gas exchange is also yet to be explored. Supply and demand ratios are likely to play a role in determining assimilation and export and, as yet, no studies of circadian regulation in wheat have been carried out in plants during grain filling when grains determine sink strength. With the development of non-destructive phenotyping methods, it will become possible to collect plant data over time and examine the kinematics of plant physiology. Optimal hydraulic conductance for drought and heat tolerance Hydraulic conductance is a measure of the flow induced by a pressure or water potential gradient normalized to the plant/organ geometry. Caldeira et al. (2014b) proposed that circadian oscillations of hydraulic conductance accounted for fluctuating growth (leaf elongation rates) in Arabidopsis. The degree of oscillation was highly dependent on evaporative demand and water stress. High root hydraulic conductance oscillation under water deficit likely led to the ability to control water uptake in response to available soil water when needed. Soil water status regulates the root hydraulic conductance of maize (Caldeira et al., 2014a) adjusting growth to water availability. Maintenance of high hydraulic conductance in spikes of long-awned cultivars of wheat significantly reduces spike temperature during grain filling (Maydup et al., 2014). The end of grain filling correlates with a loss of hydraulic conductance at the rachis-xylem conduit (Neghliz et al., 2016). Thus, we hypothesize that by maintaining optimal hydraulic conductance in the different tissues under drought and heat stress (Fig. 1), wheat plants could extend grain filling duration, cool down grain and spike, and optimize water uptake for expansive growth. In grapevine, soil–leaf differences in water potential among genotypes were shown to be less related to sensitivity of transpiration to soil water deficit than to change in soil–leaf hydraulic conductance, likely due to rapid changes in water transport within the plant (Scharwies and Tyerman, 2017). The ability to partition and channel water between stem, leaf, tillers, and spikes determines both expansive growth in these tissues and remobilization of assimilates following stress. Differences in hydraulic resistances in different tissues influence water transport capacity and drought and heat tolerance (Coupel-Ledru et al., 2014; Bramley et al., 2015). Hydraulic resistance may be determined by differences in structure and architecture of stems, peduncles, and rachis, and differences in xylem vessel diameter and leaf venation (Scharwies and Tyerman, 2017). Vessel structure has an important role in the control of water conductivity in plants in water-limited environments (Tixier et al., 2013; Caringella et al., 2015; Kadam et al., 2015). In wheat, Barlow et al. (1980) demonstrated that a xylem discontinuity at the base of the peduncle permitted the isolation of spike hydraulics from the rest of the plant, and that this anatomical feature was crucial during water scarcity, resulting in the independence of water relations in the spike from the rest of the plant. The xylem in wheat is also discontinuous between rachis and grains, isolating grains and, potentially, preventing water loss during stress (Zee and O’brien, 1970). Photoperiod response (Ppd loci) genes have pleiotropic effects on plant growth and development (Cockram et al., 2007) that can modify plant hydraulics. The photoperiod sensitive allele Ppd-D1 increases daytime and night-time transpiration while decreasing whole-plant leaf area in response to VPD increase in wheat (Schoppach et al., 2016). This suggests that whole-plant hydraulics are developmentally controlled. Deciphering the relationship between vessel structure and plant hydraulics and the genetic control of plant development in wheat will provide a better understanding of the involvement of these physiological mechanisms in tolerance to combined drought and heat stress and their potential for breeding tolerant varieties. Competition for assimilates under drought and heat stress Redox balance is crucial for the normal function of many cellular processes. Its fine control is essential for a proper integration of environmental and developmental stimuli and signal transduction (Choudhury et al., 2017). Recent studies demonstrated the important role of photorespiration in maintaining redox homeostasis (Scheibe and Dietz, 2012), mitigating oxidative stress and protecting the photosynthetic apparatus from photoinhibition (Rivero et al., 2009; Peterhansel and Maurino, 2011; Voss et al., 2013). With either drought or heat stress, net photosynthesis is reduced and photorespiration increased (Long and Ort, 2010), but the relative contributions of photorespiration and mitochondrial respiration to combined drought and heat stress tolerance in wheat are unknown and genetic variation for this ratio has not been explored. Heat stress affects membrane stability and the quantum efficiency of photosystem II, which can be measured, respectively, as cell viability and chlorophyll fluorescence (Blum, 1988; Mohammed and Tarpley, 2009). Drought stress also affects chlorophyll fluorescence with a dramatic decrease of Fv/Fm ratio in susceptible wheat compared with tolerant lines (Izanloo et al., 2008). QTLs have been reported for chlorophyll fluorescence in drought- or heat-stressed wheat (Table 1), but studies in other species suggest that responses to combined drought and heat stress are unique in comparison with either individual stress (Mittler, 2006). At the ecosystem level, drought may actually reduce heat-driven increases in plant respiration due to reduction in carbon substrates available (Schauberger et al., 2017). However, if stored carbohydrates are used for respiration and less available for remobilization following heat stress, drought may exacerbate the effect of heat stress-induced increases in respiration. The rate of grain filling from stem reserves is increased with increasing temperature, reducing grain filling duration (Blum et al., 1994). Tolerance to drought and heat stress will then depend on both the initial concentration of remobilizable carbohydrates and the use of these reserves for respiration. Genetic variation for stem water-soluble carbohydrate content has been explored with known QTLs in drought or heat stress and in combined drought and heat stress (Table 1). Yang et al. (2007) also investigated genotype × environment (G×E) interactions for QTLs for stem water-soluble carbohydrate content and remobilization efficiency under water stress in wheat and found significant interactions for all traits. They showed that not all reserves were translocated to grain following water stress and suggested that losses due to respiration could be significant. Zhang et al. (2014) explicitly investigated water-soluble carbohydrate QTLs under drought, heat, and combined drought and heat stress and were able to identify additive effects and combinations of favourable alleles for both content and remobilization, suggesting that the genetic mechanisms underlying tolerance will not depend purely on accumulation of stored carbohydrates. QTLs for respiration are now being studied in wheat for the first time under the International Wheat Yield Partnership umbrella (http://iwyp.org/wp-content/; accessed 5 February 2018). Under prolonged stress exposure, photosynthetic activity is further inhibited by excessive accumulation of reactive oxygen species (ROS), causing damage to the membranes, proteins, and chlorophyll molecules of the photosynthetic apparatus (Price and Hendry, 1991; Jiang and Huang, 2001; Allakhverdiev et al., 2008; Silva et al., 2010; Redondo-Gómez, 2013; Awasthi et al., 2014; Das et al., 2016). Plants use a complex antioxidant system to regulate ROS levels and avoid toxicity, but changes in redox status are also perceived by plants as a signature of a specific stress that will result in a corresponding acclimation response (Foyer and Noctor, 2005; Choudhury et al., 2017). The balance between accumulation of ROS in response to stress and their signalling role under stress is yet to be defined. ROS scavenging is generally induced under drought and heat stress, and higher antioxidant capacity is generally correlated with tolerance to stress (Koussevitzky et al., 2008; Suzuki et al., 2014; Wang et al., 2014a). In some wheat genotypes, tolerance to drought or heat stress was associated with increased antioxidant capacity and reduced oxidative damage (Sairam and Saxena, 2000; Sairam et al., 2000; Lascano et al., 2001; Almeselmani et al., 2006; Sečenji et al., 2010; Lu et al., 2017; Zang et al., 2017; Zhang et al., 2017). The effects of combined drought and heat on the ROS system in wheat are unknown, but recent studies highlight the importance of modulation of ROS scavenging, some pathways being specifically induced by combined stress (Rizhsky et al., 2002; Koussevitzky et al., 2008; Demirevska et al., 2010; Zandalinas et al., 2017). The alleles that regulate photorespiration, membrane stability and antioxidant capacity under drought and heat are yet to be discovered in wheat. As genomics and phenomics advance, the ability to analyse differences in physiological traits in empirical experiments has improved. Important advances in phenotyping with imaging or other equipment mean that it is possible to, for example, measure senescence or canopy temperature in real time in fields (Araus and Cairns, 2014). Further advances that allow, for example, field-scale simultaneous measurements of gas exchange, or non-destructive measurements of water-soluble carbohydrate movement can be anticipated. For researchers, these will provide a wealth of previously unquantifiable data for physiological traits. Breeding for stability, plasticity, and G×E interaction under drought and heat In past breeding of tolerant varieties, efforts have been concentrated on the search for stable QTLs that show the same allelic effect across environments to produce generalist, high-yielding varieties (Eberhart and Russell, 1966). Acuña-Galindo et al. (2015) conducted a meta-QTL analysis of 24 genetic studies where QTLs had been mapped for drought, heat, or combined stress in wheat. Co-localization with meta-QTLs for yield was only significant (at P<0.1) for the maturity/date of anthesis, spike weight/density, plant height, and canopy temperature depression QTLs. This analysis underscored the pleiotropic effects of phenology and dwarfing alleles on wheat stress response. These generalist QTLs are already bred for with Ppd and Vrn alleles routinely used in marker-assisted selection. Other stress tolerance QTLs are not generalist and have strong G×E interaction. In wheat, directional selection (Chapman et al., 2012) has been used to breed varieties that respond consistently to the target environment and management practice. Whilst this approach has been successful in achieving yield gains in some tested environments, strong G×E interactions mean that it is difficult to identify genotypes responding consistently positively in a range of stressful environments, even for a single physiological trait (Reynolds et al., 2009; Lopes et al., 2012). When testing lines bred in high- and low-moisture and reciprocal environments at different sites, Kirigwi et al. (2004) found significant environment × selection regime interactions. In this study, development in alternating high-to-low or low-to-high-moisture regimes facilitated the selection of lines that performed well for yield in both, whereas lines selected in either continuous high- or continuous low-moisture regimes had lower yields in these respective environments. The authors suggested that selection under these alternating environmental conditions favoured retention of both high yield under stress and high responsiveness to water input. In a changing environment, trait plasticity is theoretically beneficial (Bradshaw, 1965; Aspinwall et al., 2015). Plasticity can be defined as the variance in genotypic response across an environmental gradient – that is the slope of its reaction to change, with a steeper slope indicating higher plasticity (Nicotra et al., 2010). Plasticity can be measured as phenotype versus an environmental range for any trait and considered as a trait in itself (Sadras and Slafer, 2012), i.e. it has its own genetic variation and underlying QTLs. Phenotypic plasticity should be advantageous for fitness in variable environments and neutral in stable environments (Bradshaw, 1965; Nicotra et al., 2010). It can be argued that selection for plasticity QTLs, against the background of the increased pace of climate change, will prove beneficial for maintaining or improving agricultural yields (Aspinwall et al., 2015). However, plasticity is particular to the trait. For example, Sadras et al. (2009) found that high yield plasticity in wheat was disadvantageous in low-yield environments when it was associated with low plasticity of post-anthesis development. Breeding for plasticity in grain yield components coupled with plasticity for the length of the grain-filling phase will be useful but is limited due to a trade-off between low plasticity in grain size and high plasticity in grain number during this stage. Many QTLs have been found for grain production in dry and hot climates (Table 1), but very few (possibly none) are used in breeding programs. The main limiting factor to the deployment of these QTLs in breeding is the inconsistency in performances of the introgressed lines due to the strong QTL×E interaction. Although only field experiments are relevant for evaluating crop tolerance to stress as performance in an agricultural system, most studies fail to explain why a QTL is significant in one environment and not in another. Field trials are usually considered as a qualitative factor, which enables detection of G×E interactions but not its measurement (Acuña-Galindo et al., 2015). Recent development in phenomics and sensors means that we can now continuously measure soil water potential and air temperature across the crop cycle in field conditions. But how can we use these data to understand G×E? Uncoupling responsive and adaptive physiological traits is often complex and disentangling the effect of a specific environmental condition is not simple in experiments and often requires complex analysis and modelling (reviewed by Parent and Tardieu, 2014). Parent et al. (2017) described new models that exploit such data and measure a plant’s response to quantitative variations in drought and heat stress. Applied to lines that segregated for specific yield QTLs, such models revealed, in Australian wheats, that a QTL on chromosome 1B was constitutively expressed under various combinations of soil water potential and high temperature, while a QTL on chromosome 3B was heat responsive with a positive effect of the drought-tolerant parental line RAC875 when temperature was above 23 °C around flowering stage (Parent et al., 2017). This information is highly valuable as it enables us to understand a QTL’s function and use it in appropriate environments. By equipping national variety trials with sensors to measure soil moisture and air temperature, such models could also predict the level of tolerance of new varieties to quantified drought and heat. Combined with whole genome genotyping, this would provide information on the effects of haplotypes on yield in response to specific environmental conditions. Find the drought and heat tolerance genes and design the genome Another obstacle in using yield QTLs in breeding programmes is the small effect of a single QTL and the need to introgress several QTLs to gain a significant increment in yield improvement. As breeders can only recombine as many loci as the size of their breeding programmes allows, they prioritize those with strong and stable effects, such as phenology, plant height, and disease resistance, and select for yield under dry and hot environment empirically or, more recently, by genomic selection (GS). So, were the efforts in finding QTLs for drought and heat tolerance wasted? The answer is probably yes, unless we carry on the positional cloning of these QTLs and find the genes controlling combined drought and heat tolerance. Gene-level knowledge of the control of drought and heat tolerance will enable the identification and creation of new sequence variants. Although many QTLs have been found for drought or heat tolerance (Table 1), little is known about the genes underlying these effects in wheat. The molecular network of drought and heat stress response in model species includes heat shock proteins (HSPs, chaperone proteins that protect the cell machinery), a number of drought stress response or heat stress transcription factors (DSF, HSF), and signal transduction proteins (Mittler et al., 2012). A study in adult durum plants identified genes that respond specifically to combined drought and heat including a chaperone homologous to a putative t-complex protein 1 theta chain (Rizhsky et al., 2002, 2004; Rampino et al., 2012). Two classes of heat shock factors, A6 and C2, have been shown recently to enhance heat tolerance in transgenic wheat (Xue et al., 2014; Hu et al., 2018). Over-expression of TaHsfC2a-B in transgenics up-regulated a cascade of HSP genes in grains during grain filling under heat and also in leaves under drought stress. Combining positive alleles of HSF and DSF such as dehydration-responsive element-binding (DREB) proteins (Morran et al., 2011) might be a way to enhance wheat tolerance to simultaneous stress, but the positive effects will need to be tested in the field in dry and hot climates and redundancy and interactions measured. The forward genetics approach starting with a locus with a demonstrated yield effect is attractive but, to date, none of the QTLs for drought and heat tolerance (Table 1) has been cloned in wheat. While GS is an efficient tool to quickly identify the best haplotypes, it still requires the incorporation of new alleles into the breeding programme New alleles can also be found in wild relatives of wheat and landraces well adapted to local environments (Lopes et al., 2015), including hot and arid environments. Natural diversity encompasses adaptive mechanisms that wheat plants developed to cope with harsh conditions (Huang and Han, 2014). Emmer wheat and cultivated wheat’s wild relatives are sources of tolerance to high temperature or water limitation that could be used to overcome the bottleneck in genetic diversity within the cultivated wheat genepool (Feuillet et al., 2008). The usefulness of a wider germplasm is illustrated by the QTLs deriving from wild emmer wheat for drought (Peleg et al, 2005; 2009) and QTLs for salinity tolerance from Triticum monococcum (Munns et al., 2012). This is a rare example of successful introgression of a locus (Nax2) for abiotic stress tolerance in wheat, following both physiological characterization (James et al., 2006) and positional cloning of the causative gene (TmHKT1;5-A) and demonstrates the power of this approach. New alleles of known genes can also be created by deliberate mutagenesis or genome design (E. Buckler, Plant and Animal Genome conference XXVI, 2018). The ability to efficiently screen for mutations by sequencing (TILLING (Targeting Induced Local Lesions IN Genomes) by sequencing) is quite recent in wheat (Tsai et al., 2011) and is based on both an increased understanding of genomics and advances in next generation sequencing and analysis. Using this approach, Simmonds et al. (2016) were able to rapidly identify the causative mutation for the locus TaGW2-A1 and cross the mutant allele into durum and bread wheat to develop isogenic lines with increased grain weight. The advantage of a mutant collection over wild germplasm is that the new alleles are in agronomically relevant backgrounds where their effect can be readily measured. As the current sequenced collections are in English and US genetic backgrounds, namely Kronos and Cadenza (Tsai et al., 2011), the sequencing of new TILLING collections in varieties that are locally relevant to hot and dry climates is urgently needed. An alternative method is to specifically edit genes for drought and heat tolerance in a modern, relevant variety. The ability to specifically edit the wheat genome using CRISPR-cas9 ribonucleoproteins has been demonstrated in bread wheat (Liang et al., 2017). This technique promises transgene-free modification of the genome to enhance traits of agronomic interest including abiotic stress tolerance. The use of this technique, however, depends on a detailed knowledge of the sequences underlying tolerance and is likely to require cassettes of sequence edits in the case of editing for combined drought and heat tolerance for wheat. With three highly similar sub-genomes, the majority of wheat gene sequences have homeologues and the contributions of these homeologues to copy number variation and dosage-dependent expression as well as functional redundancy are often unknown in wheat but will influence the success of gene editing approaches. In some cases, a gene/QTL effect could be increased if we were to combine the positive alleles of the three homeologous copies. On a whole genome level, pan-genome data are now being used to understand and mark structural variation of this kind, for instance in maize (Lu et al., 2015). The coming together of advances in genome editing and pan-genomics in wheat should facilitate editing for the future. Conclusions Because wheat is heat tolerant when water is available (Parent et al., 2017), to improve wheat for dual tolerance, plants must be studied under the combination of stresses. Results from experiments with heat treatments and well-watered conditions are unlikely to be relevant when water is limiting in the field. There is a large body of evidence showing that water use is essential for either drought or heat tolerance and that, for tolerance of the combined stress, fine control of water relations across the growing cycle will be beneficial. This might be achieved through fine management of spatial and temporal gas exchange. For a wheat plant to be drought and heat tolerant, beneficial traits likely include the following: finely regulated transpiration through small, dense stomata, able to respond to the micro-environment (shade, water, VPD, radiation); maintenance of optimal hydraulic conductance in different tissues; a root system able to grow fast in response to water availability; water-adjustable circadian regulation of plant growth; ability to retain water in essential organs to avoid tissue dehydration; efficient HSPs to protect enzymes and membranes against high temperature; efficient carbohydrate synthesis, export, and remobilization; and an efficient ROS scavenging system (Fig. 1). The rationale for identifying and deploying alleles for combined drought and heat tolerance in wheat breeding is compelling. Improvements in phenotyping of physiological traits and genomic information are particularly encouraging as we seek to discover and incorporate, possibly, rare, novel tolerance alleles in breeding programmes. Improvement of methods capturing plant and environmental data over time will enable us to phenotype genetic populations for kinematic traits, and this will help us unravel the genetic basis of complex biological processes. Although wheat physiology under drought and heat stress is complex, this complexity and plasticity in itself provides sources of tolerance and hope. Modifying a single trait might not have a significant effect on yield under stress as some of these traits are co-dependent and would be effective only in combination. Rather than improving a single trait at a time, we might need to combine them in order to increase yield. With underscoring genetic resources and a clear picture of valuable physiological traits, combined drought and heat tolerance in wheat can now be realized in research for use in breeding programmes. Abbreviations Abbreviations G×E genotype by environment GS genomic selection HI harvest index HSF heat shock factor HSP heat shock protein QTL quantitative trait locus ROS reactive oxygen species VPD vapour pressure deficit WU water use WUE water use efficiency. Acknowledgements The authors’ research is supported by the Australian Research Council Industrial Transformation Research Hub for Genetic Diversity and Molecular Breeding for Wheat in a Hot and Dry Climate (project number IH130200027). References ABS (Australian Bureau of Statistics) . 2012 . Year Book Australia . Canberra : Australian Bureau of Statistics . Acevedo E , Hsiao TC , Henderson DW . 1971 . Immediate and subsequent growth responses of maize leaves to changes in water status . Plant Physiology 48 , 631 – 636 . Google Scholar CrossRef Search ADS Acuña-Galindo MA , Mason RE , Subramanian NK , Hays DB . 2015 . Meta-analysis of wheat QTL regions associated with adaptation to drought and heat stress . Crop Science 55 , 477 – 492 . Google Scholar CrossRef Search ADS Alexandratos N , Bruinsma J . 2012 . World agriculture towards 2030/2050: the 2012 revision . ESA Working paper No. 12-03. Rome : Food and Agriculture Organization of the United Nations . Allakhverdiev SI , Kreslavski VD , Klimov VV , Los DA , Carpentier R , Mohanty P . 2008 . Heat stress: an overview of molecular responses in photosynthesis . Photosynthesis Research 98 , 541 – 550 . Google Scholar CrossRef Search ADS Almeselmani M , Deshmukh PS , Sairam RK , Kushwaha SR , Singh TP . 2006 . Protective role of antioxidant enzymes under high temperature stress . Plant Science 171 , 382 – 388 . Google Scholar CrossRef Search ADS Altenbach SB , DuPont FM , Kothari KM , Chan R , Johnson EL , Lieu D . 2003 . Temperature, water and fertilizer influence the timing of key events during grain development in a US spring wheat . Journal of Cereal Science 37 , 9 – 20 . Google Scholar CrossRef Search ADS Aprile A , Havlickova L , Panna R et al. 2013 . Different stress responsive strategies to drought and heat in two durum wheat cultivars with contrasting water use efficiency . BMC Genomics 14 , 821 . Google Scholar CrossRef Search ADS Araus JL , Cairns JE . 2014 . Field high-throughput phenotyping: the new crop breeding frontier . Trends in Plant Science 19 , 52 – 61 . Google Scholar CrossRef Search ADS Araus JL , Slafer GA , Reynolds MP , Royo C . 2002 . Plant breeding and drought in C3 cereals: what should we breed for ? Annals of Botany 89 Spec No , 925 – 940 . Google Scholar CrossRef Search ADS Asana R , Williams R . 1965 . The effect of temperature stress on grain development in wheat . Australian Journal of Agricultural Research 16 , 1 – 13 . Google Scholar CrossRef Search ADS Aspinwall MJ , Loik ME , Resco de Dios V , Tjoelker MG , Payton PR , Tissue DT . 2015 . Utilizing intraspecific variation in phenotypic plasticity to bolster agricultural and forest productivity under climate change . Plant, Cell & Environment 38 , 1752 – 1764 . Google Scholar CrossRef Search ADS Awasthi R , Kaushal N , Vadez V , Turner NC , Berger J , Siddique KHM , Nayyar H . 2014 . Individual and combined effects of transient drought and heat stress on carbon assimilation and seed filling in chickpea . Functional Plant Biology 41 , 1148 – 1167 . Google Scholar CrossRef Search ADS Barlow E , Lee J , Munns R , Smart M . 1980 . Water relations of the developing wheat grain . Functional Plant Biology 7 , 519 – 525 . Barnabás B , Jäger K , Fehér A . 2008 . The effect of drought and heat stress on reproductive processes in cereals . Plant, Cell & Environment 31 , 11 – 38 . Bennett D , Reynolds M , Mullan D , Izanloo A , Kuchel H , Langridge P , Schnurbusch T . 2012 . Detection of two major grain yield QTL in bread wheat (Triticum aestivum L.) under heat, drought and high yield potential environments . Theoretical and Applied Genetics 125 , 1473 – 1485 . Google Scholar CrossRef Search ADS Berry J , Bjorkman O . 1980 . Photosynthetic response and adaptation to temperature in higher plants . Annual Review of Plant Physiology 31 , 491 – 543 . Google Scholar CrossRef Search ADS Blum A . 1988 . Plant breeding for stress environments . Boca Raton, FL, USA : CRC Press . Blum A . 2005 . Drought resistance, water-use efficiency, and yield potential are they compatible, dissonant, or mutually exclusive ? Australian Journal of Agricultural Research 56 , 1159 – 1168 . Google Scholar CrossRef Search ADS Blum A . 2009 . Effective use of water (EUW) and not water-use efficiency (WUE) is the target of crop yield improvement under drought stress . Field Crops Research 112 , 119 – 123 . Google Scholar CrossRef Search ADS Blum A , Sinmena B , Mayer J , Golan G , Shpiler L . 1994 . Stem reserve mobilisation supports wheat-grain filling under heat stress . Functional Plant Biology 21 , 771 – 781 . Bonneau J , Taylor J , Parent B , Bennett D , Reynolds M , Feuillet C , Langridge P , Mather D . 2013 . Multi-environment analysis and improved mapping of a yield-related QTL on chromosome 3B of wheat . Theoretical and Applied Genetics 126 , 747 – 761 . Google Scholar CrossRef Search ADS Bradshaw AD . 1965 . Evolutionary significance of phenotypic plasticity in plants . Advances in Genetics 13 , 115 – 155 . Google Scholar CrossRef Search ADS Bramley H , Bitter R , Zimmermann G , Zimmermann U . 2015 . Simultaneous recording of diurnal changes in leaf turgor pressure and stem water status of bread wheat reveal variation in hydraulic mechanisms in response to drought . Functional Plant Biology 42 , 1001 – 1009 . Google Scholar CrossRef Search ADS Caldeira CF , Bosio M , Parent B , Jeanguenin L , Chaumont F , Tardieu F . 2014a. A hydraulic model is compatible with rapid changes in leaf elongation under fluctuating evaporative demand and soil water status . Plant Physiology 164 , 1718 – 1730 . Google Scholar CrossRef Search ADS Caldeira CF , Jeanguenin L , Chaumont F , Tardieu F . 2014b. Circadian rhythms of hydraulic conductance and growth are enhanced by drought and improve plant performance . Nature Communications 5 , 5365 . Google Scholar CrossRef Search ADS Caringella MA , Bongers FJ , Sack L . 2015 . Leaf hydraulic conductance varies with vein anatomy across Arabidopsis thaliana wild-type and leaf vein mutants . Plant, Cell & Environment 38 , 2735 – 2746 . Google Scholar CrossRef Search ADS Chapman SC , Chakraborty S , Dreccer MF , Howden SM . 2012 . Plant adaptation to climate change—opportunities and priorities in breeding . Crop and Pasture Science 63 , 251 – 268 . Google Scholar CrossRef Search ADS Choudhury FK , Rivero RM , Blumwald E , Mittler R . 2017 . Reactive oxygen species, abiotic stress and stress combination . The Plant Journal 90 , 856 – 867 . Google Scholar CrossRef Search ADS Christopher JT , Manschadi AM , Hammer GL , Borrell AK . 2008 . Developmental and physiological traits associated with high yield and stay-green phenotype in wheat . Australian Journal of Agricultural Research 59 , 354 – 364 . Google Scholar CrossRef Search ADS Claverie E , Meunier F , Javaux M , Sadok W . 2017 . Increased contribution of wheat nocturnal transpiration to daily water use under drought . Physiologia Plantarum 162 , 290 – 300 . Google Scholar CrossRef Search ADS Cockram J , Jones H , Leigh FJ , O’Sullivan D , Powell W , Laurie DA , Greenland AJ . 2007 . Control of flowering time in temperate cereals: genes, domestication, and sustainable productivity . Journal of Experimental Botany 58 , 1231 – 1244 . Google Scholar CrossRef Search ADS Condon A , Farquhar G , Richards R . 1990 . Genotypic variation in carbon isotope discrimination and transpiration efficiency in wheat. Leaf gas exchange and whole plant studies . Functional Plant Biology 17 , 9 – 22 . Condon AG , Richards RA , Rebetzke GJ , Farquhar GD . 2002 . Improving intrinsic water-use efficiency and crop yield . Crop Science 42 , 122 – 131 . Google Scholar CrossRef Search ADS Coupel-Ledru A , Lebon É , Christophe A , Doligez A , Cabrera-Bosquet L , Péchier P , Hamard P , This P , Simonneau T . 2014 . Genetic variation in a grapevine progeny (Vitis vinifera L. cvs Grenache×Syrah) reveals inconsistencies between maintenance of daytime leaf water potential and response of transpiration rate under drought . Journal of Experimental Botany 65 , 6205 – 6218 . Google Scholar CrossRef Search ADS Czyczyło-Mysza I , Marcińska I , Skrzypek E et al. 2011 . Mapping QTLs for yield components and chlorophyll a fluorescence parameters in wheat under three levels of water availability . Plant Genetic Resources 9 , 291 – 295 . Google Scholar CrossRef Search ADS Das A , Eldakak M , Paudel B , Kim DW , Hemmati H , Basu C , Rohila JS . 2016 . Leaf proteome analysis reveals prospective drought and heat stress response mechanisms in soybean . BioMed Research International 2016 , 6021047 . Google Scholar CrossRef Search ADS Dashti H , Yazdisamadi B , Bihamta Naghavi MR , Quarrie S . 2007 . QTL analysis for drought resistance in wheat using doubled haploid lines . International Journal of Agriculture and Biology 9 , 98 – 101 . Demirevska K , Simova-Stoilova L , Fedina I , Georgieva K , Kunert K . 2010 . Response of oryzacystatin I transformed tobacco plants to drought, heat and light stress . Journal of Agronomy and Crop Science 196 , 90 – 99 . Google Scholar CrossRef Search ADS Diab AA , Kantety RV , Ozturk NZ , Benscher D , Nachit MM , Sorrells ME . 2008 . Drought-inducible genes and differentially expressed sequence tags associated with components of drought tolerance in durum wheat . Scientific Research and Essays 3 , 009 – 026 . Eberhart SA , Russell WA . 1966 . Stability parameters for comparing varieties . Crop Science 6 , 36 – 40 . Google Scholar CrossRef Search ADS Ehrler WL , Idso SB , Jackson RD , Reginato RJ . 1978 . Wheat canopy temperature: relation to plant water potential . Agronomy Journal 70 , 251 – 256 . Google Scholar CrossRef Search ADS Feuillet C , Langridge P , Waugh R . 2008 . Cereal breeding takes a walk on the wild side . Trends in Genetics 24 , 24 – 32 . Google Scholar CrossRef Search ADS Fischer RA . 2011 . Wheat physiology: a review of recent developments . Crop and Pasture Science 62 , 95 – 114 . Google Scholar CrossRef Search ADS Fischer RA , Maurer R . 1978 . Drought resistance in spring wheat cultivars. I. Grain yield responses . Australian Journal of Agricultural Research 29 , 897 – 912 . Google Scholar CrossRef Search ADS Foyer CH , Noctor G . 2005 . Oxidant and antioxidant signalling in plants: a re-evaluation of the concept of oxidative stress in a physiological context . Plant, Cell & Environment 28 , 1056 – 1071 . Google Scholar CrossRef Search ADS Gavuzzi P , Rizza F , Palumbo M , Campanile RG , Ricciardi GL , Borghi B . 1997 . Evaluation of field and laboratory predictors of drought and heat tolerance in winter cereals . Canadian Journal of Plant Science 77 , 523 – 531 . Google Scholar CrossRef Search ADS Golabadi M , Arzani A , Mirmohammadi Maibody SAM , Sayed Tabatabaei BE , Mohammadi SA . 2011 . Identification of microsatellite markers linked with yield components under drought stress at terminal growth stages in durum wheat . Euphytica 177 , 207 – 221 . Google Scholar CrossRef Search ADS Hill CB , Taylor JD , Edwards J , Mather D , Langridge P , Bacic A , Roessner U . 2015 . Detection of QTL for metabolic and agronomic traits in wheat with adjustments for variation at genetic loci that affect plant phenology . Plant Science 233 , 143 – 154 . Google Scholar CrossRef Search ADS Hochman Z . 1982 . Effect of water stress with phasic development on yield of wheat grown in a semi-arid environment . Field Crops Research 5 , 55 – 67 . Google Scholar CrossRef Search ADS Hu X-J , Chen D , Lynne Mclntyre C , Fernanda Dreccer M , Zhang Z-B , Drenth J , Kalaipandian S , Chang H , Xue G-P . 2018 . Heat shock factor C2a serves as a proactive mechanism for heat protection in developing grains in wheat via an ABA-mediated regulatory pathway . Plant, Cell & Environment 41 , 79 – 98 . Google Scholar CrossRef Search ADS Huang X , Han B . 2014 . Natural variations and genome-wide association studies in crop plants . Annual Review of Plant Biology 65 , 531 – 551 . Google Scholar CrossRef Search ADS Ilyas M , Ilyas N , Arshad M , Kazi AG . 2014 . QTL mapping of wheat doubled haploids for chlorophyll content and chlorophyll fluorescence kinetics under drought stress imposed at anthesis stage . Pakistan Journal of Botany 46 , 1889 – 1897 . Izanloo A , Condon AG , Langridge P , Tester M , Schnurbusch T . 2008 . Different mechanisms of adaptation to cyclic water stress in two South Australian bread wheat cultivars . Journal of Experimental Botany 59 , 3327 – 3346 . Google Scholar CrossRef Search ADS James RA , Davenport RJ , Munns R . 2006 . Physiological characterization of two genes for Na+ exclusion in durum wheat, Nax1 and Nax2 . Plant Physiology 142 , 1537 – 1547 . Google Scholar CrossRef Search ADS Jenner C . 1994 . Starch synthesis in the kernel of wheat under high temperature conditions . Functional Plant Biology 21 , 791 – 806 . Jiang Y , Huang B . 2001 . Drought and heat stress injury to two cool-season turfgrasses in relation to antioxidant metabolism and lipid peroxidation . Crop Science 41 , 436 – 442 . Google Scholar CrossRef Search ADS Kadam NN , Yin X , Bindraban PS , Struik PC , Jagadish KS . 2015 . Does morphological and anatomical plasticity during the vegetative stage make wheat more tolerant of water deficit stress than rice ? Plant Physiology 167 , 1389 – 1401 . Google Scholar CrossRef Search ADS Kadam S , Singh K , Shukla S , Goel S , Vikram P , Pawar V , Gaikwad K , Khanna-Chopra R , Singh N . 2012 . Genomic associations for drought tolerance on the short arm of wheat chromosome 4B . Functional & Integrative Genomics 12 , 447 – 464 . Google Scholar CrossRef Search ADS Kirigwi FM , van Ginkel M , Trethowan R , Sears RG , Rajaram S , Paulsen GM . 2004 . Evaluation of selection strategies for wheat adaptation across water regimes . Euphytica 135 , 361 – 371 . Google Scholar CrossRef Search ADS Kirigwi FM , Van Ginkel M , Brown-Guedira G , Gill BS , Paulsen GM , Fritz AK . 2007 . Markers associated with a QTL for grain yield in wheat under drought . Molecular Breeding 20 , 401 – 413 . Google Scholar CrossRef Search ADS Koussevitzky S , Suzuki N , Huntington S , Armijo L , Sha W , Cortes D , Shulaev V , Mittler R . 2008 . Ascorbate peroxidase 1 plays a key role in the response of Arabidopsis thaliana to stress combination . The Journal of Biological Chemistry 283 , 34197 – 34203 . Google Scholar CrossRef Search ADS Lascano HR , Antonicelli GE , Luna CM , Melchiorre MN , Gómez LD , Racca RW , Trippi VS , Casano LM . 2001 . Antioxidant system response of different wheat cultivars under drought: field and in vitro studies . Functional Plant Biology 28 , 1095 – 1102 . Google Scholar CrossRef Search ADS Liang Z , Chen K , Li T et al. 2017 . Efficient DNA-free genome editing of bread wheat using CRISPR/Cas9 ribonucleoprotein complexes . Nature Communications 8 , 14261 . Google Scholar CrossRef Search ADS Liu B , Asseng S , Muller C et al. 2016 . Similar estimates of temperature impacts on global wheat yield by three independent methods . Nature Climate Change 6 , 1130 – 1136 . Google Scholar CrossRef Search ADS Lohraseb I , Collins NC , Parent B . 2017 . Diverging temperature responses of CO2 assimilation and plant development explain the overall effect of temperature on biomass accumulation in wheat leaves and grains . AoB Plants 9 , plw092 . Long SP , Ort DR . 2010 . More than taking the heat: crops and global change . Current Opinion in Plant Biology 13 , 241 – 248 . Google Scholar CrossRef Search ADS Lopes MS , El-Basyoni I , Baenziger PS et al. 2015 . Exploiting genetic diversity from landraces in wheat breeding for adaptation to climate change . Journal of Experimental Botany 66 , 3477 – 3486 . Google Scholar CrossRef Search ADS Lopes MS , Reynolds MP , Jalal-Kamali MR et al. 2012 . The yield correlations of selectable physiological traits in a population of advanced spring wheat lines grown in warm and drought environments . Field Crops Research 128 , 129 – 136 . Google Scholar CrossRef Search ADS Lu F , Romay MC , Glaubitz JC et al. 2015 . High-resolution genetic mapping of maize pan-genome sequence anchors . Nature Communications 6 , 6914 . Google Scholar CrossRef Search ADS Lu Y , Li R , Wang R , Wang X , Zheng W , Sun Q , Tong S , Dai S , Xu S . 2017 . Comparative proteomic analysis of flag leaves reveals new insight into wheat heat adaptation . Frontiers in Plant Science 8 , 1086 . Google Scholar CrossRef Search ADS Maccaferri M , Sanguineti MC , Corneti S et al. 2008 . Quantitative trait loci for grain yield and adaptation of durum wheat (Triticum durum Desf.) across a wide range of water availability . Genetics 178 , 489 – 511 . Google Scholar CrossRef Search ADS Machado S , Paulsen GM . 2001 . Combined effects of drought and high temperature on water relations of wheat and sorghum . Plant and Soil 233 , 179 – 187 . Google Scholar CrossRef Search ADS Marks CO , Lechowicz MJ . 2007 . The ecological and functional correlates of nocturnal transpiration . Tree Physiology 27 , 577 – 584 . Google Scholar CrossRef Search ADS Mason RE , Mondal S , Beecher FW , Pacheco A , Jampala B , Ibrahim AMH , Hays DB . 2010 . QTL associated with heat susceptibility index in wheat (Triticum aestivum L.) under short-term reproductive stage heat stress . Euphytica 174 , 423 – 436 . Google Scholar CrossRef Search ADS Maydup ML , Antonietta M , Graciano C , Guiamet JJ , Tambussi EA . 2014 . The contribution of the awns of bread wheat (Triticum aestivum L.) to grain filling: Responses to water deficit and the effects of awns on ear temperature and hydraulic conductance . Field Crops Research 167 , 102 – 111 . Google Scholar CrossRef Search ADS Merchuk-Ovnat L , Fahima T , Krugman T , Saranga Y . 2016 . Ancestral QTL alleles from wild emmer wheat improve grain yield, biomass and photosynthesis across environments in modern wheat . Plant Science 251 , 23 – 34 . Google Scholar CrossRef Search ADS Mittler R . 2006 . Abiotic stress, the field environment and stress combination . Trends in Plant Science 11 , 15 – 19 . Google Scholar CrossRef Search ADS Mittler R , Finka A , Goloubinoff P . 2012 . How do plants feel the heat ? Trends in Biochemical Sciences 37 , 118 – 125 . Google Scholar CrossRef Search ADS Mohammed A-R , Tarpley L . 2009 . Impact of high nighttime temperature on respiration, membrane stability, antioxidant capacity, and yield of rice plants . Crop Science 49 , 313 – 322 . Google Scholar CrossRef Search ADS Mondal B , Singh A , Yadav A , Singh Tomar RS , Vinod Singh GP , Prabhu KV . 2017 . QTL mapping for early ground cover in wheat (Triticum aestivum L.) under drought stress . Current Science 112 , 1266 – 1271 . Google Scholar CrossRef Search ADS Mooney HA , Di Castri F . 1973 . Mediterranean type ecosystems: origin and structure . Berlin, Heidelberg : Springer-Verlag . Google Scholar CrossRef Search ADS Morran S , Eini O , Pyvovarenko T et al. 2011 . Improvement of stress tolerance of wheat and barley by modulation of expression of DREB/CBF factors . Plant Biotechnology Journal 9 , 230 – 249 . Google Scholar CrossRef Search ADS Munns R , James RA , Xu B et al. 2012 . Wheat grain yield on saline soils is improved by an ancestral Na⁺ transporter gene . Nature Biotechnology 30 , 360 – 364 . Google Scholar CrossRef Search ADS Neghliz H , Cochard H , Brunel N , Martre P . 2016 . Ear rachis xylem occlusion and associated loss in hydraulic conductance coincide with the end of grain filling for wheat . Frontiers in Plant Science 7 , 920 . Google Scholar CrossRef Search ADS Nicotra AB , Atkin OK , Bonser SP et al. 2010 . Plant phenotypic plasticity in a changing climate . Trends in Plant Science 15 , 684 – 692 . Google Scholar CrossRef Search ADS Ogbonnaya FC , Rasheed A , Okechukwu EC , Jighly A , Makdis F , Wuletaw T , Hagras A , Uguru MI , Agbo CU . 2017 . Genome-wide association study for agronomic and physiological traits in spring wheat evaluated in a range of heat prone environments . Theoretical and Applied Genetics 130 , 1819 – 1835 . Google Scholar CrossRef Search ADS Paliwal R , Röder MS , Kumar U , Srivastava JP , Joshi AK . 2012 . QTL mapping of terminal heat tolerance in hexaploid wheat (T. aestivum L.) . Theoretical and Applied Genetics 125 , 561 – 575 . Google Scholar CrossRef Search ADS Parent B , Bonneau J , Maphosa L , Kovalchuk A , Langridge P , Fleury D . 2017 . Quantifying wheat sensitivities to environmental constraints to dissect Genotype × Environment interactions in the field . Plant Physiology 174 , 1669 – 1682 . Google Scholar CrossRef Search ADS Parent B , Shahinnia F , Maphosa L , Berger B , Rabie H , Chalmers K , Kovalchuk A , Langridge P , Fleury D . 2015 . Combining field performance with controlled environment plant imaging to identify the genetic control of growth and transpiration underlying yield response to water-deficit stress in wheat . Journal of Experimental Botany 66 , 5481 – 5492 . Google Scholar CrossRef Search ADS Parent B , Tardieu F . 2012 . Temperature responses of developmental processes have not been affected by breeding in different ecological areas for 17 crop species . New Phytologist 194 , 760 – 774 . Google Scholar CrossRef Search ADS Parent B , Tardieu F . 2014 . Can current crop models be used in the phenotyping era for predicting the genetic variability of yield of plants subjected to drought or high temperature ? Journal of Experimental Botany 65 , 6179 – 6189 . Google Scholar CrossRef Search ADS Passioura JB . 1977 . Grain yield, harvest index, and water use of wheat . Journal of the Australian Institute of Agricultural Sciences 43 , 117 – 120 . Passioura JB . 1996 . Drought and drought tolerance . Plant Growth Regulation 20 , 79 – 83 . Google Scholar CrossRef Search ADS Peleg Z , Fahima T , Abbo S , Krugman T , Nevo E , Yakir D , Saranga Y . 2005 . Genetic diversity for drought resistance in wild emmer wheat and its ecogeographical associations . Plant, Cell & Environment 28 , 176 – 191 . Google Scholar CrossRef Search ADS Peleg Z , Fahima T , Krugman T , Abbo S , Yakir D , Korol AB , Saranga Y . 2009 . Genomic dissection of drought resistance in durum wheat × wild emmer wheat recombinant inbreed line population . Plant, Cell & Environment 32 , 758 – 779 . Google Scholar CrossRef Search ADS Peng J , Richards DE , Hartley NM et al. 1999 . ‘Green revolution’ genes encode mutant gibberellin response modulators . Nature 400 , 256 – 261 . Google Scholar CrossRef Search ADS Perdomo JA , Capó-Bauçà S , Carmo-Silva E , Galmés J . 2017 . Rubisco and Rubisco activase play an important role in the biochemical limitations of photosynthesis in rice, wheat, and maize under high temperature and water deficit . Frontiers in Plant Science 8 , 490 . Google Scholar CrossRef Search ADS Perdomo JA , Conesa MÀ , Medrano H , Ribas-Carbó M , Galmés J . 2015 . Effects of long-term individual and combined water and temperature stress on the growth of rice, wheat and maize: relationship with morphological and physiological acclimation . Physiologia Plantarum 155 , 149 – 165 . Google Scholar CrossRef Search ADS Peterhansel C , Maurino VG . 2011 . Photorespiration redesigned . Plant Physiology 155 , 49 – 55 . Google Scholar CrossRef Search ADS Pinto RS , Reynolds MP . 2015 . Common genetic basis for canopy temperature depression under heat and drought stress associated with optimized root distribution in bread wheat . Theoretical and Applied Genetics 128 , 575 – 585 . Google Scholar CrossRef Search ADS Pinto RS , Reynolds MP , Mathews KL , McIntyre CL , Olivares-Villegas JJ , Chapman SC . 2010 . Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects . Theoretical and Applied Genetics 121 , 1001 – 1021 . Google Scholar CrossRef Search ADS Pradhan GP , Prasad PVV , Fritz AK , Kirkham MB , Gill BS . 2012 . Effects of drought and high temperature stress on synthetic hexaploid wheat . Functional Plant Biology 39 , 190 – 198 . Google Scholar CrossRef Search ADS Prasad PVV , Pisipati SR , Momčilović I , Ristic Z . 2011 . Independent and combined effects of high temperature and drought stress during grain filling on plant yield and chloroplast EF-Tu expression in spring wheat . Journal of Agronomy and Crop Science 197 , 430 – 441 . Google Scholar CrossRef Search ADS Price AH , Hendry GAF . 1991 . Iron-catalysed oxygen radical formation and its possible contribution to drought damage in nine native grasses and three cereals . Plant, Cell & Environment 14 , 477 – 484 . Google Scholar CrossRef Search ADS Quarrie SA , Steed A , Calestani C et al. 2005 . A high-density genetic map of hexaploid wheat (Triticum aestivum L.) from the cross Chinese Spring × SQ1 and its use to compare QTLs for grain yield across a range of environments . Theoretical and Applied Genetics 110 , 865 – 880 . Google Scholar CrossRef Search ADS Rajaram S , van Ginkel M , Fischer RA . 1994 . CIMMYT’s wheat breeding mega-environments (ME) . In: Proceedings of the 8th International Wheat Genetics Symposium, Beijing, 20–25 July 1993 . Beijing : Institute of Genetics, Chinese Academy of Sciences , 1101 – 1106 . Rampino P , Mita G , Fasano P , Borrelli GM , Aprile A , Dalessandro G , De Bellis L , Perrotta C . 2012 . Novel durum wheat genes up-regulated in response to a combination of heat and drought stress . Plant Physiology and Biochemistry 56 , 72 – 78 . Google Scholar CrossRef Search ADS Redondo-Gómez S . 2013 . Abiotic and biotic stress tolerance in plants . In: Rout GR , Das AB , eds. Molecular stress physiology of plants . New Delhi : Springer India , 1 – 20 . Google Scholar CrossRef Search ADS Resco de Dios V , Loik ME , Smith R , Aspinwall MJ , Tissue DT . 2016 . Genetic variation in circadian regulation of nocturnal stomatal conductance enhances carbon assimilation and growth . Plant, Cell & Environment 39 , 3 – 11 . Google Scholar CrossRef Search ADS Reynolds M , Foulkes MJ , Slafer GA , Berry P , Parry MA , Snape JW , Angus WJ . 2009 . Raising yield potential in wheat . Journal of Experimental Botany 60 , 1899 – 1918 . Google Scholar CrossRef Search ADS Reynolds MP , Pellegrineschi A , Skovmand B . 2005 . Sink-limitation to yield and biomass: a summary of some investigations in spring wheat . Annals of Applied Biology 146 , 39 – 49 . Google Scholar CrossRef Search ADS Reynolds MP , Pierre CS , Saad ASI , Vargas M , Condon AG . 2007 . Evaluating potential genetic gains in wheat associated with stress-adaptive trait expression in elite genetic resources under drought and heat stress . Crop Science 47 , S172 – S189 . Google Scholar CrossRef Search ADS Richards RA , Rawson HM , Johnson DA . 1986 . Glaucousness in wheat: Its development and effect on water-use efficiency, gas exchange and photosynthetic tissue temperatures . Functional Plant Biology 13 , 465 – 473 . Rivero RM , Shulaev V , Blumwald E . 2009 . Cytokinin-dependent photorespiration and the protection of photosynthesis during water deficit . Plant Physiology 150 , 1530 – 1540 . Google Scholar CrossRef Search ADS Rizhsky L , Liang H , Mittler R . 2002 . The combined effect of drought stress and heat shock on gene expression in tobacco . Plant Physiology 130 , 1143 – 1151 . Google Scholar CrossRef Search ADS Rizhsky L , Liang H , Shuman J , Shulaev V , Davletova S , Mittler R . 2004 . When defense pathways collide. The response of Arabidopsis to a combination of drought and heat stress . Plant Physiology 134 , 1683 – 1696 . Google Scholar CrossRef Search ADS Roche D . 2015 . Stomatal conductance is essential for higher yield potential of C3 crops . Critical Reviews in Plant Sciences 34 , 429 – 453 . Google Scholar CrossRef Search ADS Sadok W . 2016 . The circadian life of nocturnal water use: when late-night decisions help improve your day . Plant, Cell & Environment 39 , 1 – 2 . Google Scholar CrossRef Search ADS Sadras VO , Reynolds MP , de la Vega AJ , Petrie PR , Robinson R . 2009 . Phenotypic plasticity of yield and phenology in wheat, sunflower and grapevine . Field Crops Research 110 , 242 – 250 . Google Scholar CrossRef Search ADS Sadras VO , Slafer GA . 2012 . Environmental modulation of yield components in cereals: Heritabilities reveal a hierarchy of phenotypic plasticities . Field Crops Research 127 , 215 – 224 . Google Scholar CrossRef Search ADS Saini HS , Aspinall D . 1982 . Abnormal sporogenesis in wheat (Triticum aestivum L.) induced by short periods of high temperature . Annals of Botany 49 , 835 – 846 . Google Scholar CrossRef Search ADS Saini HS , Lalonde S . 1997 . Injuries to reproductive development under water stress, and their consequences for crop productivity . Journal of Crop Production 1 , 223 – 248 . Google Scholar CrossRef Search ADS Sairam RK , Saxena DC . 2000 . Oxidative stress and antioxidants in wheat genotypes: possible mechanism of water stress tolerance . Journal of Agronomy and Crop Science 184 , 55 – 61 . Google Scholar CrossRef Search ADS Sairam RK , Srivastava GC , Saxena DC . 2000 . Increased antioxidant activity under elevated temperatures: a mechanism of heat stress tolerance in wheat genotypes . Biologia Plantarum 43 , 245 – 251 . Google Scholar CrossRef Search ADS Salter PJ , Goode JE . 1967 . Crop responses to water at different stages of growth . Farnham Royal, Bucks, England : Commonwealth Agricultural Bureaux . Scharwies JD , Tyerman SD . 2017 . Comparison of isohydric and anisohydric Vitis vinifera L. cultivars reveals a fine balance between hydraulic resistances, driving forces and transpiration in ripening berries . Functional Plant Biology 44 , 324 – 338 . Google Scholar CrossRef Search ADS Schauberger B , Archontoulis S , Arneth A et al. 2017 . Consistent negative response of US crops to high temperatures in observations and crop models . Nature Communications 8 , 13931 . Google Scholar CrossRef Search ADS Scheibe R , Dietz KJ . 2012 . Reduction-oxidation network for flexible adjustment of cellular metabolism in photoautotrophic cells . Plant, Cell & Environment 35 , 202 – 216 . Google Scholar CrossRef Search ADS Schoppach R , Claverie E , Sadok W . 2014 . Genotype-dependent influence of night-time vapour pressure deficit on night-time transpiration and daytime gas exchange in wheat . Functional Plant Biology 41 , 963 – 971 . Google Scholar CrossRef Search ADS Schoppach R , Sadok W . 2013 . Transpiration sensitivities to evaporative demand and leaf areas vary with night and day warming regimes among wheat genotypes . Functional Plant Biology 40 , 708 – 718 . Google Scholar CrossRef Search ADS Schoppach R , Taylor JD , Majerus E , Claverie E , Baumann U , Suchecki R , Fleury D , Sadok W . 2016 . High resolution mapping of traits related to whole-plant transpiration under increasing evaporative demand in wheat . Journal of Experimental Botany 67 , 2847 – 2860 . Google Scholar CrossRef Search ADS Sečenji M , Hideg É , Bebes A , Györgyey J . 2010 . Transcriptional differences in gene families of the ascorbate–glutathione cycle in wheat during mild water deficit . Plant Cell Reports 29 , 37 – 50 . Google Scholar CrossRef Search ADS Shah NH , Paulsen GM . 2003 . Interaction of drought and high temperature on photosynthesis and grain-filling of wheat . Plant and Soil 257 , 219 – 226 . Google Scholar CrossRef Search ADS Shahinnia F , Le Roy J , Laborde B , Sznajder B , Kalambettu P , Mahjourimajd S , Tilbrook J , Fleury D . 2016 . Genetic association of stomatal traits and yield in wheat grown in low rainfall environments . BMC Plant Biology 16 , 150 . Google Scholar CrossRef Search ADS Sharma D , Singh R , Rane J , Gupta VK , Mamrutha HM , Tiwari R . 2016 . Mapping quantitative trait loci associated with grain filling duration and grain number under terminal heat stress in bread wheat (Triticum aestivum L.) . Plant Breeding 135 , 538 – 545 . Google Scholar CrossRef Search ADS Shiferaw B , Smale M , Braun H-J , Duveiller E , Reynolds M , Muricho G . 2013 . Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security . Food Security 5 , 291 – 317 . Google Scholar CrossRef Search ADS Shirdelmoghanloo H , Taylor JD , Lohraseb I et al. 2016 . A QTL on the short arm of wheat (Triticum aestivum L.) chromosome 3B affects the stability of grain weight in plants exposed to a brief heat shock early in grain filling . BMC Plant Biology 16 , 100 . Google Scholar CrossRef Search ADS Silva EN , Ferreira-Silva SL , Fontenele Ade V , Ribeiro RV , Viégas RA , Silveira JA . 2010 . Photosynthetic changes and protective mechanisms against oxidative damage subjected to isolated and combined drought and heat stresses in Jatropha curcas plants . Journal of Plant Physiology 167 , 1157 – 1164 . Google Scholar CrossRef Search ADS Simmonds J , Scott P , Brinton J , Mestre TC , Bush M , Del Blanco A , Dubcovsky J , Uauy C . 2016 . A splice acceptor site mutation in TaGW2-A1 increases thousand grain weight in tetraploid and hexaploid wheat through wider and longer grains . Theoretical and Applied Genetics 129 , 1099 – 1112 . Google Scholar CrossRef Search ADS Spielmeyer W , Hyles J , Joaquim P , Azanza F , Bonnett D , Ellis ME , Moore C , Richards RA . 2007 . A QTL on chromosome 6A in bread wheat (Triticum aestivum) is associated with longer coleoptiles, greater seedling vigour and final plant height . Theoretical and Applied Genetics 115 , 59 – 66 . Google Scholar CrossRef Search ADS Stone P , Nicolas M . 1995 . A survey of the effects of high temperature during grain filling on yield and quality of 75 wheat cultivars . Australian Journal of Agricultural Research 46 , 475 – 492 . Google Scholar CrossRef Search ADS Sun X , Cahill J , Van Hautegem T et al. 2017 . Altered expression of maize PLASTOCHRON1 enhances biomass and seed yield by extending cell division duration . Nature Communications 8 , 14752 . Google Scholar CrossRef Search ADS Suzuki N , Rivero RM , Shulaev V , Blumwald E , Mittler R . 2014 . Abiotic and biotic stress combinations . New Phytologist 203 , 32 – 43 . Google Scholar CrossRef Search ADS Tahmasebi S , Heidari B , Pakniyat H , McIntyre CL . 2017 . Mapping QTLs associated with agronomic and physiological traits under terminal drought and heat stress conditions in wheat (Triticum aestivum L.) . Genome 60 , 26 – 45 . Google Scholar CrossRef Search ADS Talukder SK , Babar MA , Vijayalakshmi K , Poland J , Prasad PV , Bowden R , Fritz A . 2014 . Mapping QTL for the traits associated with heat tolerance in wheat (Triticum aestivum L.) . BMC Genetics 15 , 97 . Google Scholar CrossRef Search ADS Tardieu F , Parent B , Caldeira CF , Welcker C . 2014 . Genetic and physiological controls of growth under water deficit . Plant Physiology 164 , 1628 – 1635 . Google Scholar CrossRef Search ADS Tester M , Langridge P . 2010 . Breeding technologies to increase crop production in a changing world . Science 327 , 818 – 822 . Google Scholar CrossRef Search ADS Tilman D , Balzer C , Hill J , Befort BL . 2011 . Global food demand and the sustainable intensification of agriculture . Proceedings of the National Academy of Sciences, USA 108 , 20260 – 20264 . Google Scholar CrossRef Search ADS Tixier A , Cochard H , Badel E , Dusotoit-Coucaud A , Jansen S , Herbette S . 2013 . Arabidopsis thaliana as a model species for xylem hydraulics: does size matter ? Journal of Experimental Botany 64 , 2295 – 2305 . Google Scholar CrossRef Search ADS Tricker PJ , Haefele SM , Okamoto M . 2016 . The interaction of drought and nutrient stress in wheat . In: Ahmad P , ed. Water stress and crop plants: A sustainable approach . Chichester : John Wiley & Sons, Ltd , 695 – 710 . Google Scholar CrossRef Search ADS Tsai H , Howell T , Nitcher R et al. 2011 . Discovery of rare mutations in populations: TILLING by sequencing . Plant Physiology 156 , 1257 – 1268 . Google Scholar CrossRef Search ADS USDA . 2017 . World agricultural production . Washington, DC, USA : United States Department of Agriculture Foreign Agricultural Service . Vadez V , Kholova J , Medina S , Kakkera A , Anderberg H . 2014 . Transpiration efficiency: new insights into an old story . Journal of Experimental Botany 65 , 6141 – 6153 . Google Scholar CrossRef Search ADS Verma V , Foulkes MJ , Worland AJ , Sylvester-Bradley R , Caligari PDS , Snape JW . 2004 . Mapping quantitative trait loci for flag leaf senescence as a yield determinant in winter wheat under optimal and drought-stressed environments . Euphytica 135 , 255 – 263 . Google Scholar CrossRef Search ADS Vettakkorumakankav NN , Falk D , Saxena P , Fletcher RA . 1999 . A crucial role for gibberellins in stress protection of plants . Plant and Cell Physiology 40 , 542 – 548 . Google Scholar CrossRef Search ADS Vijayalakshmi K , Fritz AK , Paulsen GM , Bai G , Pandravada S , Gill BS . 2010 . Modeling and mapping QTL for senescence-related traits in winter wheat under high temperature . Molecular Breeding 26 , 163 – 175 . Google Scholar CrossRef Search ADS Voss I , Sunil B , Scheibe R , Raghavendra AS . 2013 . Emerging concept for the role of photorespiration as an important part of abiotic stress response . Plant Biology 15 , 713 – 722 . Google Scholar CrossRef Search ADS Wang X , Cai J , Liu F , Dai T , Cao W , Wollenweber B , Jiang D . 2014a. Multiple heat priming enhances thermo-tolerance to a later high temperature stress via improving subcellular antioxidant activities in wheat seedlings . Plant Physiology and Biochemistry 74 , 185 – 192 . Google Scholar CrossRef Search ADS Wang Y , Chen L , Du Y , Yang Z , Condon AG , Hu Y-G . 2014b. Genetic effect of dwarfing gene Rht13 compared with Rht-D1b on plant height and some agronomic traits in common wheat (Triticum aestivum L.) . Field Crops Research 162 , 39 – 47 . Google Scholar CrossRef Search ADS Wardlaw I , Wrigley C . 1994 . Heat tolerance in temperate cereals: an overview . Functional Plant Biology 21 , 695 – 703 . Weigand C . 2011 . Wheat import projections towards 2050 . Arlington, VA, USA : US Wheat Associates . Weldearegay DF , Yan F , Jiang D , Liu F . 2012 . Independent and combined effects of soil warming and drought stress during anthesis on seed set and grain yield in two spring wheat varieties . Journal of Agronomy and Crop Science 198 , 245 – 253 . Google Scholar CrossRef Search ADS Wheeler T . 2012 . Agriculture: Wheat crops feel the heat . Nature Climate Change 2 , 152 – 153 . Google Scholar CrossRef Search ADS Xu Y-F , Li S-S , Li L-H , Ma F-F , Fu X-Y , Shi Z-L , Xu H-X , Ma P-T , An D-G . 2017 . QTL mapping for yield and photosynthetic related traits under different water regimes in wheat . Molecular Breeding 37 , 34 . Google Scholar CrossRef Search ADS Xue GP , Sadat S , Drenth J , McIntyre CL . 2014 . The heat shock factor family from Triticum aestivum in response to heat and other major abiotic stresses and their role in regulation of heat shock protein genes . Journal of Experimental Botany 65 , 539 – 557 . Google Scholar CrossRef Search ADS Yang DL , Jing RL , Chang XP , Li W . 2007 . Identification of quantitative trait loci and environmental interactions for accumulation and remobilization of water-soluble carbohydrates in wheat (Triticum aestivum L.) stems . Genetics 176 , 571 – 584 . Google Scholar CrossRef Search ADS Zandalinas SI , Balfagón D , Arbona V , Gómez-Cadenas A . 2017 . Modulation of antioxidant defense system is associated with combined drought and heat stress tolerance in Citrus . Frontiers in Plant Science 8 , 953 . Google Scholar CrossRef Search ADS Zandalinas SI , Mittler R , Balfagón D , Arbona V , Gómez-Cadenas A . 2018 . Plant adaptations to the combination of drought and high temperatures . Physiologia Plantarum 162 , 2 – 12 . Google Scholar CrossRef Search ADS Zang X , Geng X , Wang F et al. 2017 . Overexpression of wheat ferritin gene TaFER-5B enhances tolerance to heat stress and other abiotic stresses associated with the ROS scavenging . BMC Plant Biology 17 , 14 . Google Scholar CrossRef Search ADS Zee S , O’brien T . 1970 . A special type of tracheary element associated with “xylem discontinuity” in the floral axis of wheat . Australian Journal of Biological Sciences 23 , 783 – 792 . Google Scholar CrossRef Search ADS Zhang B , Li W , Chang X , Li R , Jing R . 2014 . Effects of favorable alleles for water-soluble carbohydrates at grain filling on grain weight under drought and heat stresses in wheat . PLoS One 9 , e102917 . Google Scholar CrossRef Search ADS Zhang G , Zhang M , Zhao Z , Ren Y , Li Q , Wang W . 2017 . Wheat TaPUB1 modulates plant drought stress resistance by improving antioxidant capability . Scientific Reports 7 , 7549 . Google Scholar CrossRef Search ADS © The Author(s) 2018. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

Journal of Experimental BotanyOxford University Press

Published: Mar 17, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off