Physiological Traits for Improving Heat Tolerance in Wheat

Abstract

Wheat (Triticum aestivum) represents about 30% of the world’s cereal area, with over 220 million ha cultivated worldwide, often under abiotic stress. Wheat growth can be impaired by heat stress (HS) at any developmental stage, and modeling scenarios predict even warmer temperatures in the future (Easterling and Apps, 2005). The worst impacts of rising temperatures will occur at low latitudes (where approximately 100 million ha of wheat are cultivated, producing approximately 280 million tons of grain), while some benefits at high latitudes are expected. In terms of breeding targets, 12 different wheat mega-environments have been defined worldwide based on cropping system (e.g. rain fed versus irrigated, spring versus winter type) together with biotic and abiotic constraints (Braun et al., 2010). While mega-environment 5 encompasses 7 million ha under continuous HS (e.g. in Sudan and south and central India), over one-half of the total wheat area is prone to periods of HS already, and climate models suggest further increases in average temperatures (Fig. 1; Supplemental Table S1) as well as extreme temperature anomalies, which are already detectable (Hansen et al., 2012). Yield penalties are associated with both chronically high temperatures (mean temperature of the growth cycle being 18°C–25°C, and maximum day temperatures up to 32°C during grain filling) as well as heat shocks, where temperatures greater than 32°C occur during mid or late reproductive wheat stages, including grain filling (Wardlaw and Wrigley, 1994). A recent analysis of extensive international nursery data suggests that spring wheat breeding targeted for abiotic stress delivers better genetic gains in warmer environments (S.M. Gourdji, K.L. Mathews, M.P. Reynolds, J. Crossa, and D.B. Lobell, unpublished data). This Update considers the physiological processes and traits for which there is evidence that genetic improvement could improve wheat adaptation to HS. The issue of biotic threats to wheat is beyond the scope of this review, and readers are referred to other sources (Legreve and Duveiller, 2010). Predicted average growing season temperatures for 2050 in each wheat mega-environment (circle color; adapted from Ramirez and Jarvis, 2008). Red numbers show average growing season temperatures above 18°C (indicative of chronically high temperature stress according to Wardlaw and Wrigley, 1994). Despite advances in our understanding of genes of major effect conferring disease resistance in wheat (Krattinger et al., 2009), the genetic basis of heat adaptation is poorly understood. Currently, no “heat-tolerance” genes have been cloned. For the time being, physiological traits (PTs) associated with heat adaptation constitute the best available “handle” for genetic improvement of crops, since they represent de facto favorable combinations of alleles. Such alleles are still quite elusive using the quantitative trait locus (QTL) approach, because they show interaction with both environment and genetic background, which typically includes variation in genes of major effect (Reynolds and Tuberosa, 2008; Pinto et al., 2010). While the outcome of combining heat-adaptive PTs in hybridization is, similarly, not always predictable in terms of the net effect on crop productivity, especially over a wide range of environments, crosses made between parents with different but potentially complementary PTs increase the probability of cumulative gene action (Reynolds et al., 2009; Reynolds and Rebetzke, 2011). Both gene discovery and physiological breeding are being facilitated by (1) understanding how adaptive traits enhance yield in heat-stressed environments as well as (2) the development and application of phenotyping platforms to (3) screen experimental populations in order to pinpoint alleles of interest and (4) identify promising new genetic resources. A conceptual model of genetically determined PT to improve heat tolerance in wheat is proposed (Fig. 2), based partly on similar approaches for improving drought adaptation (Reynolds and Tuberosa, 2008), which resulted in the distribution of a new drought-adapted wheat germplasm targeted to mega-environment 4 (Reynolds et al., 2009; http://apps.cimmyt.org/english/wps/obtain_seed/iwin/index.htm). The model is based on the notion that when water and nutrients are not limiting, yield will be a function of three main genetically determined drivers of yield: (1) light interception (LI); (2) radiation use efficiency (RUE); and (3) partitioning of total assimilates. Conceptual model of heat-adaptive traits grouped by three main drivers of yield (i.e. yield = LI × RUE × HI) in the absence of water limitations (adapted from Reynolds et al., 2007). ABA, Abscisic acid. The physiological breeding approach aims to combine traits associated with all three drivers of yield to result in a cumulative genetic effect on yield. Traits for which there is reasonable evidence of being heat adaptive will now be discussed in this