An assessment of wheat yield sensitivity and breeding gains in hot environments

Abstract

Genetic improvements in heat tolerance of wheat provide a potential adaptation response to long-term warming trends, and may also boost yields in wheat-growing areas already subject to heat stress. Yet there have been few assessments of recent progress in breeding wheat for hot environments. Here, data from 25 years of wheat trials in 76 countries from the International Maize and Wheat Improvement Center (CIMMYT) are used to empirically model the response of wheat to environmental variation and assess the genetic gains over time in different environments and for different breeding strategies. Wheat yields exhibited the most sensitivity to warming during the grain-filling stage, typically the hottest part of the season. Sites with high vapour pressure deficit (VPD) exhibited a less negative response to temperatures during this period, probably associated with increased transpirational cooling. Genetic improvements were assessed by using the empirical model to correct observed yield growth for changes in environmental conditions and management over time. These 'climatecorrected' yield trends showed that most of the genetic gains in the high-yield-potential Elite Spring Wheat Yield Trial (ESWYT) were made at cooler temperatures, close to the physiological optimum, with no evidence for genetic gains at the hottest temperatures. In contrast, the Semi-Arid Wheat Yield Trial (SAWYT), a lower-yielding nursery targeted at maintaining yields under stressed conditions, showed the strongest genetic gains at the hottest temperatures. These results imply that targeted breeding efforts help us to ensure progress in building heat tolerance, and that intensified (and possibly new) approaches are needed to improve the yield potential of wheat in hot environments in order to maintain global food security in a warmer climate.

Strategic crossing of biomass and harvest index—source and sink—achieves genetic gains in wheat

“…Relatively small increases in yield represented by genetic gains in the annual to decadal long timescale, while important for farmers, are not easily ratified by stringent statistical tests due to the relatively large errors (e.g. plot to plot variation) compared to the absolute magnitude of genetic gain (typically 0.5-5% in this timeframe); therefore, demonstrations of breeding progress typically consider genetic gains over several decades (Gourdji et al 2012;Sharma et al 2012;Fischer et al 2014;Crespo-Herrera et al 2017). Despite the extremely restricted number of years for the current study, demonstration of proof of concept requires formal tests.…”

“…Breeding is conducted at a few strategic research hubs to annually develop up to 1000 new high yielding, disease-resistant lines targeted to major agro-ecologies. Germplasm delivered is used as sources of outstanding traits for breeding; as candidates for variety release; and for research into local adaptation (Braun et al 2010;Gourdji et al 2012;.…”

Ppd1, Vrn1, ALMT1 and Rht genes and their effects on grain yield in lower rainfall environments in southern Australia

“…The first was mean maximum temperature for 15 September-31 October. The second, vapour-pressure deficit, was calculated because it was known to influence grain yield in semi-arid environments around flowering and early grain-filling (Dreccer et al 2007;Gourdji et al 2013). The parameter we calculated was the same as the parameter described by Dreccer et al (2007).…”

Improving agricultural knowledge management: The AgTrials experience

“…Agronomic data can also be used as a benchmark in yield gap studies for what farmers might be able to achieve under improved conditions and management ( Gustafson et al , 2014). Trial data collected across a large geographic extent and over decades can be useful to monitor climate change or the spread of pests and diseases ( Gourdji et al , 2012; Lampe et al , 2014; Lobell et al , 2011), to understand the drivers of technology adoption, to set research and development priorities and to conduct both ex-ante and ex-post impact analysis ( Badu-Apraku et al , 2011; Hyman et al , 2016; Renkow & Byerlee, 2010; Setimela et al , 2005). One of the most obvious uses of agricultural trial data is to calibrate crop models, for a single location or for spatially explicit models covering countries, regions or the entire globe.…”