doi: 10.1098/rspb.2012.2190, 280 2013 Proc. R. Soc. B

 Sharon M. Gourdji, Ky L. Mathews, Matthew Reynolds, José Crossa and David B. Lobell in hot environmentsAn assessment of wheat yield sensitivity and breeding gains  

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ResearchCite this article: Gourdji SM, Mathews KL,

Reynolds M, Crossa J, Lobell DB. 2012 An

assessment of wheat yield sensitivity and

breeding gains in hot environments. Proc R Soc

B 280: 20122190.

http://dx.doi.org/10.1098/rspb.2012.2190

Received: 14 September 2012

Accepted: 9 November 2012

Subject Areas:environmental science, plant science

Keywords:climate change, wheat, heat tolerance,

breeding

Author for correspondence:Sharon M. Gourdji

e-mail: sgourdji@stanford.edu

Electronic supplementary material is available

at http://dx.doi.org/10.1098/rspb.2012.2190 or

via http://rspb.royalsocietypublishing.org.

& 2012 The Author(s) Published by the Royal Society. All rights reserved.

An assessment of wheat yieldsensitivity and breeding gains inhot environments

Sharon M. Gourdji1,2, Ky L. Mathews3, Matthew Reynolds3, Jose Crossa3

and David B. Lobell1,2

1Department of Environmental Earth System Science, Stanford University, Stanford, CA 94305, USA2Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA3International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641,06600 Mexico D.F., Mexico

Genetic improvements in heat tolerance of wheat provide a potential adap-

tation 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 Inter-

national 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 differ-

ent 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 nega-

tive 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 ‘climate-

corrected’ 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 gen-

etic 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.

1. IntroductionWheat is the most widely grown crop in the world in terms of total harvested

area [1], and currently provides an average of about 20 per cent of human cal-

orie consumption [2]. Improvements in yield are essential to keep pace with

population growth and increased demand, yet long-term climate trends threa-

ten to reduce wheat yields, or at least slow yield growth, in many regions.

Spring wheat is already grown in many tropical and sub-tropical environments

near or past the optimal temperatures for wheat [3], particularly during the

later grain-filling portion of the season [4–6]. Modelling studies have shown

that even with adaptive agronomic changes to planting date and cultivar, pro-

jected warming will still have a negative impact on wheat yields around the

globe [7]. Although elevated CO2 levels associated with warming may impart

benefits, which in many regions could outweigh the negative impacts of warm-

ing for the next few decades [8], a warming climate still represents an important

adaptation challenge to the maintenance of past productivity gains.

0–2

wheat yields (tonnes ha–1)

2–44–66–8

> 8

Figure 1. Map of 349 trial locations included in analysis, colour-coded by average yields. Also shown (in grey) are the 31 877 stations used for weatherinterpolation.

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Beyond relatively straightforward agronomic changes, an

often cited adaptation strategy is to breed new wheat var-

ieties that combine improved heat tolerance with other

desirable traits, such as disease resistance and high yield

potential. The International Maize and Wheat Improvement

Center (CIMMYT), based in Mexico, has been a leader in

breeding and disseminating improved varieties of wheat in

developing countries since its inception in 1943, funded by

the Rockefeller Foundation and the government of Mexico.

In the 1990s, it was estimated that 90 per cent of bread

wheat releases in developing countries contained ancestry

from one or more CIMMYT varieties [9], and today, more

than 75 per cent of the area planted to modern wheat

varieties in developing countries uses varieties developed

by CIMMYT or its national-level partners (http://

www.cimmyt.org/en/about-us/who-we-are). Recent studies

assessing long-term genetic gains of wheat lines released by

the CIMMYT Global Wheat Program show a continuous

yield increase of approximately 0.7 per cent per year in

both low-yielding areas, and well-irrigated and high-rainfall

areas [10,11].

Given the major role of CIMMYT in international wheat

improvement, and the evidence of widespread warming

in major wheat growing regions in the past few decades

[6,12], a relevant issue is the relative performance of

CIMMYT lines under different temperature conditions.

A related question is whether nurseries that focus on breed-

ing for targeted drought or heat stress show evidence of

more rapid yield gains at high temperatures than the more

standard approach of breeding for high yield potential

under optimal management, since this knowledge could

help us to guide future efforts.

The current study addresses these questions using histori-

cal datasets from three different spring bread wheat nurseries

at CIMMYT with different breeding goals: the Elite Spring

Wheat Yield Trial (ESWYT), which contains the highest-yield-

ing varieties under ideal environmental and management

conditions; the Semi-Arid Wheat Yield Trial (SAWYT),

where wheat is specifically bred to maintain yields under

dry conditions that are frequently accompanied by heat

stress; and the High Temperature Wheat Yield Trial

(HTWYT), where wheat is bred for high temperature, irri-

gated environments. Data from these nurseries are first

used in regression analysis to define the sensitivity of wheat

yields to temperature and other environmental parameters.

These regressions are then used to adjust observed yields

for changes in the locations and environmental conditions

of trials over time, a step necessary in order to assess

true genetic gains under theoretically constant conditions.

Inferred genetic gains are then compared across a range of

cool to hot temperatures in the grain-filling stage, in order

to determine the relative rate of gains in hot environments.

2. Methods(a) DatasetsFor each year and breeding nursery, a new set of varieties is

sent annually by CIMMYT to a network of international collab-

orators (called the International Wheat Improvement Network,

IWIN), who grow this common germplasm under a range of

environmental conditions. For example, seasonal average temp-

eratures vary in the database between 78 and 278C (see figure S1

in the electronic supplementary material). The IWIN serves pri-

marily to distribute improved germplasm globally, but the data

returned from these trials provide a valuable resource to assess

genotype � environment (G � E) interactions and long-term

trends in breeding. IWIN trial datasets have been used in

studies to help us understand the impact of breeding nurseries

such as the ESWYT [10,13], SAWYT [11,14] and HTWYT [15],

among others.

All trials in this dataset were generally requested to be well

managed in terms of water and fertilizer application, with

trials affected by lodging or disease filtered out. One exception

is in the SAWYT nursery, where collaborators were encouraged

to apply only enough irrigation to achieve germination, with

final yields being largely dependent on in-season rainfall and/

or stored soil moisture. As might be expected in this 76-country,

25-year dataset, the implementation of management instructions

most probably varied across trials within the database, as evi-

denced by the wide range of yields in the database (from

approx. 1 to approx. 10 tonnes ha21 grown with the same

varieties in any given year; figure 1). However, among inter-

national agricultural datasets, this one contains a relatively

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minimal amount of confounding factors, along with a wide

range of environmental conditions, thereby enabling us to

empirically assess relationships between wheat yield and

environmental parameters throughout the crop life cycle. Such

an empirical analysis can both confirm current understanding

and elucidate mechanisms for future prediction of crop yields

in a changing climate.

Yield data in this study represent means across genotypes

and replications for a given trial location, sowing date and nur-

sery. The trial means were calculated using only the subset of

genotypes within a nursery each year that had similar phenology

(i.e. +3.5 days of the mean trial heading date) in high-yielding

(i.e. more than 5 tonnes ha21) environments, in order to exclude

the confounding effects of large maturity ranges in stress

environments. However, mean yields for the selected genotypes

were calculated for all trials, and included in the empirical model

regardless of yield level.

Yield data were paired with reconstructed daily weather data,

as described in the detailed methods section in the electronic

supplementary material. In short, daily temperature data were

obtained by combining high-spatial-resolution climatologies

with interpolation of anomalies from nearby station data, while

daily relative humidity and radiation were obtained from satel-

lite-based datasets. While water was assumed not to be a

limiting factor for the irrigated trials, unfortunately little infor-

mation was available regarding timing and amount of irrigation

water, nor were suitable soil moisture datasets available.

(b) Empirical modelMean yields from a total of 1353 trials, pooled across nurseries

and planted from 1980 to 2009 in 349 unique locations

(figure 1), were paired with weather data in a panel regression:

yield ¼ cj þ an þ (gn � year)þ (b�W)þ 1;

where cj are country fixed effects, an are nursery fixed effects, gn

are yield trends by nursery, W is a set of environmental variables

defined by growth stage and b are the coefficients on these

variables.

The environmental variables in W include: air temperature

(both linear and squared terms), diurnal temperature range

(DTR), shortwave radiation, day length, vapour pressure deficit

(VPD), and interaction terms between VPD and temperature

(linear and quadratic). Vapour pressure deficit was calculated

as the difference between saturation and actual vapour pressures,

which were derived from daily minimum and maximum temp-

eratures and relative humidity data. Each environmental

variable included in the regression was averaged for three

stages throughout the growing season [16]: vegetative (from

sowing to 300 growing degree-days, GDD, before heading),

reproductive (from 300 GDD before heading to 100 GDD after)

and grain-filling (from 100 GDD after heading to harvest).

The linear and quadratic temperature terms allowed the

model to choose an ‘optimal’ temperature per growth stage,

while DTR allowed for a differential response to day-versus

night-time temperatures. Radiation and day length affect photo-

synthesis and development rates, respectively, and while

radiation tends to covary with temperature (especially in the

vegetative stage), day length, along with temperature, is also

an important determinant of phenology. VPD interacts with air

temperature through its impact on transpiration and, hence,

canopy temperatures. A number of alternative models were

also tested (e.g. excluding day length and/or DTR, excluding

the temperature quadratic terms, or additionally including

stage length terms and their interaction with temperature). The

results using these alternative models confirmed that the main

conclusions of the study were not sensitive to model formulation.

Country fixed effects in the model accounted for average

differences in management or soil type by country, after

accounting for variability explained by the weather-based predic-

tors in the regression. We assumed that any remaining variations

in management or soil type within countries were not correlated

with weather, and therefore did not bias our regression estimates.

Trials were pooled across nurseries into a single model in

order to increase statistical power, and because of large differ-

ences in the number of trials per nursery (959 from ESWYT

versus 259 from SAWYT and 135 from HTWYT). However, struc-

tural differences exist in germplasm, environment and

management between the nurseries (e.g. irrigation in ESWYT

and HTWYT, but none in SAWYT). Nursery fixed effects and

nursery-specific year trends, corresponding to varying levels of

genetic yield growth, help us to account for these differences.

As a sensitivity test, we also ran three separate nursery-specific

regression models.

(c) Assessment of genetic gains by nurseryand temperature bins

Genetic gains were assessed by using the regression model to

correct observed yield trends for changing environmental and

management conditions over time. (Here, ‘genetic gains’ refers

to the relative performance of the changing germplasm in the

trial means over the lifetime of the nurseries.) Specifically, the

regression model was used to predict yield changes in the dataset

caused by changes in environmental variables and country

effects over time, and these partial fitted values are then sub-

tracted from the observed yields. Linear time trends are finally

fitted to the residuals to assess ‘climate-corrected’ yield trends,

or inferred genetic gains. Time trends in residuals can also be

assessed for subsets of the data (e.g. by nursery and/or tempera-

ture ranges). For this analysis, four temperature bins were

defined based on average temperature quartiles during the

grain-filling period, typically the hottest portion of the season.

Genetic gains were not analysed for HTWYT, given the short life-

span (1993–2004) and lack of significant observed yield trends in

this nursery. (Regardless, the HTWYT trials were retained in the

empirical model in order to help increase statistical power.)

Trends in environmental variables in the database primarily

reflect the changing mix of sites over time, rather than the cli-

matic trends at the sites themselves. For example, there was a

strong warming trend across trials in the grain-filling stage,

which rose from an average daily temperature of 198C in 1983

to 248C in 2009. However, the annual average global warming

trend in the station database compiled for this study was only

approximately 0.88C over this same period. There was also a

significantly positive trend in radiation (by about 7%) in the

grain-filling stage over the period. These strong trends in temp-

erature and radiation in the trial dataset were probably because

of the growing proportion of trials in India, which rose from 5

per cent in the earliest decade (1983–1992) to 30 per cent in the

last decade (2000–2009). India has some of the highest average

temperatures in the grain-filling stage (26.08C versus a mean of

21.98C), which may have depressed overall observed yield

trends in recent years, although the higher radiation would

have had an opposing effect. Our method for assessing genetic

gains should correct for any trends in environmental variables

and country makeup in the database, regardless of their source.

3. Results and discussion(a) Results from empirical modelThe regression results exhibited a clear influence of tempera-

ture on trial mean yields, with significant interactions

between temperature and VPD. Nearly half of all yield

variability was captured by the regression model (adjusted

−4

−3

−2

−1

0

1

veg − high VPDveg − low VPD

rep − high VPDrep − low VPD

GF − high VPDGF − low VPD

0 5 10 15 20 25 30average temperatures by growth stage (°C)

yiel

d re

spon

se (

tonn

es h

a–1)

Figure 2. Inferred yield response to temperature from regression model forthree growth stages (veg ¼ vegetative; rep ¼ reproductive; GF ¼ grainfilling), with the response curves fitted separately for high and low VPD trials.The curves have been normalized to equal 0 at 128C. The line thicknesscorresponds to the significance of the slope (i.e. thin: NS, medium: p � 0.1,thick: p � 0.05, where the p-values are from a two-sided t-test.)

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r2 ¼ 0.44), with weather providing a substantial fraction of the

explanatory power (i.e. the adjusted r2 ¼ 0.23 for a model with

only weather and no country fixed effects). The country fixed

effects showed a substantial and significant variation across

countries, with countries such as Zimbabwe and Canada

having a strong yield benefit (approx. 3 tonnes ha21) relative

to what is predicted by weather alone, and Nepal and Algeria

having a significant yield penalty (approx. 21 tonnes ha21).

Figure 2 shows the inferred yield response to temperature

from the regression model for each growth stage under both

high and low VPD conditions. These curves represent the

partial fitted values from the model, by growth stage, of

temperature (linear and quadratic terms), VPD, and the inter-

action terms between temperature and VPD. Response curves

are shown for both high and low VPD in order to illustrate

the importance of temperature � VPD interactions. (In these

curves, the VPD values at each temperature were specified

as the 10th and 90th percentiles of VPD at that temperature,

as predicted from a quantile regression.)

Yields decline significantly more rapidlyat high temperatures

under low VPD, or humid conditions, than under high VPD,

especially in the grain-filling stage (figure 2). This relationship

probably reflects the fact that, assuming sufficient soil moisture,

more plant transpiration occurs under high VPD conditions, in

which canopy temperatures are cooled below air temperature.

This result agrees with past observations that yields in hot,

low-humidity environments are strongly positively correlated

with stomatal conductance and canopy temperature depression

[17–19]. While VPD is positively correlated with radiation

(correlation is roughly 0.35 for each of the three growth stages),

the regression model includes a separate term for radiation,

and thus the VPD� temperature interaction is unlikely to be

an artefact of higher radiation in high-VPD environments.

In the reproductive stage, interactions between tempera-

ture and VPD were less significant than in the vegetative or

grain-filling stages. In this stage, it is probable that higher

VPD for a given temperature has opposing effects on yield

(i.e. higher transpiration cooling, but also more sensitivity

to water stress, and hence closed stomata). For example, in

models fitted to just the SAWYT and HTWYT trials

(see electronic supplementary material, figure S2a–c), there

was a more negative response to warming for the high rela-

tive to low VPD trials in the reproductive stage, whereas

the converse is true for an ESWYT-only model, which was

fitted to trials with presumably more sufficient soil moisture.

Especially for the non-irrigated trials in SAWYT, it is probable

that a higher exposure to water stress during this period

played a role in negating the benefits of high VPD.

Overall, warming was beneficial during the vegetative

stage up to approximately 208C. In the reproductive stage,

optimal temperatures were approximately 128C for both high

and low VPD trials, with significantly negative impacts from

warming at temperatures more than 168C. In the grain-filling

stage, warming had a negative impact on yields across the

full range of temperatures in the database. Given the higher

average air temperatures during grain-filling relative to those

earlier in the season (see the electornic supplementary

material, figure S3a–c), it may be that canopy temperatures

in this growth stage (especially in humid conditions) often

reached physiological limits in terms of plant metabolism [20].

The regression results also allowed us to infer relation-

ships between the ancillary variables and yield, in addition

to temperature (results not shown). For example, we saw a

very positive and significant relationship between radiation

and yield during the grain-filling stage with an inferred coef-

ficient of 0.1 tonnes ha21 (MJ (m2 � day)21)21. Coefficients

on radiation were negative, but insignificant, during the

vegetative and reproductive stage, most probably because

of their correlation with other variables and/or processes

counteracting what would otherwise be a positive associ-

ation. Day length has a significantly negative coefficient in

the vegetative stage, most probably because of faster develop-

ment and lower potential grain number associated with

longer photoperiod [21]. Although day- and night-time temp-

eratures have been shown to have differential impacts on

grain yield in previous studies [22], results here did not

show a significant relationship between diurnal temperature

range (DTR) and yield in any of the three growth stages.

(b) Inferred response to þ28C warmingAs an overall summary of the regression results, figure 3 dis-

plays the estimated yield loss (or gain) in tonnes ha21 from

28C warming throughout the growing season for trials in

the 1990s and 2000s. The projected yield changes owing to

warming were calculated by comparing the actual fitted

values from the regression with recomputed fitted values

that reflect historical temperatures þ28C across stages,

along with associated changes in VPD and DTR. Radiation

and relative humidity values were assumed to stay constant.

Radiation trends are primarily affected by trends in air pol-

lution and aerosol-cloud feedback effects [23,24], whereas

relative humidity is projected to stay constant on a global

basis with greenhouse-gas-induced warming [25,26].

The model predicted that 95 per cent of trials would have

a lower mean yield from a þ28C warming, with a mean

loss of approximately 0.3 tonnes ha21, and a range of

0.3 tonnes ha21 gain to 1.4 tonnes ha21 loss. This translated

into an average loss of approximately 11 per cent of current

yields across the globe. In general, the regions that were

most subject to warming-related losses already had high sea-

sonal average temperatures (see the electronic supplementary

<–0.8–0.8 to – 0.6–0.6 to – 0.4

–0.2 to 0> 0

–0.4 to – 0.2

losses from +2°Cwarming (tonnes ha–1)

Figure 3. Map of trial locations since 1990 with estimated loss/gain from þ28C warming; multiple years and sites clustered within a 100 km distance are averagedfor illustration purposes.

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material, figure S4), such as in Sudan, Myanmar and Paraguay,

where projected losses average approximately 61, 58 and 35 per

cent, respectively, of current yields. Humidity also played a

role, with regions such as the Nile basin in Egypt, Iran and

northwest Mexico showing only modest projected losses for

relatively high current seasonal temperatures, because of

their dry, high VPD conditions. Overall, the Mediterranean

basin showed the least amount of losses from warming, and

in some cases slight gains, owing to low humidity and lower

temperatures associated with winter planting in the region.

Nursery-specific models fitted to only ESWYT or SAWYT

trials showed that SAWYT germplasm is more resilient than

ESWYT to warming up to approximately 218C, when both

models began to converge in terms of their negative response

to future warming (see the electronic supplementary

material, figure S4b). Finally, we note that higher atmospheric

CO2 should offset some of the temperature-related declines in

yield shown here for wheat, a C3 crop sensitive to CO2

fertilization. However, the magnitude of CO2 fertilization in

field conditions, with associated interactions between nutri-

ent, water and temperature limitations, is still subject to

debate [27–29].

(c) Estimated genetic gains by nursery and temperaturebins

Both the observed and climate-corrected yield trends in

ESWYT were positive in all of the grain-filling temperature

bins since 1983 (figure 4a), although trends were only signifi-

cant in the two coolest bins, closer to the optimal temperature

for wheat yields [30–32]. Inferred genetic gains in the

warmer bins were insignificant after accounting for trends

in environment and location (i.e. country effects). The

environmental trends since 1983 had small net effects in

ESWYT on average, with negative impacts of a warming

trend counteracted by other positive environmental effects

(mainly in radiation; figure 4c).

In contrast to ESWYT, the largest and only significant pro-

gress for climate-corrected yields for SAWYT was observed in

the hottest temperature bin (figure 4b). Also in contrast to

ESWYT, the overall trends in environmental effects were

negative for SAWYT because of rising temperature trends

across growth stages, which were not counteracted by radi-

ation increases (figure 4d ). For both ESWYT and SAWYT,

country effects have been negative, because of the increasing

proportion of trials in South Asia (figure 4c,d ).

Two robustness checks help one to support the finding

that ESWYT gains were concentrated at cooler sites, while

SAWYT gains came mainly from hotter sites. First, trends

were computed for varying start years from the beginning

of the nursery to 10 years before the end (i.e. 2000). We

show a version of figure 4a based on a start date of 1993,

which is the year in which SAWYT started (see the electronic

supplementary material, figure S5). Over this shorter period,

the ESWYT climate-corrected yield trends, or genetic gains,

were even more skewed towards the coolest grain-filling

temperature bin (less than 19.58C), with a ratio of 21 times

growth in the coolest relative to the warmest bin.

Second, the analysis was repeated using nursery-specific

models to correct for environment and country effects.

Results were very similar (see the electronic supplementary

material, figure S6), with the exception that SAWYT trials

showed stronger genetic gains across all temperature bins

as compared with results from the pooled model. This dif-

ference reflects the fact that the SAWYT-specific model

exhibited stronger responses to warming, and therefore pro-

duced a stronger correction for the observed warming trends.

Overall, these results demonstrate that genetic gains can

diverge strongly from observed yield trends, especially so

in the SAWYT nursery, where relatively flat yield trends

mask much stronger genetic gains evident in this breeding

programme. Moreover, the significant genetic gains at high

temperatures in SAWYT, but not ESWYT, indicate that a tar-

geted breeding programme helps one to ensure success in

breeding for heat tolerance. It should be noted that these

results can also be explained by the environments in

Mexico, in which new varieties were sown and selected for

the two nurseries. For example, the median seasonal temp-

erature across ESWYT trials in Mexico is 17.98C, whereas

that for SAWYT is 20.18C, with most probable even higher

canopy temperatures owing to drought conditions and a

lack of evaporative cooling.

0.15

(a) (b)

(c) (d )

0.10

0.05

tren

d (t

onne

s ha

–1 y

ear–1

)tr

end

(ton

nes

ha–1

yea

r–1)

–0.05

start year of trend start year of trend

1985 1990 1995 2000

<19.5°C 19.5–21.8°C 21.8–24.8°C ≥24.8°C <19.5°C 19.5–21.8°C 21.8–24.8°C ≥24.8°C

0

0.10

observedclimate-corrected

1985 1990 1995 2000

observedenvironmentcountrygenetic

0.05

–0.05

0

Figure 4. (a,b) Observed and climate-corrected yield trends from earliest start year of nursery, binned by average temperatures in the grain-filling period for(a) ESWYT and (b) SAWYT. The error bars are at a p ¼ 0.05 significance level from a one-sided t-test. (c,d) Trends in environmental and country effects, andobserved and ‘climate-corrected’ yields plotted as a function of the start year of the trend for (c) ESWYT and (d ) SAWYT nurseries.

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CO2 fertilization has also probably played a role in the

inferred ‘genetic’ gains shown here for both nurseries, given

a 45 ppm rise in atmospheric CO2 from 1983 to 2009 (as

measured at Mauna Loa, HI). Rising atmospheric CO2 may

have especially promoted yield gains in SAWYT, because of

decreased stomatal conductance and increased water savings

at higher CO2 under drought conditions [33]. However, given

the covariance between variety improvement and increasing

atmospheric CO2 in recent years, it is difficult to statistically

identify the CO2 effect in this study.

Understanding the underlying mechanisms behind the

differential yield progress for ESWYT and SAWYT at hot

temperatures is beyond the scope of this paper. However,

we offer a few observations. First, one strategy to withstand

hotter temperatures while maintaining similar growth dur-

ations would be to lengthen the accumulated temperature

(or GDD) requirements for development. In the CIMMYT

database, both ESWYT and SAWYT showed positive trends

for GDD in the vegetative stage. Consistent with the greater

inferred genetic gains for SAWYT versus ESWYT at hot temp-

eratures in the grain-filling stage, the positive trend in

vegetative GDD requirements was more than two times

higher for SAWYT than for ESWYT over a common time-

frame (1993–2009, 13 versus 5 degree-days per year). GDD

requirements for the vegetative stage increased by 23 per

cent in SAWYT over the lifetime of the nursery, which was

enough to maintain a constant duration of this period,

despite significant warming because of a combination of cli-

mate trends and a changing mix of sites. Since the potential

grain number is positively associated with both vegetative

duration [21,34] and yields, the increased GDD requirements

in this period have probably played a role in maintaining

yield performance in hot conditions.

A second potential mechanism relates to grain-filling

rates. The grain-filling period became significantly shorter

in SAWYT over time owing to rising temperatures in this

growth stage (i.e. 12 days shorter for a 4.58C average rise

from 1993 to 2009, with no evidence of increasing thermal

requirements in this final growth stage). Yet grain weight

data, available for only approximately 20 per cent of the

records in the database, show a 4 per cent increase for

SAWYT. A higher grain weight, together with a shorter

grain-filling period, implies an increased grain-filling rate

per day (perhaps to a small extent owing to temperature

[35], but probably owing to variety improvement). The data

support a significant increase in grain-filling rates for both

ESWYT and SAWYT of 0.004 and 0.008 mg (kernel �day)21 year21, respectively.

Thus, increased thermal time to flowering and higher

grain-filling rates appear to be two sources of yield

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maintenance and growth at high temperatures in the SAWYT

database. Increased water savings at higher atmospheric CO2

may also play a role, given increased evaporative demand at

higher temperatures. However, it should be noted that increas-

ing GDD requirements, faster grain-filling rates and reduced

stomatal conductance at higher atmospheric CO2 will not

prevent irreversible damage from extreme heat episodes [36],

particularly during the reproductive period. Therefore, other

breeding strategies, such as speeding development to force

flowering earlier in the season, may be beneficial in some

environments and for risk-averse farmers [20].

ProcRSocB

280:20122190

4. ConclusionsDecades of wheat breeding efforts at CIMMYT have resulted in

an extensive trial database of wheat yields under varying

environmental conditions. This database provides a valuable

means of empirically assessing the response of wheat to environ-

mental variation, and also the genetic gains over time and in

different environments that are associated with different breed-

ing strategies. Consistent with previous studies, our empirical

model showed the most negative response to high temperatures

in the grain-filling phase under low VPD, or humid conditions.

Assuming sufficient water supply, higher VPD for a given temp-

erature leads to more transpiration cooling, lower canopy

temperatures and a less negative response to warming. A nega-

tive response to warming was also seen during the reproductive

phase at average temperatures above 138C, but a higher sensi-

tivity to water stress during this phase reduced the relative

advantage of high VPD trials.

With current breeding strategies, projected future climate

change will probably put a drag on growth in global spring

wheat yields, and may even depress them, especially in

locations where wheat is already grown in hot conditions

(particularly in south Asia, and also parts of sub-Saharan

Africa, the Middle East and Latin America). Agronomic

changes (e.g. shifts in planting dates or locations, and

improved access to inputs) in combination with CO2 fertiliza-

tion, can potentially help to mitigate these losses. However,

new varieties of wheat with high yield potential in hot

environments are required in order to adequately prepare

for projected temperature rises of approximately 28C by 2050.

ESWYT and SAWYT epitomize two different breeding

strategies, to develop wheat varieties that are (i) high-yielding

in irrigated and high rainfall conditions, but potentially sen-

sitive to abiotic stresses, and (ii) tolerant to drought and heat

stress under rain-fed conditions, but with lower yield poten-

tial. This study finds that most progress in ESWYT to date has

been achieved at the cooler temperatures in the grain-filling

phase, closest to the optimal temperatures for wheat pro-

duction. In contrast, progress has been made in SAWYT

across temperature bins, but most significantly in the hottest

bin, thereby building greater amounts of heat tolerance into

the germplasm. Two potential mechanisms for the relatively

higher genetic gains in SAWYT at high temperatures relate to

longer vegetative GDD requirements and faster grain-filling

rates. It will also be imperative to build resilience to extreme

heat into future germplasm, especially during the reproduc-

tive phase, in order to avoid the risk of complete crop

failure with more frequent heat waves.

The lack of yield increase to date for the highest-yielding

varieties under hot conditions, as shown in this study, indicates

the need for new and intensified efforts to achieve these gains.

This will require a combined effort, using genetic diversity with

physiological and molecular breeding, and bioinformatic tech-

nologies, along with the adoption of improved agronomic

practices by farmers. Although many trade-offs exist between

high yield potential and stress adaptation, in our view it

should be feasible to achieve both goals as long as hot environ-

ments are systematically included in the selection process for a

breeding strategy like ESWYT. Given that disease resistance,

pest resistance and maintaining grain quality will also continue

to be priorities, additional resources may be needed to simul-

taneously achieve all of these targets.

We gratefully acknowledge Mateo Vargas, who prepared much of thedata for analysis in this study, and Thomas Payne, who contributedvaluable interpretation of the results. This work was supported by agrant from the Rockefeller Foundation. The data associated with thisstudy are deposited in the Dryad Repository: http://dx.doi.org/10.5061/dryad.525vm.

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