1. doi: 10.1098/rspb.2012.2190
,2802013Proc. R. Soc. B
Sharon M. Gourdji, Ky L. Mathews, Matthew Reynolds, José Crossa and David B. Lobell
in hot environments
An assessment of wheat yield sensitivity and breeding gains
Supplementary data
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"Data Supplement"
References
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Research
Cite 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.
An assessment of wheat yield
sensitivity and breeding gains in
hot environments
Sharon M. Gourdji1,2, Ky L. Mathews3, Matthew Reynolds3, Jose´ Crossa3
and David B. Lobell1,2
1
Department of Environmental Earth System Science, Stanford University, Stanford, CA 94305, USA
2
Center on Food Security and the Environment, Stanford University, Stanford, CA 94305, USA
3
International 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. Introduction
Wheat 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.
& 2012 The Author(s) Published by the Royal Society. All rights reserved.
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3. 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) Datasets
For 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
0–2
wheat yields (tonnes ha–1)
2–4
4–6
6–8
>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 weather
interpolation.
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4. 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 model
Mean 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 nursery
and 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 model
The 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
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5. 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.)
Yieldsdeclinesignificantlymorerapidlyathightemperatures
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 Â 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 warming
As 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
−4
−3
−2
−1
0
1
veg − high VPD
veg − low VPD
rep − high VPD
rep − low VPD
GF − high VPD
GF − low VPD
0 5 10 15 20 25 30
average temperatures by growth stage (°C)
yieldresponse(tonnesha–1
)
Figure 2. Inferred yield response to temperature from regression model for
three growth stages (veg ¼ vegetative; rep ¼ reproductive; GF ¼ grain
filling), with the response curves fitted separately for high and low VPD trials.
The curves have been normalized to equal 0 at 128C. The line thickness
corresponds 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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6. 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 temperature
bins
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.
<–0.8
–0.8 to – 0.6
–0.6 to – 0.4
–0.2 to 0
> 0
–0.4 to – 0.2
losses from +2°C
warming (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 averaged
for illustration purposes.
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7. 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 Â
day)21
year21
, respectively.
Thus, increased thermal time to flowering and higher
grain-filling rates appear to be two sources of yield
0.15
(a) (b)
(c) (d)
0.10
0.05
trend(tonnesha–1year–1)trend(tonnesha–1year–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
observed
climate-corrected
1985 1990 1995 2000
observed
environment
country
genetic
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, and
observed 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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8. 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].
4. Conclusions
Decades 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 empiricallyassessing 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 the
data for analysis in this study, and Thomas Payne, who contributed
valuable interpretation of the results. This work was supported by a
grant from the Rockefeller Foundation. The data associated with this
study are deposited in the Dryad Repository: http://dx.doi.org/
10.5061/dryad.525vm.
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