Technical report
Project title: Validation of late sowing to identify heat stress tolerance in wheat and investigation
of QTL for heat stress tolerance
Project no: APG00010
Researcher in charge: Paul Telfer – Australian Grain Technologies
Contact: [email protected]
Research methodologies
Delayed sowing at the managed environment facilities (MEF) was used to further our understanding
of the effects of heat stress on wheat production. This pushes crop development later in the season,
increasing the crops’ exposure during the sensitive flowering and grain filling stages to heat stress,
providing opportunities to understand crop performance under such conditions. This technique is
used routinely by CIMMYT in Mexico for research into crop physiology and to screen and select within
CIMMYT breeding material.
In this study three times of sowing were used to achieve a range of potential heat stress environments
at each of the MEF locations. Ben Biddulph/DAFWAs’ benchmarking experiments were used as the
conventional time of sowing, to reduce the number of plots required by this project and was the least
heat stressed environment at each location, in each season. Delayed sowing at one and two months
after the conventionally sown experiment exposed experiments to increased heat stress. A core set
of genotypes were used for the duration of the project, with known variation in heat stress tolerance,
to understand the response in grain yield and related traits. Additional genotypes were included in
delayed sowing experiments to investigate QTL previously identified as having an effect on heat stress
tolerance.
All experiments included in this study received supplementary irrigation throughout the season, to
remove the confounding effects of severe drought stress. The amount of irrigation applied was
determined with the aid of Stressmater, aiming to subject the experiments to ‘environment type 2’,
as described in Chenu et al (2013a) and Chenu et al (2013b). Irrigation was generally necessary later
in the season to offset terminal drought, particularly in the late sown experiments.
To determine the impact of heat stress on grain yield, the anthesis date of each plot was recorded and
climatic parameters were calculated for the anthesis and grain filling periods for each plot. The
description of these climatic parameters are summarised in Table 1. Determining the climatic variables
on an individual plot basis was important to accurately capture spatial and genotypic variation for
maturity and subsequent variance in exposure to heat stress events.
1
Table 1. Descriptions of growth stages and climatic variables used in this project (developed by Scott
Chapman and Bangyou Zhang, CSIRO).
Growth stage
Flowering
Grain filling
Degree day range relative to anthesis
300°Cd before anthesis to 100°Cd post
100°Cd post anthesis to 600°Cd post
Climatic variable
Average temperature
Total thermal time
Average minimum temperature
Average maximum temperature
Number of hot days
Number of very hot days
Number of frost days
Total thermal time of hot days
Total thermal time of very hot days
Total thermal time of frost days
Abbreviation
avgt
sum_tt
avg_mint
avg_maxt
hot_days
very_hot_days
frost_days
hot_sum
very_hot_sum
frost_sum
Growing season rainfall
gs_rainfall
Explanation
No. days >30°C
No. days >35°C
No. days below 0°C
Total rainfall – from month of
seeding to harvest
The experiments were run from 2010 to 2013. However, a greater focus was applied to the 2012 and
2013 data due to superior data quality and a refined selection of genotypes in these experiments. All
analyses were carried out using the ASREML R package. A multi-experiment factor analytic (FA) MET
analysis was used, allowing experiment loadings and genotype scores to be calculated. These loadings
and scores were then further explored for their correlation with climatic variables outlined in Table 1
and genotype tolerance.
A single stage variance component MET analysis was used to determine the grain yield response of
the QTL tail genotypes to climatic (heat) variables.
Effects of heat stress on production (Milestones 1 and 2, Output 1)
The range of sowing dates and sites used in this study successfully exposed wheat genotypes to a
range of heat stress environments with a range of severity (table 2).
Across the 12 irrigated experiments from 2012 and 2013, average temperature during flowering and
grain fill explained 13.9% and 23.3% of the grain yield variation, respectively (Figure 1). Yield related
traits measured in the MEFs also displayed varying ranges of response to heat stress in the same
analysis. In general, a reduction in grain size was shown in response to increasing average and average
maximum temperatures during grain filling (Figure 2). Other physical quality and grain yield
components, including hectolitre weight, spikelet fertility and screenings percentage, did not display
any consistent significant effects resulting from heat stress in this study. The effects of heat stress on
grain yield and associated traits observed in this experiment is smaller than has been reported
previously (Bennett et al., 2012; Kuchel et al., 2007; Telfer et al., 2013). This was possibly due to the
contrasting production environments where the experiments were sown in this study, the influence
of delayed sowing, which causes plant development to accelerate and the timing of irrigation.
2
Table 2. The range of mean experiment climate variables experienced across all irrigated
(conventional and late sowing dates) experiments in 2012 and 2013 for both flowering (fl), grain filling
(gf) and growing season (gs).
Climate Variable
fl_avgt
fl_avg_mint
fl_avg_maxt
fl_hot_days
fl_very_hot_days
fl_frost_days
fl_hot_sum
fl_very_hot_sum
fl_frost_sum
gf_avgt
gf_avg_mint
gf_avg_maxt
gf_hot_days
gf_very_hot_days
gf_frost_days
gf_hot_sum
gf_very_hot_sum
gf_frost_sum
gs_rainfall
Min
14.3
6.1
21.2
0.3
0
0
0.2
0
0
14.6
5.3
22.7
0.1
0
0
0.14
0
0
220.8
Max
18.8
10.0
27.9
8.3
2.0
1.4
22.4
1.9
1.45
20.9
13.8
30.3
13.3
2
2.2
38.4
3.0
4.5
367.6
3500
3500
Grain yield (kg ha-1)
B 4000
Grain yield (kg ha-1)
A 4000
3000
2500
2000
1500
1000
500
R² = 0.139
0
14
16
18
Unit
°C
°C
°C
no.
no.
no.
°C
°C
°C
°C
°C
°C
no.
no.
no.
°C
°C
°C
mm
3000
2500
2000
1500
1000
500
R² = 0.2332
0
14
20
16
18
20
22
Grain fill avg temp (°C)
Flowering avg temp (°C)
Figure 1. Mean experiment grain yield plotted against mean experiment average temperature during
flowering (A) and grain filling (B) for 12 irrigated sites in 2012 and 2013.
3
B 50
40
30
20
10
R² = 0.1113
0
14
16
18
20
Thousand grain weight (g)
Thousand grain weight (g)
A 50
22
40
30
20
10
R² = 0.1009
0
22
Grain fill avg temp (°C)
24
26
28
30
32
Grain fill avg max temp (°C)
Figure 2. Mean experiment thousand grain weight plotted against mean experiment average
temperature (A) and average maximum temperature (B) during grain filling for 11 irrigated sites in
2012 and 2013.
Factor analytical multi environment trial (FA-MET) analysis (Milestones 1 and 2, Output 2)
A FA-MET analysis was used to characterise the GxE involved with grain yield in the MEF experiments.
Six factors accounted for 100% of the variance in GxE for grain yield in this dataset (Table 3).
Experiment loadings for each factor were correlated against the average climate variables at each
location. This showed how climate may be related to each of the factors and indicates how each of
the factors influenced the GxE of grain yield in the environments in the study. The findings (Table 3)
reiterated the importance of water availability to grain yield, with growing season rainfall accounting
for 44.6 % of factor 1 (the factor explaining the largest proportion of GxE). Heat related climate
variables were also found to be related to a number of the factors, indicating the role of heat stress in
determining final grain yield, but also the complexity with which heat interacts with genotype to affect
grain yield.
Table 3. Factors contributing to GxE of grain yield in the irrigated MEF experiments and climate
variables correlating to factor loadings.
Factor 1
Average
percentage of
Environmental variables related to factor
GxE explained
by factor
37.3
Growing season rainfall
Factor 2
16.8
Frost parameters during flowering - grain number control
Factor 3
11.43
Heat parameters during grain filling
Factor 4
17.2
Heat parameters during flowering and frost parameters
during grain filling
Factor 5
12.0
Heat parameters and grain yield in both flowering and
grain filling
Factor 6
5.4
Factor
4
Factor 3 accounted for 22.1 % of variation in grain yield between genotypes in the late sown (date 2)
experiment at Yanco in 2012 (YAD212), which had the highest average temperature during grain filling,
as well as receiving a high average maximum temperature and larger number of hot days relative to
the rest of the experiments. Factor 4 in the irrigated benchmarking trial at Narrabri in 2013 (NABIR13)
accounted for 52.9 % of yield variation and experienced the highest flowering average maximum
temperature and second highest flowering average temperature. As well as this, NABIR13 also
experienced the highest number of grain filling frost events and had the lowest grain filling average
minimum temperature. Similar was also seen for frost in the late sown Merredin trial in 2012
(MED112) where Factor 2 accounted for 14.5 % of yield variation and also experienced the greatest
number of frost events during flowering.
To understand the GxE involved with genotype performance in the MEFs, genotypic data from the
AGT/SAGIT funded controlled environment screening facility at Roseworthy was used to further
investigate GxE. In the controlled environment screening, plants are stressed for three days at 10 days
after the main tiller finishes anthesis, with effects on grain number, grain size, fertility and harvest
index determined as a measure of heat stress tolerance when compared against unstressed control
plants. Many of the genotypes included in the MEF experiments have been screened through this
controlled environment assay. When correlated to genotype scores from the FA-MET analysis, the
field data can be compared to the controlled environment data. Genotype performance in the
controlled environment was correlated to a number of the genotype scores from the MEF FA-MET
analysis. Heat stress tolerance, as determined by maintenance of fertility, grain weight and grain
number, were found to be strongly correlated with genotypic scores from factors 3 and 4. This is a
significant result as it provides further field validation that the controlled environment heat stress
assay has the ability to accurately identify genotypes with improved levels of heat stress tolerance.
QTL tail performance, QTL response (Milestones 3, Output 3)
Three sets of QTL tails (10 positive and 10 negative individuals for each QTL) were included all in MEF
experiments in 2012 and 2013 to investigate the value of these QTL in heat stress adaptation. The QTL
tails were;



1A (Trident/Molineux) Grain size and grain yield QTL associated with heat stress conditions.
7A (Kukri/RAC875) – Associated with fertility and grain yield under heat stress conditions.
3B (Kukri/RAC875) – identified in late sown experiments in Mexico related to grain yield,
biomass production and grain size.
Trident/Molineux 1A QTL
In the MEFs, the QTL allele effects and the interaction between the alleles and the climatic variables
was found to have a significant effect (P<0.1) on thousand grain weight, for the number of days over
30°C and the sum of thermal time exceeding 30°C during grain filling (Table 4). Significant interactions
were found between head harvest index and daily maximum average temperature, number of days
over 30°C and the sum of thermal time for days over 30°C during grain filling (Table 4). The B allele
conferred a greater value for both traits. However, it was the A allele that was less responsive to
increasing heat stress and could therefore be considered more tolerant (Figure 3). The QTL main and
interaction effects did not have a significant effect on grain yield.
5
Table 4. Allele, climatic variables and interactions between allele and climatic variables for the
Trident/Molineux 1A QTL from the MEF experiments for TGW and head harvest index.
climatic
variable
gf_hot_days
gf_hot_sum
gf_avg_maxt
gf_hot_days
gf_hot_sum
Trait
Thousand grain weight
Head harvest index
P-value
allele
0.089
0.075
ns
ns
ns
.
.
P-value climatic
variable
<0.001
<0.001
<0.001
<0.001
<0.001
***
***
***
***
***
P-value
interaction
0.077
0.058
0.005
0.018
0.0028
.
.
**
*
**
38
YND213
36
34
NAD113
YND113
NAD113
YND213
32
TGW (g)
YND113
R² = 0.7774
30
28
A
26
NAD213
R² = 0.6759
24
B
NAD213
22
20
20
25
30
35
40
Sum of degree over 30°C during grain-fill
Figure 3. The relationship between the sum of thermal time (degree days) during grain filling and
thousand grain weight found in the Trident/Molineux 1A QTL.
The genotypes from the Trident/Molineux population were only grown for one year in 2013, providing
a limited dataset, with more data required to confirm these initial results. Previously the 1A QTL had
been found to have an effect on grain size and grain yield. To a degree this was found to be true with
thousand grain weight effects and interactions with climate variables. However, no grain yield effect
of this locus was identified in the MEFs. This may be due to the complex GxE interaction, the small
data set, perhaps insufficient number of genotypes included as tails and despite the range in maturity
being restricted, and other confounding alleles (i.e. these weren’t NILs for the 1A locus). However, our
results have confirmed that the B allele conferred a greater performance advantage, while the A allele
appeared to be more tolerant to heat stress conditions.
Kukri/RAC875 7A QTL
In the MEF experiments neither allele had a statistically significant advantage for grain yield or other
physiological traits. However, some allele by climatic variable interactions were significant (Table 5).
The A allele conferred a greater performance advantage for thousand grain weight as average
flowering temperatures increased for significant interactions (Figure 4), which suggests a higher level
of tolerance to heat stress conditions. However, performance advantages for the A allele were small.
6
Table 5. Allele, climatic variables and interactions between allele and climatic variables from the MEF
experiments for the Kukri/RAC875 7A QTL for TGW, HLW and head harvest index.
Trait
climatic variable
P-value
allele
Thousand grain weight
fl_avgt
fl_hot_days
fl_hot_sum
gf_very_hot_days
gf_avgt
gf_hot_days
ns
ns
ns
ns
ns
ns
Hectolitre weight
Head harvest index
P-value
climatic
variable
0.015
ns
ns
ns
ns
<0.001
P-value
interaction
*
0.066
0.016
0.038
0.011
0.022
0.06
***
.
*
*
*
*
.
50
YAD212
YAD112
YND113
45
TGW (g)
40
NAIR12
R² = 0.1315
NARF12YND213
NAD113
35
NAD213
MED112
A
B
30
R² = 0.1661
25
20
14
15
16
17
18
19
20
Flowering average daily temperature (°C)
Figure 4. The relationship between the average daily temperature during flowering and thousand
grain weight for the Kukri/RAC875 7A QTL.
The 7A QTL has previously been identified to have a potential role in improved fertility and grain yield
under heat stress conditions (Bennett et al., 2012). In this study this relationship was a little less clear.
However, the A allele did appear to have a small improvement for heat tolerance over the B allele for
thousand grain weight and hectolitre weight.
Kukri/RAC875 3B QTL
A grain yield interaction between allele and average maximum temperature during flowering was
significant at the P<0.1 level where the B allele conferred a small advantage (Table 6 and Figure 5) at
both Narrabri and Yanco. Head harvest index displayed interactions with a number of heat related
climatic variables during both flowering and grain filling.
7
Table 6. Allele, climatic variables and interactions between allele and climatic variables from the MEFs
for thousand grain weight, hectolitre weight and head harvest index for the Kukri/RAC875 3B QTL.
Trait
climatic variable
Grain yield
Head harvest index
fl_avg_maxt
fl_avgt
fl_avg_maxt
gf_avg_maxt
gf_hot_sum
gf_very_hot_sum
P-value
allele
ns
ns
ns
ns
ns
ns
B
4000
P-value
interaction
0.06
0.031
0.025
0.089
0.017
0.048
.
*
*
.
*
*
3500
3000
3500
R² = 0.1185
3000
A
B
2500
2000
R² = 0.1297
Grain Yield (kg ha-1)
Grain Yield (kg ha-1)
A 4500
P-value climatic
variable
0.0037
**
ns
ns
<0.001
***
0.026
*
0.049
*
2500
R² = 0.1212
A
2000
B
1500
R² = 0.1081
1000
1500
24
26
28
Flowering average maximum
temperature (°C)
22
30
23
24
25
Flowering average maximum
temperature (°C)
26
Figure 5. The relationship between the average daily maximum temperature during flowering and
grain yield in the MEF experiments for the Kukri/RAC857at (A), Narrabri and (B), Yanco.
The 3B QTL identified in the Kukri/RAC875 population was originally detected in late sown
experiments in Mexico and related to grain yield performance, biomass production and grain size
(Bennett et al., 2012). This environment is most similar to the conditions experienced in the MEF at
Narrabri. It was observed that the B allele did confer a small advantage but not enough to be
statistically significant. It is possible that the effect of the QTL are real and of potential significance,
although it was not confirmed in this study, potentially due to the complex GxE interaction and other
reasons discussed earlier.
Benchmarking of Biomass and harvest index (Milestones 4, Output 2)
Similar issues regarding GxE were faced when analysing anthesis biomass and harvest index as
discussed earlier with regard to grain yield, reducing the confidence of directly comparing genotypes
across the environments in the MEFs. Anthesis biomass showed a significant decline (P<0.001) in
response to both increasing average temperature and average maximum temperature during grain
filling (Figure 6). This was a comparable response to what was exhibited for grain yield (Figure 1).
Similarly, harvest index showed a significant decline (P<0.001) under increasing average temperatures
and increasing average maximum temperatures during grain filling (Figure 7), although this was not
as severe as grain yield and biomass.
Evaluation of biomass and harvest index is ongoing within the Benchmarking project (Ben
Biddulph/DAFWA) with further reporting to be presented in the final report for that project.
8
8000
8000
7000
6000
5000
4000
3000
2000
1000
R² = 0.4103
0
14
16
18
20
22
Antheisi biomass yield (kg ha-1)
B 9000
Antheisi biomass yield (kg ha-1)
A 9000
7000
6000
5000
4000
3000
2000
1000
R² = 0.3216
0
20
Grain fill average temp (°C)
25
30
35
Grain fill average maximum temp (°C)
Figure 6. Mean experiment anthesis biomass yield against mean experiment average temperature (A)
and average maximum temperature (B) during grain filling for 10 irrigated sites in 2012 and 2013.
B 0.45
A 0.45
0.4
0.4
0.35
0.35
0.3
Harvest index
Harvest index
0.3
0.25
0.2
0.15
0.1
0.05
14
16
18
20
0.2
0.15
0.1
0.05
R² = 0.134
0
0.25
R² = 0.1009
0
22
22
24.5
27
29.5
32
Grain fill average temperature
Grain fill average temperature (°C)
Figure 7. Mean experiment harvest index against mean experiment average temperature (A) and
average maximum temperature (B) during grain filling for 9 irrigated sites in 2012 and 2013.
Lessons learnt from MEF heat stress tolerance studies on wheat
This study identified some shortcomings in the use of delayed sowing for assessment of heat stress
tolerance. By using delayed sowing the treatment induced on the experiments is that of time of sowing
and not specifically heat stress. Changing the time of sowing, in this case by as much as a two month
delay, changes the growing season dramatically from local agronomic best practice. During vegetative
growth, the later sowings are exposed to cooler temperatures, which reduce early plant growth, then
higher temperatures are present earlier in developmental growth stages, not only inducing
temperature stress on plants, but also accelerating plant development, shortening the growing season
and reducing grain yield potential in comparison to best practice methods of sowing time. Different
plant types and maturities may respond to this differently, producing confounding effects that may
lead to potentially misleading results for Australian grain growers or typical growing environments.
The confounding effects of delayed sowing may also have affected the interaction of QTL being
examined and the analysis of their performance.
9
Targeting diverse environments is important to understanding heat stress tolerance, but more work
needs to be done on unravelling interactions with Australian macro environments. It was evident while
conducting this study that experiments conducted in each of the facilities responded quite differently
for both yield, yield components and related traits. While experiments conducted at each location did
not always group together it was evident that location effects were contributing significantly to GxE.
More extensive experimentation would need to occur in each of these regions to be sure of
environmental responses and interactions with heat stress. It is also likely that very different types of
heat stress add further complexity to genotype responses to heat stress. In northern NSW and QLD,
heat stress tends to be characterised by extended periods of elevated temperature during flowering
and grain filling, while short periods of elevated temperatures during flowering and grain filling (heat
shock) are often seen in the southern and western regions. Not enough data was collected from each
region in this study to be confident of dissecting the differences in genotype response to these two
potentially different types of heat stress.
Results from analysing the QTL tails did produce some interesting results. As described regarding the
response of the Trident/Molineux 1A QTL the A allele did exhibit tolerance to heat stress conditions.
However, this was not to a level that would benefit breeders, but did show that heat stress tolerance
QTL do exist within Australian germplasm and if combined with other QTL could produce superior
performance under heat stress conditions. Analysis of the two QTL from Kukri RAC875, produced less
positive results. As stated earlier, the findings from the QTL tails may have been affected by a number
of factors. This includes the complicated GxE across the experiments, the small number of genotypes
selected for each allele and that these genotypes are from a doubled haploid population rather than
being NILs, so other traits affecting adaptation may be reducing the significants of the QTL allele in
these findings.
Recommendations for the future
As a research platform, the MEFs offer great potential for trait based research to benefit Australian
growers. Core to this is the well characterised environments in which the MEFs are situated, the high
density of physiological plant measurements and the availability of irrigation. However, it was also
these factors that in part hindered the ideal outcomes from this project being achieved. While the
diverse environments that the MEFs are situated in facilitated diverse heat stress conditions, the
extent of environment heterogeneity in this project was also a source of complexity that appears to
have reduced our ability to improve our understanding of heat stress response in Australian
germplasm. Delayed sowing was another limitation of the research conducted. Although successful in
increasing heat stress present within experiments, the changes in plant growth as a result, not only
increased GxE, but also greatly reduce the relevance of the outputs. As such, it is the opinion of the
researchers that datasets sourced from sites with a greater level of homogeneity and representative
sowing times, while still achieving a range in heat stress conditions, will improve the outcomes of
investigations into heat stress tolerance within Australian germplasm.
10
References
Bennett, D., Izanloo, A., Reynolds, M., Kuchel, H., Langridge, P., and Schnurbusch, T. (2012). Genetic
dissection of grain yield and physical grain quality in bread wheat (Triticum aestivum L.) under
water-limited environments. Theoretical and Applied Genetics 125, 255-271.
Chenu, K., Deihimfard, R., and Chapman, S. C. (2013a). Large-scale characterization of drought pattern:
a continent-wide modelling approach applied to the Australian wheatbelt – spatial and
temporal trends. New Phytologist 198, 801-820.
Chenu, K., Doherty, A., Rebetzke, G. J., and Chapman, S. C. (2013b). StressMaster: a web application
for dynamic modelling of the environment to assist in crop improvement for drought
adaptation. In "7th International Conference on Functional-Structural Plant Models " (R. N.
Sievänen, E., C. L. Godin, A. and P. Nygren, eds.), pp. 357-359, Saariselkä, Finland
Kuchel, H., Williams, K., Langridge, P., Eagles, H. A., and Jefferies, S. P. (2007). Genetic dissection of
grain yield in bread wheat. II. QTL-by-environment interaction. Theoretical and Applied
Genetics 115, 1015-1027.
Telfer, P., Edwards, J., Kuchel, H., Reinheimer, J., and Bennett, D. (2013). Heat stress tolerance of
wheat. In "GRDC Advisor Update", pp. 199-203. GRDC, Ballarat VIC.
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