Water use efficiency and barley
production on the Canadian
Prairies
Dr. Anthony Anyia
Senior Scientist & Acting Manager, Bioresource Technologies,
Alberta Innovates – Tech Futures
June 23, 2010
21st BMBRI Triennial Barley Improvement Meeting held in Guelph
Challenges of Crop Production on the Prairies –
demonstrated through News Releases by CWB
1.
2.
3.
4.
5.
6.
7.
June 11, 2010: Wet weather severely impairs crop prospects across
the Prairies
June 11, 2009: Cold spring, dry fields lower 2009 crop prospects in
Western Canada
June 12, 2008: Rains help boost 2008 crop estimates, cold spring a
concern
June 14, 2007: Wet spring lowers Prairie wheat acres, increases barley
June 10, 2004: Moisture conditions improve across Western Canada
but dry pockets remain
June 12, 2003: Improved moisture conditions good news for prairie
farmers
August 6, 2003: Hot, dry July plays havoc with crops across the
prairies
Characteristics of Canadian Prairies
Vegreville AB, April 2007
 Short and dry growing season
 Insufficient growing season rainfall
 Drought and heat stress in summer
 Long and cold winter
 Spring and fall frost common
Vegreville AB, 2002, courtesy AAFC

Occasional flooding and water
logging in spring

Seeding delayed due to water
logged field

Fields may be abandoned due
to water logged soils or
drought
Canada is a major world
producer of barley
Despite the challenges,
7.0
Production
11.8
6.0
Yield
11.7
5.0
11.0
9.5
4.0
7.4
5.9
3.0
5.1
4.6
3.1
2.0
1.0
ar
k
De
nm
US
A
UK
lia
Au
st
ra
Fr
an
ce
Tu
rk
ey
an
y
G
er
m
Sp
ai
n
0.0
Canadian yields are lower
than most other leading
producers
Tonnes/Ha
15.7
a
Ca
na
da
18.0
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0
Ru
ss
i
Canada is a major
producer of barley
Million tonnes
2007 Barley production & yield by 10 top countries (FAO Stat)
Canadian barley and wheat yields in comparison to
yields in China
6.00
Wheat-Canada
5.00
Barley-Canada
Tonnes/Ha
Wheat-China
4.00
Barley-China
3.00
Canada: W = 5%;
B = 0%
2.00
1.00
China: W = 50%;
B = 60%
Source: FAOStat
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
0.00
Severe drought
year in Alberta

Breeding progress is masked by genotype by location variation in yield

Low yields can be attributed to poor growing conditions prevalent on the prairies
Barley yield depend on both moisture and temperature
250
70
30
60
25
200
20
150
15
100
10
50
0
2002
Average yield (bu/acre)
35
2003
2004
2005
2006
2007
Yield (data label = tonnes/ha)
3.5
40
R2 = 0.7476
50
100
150
200
250
3.1
3.0
2.2
30
20
5
10
0
0
2002
2003
Good moisture + high temp
= below average yield
y = 0.121x + 28.541
3.2
50
 Low moisture + high
temp = very low yield
70
65
60
55
50
45
40
35
30
3.6
3.0
2004
Average yield (bu/acre)
300
80
40
Yield (bu/acre)
Weather conditions, Vegreville
July max temp (°C)
Within season rainfall (mm)
350
2005
2006
2007
10YAvg
70
65
60
55
50
45
40
y = -3.0434x + 154.86
R2 = 0.5878
35
30
300
Within season rainfall (mm)
Good moisture + moderate temp = above
average yield
28
30
32
34
Maximum temperature in July (°C)
36
Can we further improve yield and yield
stability?
 Improved management of the cropping systems (Agronomic
research still essential)
 Genetic improvement (direct vs. indirect selection)
 Experience show that targeting of underlying physiological
traits that limit yield can contribute to substantial yield
improvements (there are only few successful examples)
 To be useful, physiological traits should be easy to score and
have no yield penalty under favorable conditions
 Many breeders are already taking advantage of advances in
genomics and genetic mapping in breeding programs (more
still need to be done)
Bridging the gap between breeders, physiologists & ‘omics
Life-cycle of a typical cereal crop
Vegetative
Stages:
Reproductive
Establishment &
Growth Foundation
Soil moisture (mm)
for future yield
Growth
Conditions
Pre-Anthesis
Post-Anthesis
Formation of
yield potential
determinant of
actual yield
150
40
30
100
20
50
0
21-May 31-May
10
0
10-Jun
20-Jun
Usually good moisture
30-Jun
10-Jul
20-Jul
30-Jul
Moisture is limiting
09-Aug
Daily max temp
(°C)
Phase:
19-Aug
Drought and heat stress
Adapted from Anyia et al., 2008
Adapted from Anyia et al., 2008
Genetic improvement of crops
Can we design new smart varieties that:
Use more of the water supply
- Increase water use
- Decrease soil evaporation
Early seedling vigour
(Leaf area, SLA, LAI)
Better exchange of water for CO2
Increase TE
Convert more biomass into grain
Increase stem reserves
-Increase water use efficiency
- Increase harvest index
(carbon isotope discrimination)
(non structural CH2O)
Advantage in grain yield (%)
Wheat lines selected for low CID (Rebetzke et al. 2002)
12
10
8
6
4
2
0
200
250
300
350
400
Growing-season rainfall (mm)
450
Relationship between WUE and CID
(Adapted from Anyia et el., 2007)
5
5
Well watered
4
Well stressed
y = -0.5242x + 15.211
R2 = 0.8943
3
WUE (g Kg-1)
WUE (g Kg-1)
Well watered
4
Well stressed
3
y = -0.5318x + 15.22
R2 = 0.8359
2
2
20
21
22
23
Carbon isotope discrimination (‰)
Two-row barley
24
25
20
21
22
23
24
Carbon isotope discrimination (‰)
Six-row barley
25
Rank stability of leaf-CID across locations & years
22.0
22.0
Two years
21.0
20.0
19.0
Merit
18.0
H93174006
17.0
16.0
AC Metcalfe
Two-row barley
Δ13C (‰) from Vegreville 2007
Carbon isotope discrimination (‰)
Data from Chen et al. 2010, in-press
21.5
21.0
20.5
R2 = 0.52
20.0
Xena
19.5
16.0
15.0
Lac-2005
Veg-2005
Veg-2006
Veg-2007
16.5
Cas-2007
17.0
17.5
18.0
18.5
19.0
19.5
20.0
13
Δ C (‰) from Vegreville 2006
21.6
21.4
21
20
19
18
160049
17
W89001002003
16
Six-row barley
Δ13C (‰) from Vegreville 2007
Cabon isotope discrimination (‰)
22
21.2
21.0
20.8
20.6
20.4
20.2
R2 = 0.67
20.0
19.8
Kasota
19.6
M92081001
19.4
20.0
15
Two locations
20.2
20.4
13
20.6
20.8
Δ C (‰) from Castor 2007
Lac-2005
Veg-2005
Veg-2006
Veg-2007
Cas-2007
21.0
21.2
CID & protein distribution of F5 RIL population
CID Distribution of Merit x H93174006 mapping population
21.00
20.50
20.00
19.50
19.00
18.50
18.00
17.50
17.00
16.50
36
37
58
17
2
4
15
6
10
9
82
38
16
8
39
16
25
11
9
44
Protein distribution of Merit x H93174006
18.0
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
RIL
0
3
9
0
17
16
16
59
12
6
10
9
43
9
69
13
2
10
4
3
64
18
7
11
82
3
48
15
29
6
1
0
13
12
18
1
0.0
24
Protein content (%)
47
42
15
18
9
16
6
11
6
26
10
0
17
8
79
19
0
P1
90
14
2
96
14
1
13
3
70
16.00
2
0
-3
0
3
Discriminant Analyses on
Merit x H93174006 RIL
population
2
-2
Variables; DM
and Seed
Weight and HI
Variables: Protein,
DM, and Seed
weight
1
-4
2
0
Variables; DM
and Seed
Weight
-3
0
-1
-2
1.5
3
-2
0
-1
0
-1
1
-2
-1.5
1
2
*** Protein had a significant
–ve corr with HI
Summary DArT diversity in a population of 188 RILs and the
two parental lines
Wheat DH population
Merit x H93174006 RIL population
Parent 1
Parent 2
# markers
Parent
1
1
0
193
1
0
184
1
1
254 ??
1
1
6
1
-
1
-
5
0
0
140 ??
0
0
0
1
146
0
1
0
-
6
0
-
-
1
4
-
1
743
Parent 2
# markers
221
2
418
Relationship between water/nitrogen use
efficiencies & protein
We tested the following hypotheses
 For the same nitrogen supply, higher levels of soil moisture will
lower protein content, whereas drier conditions lead to higher
protein content.
 When moisture is limiting, water use efficient varieties will
improve yield and hence decrease nitrogen concentration leading
to lower protein content (implies a negative correlation)
The Results of greenhouse studies
AC Metcalfe
Copeland
CDC Cowboy
Niobe
AC Metcalfe
19.75
17.99
20
15.56
15
16.6117.05
14.46
13.1 13.59
12.23
11.6211.72
9.85 9.29
10
8.52 8.86
9.67
WUE (g/Kg)
Protein content (%)
25
5
0
WD-N-50%
WD-N-100%
AC Metcalfe
WW-N-50%
Copeland
100
CDC Cow boy
52.32
40
43.2
30
20
27.28
19.76
10.64
10
37.84
32.44
37.52
28 31.28
41.64
7.36
0
WD-N-50%
WD-N-100%
WW-N-50%
WW-N-100%
CDC Cowboy
Niobe
4.42
4.28
4.04
3.53
WD-N-100%
AC Metcalfe
CID (‰)
NUE
60
4.45
4.16
3.63
Niobe
70
50
4.15
WD-N-50%
77.36
74.08
80
4.15 4.07
Copeland
3.71
3.52
3.87
3.71 3.69
2.5
2
1.5
1
0.5
0
WW-N-100%
91.5290.64
90
5
4.5
4
3.5
3
4.66
25
24.5
24
23.5
23
22.5
22
21.5
21
20.5
20
19.5
WW-N-50%
Copeland
CDC Cowboy
23.9
23
22.8 22.8
23 23.1
WW-N-100%
Niobe
24.3
24.2
23.7
22.7
24
23.8
23.6
22.7
21.8
21.2
WD-N-50%
WD-N-100%
WW-N-50%
Two N levels under WW and WD conditions
WW-N-100%
Correlations amongst WUE, NUE and
protein under drought in GH
22
20
40
35
30
Protein content (%)
Nitrogen use efficiency
50
45
y = 27.985x - 91.733
R2 = 0.4544
25
20
15
10
18
16
14
12
y = -0.2723x + 22.032
R2 = 0.3575
10
5
0
8
3
3.5
4
4.5
5
10
15
Water use efficiency (g/Kg)
25
30
4.8
20
19
4.6
18
17
16
15
14
13
12
Water use efficiency
Protein content (%)
20
Grain yield (g)
y = -4.1562x + 33.444
R2 = 0.2993
11
10
4.4
4.2
4
3.8
3.6
y = -0.351x + 12.133
R2 = 0.7341
3.4
3.2
3
3.5
4
4.5
Water use efficiency (g/Kg)
5
3
21
21.5
22
22.5
23
23.5
Carbon Isotope discrimination
Two N levels under WD conditions
24
24.5
Results of field studies with 7 varieties
1626
1650
60.0
1603
1550
1500
55.6
48.3
50.0
1514
1479
1454
1445
1450
1393
1400
Harvest index
Aerial biomass (g)
1600
51.8
51.2
CDC
Meredith
CDC
Reserve
50.4
49.1
44.4
40.0
30.0
20.0
1350
10.0
1300
1250
0.0
AC Metcalfe
Bentley
CDC
Cowboy
CDC
Meredith
CDC
Reserve
Copeland
Niobe
AC Metcalfe
Bentley
CDC
Cowboy
Copeland
Niobe
15.0
14.8
Protein content (%)
14.7
14.5
14.3
14.3
14.0
13.8
13.5
13.5
13.3
13.0
12.5
AC Metcalfe
Bentley
CDC
Cowboy
CDC
Meredith
CDC
Reserve
Copeland
Niobe
Protein content (%)
15.0
14.8
14.6
14.4
14.2
14.0
13.8
13.6
13.4
13.2
13.0
40.0
y = -0.1397x + 21.091
R2 = 0.7182
45.0
50.0
Harvest index
55.0
60.0
Conclusions
 To maintain/improve on the yield progress already
made by our breeders, new tools are needed to target
specific traits and growth conditions that limit yield
 The new tools must be complementary to existing
tools and easy to deploy in existing breeding
programs
 Although several physiological traits have been
proposed, only a few have been successfully used to
improve yield
 Improvement in one trait can have the unintended
consequence of leading to a decline in another
 Pyramiding of several traits such as WUE and NUE
related traits may lead to progress in achieving
yield stability
 Narrow genetic base of modern varieties may
impede progress (new sources of variations are
necessary to overcome this)
 Advances in genomics and genetic mapping are
making it faster and cheaper to combine several
polygenetic traits in new varieties
 Identifying QTLs and their linked markers will
potentially reduce time and cost to make the use of
physiological traits more attractive in barley
breeding
Acknowledgements
Funding:
• Brewing & Malting Barley Res. Institute
• Alberta Agricultural Research Institute
• Alberta Crop Industry Development Fund
• Alberta Barley Commission
Project Staff:
• Jing Chen
• Ludovic Capo-Chichi
• Sharla Eldridge
Collaborators/Institutions:
FCDC Lacombe
• Dr. Pat Juskiw
• Dr. Joseph Nyachiro
• Jennifer Zantinge
University of Alberta
• Dr. Scott Chang