Dr. Anthony AnyiaSenior Scientist & Acting Manager, Bioresource Technologies, Alberta Innovates – Tech Futures

June 23, 2010

Water use efficiency and barley production on the

Canadian Prairies

21st BMBRI Triennial Barley Improvement Meeting held in Guelph

Challenges of Crop Production on the Prairies –demonstrated through News Releases by CWB

1. June 11, 2010: Wet weather severely impairs crop prospects across the Prairies

2. June 11, 2009: Cold spring, dry fields lower 2009 crop prospects in Western Canada

3. June 12, 2008: Rains help boost 2008 crop estimates, cold spring a concern

4. June 14, 2007: Wet spring lowers Prairie wheat acres, increases barley

5. June 10, 2004: Moisture conditions improve across Western Canada but dry pockets remain

6. June 12, 2003: Improved moisture conditions good news for prairie farmers

7. August 6, 2003: Hot, dry July plays havoc with crops across the prairies

Vegreville AB, 2002, courtesy AAFC

Short and dry growing season

Insufficient growing season rainfall

Drought and heat stress in summer

Long and cold winter

Spring and fall frost common

Characteristics of Canadian Prairies

Vegreville AB, April 2007

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

Canadian yields are lower than most other leading producers

Canada is a major producer of barley

Despite the challenges, Canada is a major world producer of barley

2007 Barley production & yield by 10 top countries (FAO Stat)

15.7

11.8 11.7 11.09.5

7.45.9

5.1 4.63.1

0.02.04.06.08.0

10.012.014.016.018.0

Mill

ion

tonn

es

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Ton

nes/

Ha

Production

Yield

Breeding progress is masked by genotype by location variation in yield

Low yields can be attributed to poor growing conditions prevalent on the prairies

Canadian barley and wheat yields in comparison to yields in China

0.00

1.00

2.00

3.00

4.00

5.00

6.00

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Ton

nes/

Ha

Wheat-Canada

Barley-Canada

Wheat-China

Barley-China

China: W = 50%; B = 60%

Canada: W = 5%; B = 0%

Severe drought year in Alberta

Source: FAOStat

Low moisture + high temp = very low yield

Good moisture + high temp = below average yield

2.2

3.0

3.5 3.6

3.23.0

3.1

0

10

20

30

40

50

60

70

80

2002 2003 2004 2005 2006 2007 10YAvg

Yie

ld (

bu

/acr

e)

Yield (data label = tonnes/ha)

0

50

100

150

200

250

300

350

2002 2003 2004 2005 2006 2007

With

in s

easo

n ra

infa

ll (m

m)

0

5

10

15

20

25

30

35

40

July

max

tem

p (°

C)

Weather conditions, Vegreville

Barley yield depend on both moisture and temperature

y = -3.0434x + 154.86

R2 = 0.5878

30

35

40

45

50

55

60

65

70

28 30 32 34 36

Maximum temperature in July (°C)

Ave

rag

e y

ield

(b

u/a

cre

)

y = 0.121x + 28.541

R2 = 0.7476

3035

4045

5055

6065

70

50 100 150 200 250 300

Within season rainfall (mm)

Ave

rag

e y

ield

(b

u/a

cre

)

Good moisture + moderate temp = above average yield

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)

Can we further improve yield and yield stability?

Bridging the gap between breeders, physiologists & ‘omics

Life-cycle of a typical cereal crop

Stages: Establishment & Growth Foundation for future yield

Post-Anthesis determinant of actual yield

Phase: ReproductiveVegetative

Pre-Anthesis Formation of yield potential

Adapted from Anyia et al., 2008

0

50

100

150

21-May 31-May 10-Jun 20-Jun 30-Jun 10-Jul 20-Jul 30-Jul 09-Aug 19-Aug

Soi

l moi

stur

e (m

m)

0

10

20

30

40

Dai

ly m

ax t

emp

(°C

)

Usually good moisture Moisture is limiting Drought and heat stressGrowth Conditions

Adapted from Anyia et al., 2008

Genetic improvement of crops

Use more of the water supply- Increase water use- Decrease soil evaporation

Better exchange of water for CO2-Increase water use efficiency

Early seedling vigour(Leaf area, SLA, LAI)

Increase TE(carbon isotope discrimination)

Increase stem reserves(non structural CH2O)

Convert more biomass into grain - Increase harvest index

Can we design new smart varieties that:

200 250 300 350 400 450

Growing-season rainfall (mm)

0

2

4

6

8

10

12

Advanta

ge in g

rain

yie

ld (%

)

Wheat lines selected for low CID (Rebetzke et al. 2002)

Relationship between WUE and CID

(Adapted from Anyia et el., 2007)

y = -0.5242x + 15.211R2 = 0.8943

2

3

4

5

20 21 22 23 24 25

Carbon isotope discrimination (‰)

WU

E (

g K

g-1)

y = -0.5318x + 15.22R2 = 0.8359

2

3

4

5

20 21 22 23 24 25

Carbon isotope discrimination (‰)

WU

E (

g K

g-1)

Six-row barley

Two-row barley

Well watered

Well stressed

Well watered

Well stressed

R2 = 0.67

19.4

19.6

19.8

20.0

20.2

20.4

20.6

20.8

21.0

21.2

21.4

21.6

20.0 20.2 20.4 20.6 20.8 21.0 21.2

Δ13C (‰) from Castor 2007

Δ1

3C

(‰

) fr

om

Veg

revi

lle 2

007

15

16

17

18

19

20

21

22

Lac-2005 Veg-2005 Veg-2006 Veg-2007 Cas-2007

Cab

on is

otop

e di

scrim

inat

ion

(‰)

160049

W89001002003

Kasota

M92081001Six-row barley

15.0

16.0

17.0

18.0

19.0

20.0

21.0

22.0

Lac-2005 Veg-2005 Veg-2006 Veg-2007 Cas-2007

Car

bon

isot

ope

disc

rimin

atio

n (‰

)

Merit

H93174006

AC Metcalfe

XenaTwo-row barley

Rank stability of leaf-CID across locations & years

R2 = 0.52

19.5

20.0

20.5

21.0

21.5

22.0

16.0 16.5 17.0 17.5 18.0 18.5 19.0 19.5 20.0

Δ13C (‰) from Vegreville 2006

Δ1

3 C (

‰)

fro

m V

eg

reville

2007

Two locations

Two years

Data from Chen et al. 2010, in-press

CID & protein distribution of F5 RIL population

CID Distribution of Merit x H93174006 mapping population

16.00

16.50

17.00

17.50

18.00

18.50

19.00

19.50

20.00

20.50

21.00

Protein distribution of Merit x H93174006

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

RIL

Pro

tein

co

nte

nt

(%)

-4

-2

0

2

-3 0 3

Discriminant Analyses on Merit x H93174006 RIL population

-2

-1

0

1

2

-2 -1 0 1 2

Variables; DM and Seed Weight

-2

-1

0

1

2

-3 -1.5 0 1.5 3

Variables: Protein, DM, and Seed weight

Variables; DM and Seed Weight and HI

*** Protein had a significant –ve corr with HI

Merit x H93174006 RIL population

Parent 1 Parent 2 # markers

1 0 193

1 1 254 ??

1 -  

0 0 140 ??

0 1 146

0 - 6

- 1 4

  743

Wheat DH population

Parent 1 Parent 2 # markers

1 0 184

1 1 6

1 - 5

0 0

0 1 221

0 -

- 1 2

  418

Summary DArT diversity in a population of 188 RILs and the two parental lines

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

15.56

17.99

9.8511.62

13.1

19.75

9.29

11.7213.59

16.61

8.529.67

14.46

17.05

8.86

12.23

0

5

10

15

20

25

WD-N-50% WD-N-100% WW-N-50% WW-N-100%

Pro

tein

co

nte

nt

(%)

AC Metcalfe Copeland CDC Cowboy Niobe

4.15 4.16

3.53 3.714.07

3.63 3.71 3.69

4.664.45

4.044.42

4.15 4.28

3.523.87

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

WD-N-50% WD-N-100% WW-N-50% WW-N-100%

WU

E (

g/K

g)

AC Metcalfe Copeland CDC Cowboy Niobe

28

10.64

74.08

37.8431.28

7.36

77.36

32.44

43.2

27.28

91.52

52.32

37.52

19.76

90.64

41.64

0

10

20

30

40

50

60

70

80

90

100

WD-N-50% WD-N-100% WW-N-50% WW-N-100%

NU

E

AC Metcalfe Copeland CDC Cow boy Niobe

22.823

23.9 24

22.823.1

24.2

23.6

21.8

21.2

23.7

22.723

22.7

24.3

23.8

19.520

20.521

21.522

22.523

23.524

24.525

WD-N-50% WD-N-100% WW-N-50% WW-N-100%

CID

(‰

)

AC Metcalfe Copeland CDC Cowboy Niobe

Two N levels under WW and WD conditions

y = 27.985x - 91.733

R2 = 0.4544

0

5

10

15

20

25

30

35

40

45

50

3 3.5 4 4.5 5

Water use efficiency (g/Kg)

Nit

rog

en u

se e

ffic

ien

cy

y = -0.2723x + 22.032R2 = 0.3575

8

10

12

14

16

18

20

22

10 15 20 25 30

Grain yield (g)

Pro

tein

co

nte

nt

(%)

y = -4.1562x + 33.444R2 = 0.2993

10

11

12

13

14

15

16

17

18

19

20

3 3.5 4 4.5 5

Water use efficiency (g/Kg)

Pro

tein

co

nte

nt

(%)

Correlations amongst WUE, NUE and protein under drought in

GH

Two N levels under WD conditions

y = -0.351x + 12.133

R2 = 0.7341

3

3.2

3.4

3.6

3.8

4

4.2

4.4

4.6

4.8

21 21.5 22 22.5 23 23.5 24 24.5

Carbon Isotope discrimination

Wat

er u

se e

ffic

ien

cy

14791445

1514

1626

1393

1603

1454

1250

1300

1350

1400

1450

1500

1550

1600

1650

Aer

ial

bio

mas

s (g

)

AC Metcalfe Bentley CDCCowboy

CDCMeredith

CDCReserve

Copeland Niobe

48.3 50.4

44.4

51.8 51.2 49.1

55.6

0.0

10.0

20.0

30.0

40.0

50.0

60.0

Har

vest

in

dex

AC Metcalfe Bentley CDCCowboy

CDCMeredith

CDCReserve

Copeland Niobe

14.7

13.8

14.8

14.3

13.5

14.3

13.3

12.5

13.0

13.5

14.0

14.5

15.0

Pro

tein

co

nte

nt

(%)

AC Metcalfe Bentley CDCCowboy

CDCMeredith

CDCReserve

Copeland Niobe

y = -0.1397x + 21.091

R2 = 0.7182

13.0

13.2

13.4

13.6

13.8

14.0

14.2

14.4

14.6

14.8

15.0

40.0 45.0 50.0 55.0 60.0

Harvest index

Pro

tein

co

nte

nt

(%)

Results of field studies with 7 varieties

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

FCDC Lacombe• Dr. Pat Juskiw • Dr. Joseph Nyachiro• Jennifer Zantinge

Collaborators/Institutions:University of Alberta• Dr. Scott Chang

Project Staff:• Jing Chen• Ludovic Capo-Chichi• Sharla Eldridge