Understanding and quantifying the drivers of seed yield in ... · Understanding and quantifying the drivers of seed yield in pulses ... Patterns of water stress – pea vs chickpea

1

Understanding and quantifying the

drivers of seed yield in pulses

Victor Sadras & Lachlan Lake

South Australia Research & Development Institute

funded by

Grains Research & Development Corporation

Australia-India Strategic Research Fund

Yitpi Foundation

Australian Pulse Conference, 12-14 September 2016, Tamworth

http://www.fao.org

Technological innovation

new combinations of pre-existing elements

http://futuristablog.com/technological-trends-in-agriculture/

Technological innovation

new functions Darwinian pre-adaptations

Kauffman SA (2008) 'Reinventing the Sacred: A New View of Science, Reason, and Religion'

yield

5

environment E

actual technology G, M, G x M

potential technology

science, practice

obsolescence

rate

adoption

rate

innovation

rate

farmer; physiology, genetics, soil

6

short term + low aggregation: environment overrides technology

Crop E : GxE : G ratio Source

field pea in Canada 31.9 : 2.8 : 1 Yang et al. (2005)

sunflower in Argentina 4.3 : 1.5 : 1 de la Vega et al. (2007)

wheat in Australia 3.0 : 2.2 : 1 Cooper et al. (1995)

sugar beet in Europe 27.5 : 1.1 : 1 Hoffmann et al. (2009)

E is often a large source of variation two exceptions

Trait: yield

7

8 Crop Science 2015

Crop Science 56 (5) 2016

special issue GxE

13 papers

half define E as location/season

E : GxE : G 15 : 7 : 2

Slide ratio

9

10

-800 -400 0 400 800

0.0

0.2

0.4

0.6

0.8

1.0

Wa

ter

su

pp

ly/d

em

an

d r

ati

o

Thermal time centred at flowering (oCd)

Step 1


-800 -400 0 400 800

Wa

ter

su

pp

ly/d

em

an

d r

ati

o

0.0

0.2

0.4

0.6

0.8

1.0ET1

ET2

ET3

Step 2

Environment type

1 2 3

Actu

al yie

ld (

t/ha)

0

2

4

Step 3

12

Sadras et al 2012 Crop and Pasture Science 63, 33

Spatial, probabilistic pattern of drought types for field pea

StressMaster to target specific environment

Soil

Climate (on-site sensor)

Management-to-date

Irrigation scenarios

- Sowing

- Fertilisation

- Irrigation

Report by email

Chenu K, Doherty A, Rebetzke GJ and Chapman SC (2013) StressMaster: a web application for dynamic modelling of the

environment to assist in crop improvement for drought adaptation. In: Sievnen R, Nikinmaa E, Godin C, Lintunen A and

Nygren P (eds) 7th International Conference on Functional-Structural Plant Models, Saariselk, Finland, pp 357-359.

http://ojs.metla.fi/index.php/fspm2013/article/view/872/876

Chenu K, Doherty A, Brider J, Rebetzke GJ, McDonald G, Mayfield A, Bechaz K, Pattison A, Murfit M, Mayer JE. 2016.

Using crop modeling to get better field data. International Crop Modelling Symposium (15-17 March, Berlin, Germany). 2pp.

Chenu et al. (2013) In: Sievnen et al. (eds) 7th International Conference on Functional-Structural Plant Models, Saariselk, Finland,

pp 357-359. http://ojs.metla.fi/index.php/fspm2013/article/view/872/876

Chenu et al. (2016) Proceedings of the International Crop Modelling Symposium , Berlin, Germany. 2pp.

13

http://ojs.metla.fi/index.php/fspm2013/article/view/872/876http://ojs.metla.fi/index.php/fspm2013/article/view/872/876

14 Sadras et al. 2013 Field Crops Research 150, 63

16

Terminal drought a misleading concept

Chenu et al 2013 wheat, Australia

cognitive structuresinteract with grammarprovide conditions for language use

Chomsky 1998

Patterns of water stress pea vs chickpea

Pea: Sadras et al 2013 Crop & Pasture Science 63:33

Chickpea: Lake et al 2016 Crop & Pasture Science 67:204 17


-1500 -1000 -500 0 500 1000

field pea

-1500 -1000 -500 0 500 1000

Wa

ter

su

pp

ly/d

em

an

d r

ati

o

0.0

0.2

0.4

0.6

0.8

1.0

chickpea

Abbo et al 2003 Quarterly Review of Biology 78, 435

The presence of founder crops in the archaeological record of the Levant

19

Vernalisation:

Abbo 2002 New Phytol 54, 695

Berger et al. 2005 Australian J Agr Res 56, 1191

Species

Temperature (oC)

onset peak

Pea

73.6 82.2

Chickpea

80.3 91.3

Soybean

85.6 91.1

Onset and peak temperature for denaturation of seed protein isolates

Withana-Gamage et al. 2011 J Sci Food Agric 91:1022 20

Chickpea Dry Weight (g m-2)

0 500 1000 1500 2000

Fie

ldp

ea

Dry

We

igh

t (g

m-2

)

0

500

1000

1500

2000

Chickpea dry weight (g m-2)

0 200 400 600 800 1000

Fie

ldpea d

ry w

eig

ht (g

m-2

)

0

200

400

600

800

1000

shoot

root

experimental

modelled

Lake et al 2016 Crop Pasture Science 67:204 21

y = x

y = x

Crop growth rate derived from NDVI (g m-2

oCd

-1)

0.0 0.2 0.4 0.6 0.8 1.0

Yie

ld (

t/ha)

0

1

2

3

4

5

field pea

0.0 0.5 1.0 1.5 2.0 2.5

chickpea

Chickpea: Lake et al 2016 European J Agronomy in press

Pea: Sadras et al 2013 Field Crops Research 150, 63

Physiological approach to phenotyping

20 varieties

8 environments

29 varieties

10 environments

22

Thermal time centered at the beginning of flowering (oCd)

-1000 -500 0 500 1000

Tem

pera

ture

(oC

)

0

5

10

15

20

25

30

MAX1 (23%)

MAX2 (27%)

MAX3 (50%)

MIN1 (23%)

MIN2 (42%)

MIN3 (35%)

23 Lake et al 2016 Crop Pasture Science 67:204

-1000 -500 0 500 1000

Tem

pera

ture

(oC

)

0

10

20

30

40

-1000 -500 0 500 1000


maximum minimum

Probabilistic thermal regimes for chickpea in Australia

ET3

0 500 km

20406080100

ET2

20406080100

ET1

20406080100

ET3

0 500 km

20406080100

ET2

20406080100

ET1

20406080100

MAX1

MAX2

MAX3

MIN1

MIN2

MIN3

maximum temperature minimum temperature

24

25

1957-2014

Month

1 2 3 4 5 6 7 8 9 10 11 12

Min

era

lisati

on

(kg

N h

a-1

mo

nth

-1)

0

10

20

30

40

erratic summer rainfall

warm soil

autumn rainfall break

mild temp

wet but cold winter

end rainfall season

mild spring temp

Sadras et al unpublished

How reliable are simulation models?

Growth ****

Phenology ****

Water budget ***

Nitrogen budget **

Biomass partitioning **

Extreme events *

Pests *

Biological aspects of rotations - 26

Plasticity: one genotype producing alternative

phenotypes in response to environment

helmeted - stress + stress

Woltereck 1909 Verhandlungen der Deutschen Zoologischen Gesellschaft 19, 110 27

Environment

Reaction norm is the mathematical function

phenotype = f (environment)

slope = plasticity

Ph

en

oty

pe

Woltereck 1909 Verhandlungen der Deutschen Zoologischen Gesellschaft 19, 110 28

29

Methods to quantify plasticity

Slope of reaction norm Allows for non-linearity

Difficult to identify main E

Sadras & Richards 2014 J Exp Bot 65, 1981

Variance ratio Single value of plasticity

Applies irrespective of E-driver

Environmental mean yield

Yie

ld o

f cultiv

ar

1 o

r 2

cv 1

cv 2

30 Sadras & Richards 2014 J Exp Bot 65, 1981; Grogan et al. 2016 Crop Sci

0.6 0.8 1.0 1.2 1.4

0

2

4

P = 0.34

0.6 0.8 1.0 1.2 1.4

0

2

4

6

8

P = 0.38

b = -3.6*** 0.32 t ha-1

0.4 0.6 0.8 1.0 1.2 1.4Y

ield

(t

ha

-1)

0

1

2

3

4

5

Phenotypic plasticiy of grain yield

0.6 0.9 1.2 1.5

0

4

8

12

b = 2.0*** 0.31 t ha-1

b = -1.7** 0.54 t ha-1

b = 2.5** 0.75 t ha-1

b = 1.5*** 0.23 t ha-1

b = 0.5* 0.05 t ha-1

field pea in Australia sunflower in Argentina

wheat in Mexico rye in Finland

Environment

potential

stress

Yield stability another misleading concept

31 Sadras & Richards 2014 J Exp Bot 65, 1981

0.6 0.8 1.0 1.2 1.4

0

2

4

P = 0.34

0.6 0.8 1.0 1.2 1.4

0

2

4

6

8

P = 0.38

b = -3.6*** 0.32 t ha-1

0.4 0.6 0.8 1.0 1.2 1.4Y

ield

(t

ha

-1)

0

1

2

3

4

5


0.6 0.9 1.2 1.5

0

4

8

12

b = 2.0*** 0.31 t ha-1

b = -1.7** 0.54 t ha-1

b = 2.5** 0.75 t ha-1

b = 1.5*** 0.23 t ha-1

b = 0.5* 0.05 t ha-1



Environment

potential

stress


0.6 0.8 1.0 1.2 1.4

0

2

4

P = 0.34

0.6 0.8 1.0 1.2 1.4

0

2

4

6

8

P = 0.38

b = -3.6*** 0.32 t ha-1

0.4 0.6 0.8 1.0 1.2 1.4Y

ield

(t

ha

-1)

0

1

2

3

4

5


0.6 0.9 1.2 1.5

0

4

8

12

b = 2.0*** 0.31 t ha-1

b = -1.7** 0.54 t ha-1

b = 2.5** 0.75 t ha-1

b = 1.5*** 0.23 t ha-1

b = 0.5* 0.05 t ha-1



Environment

potential

stress

34 Alvarez Prado et al. 2014 J Exp Bot 65, 4479

35

Plasticity and GxE have been running in parallel for over a century

12k plasticity papers 1967-2013

Milestones

Richard Woltereck 1909

Anthony Bradshaw 1965

Mary Jane West-Eberhard 2003

36

Plasticity and GxE have been running in parallel for over a century

Milestones

Richard Woltereck 1909

Anthony Bradshaw 1965

Plasticity is a trait of its own, with

its own genetic control

Mary Jane West-Eberhard 2003

Plasticity of 13C

Plasticity of flowering

Plasticity of N fixation

Plasticity of N uptake

Plasticity of seeds per m2

Plasticity of yield

Chr1 Chr2 Chr3 Chr4 Chr5 Chr6 Chr7 Chr8

FS

T

Genetic regions associated with trait plasticity in chickpea

Sadras et al. 2016 J. Exp. Bot. 67, 4339

37

Genetics of crop and plant yield do not match

Lake et al (2016) Field Crops Research in press

FS

T

Seed yield under relaxed competition

Seed yield in crop stand

1 2 3 4 5 6 7 8

Chromosome

38

39

thank you [email protected]

Ross Ballard

Jorge Casal

Karine Chenu

Liz Farquharson

Kristy Hobson

Anthony Leonforte

Yongle Li

Larn McMurray

Richard Richards

Garry Rosewarne

Tim Sutton

Jenny Wood

funded by

GRDC

AISRF

Yitpi Foundation

Documents

Understanding and quantifying the drivers of seed yield in ... · Understanding and quantifying the drivers of seed yield in pulses ... Patterns of water stress – pea vs chickpea