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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
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http://www.fao.org
Technological innovation
new combinations of pre-existing elements
http://futuristablog.com/technological-trends-in-agriculture/
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Technological innovation
new functions Darwinian pre-adaptations
Kauffman SA (2008) 'Reinventing the Sacred: A New View of Science, Reason, and Religion'
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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
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6
short term + low aggregation: environment overrides technology
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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
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8 Crop Science 2015
Crop Science 56 (5) 2016
special issue GxE
13 papers
half define E as location/season
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E : GxE : G 15 : 7 : 2
Slide ratio
9
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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
Thermal time centred at flowering (oCd)
-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
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12
Sadras et al 2012 Crop and Pasture Science 63, 33
Spatial, probabilistic pattern of drought types for field pea
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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
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14 Sadras et al. 2013 Field Crops Research 150, 63
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16
Terminal drought a misleading concept
Chenu et al 2013 wheat, Australia
cognitive structuresinteract with grammarprovide conditions for language use
Chomsky 1998
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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
Thermal time centred at flowering (oCd)
-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
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Abbo et al 2003 Quarterly Review of Biology 78, 435
The presence of founder crops in the archaeological record of the Levant
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Vernalisation:
Abbo 2002 New Phytol 54, 695
Berger et al. 2005 Australian J Agr Res 56, 1191
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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
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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
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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
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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
Thermal time centred at flowering (oCd)
maximum minimum
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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
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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
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How reliable are simulation models?
Growth ****
Phenology ****
Water budget ***
Nitrogen budget **
Biomass partitioning **
Extreme events *
Pests *
Biological aspects of rotations - 26
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Plasticity: one genotype producing alternative
phenotypes in response to environment
helmeted - stress + stress
Woltereck 1909 Verhandlungen der Deutschen Zoologischen Gesellschaft 19, 110 27
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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
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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
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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
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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
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
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32 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
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
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33 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
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
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34 Alvarez Prado et al. 2014 J Exp Bot 65, 4479
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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
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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
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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
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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
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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