SAMARA: A crop model for

(1)

SAMARA: A crop model for

simulating rice phenotypic

plasticity

Michael Dingkuhn, Uttam Kumar,

Ma Rebecca Laza, Richard Pasco, Gemma T. Narisma, Faye A. Cruz

IRRI, CIRAD, Manila Observatory

(2)

Hypotheses

• Simulate morphological plasticity and its effect on yield

– Resources (Light and water)

– Management (crop establishment, population, water)

• => GxExM interactions on phenotype and yield

• => Ideotypes

• Competition among plants and organs drive plasticity

• Compensatory plasticity

• Plant structure: arborescent system of sinks

• Sinks compete => feedback on number/size of new sinks

(3)

Supply vs. Demand driven growth

PAR

+ H20

+ NPK

= Yield

Biomass

Soil water reserve

Agronomic angle:

Supply

(assimilation) driven system

Botanical angle:

Demand

(organogenesis) driven system

Meristem

control of

development

(4)

SAMARA

Ic, state variable driving phenological and morphological adjustments

Ic = Supply/demand

Proxy for sugar signaling

Ensures source-sink

equilibrium as the

architecture unfolds

Supply = daily [Pn-Rm]

Demand = daily aggregate

growth commitments

Ic controls:

- Tiller outgrowth (Ic>0.5)

- Tiller mortality (Ic<1)

- Leaf mortality (Ic<1)

- Reserve formation (Ic>1)

- Reserve mobilization (Ic<1)

- Organ size (Ic << 1)

(5)

SAMARA calibration/validation for

IR72: 14 environments

Calibration

Validation: Biomass, Grain yield, tillering

Also simulated:

(6)

YC1

Plants m

-2

YC3

Panicles

tiller

-1

YC4

Spikelets

panicle

-1

YC5

Grains

spikelet

-1

YC2

Tillers

plant

-1

YC6

Weight

grain

-1

YC(N) on field area basis

YC(N+1

)

/

YC(N)

Variation of

YC(N+1) to attain

constant value on

ground-area basis

o

1

0

0 %

compe

nsa

tion

YC(N) on field area basis

YC(N+1

)

/

YC(N)

o

Compen-

sation

x

Full compensation

Partial compensation

x

80%

105%

Tillering

Surviving,

fertile tillers

Panicle

size

Spikelet

fertility

Grain filling

(7)

Compensatory plasticity, observed and simulated:

Case of increasing population

Simulated

Panicles m-2

300

400

500

600

700 Fi

lle

d

gr

ai

ns

p

an

ic

le

-1

40

60

80

100

Dry season, 25 hills m-2

Dry season, 100 hills m-2

Wet season, 25 hills m-2

Wet season, 100 hills m-2

D S: 3 783_{4 g} rain_{s m} -2 W S: 2 648_{9 g} rain s m-2 Compensation WS: 115% Compensation DS: 141%

Simulated

Panicles m-2

300

400

500

600

700 S

pi

ke

le

ts

p

an

ic

le

-1

40

60

80

100

120

W S: 3 018 4 sp ikele ts m-2 DS : 50 825_sp ikele ts m-2 Compensation DS: 154% Compensation WS: 57%

Simulated

Hills m-2

25

50

75

100 P

an

ic

le

s

hi

ll-1

0

5

10

15

20

DS: 4 49 pa_nicle s m-2 WS: 3 53 pa_nicle s m-2 Compensation DS: 96% Compensation WS: 85%

Observed

Panicles m-2

300

400

500

600

700 S

pi

ke

le

ts

p

an

ic

le

-1

40

60

80

100

120

Compensation WS: 120% Compensation DS: 69% W S: 3 018 4 sp ikele ts m-2 DS : 50 825_sp ikele ts m-2

Observed

Panicles m-2

300

400

500

600

700 Fi

lle

d

gr

ai

ns

p

an

ic

le

-1

40

60

80

100

D S: 3 783_{4 g} rain_{s m} -2 W S: 2 648_{9 g} rain s m-2 Compensation DS: 106% Compensation WS: 129%

Observed

Hills m-2

25

50

75

100 P

an

ic

le

s

hi

ll-1

0

5

10

15

20

DS: 4 49 pa_nicle s m-2 WS: 3 53 pa_nicle s m-2 Compensation DS: 87% WS: 91%

A

B

C

D

E

F

(8)

Plasticity of 12

contrasting

cultivars

Sequentially formed yield components

Ph

en

ot

yp

ic

p

la

st

ic

ity

(%

) i

nd

uc

ed

b

y

po

pu

la

tio

n

(A

C

) o

r s

ea

so

n

(B

D

)

0 20 40 60 80 100 120 _V2 V3 V5 V6 V7 V9 V10

V1, IR72 indica check cv. V11, trop. japonica X indica

V4, trop. japonica V8, temp. japonica X indica V10, indica Mean S.E.

Non-reproductive traits

0 20 40 60 Mean S.E. Time to F L (d ) Plan t hei ght a t FL LAI a t FL SLA at F L Flag leaf widt h Flag leaf are a Flag -1 le af a rea Leaf /ste m dw at F L 0 20 40 60 Hills m-2 Culm s hil l-1 a t PI Panic les c ulm-1 Spike lets panic le-1 Grai ns s pikele t-1 Wei ght g ra in-1 0 20 40

60 Mean S.E. _{Mean S.E.}

A

B

C

D

Sequentially formed

yield components

Non-reproductive

traits

Population

effect

Season

effect

 Successive compensations converge to

minimize plasticity of last component

(mean grain weight)

 Plasticity is source of yield stability

Plasticity (trait) =

Variation/mean =

Trait(T2)-Trait(T2

---

Mean Trait (T1, T2)

(9)

Pop. density effect on yield components

Cu

lm

s/h

ill a

t P

I

Cu

lm

s/h

ill a

t P

M

Cu

lm

s/a

rea

at

PM

Po

pu

lati

on

effe

ct (%)

-80

-60

-40

-20

0

20

40

60 Pla

nt

he

igh

t a

t F

L

Fla

g l

ea

f le

ng

th

LA

I a

t F

L

Le

af

dry

we

igh

t a

t F

L

a.g

. d

ry

we

igh

t a

t P

M

Gr

ain

Yi

eld

Pa

nic

les

/ar

ea

Sp

ike

let

s/p

an

icle

s

Sp

ike

let

s/a

rea

Fra

cti

on

fil

led

gr

ain

Ha

rve

st

ind

ex

Observed

Simulated

Effect of increasing

population 4-fold

(4-season means ±SE):

(10)

Example of Ic

effect on

tillering

0

20

40

60

80

100

120 Ass

imi

la

tio

n

ra

te

a

nd

R

m

(kg

h

a-1

d

-1

)

0

100

200

300

400

500

600 A (25 hills m-2)

A (100 hills m-2)

Rm (25 hills m

-2)

Rm (100 hills m

-2)

Days after sowing

0

20

40

60

80

100

120 In

de

x

of

co

mp

et

iti

on

(I

c)

0.0

0.5

1.0

1.5

2.0 C

ul

ms

hi

ll-1

0

5

10

15

20

25

30

35 Ic (25 hills m--2)

Ic (100 hills m-2)

Culms/hill

(25 hills m-2)

Culms/hill

(100 hills m-2)

Seedling

nursery

Tillering

Stem

_elongation

Grain

filling

Transplanting

A

(11)

Phenotype sensitivity to model parameters:

Many traits affect phenotype but not yield due to trait-trait

compensation

Sensitivity of TilAbility

Days after sowing

0 20 40 60 80 100 120

C

ulm

s

hill

-1

o

r L

A

I

0 10 20 30 40 0 5 10 15 20 IR72 (2.5) 3.5 1.5

Ab

ov

eg

ro

un

d

dw

(g

re

en

)

Gr

ain

y

ield

14 %

M

C

Cu

lm

s

hill

-1

LAI

Stem

reserve

Sensitivity of Phyllo (

o

Cd)

Days after sowing

0 20 40 60 80 100 120 0 5 10 15 20 0 10 20 30 40 IR72 (100) 115 85

Sensitivity of LeafLengthMax (mm)

Days after sowing

0 20 40 60 80 100 120 0 10 20 30 40

D

ry

m

at

te

r (

M

g

ha

-1

)

0 5 10 15 20 IR72 (450) 550 350

leaf

nu

mb

er

A

B

C

Sensitivity of CoeffLeafdeath

Days after sowing

0 20 40 60 80 100 120

C

ulm

s

hill

-1

o

r L

A

I

0 5 10 15 20 25 30 35 40 0 2 4 6 8 10 12 14 16 18 20 IR72 (0.045) 0 (Complete staygreen)

Sensitivity of RelMobiliInternodeMax

Days after sowing

0 20 40 60 80 100 120 0 5 10 15 20 25 30 35 40 0 2 4 6 8 10 12 14 16 18 20 IR72 (0.02) 0 (no mobilisation)

Ab

ov

eg

ro

un

d

dw

(g

re

en

)

Gr

ain

y

ield

14 %

M

C

LAI

Stem

reserve

Sensitivity of CoeffTillerDeath

Days after sowing

0 20 40 60 80 100 120 0 5 10 15 20 25 30 35 40 0 2 4 6 8 10 12 14 16 18 20 IR72 (0.12) 0

Cu

lm

s

hill

-1

D

ry

m

at

te

r (

M

g

ha

-1

)

A

B

C

(12)

Plant type optimization study

(virtual breeding)

Agdw (Mg-ha-1)

0

5

10

15

20 C

ou

nt

0

200

400

600 Virtual population

_{(ca. 15,000)}

IR72

Grain Yield (14% MC)

0

2

4

6

8

10

12 C

ou

nt

0

100

200

300

400 • Testing virtual recombinations of traits

• Step 1: Calibrate check-cv IR72

• Step 2: Identify 8 key parameters and vary them

within realistic ranges

• Step 3: Create “recombinants” (ca. 15,000 “cvs”)

• Step 4: Simulate in 2 environments, select 60

• Step 5: Simulate under different population X

season, select 20

• Step 6: Multilocation trial (India Philippines)

• Step 7: Testing under CC scenarios (GCM, multi-side)

• Step 8: Select 4 best and compare with IR72

(13)

Effect of trait variation on yield of virtual populations

(14)

Effect of increased stem CH

₂

O storage capacity

(from 33% to 43%)

CoeffResCapacityInternode

Grain Yield (14% MC)

0

2

4

6

8

10

12 Co

un

t

0

100

200

300

400 All

0.5

0.75 IR72

IR72

 Only effective in highest

yielding backgrounds

 Without it, potential yield

gains over IR72 remain

marginal

Optimized ideotype compared to IR72:

(Photosynthesis/Respiration traits not considered)

-

Slightly longer BVP (FL 85-90d)

-

Slightly reduced phyllochron

-

Partial stay-green (slightly less leaf senescence)

-

Moderate tillering

-

Increased stem [NSC] at flowering

-

Increased spikelets/panicle

(15)

S1

S2

S3

G

ra

in

Y

ie

ld

(M

g

ha

-1

)

6

8

10

12 Means, best 4

_IR72

S1

S2

S3

A

gd

w

(M

g

ha

-1

)

10

12

14

16

18

20

22 S1

S2

S3

H

ar

ve

st

In

de

x

0.4

0.5

0.6 Temperature

Scenario

S1

S2

S3

o

C

20

25

30

35 Tmin

Tmax

Tmean

+1.0

o

_{C +1.6}

o

C

1980-

2000

2010-

2029

2030-

2049

Three GCMs:

 HadGEM2

 CanSEM2

 GFDL

Climate-change scenario

(case: Hyderabad winter crop)

Yield increases because of CO

₂

fertilization (490 ppm in S3) but

some ist lost to heat damage;

Ideotype vs. IR72 gain = ca. 12%

Heat

sterility

Yield

IR72

Hyderabad winter season

Year 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 % s te ril ity 0 10 20 30 40 50