Development of a rice growth model for decision support systems Hiroe - - PowerPoint PPT Presentation

National Agriculture and Food Research Organization

Development of a rice growth model for decision support systems

Hiroe Yoshida, Kou Nakazono, Kaori Sasaki, Hiroyuki Ohno and Hiroshi Nakagawa Hiroyuki, Ohno, and Hiroshi Nakagawa

Agroinformatics division National Agricultural Research Center National Agricultural Research Center National Agriculture and Food Research Organization

Background1 g

Rice yield is determined by combinations between genotypes and environments and environments.

Site (Environment) A Site (Environment) B 300kg/ha 800kg/ha Genotype 1 500kg/ha 350kg/ha Genotype 2

To improve productivity and sustainability of rice production in Asia, we need location‐ and cultivar‐specific rice cultivation t h l i technologies.

Backgroun2 g

Crop growth simulation models for rice have played important roles to help understand its yield responses to various roles to help understand its yield responses to various environmental conditions.

(Kropff et al., 1994; Horie et al., 1995; Bouman et al., 2001) ( p )

・Evaluate plant ideotype ・Predict potential yield ・Asses the effect of climate change on crop performance ・Verify physiological hypotheses for further experimental research

A crop growth simulation model which explain the genotypic and environmental difference in rice growth and yield will be a useful tool to develop location‐ and cultivar‐ specific rice cultivation technologies in Asia.

Contents Contents

The development of new crop model for explaining genotype‐by‐ i i i i i h d i ld environment interaction in rice growth and yield 1 G by E database

• 1. G by E database
• 2. Development of the model
• 3. Model validation and genotype‐specific parameters

Simulation of the effects of genotype and N availability on rice Simulation of the effects of genotype and N availability on rice growth and yield response to an elevated atmospheric CO2 concentration concentration

The development of new crop model for The development of new crop model for explaining genotype‐by‐environment interaction in rice growth and yield in rice growth and yield

• 1. G by E database

Cross‐locational experiments for the development of rice growth database Horie et al. 2004

Genotype Type Takanari （TKA) indica×japonica IR72 （IR) indica Shanguichao （SKS) indica Ch86 （CH) indica （ ) IR65564‐44‐2‐2 （NPT) indica×javanica Nipponbare （NIP) japonica Nipponbare （NIP) japonica Takenari （TKE) japonica B t （BAN) javanica Banten （BAN) javanica WAB450‐I‐B‐P‐38‐HB （WAB) O.sativa×O.glaberrima

Because the robustness of the model structure and parameters largely depends on the environmental range that the database covers, this database played important roles in the

10

age

TKA IR

Leaf area index (LAI)

development of the model.

Env.

4 6 8

t heading sta

IR SKS CH NPT NIP

1 Iwate 2001 2 Iwate 2002 3 Nagano 2002

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

LAI at

NIP TKE BAN WAB

4 Shimane 2001 5 Shimane 2002 6 Kyoto 2001 2002

9 4 2 6 8 3 7 5 1 10 11

Environments

1200

h d ld

7 Kyoto 2002 8 Nanjing 2002 9 Yunnan 2002 10 Chiang Mai 2001

600 800 1000 1200

ry grain yield g m‐2)

Rough dry grain yield

10 Chiang Mai 2001 11 Ubon Ratchathani 2001

200 400 600

Rough d (g

9 6 4 7 5 8 3 2 1 11

Environments

The development of new crop model for The development of new crop model for explaining genotype‐by‐environment interaction in rice growth and yield in rice growth and yield

• 2. Development of the model

7 sub‐models for explaining specific processes

Phenological development ・About 1/3 G‐by‐E datasets were Biomass growth

Yoshida et al 2008 FCR 108 222 230

y utilized for sub‐model calibrations, and the rest for the model validations.

Yoshida et al., 2008,FCR, 108, 222‐230.

LAI development

Yoshida et al., 2007, FCR, 102‐228‐238.

・Simplex method S ik l t b d t i ti Yield formation

Yoshida and Horie, 2009, FCR, 113, 227‐237.

Whole system model This procedure had advantages in Spikelet number determination

Yoshida et al., 2006, FCR, 97,337‐343.

N dynamics within plant organs This procedure had advantages in… ・Understanding each physiological processes ・Step‐by‐step assessment of the model N dynamics within plant organs Plant N uptake

Yoshida and Horie, 2010, FCR, 117‐122‐130.

Step by step assessment of the model structure, algorithm and precision ・Identifying genotype‐specific parameters

Yoshida and Horie, 2010, FCR, 117 122 130.

y g g yp p p which have a specific role in individual processes.

Photosynthesis Development

Biomass growth Yield formation Phenological development

Root Sugar (Su) Maintenance respiration Attainable Yield Spikelet sterility Development DVI Storage starch accumulation Vegetative tissue growth Grain Yield (Y) Grain growth Root growth Yield Differentiation

Spikelet number

Vegetative Tissues (V) Storage starch (ST) Translocation Spikelet # Degeneration Vegetative tissue N (NVT) (leaf N + stem N) Grain N (NY) Senescence Translocation Degeneration

Plant N dynamics LAI

Vegetative tissue N accumulation Npool (leaf N + stem N) Grain N accumulation Recover ND Expansion

development

Npool accumulation Soil mineral N

Plant N uptake

Expansion LAI S N uptake Root system development Root system Indigenous supply fertilization Loss Senescence

The development of new crop model for The development of new crop model for explaining genotype‐by‐environment interaction in rice growth and yield in rice growth and yield

• 3. Model validation and genotype‐specific

parameters

1500

Rough grain yield

100

Spikelet number per area

30

Plant N uptake

1000 1500 d ( gm ‐2) 100 number (m ‐2) 20 25 30

• ntent (g m‐2)

y = 1.00x R² = 0 82 500 Simulated yiel y = 1.05x 50 ated spikelet n y = 0.99x 5 10 15 ated plant N co R = 0.82 500 1000 1500 Measured yield (g m‐2) R² = 0.78 50 100 Simula Measured spikelet number (m‐2) R² = 0.91 5 10 20 30 Simula Measured plant N content (g m‐2) y (g ) 2500

• und

Biomass growth

12

LAI dynamics

Measured plant N content (g m 2) 12 14 m‐2)

Leaf N content

1000 1500 2000 s of above‐gro wth (g m‐2) 6 8 10 mulated LAI 6 8 10 12 af N content (g y = 1.02x R² = 0.94 500 1000 lated dynamic biomass grow y = 0.97x R² = 0.78 2 4 Sim y = 0.99x R² = 0.78 2 4 6 Simulated lea 1000 2000 3000 Simu Measured dynamics of above‐ground biomass gorwth (g m‐2) 5 10 15 Measured LAI 5 10 15 Measured leaf N content (gm ‐2)

(Yoshida and Horie 2010)

5 10 15 20 25

Iwate 01

5 10 15 20 25

Iwate 02

5 60 120 180 5 60 120 180 20 25 20 25 5 10 15 60 120 180

Shimane 01

5 10 15 60 120 180

Shimane 02

• n (gm‐2)

60 120 180 60 120 180 5 10 15 20 25

Yunnan 02

5 10 15 20 25

Ubon 01

cumulatio

15 20 25 15 20 25 5 60 120 180

Yunnan 02

5 60 120 180

lant N acc

With the use of 2 empirical

2 5 10 15 60 120 180

Kyoto 01

5 10 15 60 120 180

Kyoto 02

P

p soil parameters characterizing indigenous

5 10 15 20 25

Nagano 02

5 10 15 20 25

Nanjing 02

N supply and N loss.

60 120 180

Nagano 02

60 120 180

Nanjing 02

Days after transplanting (Yoshida and Horie 2010)

Simulated growth and yield of IR72

2000 2500 2000 2500

A: Kyoto 2001 B: Yunnan 2002

Above‐ground 1000 1500 1000 1500 Above ground biomass Structural Yield 500 500

m‐2)

Structural Tissue NSC 50 100 150 50 100 150 2000 2500 2000 2500

• mass (g m

C: Iwate2001 D: Ubon 2001

1000 1500 1000 1500 2000

Bio

500 500 1000 50 100 150 50 100 150

Days after transplanting

11 genotype‐specific model parameters for representing critical

Parameter Unit Definition

traits for determining genotypic difference in rice growth and yield

symbol Unit Definition GV d Minimum number of days required to head L hr Critical day length for development LC hr Critical day length for development Th ˚C Temperature at which development rate is half the maximum TγH ˚C High temperature to induce 50% spikelet sterility

γH

g e pe a u e o duce 50% sp e e s e y TγL ˚C Critical low temperature for spikelet sterility gS mol m‐2 s‐1 Stomatal conductance for CO2 transfer k Radiation extinction coefficient A g‐1 Spikelet differentiation efficiency per unit plant N Critical leaf N content per unit leaf area below which leaves LNCmin g m‐2 Critical leaf N content per unit leaf area below which leaves start to senesce WG mg Potential single grain mass τN LAI at which root system expands to the whole root zone

(Yoshida and Horie 2009, 2010)

Simulation of the effects of genotype and N availability on rice growth and yield response y g y p to an elevated atmospheric CO2 concentration

Background g

Many experimental research revealed that there is a large genotypic and environmental variation in rice yield response to genotypic and environmental variation in rice yield response to elevated atmospheric CO2 concentration.

・Effect of the amount of nitrogen (N) fertilizer (Kim et al., 2001 and 2003, Stitt and Krapp, 1999, Yang et al., 2006 and 2009). ・Japonica genotypes vs Indica high‐yielding genotypes (Horie et al 2005 De Japonica genotypes vs Indica high‐yielding genotypes (Horie et al., 2005, De Costa et al., 2007)

Can the model explain the genotypic and N fertilization effects on rice yield response to elevated CO2?

Simulation environment

All simulations were based on ARICENET experiments

C 9 i 7 i i J Chi d Th il d i h b d i i i l l Crop: 9 rice genotypes grown at 7 sites in Japan, China and Thailand, with observed initial values at transplanting date Climate‐condition: Various climate‐conditions observed at those 7 sites during rice growth period Soil N condition: same level of soil N fertility was assumed for all the sites (The two site‐specific soil parameters for representing the rates of soil N mineralization and its loss were standardized to values of Kyoto for all the sites.)

The effect of elevated [CO2] on rice growth and yield under f l various N fertilization rates

・The current (360ppm) and elevated (700ppm) [CO2] ・Five levels of N fertilization: 4, 8, 12, 16, 20 g N m‐2

(40% for basal and 20% at 20 DATP, PI and about 20 days before heading)

2500

A: IR72

1500 2000 2500

g m-2)

Above-ground biomass: Elevated [CO2]

A: IR72

1000 1500

• mass (g

Above-ground biomass: Current [CO2] NSC: Elevated [CO2]

500

50 100 150

Bi

NSC: Current [CO2] 50 100 150

2000 2500

m-2) B: Nipponbare

1000 1500

mass (g m

500

Biom

Simulation at Kyoto with 12 g m‐2 N fertilizer

50 100 150

Days after transplanting

Yoshida et al., FCR (in press)

The model predicted that the relative yield response to elevated [CO2] (RY, the ratio

• f yield under 700 ppm [CO2] to that under 360 ppm [CO2]) increased with

1.3

d Rough dry grain yield Above-ground biomass

• f yield under 700 ppm [CO2] to that under 360 ppm [CO2]) increased with

increasing N fertilizer averaged over all genotypes and locations.

1.25

• elevated

Rough dry grain yield Above ground biomass

1.15 1.2

ent due to O2]

1.1 1.15

hanceme [CO

1.05

lative enh

1

Rel

4 8 12 16 20

Fertilized N (g m-2)

Yoshida et al., FCR (in press)

Genotypic difference in relative yield response to elevated [CO2] was related to 1 6 yp y p [

2]

that in spikelet number under the current [CO2] condition. 1.5 1.6 ue to

y=0.0065x + 0.86 R 0 88**

3 1.4 crease d d [CO2]

R=0.88

Nipponbare IR72 Shanguichao Takanari

1.2 1.3 e yield in elevated

Ch86 IR65564‐44‐2‐2 Takenari Nipponbare

1.1 Relative

Banten IR65564 44 2 2 WAB‐450‐I‐B‐P‐38‐HB

1 30 40 50 60 70 80 90 S ik l t b it d th t Spikelet number per unit area under the current [CO2] condition (with 20 g m‐2 N fertilizer)

Yoshida et al., FCR (in press)

Conclusions Conclusions

i G b i h d i ld i f h Targeting G by E in crop growth and yield is one of the major tasks of crop growth model integrated into decision support systems for rice. pp y The present model well explained the observed t i d i t l i ti i l t N genotypic and environmental variations in plant N accumulation, growth and yield of diverse rice genotypes in Asia. Further improvement of the model will help develop location and cultivar specific rice cultivation location‐ and cultivar‐specific rice cultivation technologies in Asia based on underlying physiological processes.