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 800kg/ha Genotype 1 300kg/ha 350kg/ha 500kg/ha Genotype 2 To improve productivity and sustainability of rice production in Asia, we need location ‐ and cultivar ‐ specific rice cultivation t technologies. h l i

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 ‐ environment interaction in rice growth and yield i i i i i h d i ld 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) Nipponbare （ NIP) japonica japonica Takenari （ TKE) japonica Banten （ BAN) （ BAN) B t javanica 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 development of the model. 10 Leaf area index (LAI) TKA age IR IR t heading sta 8 SKS 6 CH NPT Env. 4 LAI at NIP NIP 1 Iwate 2001 TKE 2 2 Iwate 2002 BAN 0 3 Nagano 2002 WAB 9 9 4 4 2 2 6 6 8 8 3 3 7 7 5 5 1 1 10 10 11 11 4 Shimane 2001 Environments 5 Shimane 2002 6 Kyoto 2001 1200 1200 Rough dry grain yield h d ld 7 Kyoto 2002 2002 ry grain yield 1000 8 Nanjing 2002 g m ‐ 2 ) 800 9 Yunnan 2002 (g 600 600 10 10 Chiang Mai 2001 Chiang Mai 2001 Rough d 400 11 Ubon Ratchathani 2001 200 0 0 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 y utilized for sub ‐ model calibrations, and the rest for the model Biomass growth validations. Yoshida et al 2008 FCR 108 222 230 Yoshida et al., 2008,FCR, 108, 222 ‐ 230. ・ Simplex method LAI development Yoshida et al., 2007, FCR, 102 ‐ 228 ‐ 238. Whole system Yield formation model Yoshida and Horie, 2009, FCR, 113, 227 ‐ 237. This procedure had advantages in This procedure had advantages in… S ik l t Spikelet number determination b d t i ti ・ Understanding each physiological processes Yoshida et al., 2006, FCR, 97,337 ‐ 343. ・ Step ‐ by ‐ step assessment of the model Step by step assessment of the model N dynamics within plant organs N dynamics within plant organs structure, algorithm and precision Plant N uptake ・ Identifying genotype ‐ specific parameters y g g yp p p Yoshida and Horie, 2010, FCR, 117 ‐ 122 ‐ 130. Yoshida and Horie, 2010, FCR, 117 122 130. which have a specific role in individual processes.

Phenological Biomass growth development Photosynthesis Yield formation Development Development Spikelet sterility Sugar (Su) DVI Maintenance Attainable respiration Root Yield Yield Root growth Spikelet Grain growth number Storage starch Vegetative accumulation tissue growth Differentiation Grain Yield (Y) Vegetative Tissues Storage starch Translocation (V) (ST) Spikelet # Degeneration Degeneration Plant N dynamics Vegetative tissue N (N VT ) (leaf N + stem N) Grain N (N Y ) Translocation Senescence LAI development Vegetative tissue N Recover N D accumulation Grain N N pool accumulation (leaf N + stem N) Expansion Expansion N pool accumulation LAI Plant N uptake Soil mineral N S Senescence N uptake Root system Loss Root system development Indigenous supply fertilization

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

Rough grain yield Spikelet number per area Plant N uptake 1500 1500 100 100 30 30 number (m ‐ 2 ) ontent (g m ‐ 2 ) d ( gm ‐ 2) 25 1000 20 ated spikelet n ated plant N co Simulated yiel 50 15 500 10 y = 1.00x y = 0.99x y = 1.05x 5 5 R² = 0 82 R = 0.82 Simula Simula R² = 0.78 R² = 0.91 0 0 0 0 500 1000 1500 0 50 100 0 10 20 30 Measured spikelet number (m ‐ 2 ) Measured yield (g m ‐ 2) y (g ) Measured plant N content (g m 2 ) Measured plant N content (g m ‐ 2 ) Leaf N content Biomass growth LAI dynamics 14 ound 2500 12 m ‐ 2 ) 12 12 s of above ‐ gro af N content (g 10 2000 wth (g m ‐ 2) mulated LAI 10 8 1500 8 6 biomass grow 6 6 Sim 1000 1000 Simulated lea lated dynamic 4 4 y = 1.02x y = 0.97x 500 y = 0.99x 2 R² = 0.94 2 R² = 0.78 R² = 0.78 0 0 0 0 0 Simu 0 1000 2000 3000 0 5 10 15 0 5 10 15 Measured LAI Measured dynamics of above ‐ ground Measured leaf N content (gm ‐ 2 ) biomass gorwth (g m ‐ 2) (Yoshida and Horie 2010)

25 25 Iwate 02 Iwate 01 20 20 15 15 10 10 5 5 5 5 0 0 0 60 120 180 0 60 120 180 25 25 20 20 on (gm ‐ 2 ) 15 15 10 10 Shimane 02 5 Shimane 01 5 0 0 0 0 60 60 120 120 180 180 0 0 60 60 120 120 180 180 cumulatio 25 25 Ubon 01 20 20 15 15 10 10 Yunnan 02 Yunnan 02 5 5 5 5 lant N acc 0 0 0 60 120 180 0 60 120 180 25 25 20 20 With the use of 2 empirical p 15 15 15 15 P 10 10 soil parameters Kyoto 01 5 5 Kyoto 02 characterizing indigenous 0 0 0 60 120 180 0 60 120 180 N supply and N loss. 2 25 25 20 20 15 15 10 10 5 Nagano 02 Nagano 02 5 Nanjing 02 Nanjing 02 0 0 0 60 120 180 0 60 120 180 Days after transplanting (Yoshida and Horie 2010)

Simulated growth and yield of IR72 2500 2500 A: Kyoto 2001 B: Yunnan 2002 2000 2000 Above ‐ ground Above ground biomass 1500 1500 Yield 1000 1000 Structural Structural Tissue 500 500 NSC m ‐ 2 ) 0 0 omass (g m 0 50 100 150 0 50 100 150 2500 2500 C: Iwate2001 D: Ubon 2001 2000 Bio 2000 2000 1500 1500 1000 1000 1000 500 500 0 0 0 50 100 150 0 50 100 150 Days after transplanting

11 genotype ‐ specific model parameters for representing critical traits for determining genotypic difference in rice growth and yield Parameter Unit Unit Definition Definition symbol G V d Minimum number of days required to head L C L hr hr Critical day length for development Critical day length for development T h ˚ C Temperature at which development rate is half the maximum T γ H ˚ C High temperature to induce 50% spikelet sterility g e pe a u e o duce 50% sp e e s e y γ H T γ L ˚ C Critical low temperature for spikelet sterility mol m ‐ 2 s ‐ 1 g S Stomatal conductance for CO 2 transfer k Radiation extinction coefficient g ‐ 1 A Spikelet differentiation efficiency per unit plant N Critical leaf N content per unit leaf area below which leaves Critical leaf N content per unit leaf area below which leaves g m ‐ 2 LNC min start to senesce W G mg Potential single grain mass τ N 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 CO 2 concentration