oryza2000 modeling: an introduction
DESCRIPTION
An introduction to rice modeling using ORYZA2000.TRANSCRIPT
Systems Analysis and Simulation
- An introduction -
Principles and theory of systems analysis and simulation of “the School of de Wit”,
Wageningen University, Netherlands (1965-…)
Other major modelling groups:
• USA: IBSNAT/DSSAT (CERES, CROPGRO)
• Australia: APSRU (APSIM)
All three combined in ICASA: International Consortium for the Application of Systems Analysis
Systems, Models and Simulation
System: limited part of reality that contains interrelated elements
System boundary: environment influences system, but not the other way round
Model: simplified representation of a system e.g.- scale model of ship- mathematical model
Simulation: building mathematical models and studyperformance in reference to real system
The Rice System and boundary at field level
Radiation, CO2, H2O O2 , H2O
H2O H2O, nutrients
H2OH2O Root zone
nutrients
Temperature,Wind speedVapor pressure
Mathematical model
Schematization of the production system:
Cf = kdf,m / (0.8 √(1 – σ) )
kdr,bl = 0.5 Cf / sinβ or kdr,t = kdr,bl √(1 – σ)
Ia,L = dIL /dL = k (1 ρ) I0 exp( k L)
Ia,df = dIdf,L/dL = kdf (1 ) I0,df exp(kdf LL)
Ia,dr,t = dIdr,t,L/dL = kdr,t (1 ) I0,dr exp(kdr,t LL)
Ia,dr,dr = dIdr,dr,L/dL = kdr,dr (1 ) I0,dr exp(kdr,dr LL)
Ia,sh = Ia,df (Ia,dr,t Ia,dr,dr)
Ia,dr,dr = (1 σ) I0,dr/sinβ and fsl = Cf exp(kdr,bl LL)
Light interceptionand distribution
SUBROUTINE SRDPRF (GAID, CSLV, SINB, ECPDF, RDPDR, RDPDF, & RAPSHL, RAPPPL, FSLLA)
! Reflection of horizontal and spherical leaf angle distribution TMPR1 = SQRT (1. - CSLV) RFLH = (1. - TMPR1) / (1. + TMPR1) RFLS = RFLH * 2. / (1. + 2. * SINB)
! Extinction coefficient for direct radiation and total direct flux CLUSTF = ECPDF / (0.8*TMPR1) ECPBL = (0.5/SINB) * CLUSTF ECPTD = ECPBL * TMPR1 ! Absorbed fluxes per unit leaf area: diffuse flux, total direct! flux, direct component of direct flux RAPDFL = (1.-RFLH) * RDPDF * ECPDF * EXP (-ECPDF * GAID) RAPTDL = (1.-RFLS) * RDPDR * ECPTD * EXP (-ECPTD * GAID) RAPDDL = (1.-CSLV) * RDPDR * ECPBL * EXP (-ECPBL * GAID)
Scientific equations
Computer code
Diagram of a crop growth model
Photosynthesis
Assimilatepool Biomass
Leaves
Stems
Panicles
Roots
LAI
Developmentstage
Maintenancerespiration
Growthrespiration
Partitioning
Developmentrate
N leaves
Light
Transpiration
Soil water Soil-watertension
Evaporation Rain, irrigation
Temperature
modelModel input:• Weather• Crop properties• Soil properties• Management
Model output = calculated/predicted• Crop growth and development• Yield• Water requirements• Nitrogen requirements• ……
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Run 3, WAGTRun 3, WAGT_OBSRun 3, WLVGRun 3, WLVG_OBSRun 3, WSORun 3, WSO_OBS
Simulation
Real rice system
Crop models: descriptive and explanatory
Descriptive: describe relation at same level of integration (e.g. leaf photosynthesis as function of radiation falling on that leaf)
Explanatory: explain system from underlying level of integration (e.g. assimilation of whole crop as function of leaf photosynthesis characteristics)
State variable approach
• State of a system can be defined at any time
• Changes can be expressed mathematically
State time 1 State time 2
(leaf area)
Rate of change ( leaf area)
x time step
LIGHT
PHOTOS
MAINT
BIOMASS
GROWTH
LAICONV. EFF.
ASSIMILATES
Modelling: why the fuzz?
Radiation, CO2, H2O O2 , H2O
H2O H2O, nutrients
H2OH2O Root zone
nutrients
Temperature,Wind speedVapor pressure
Study the behavior of the system in relation to (changes in) its environment
G x E
Crop ecology
The Rice System and boundary at field level
Radiation, CO2, H2O O2 , H2O
H2O H2O, nutrients
H2OH2O Root zone
nutrients
Temperature,Wind speedVapor pressure
Purpose and usefulness of modelling
• Test our knowledge and understanding• Supports experimental data analysis through process-based explanation• Mimic field experiments• Extrapolate experimental findings (time, space)• Management optimization• Crop ideotype design (breeding support)• Agro-ecological zonation, yield gap analysis, yield forecasting, climate change
ORYZA2000: a crop growth simulation model for lowland (and upland/aerobic) rice
1. Potential production
2. Water-limited production
3. Nitrogen-limited production
1970
1975
1980
1985
1990
1995
WOFOST
WOFOST 6.0
ELCROS
PAPRAN
PHOTON
MACROS(SAWAH)
ARID CROP(SAHEL)
ARID CROP
SUCROS87SUCROS
SUCROS87
SUCROS1,SUCROS2
SBFLEVO, WWFLEVO
SWHEAT
INTERCOM
BACROS
LINTUL
1965 'Photosynthesis of leaf canopies'
MICROWEATHER
ORYZA
2000 ORYZA2000
Pedigree of crop growth models from
“School of de Wit”
Some validation
IR72 at IRRI farm; 1991-1993 WS and DS:
• Different N treatments from 0 to 400 kg ha-1
• Different N application timings
• Fully irrigated treatments
IR72, DS 1992
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Leaf AreaIndex
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Total
Panicle
Leaves
?
Biomass
225 kg N ha-1 0 kg N ha-1
N uptake (kg/ ha)N supply (kg/ ha)
Yield (kg/ ha)
N supply (kg/ ha)
400 300
11
400
Observed
o Simulated
IR72; 1993 DS
17 treatments
0-400 kg N ha-1
Different splits?
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0 5 10
Yield simulated (t ha-1)
Yield observed (t ha-1)
N = 39
IR72; all data
1991-1993
39 treatments
0-400 kg N ha-1
Different splits
IR72, DS 1992: ponded water depth
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38 58 78 98 118Day of year
Ponded water depth (mm)
� ObservedSimulated
Case study 1: water management optimization (Boling et al., 2001)
IR64 at Jakenan, Indonesia; 1995-2000
Irrigated and rainfed treatments
General objectives:
• Optimize crop scheduling (best use of rain)
• Optimize irrigation water application
• Toposequence effect (low-deep groundwater)
Solar radiation, MJ m-2 d-1
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O N D J F M A M J J A S O
Rainfall, mm (10 d)-1
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P=0.20 P=0.50 P=0.80
(a)
(b) Probability of exceedance (P):
Time
O N D J F M A M J J A S O
(c)
(Gogorancah) (Walik Jerami) (Palawija)
Dry-seeded rice Transplanted rice Upland crop
Jakenan climateandcropping system
First step: model validation: crop
Calendar day
40 60 80 100 120 140 160 180 200
Dry matter, kg ha-1
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6000
9000
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15000
18000WiTnS1, measuredWrTnS1, measuredWiTnS2, measuredWiTdS1, measuredWrTdS1, measuredWrTdS2, measuredWiTnS1, simulatedWrTnS1, simulatedWrTnS2, simulated
Irrigated
Rainfed early
Rainfed late
1996
(a) April-June 1995 (walik jerami season)
A M J J A
Water table depth, cm
-140
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0
(b) December 1997-March 1998 (gogorancah season)
D J F M A
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20
measuredsimulated
(c) November 1998-February 1999 (gogorancah season)
Day of seeding
N D J F M
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Model validation: groundwater
Modelling depth ofgroundwater difficult! Use “scenarios” in the model explorations: - shallow - medium - deep
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Rainfed early; 20 cm depth
Day
kPa
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Jakenan, 1996. WrTdS2, 20 cm
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Kpa Rainfed late; 20 cm depthkPa
Model validation: soil water tension
Day of seeding
O N D J F M A M J J A S O
Simulated yield, kg ha-1
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Model exploration: irrigated and rainfed yield asfunction of sowing date
Day of seeding
O N D J F M A M J J A S O
Water requirement, mm
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Simulated yield, kg ha-1
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(b)Irrigation scenario:
rainfed
irrigated (I1), 0.34 cm3
cm-3
irrigated (I3), PI to M, 3.3 mm d-1
irrigated (I2), PI to M, 7.5 mm d-1
I1: 0.34 cm3 cm-3
I3: PI to M, 3.3 mm d-1
I2: PI to M, 7.5 mm d-1
Model exploration: effect of small irrigation applications
Day of seeding
J F M A M
Yield increase, kg ha-1 m-3 irrigation
0.0
0.2
0.4
0.6
0.8
1.0
I1: 0.34 cm3 cm-3
I3: PI to M, 3.3 mm d-1
I2: PI to M, 7.5 mm d-1
Model exploration: optimizing irrigation application
Case study 2: agro-ecological zonation and yield forecasting (European Union)
Example for wheat (SUCROS model used)
General objectives:
• Map potential and rainfed yields in EU
• Map yield gap in EU
• Predict yield in EU
Step 1: weather stations and grid cells
Step 2: soil data
Step 3: running model in GIS: potential yield
Step 3: running model in GIS: yield prediction
Step 3: running model in GIS: yield gap analysis