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

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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)

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400

Observed

o Simulated

IR72; 1993 DS

17 treatments

0-400 kg N ha-1

Different splits?

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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)

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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

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Dry matter, kg ha-1

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Irrigated

Rainfed early

Rainfed late

1996

(a) April-June 1995 (walik jerami season)

A M J J A

Water table depth, cm

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(b) December 1997-March 1998 (gogorancah season)

D J F M A

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measuredsimulated

(c) November 1998-February 1999 (gogorancah season)

Day of seeding

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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

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kPa

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Jakenan, 1996. WrTdS2, 20 cm

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Model validation: soil water tension

Day of seeding

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Simulated yield, kg ha-1

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Model exploration: irrigated and rainfed yield asfunction of sowing date

Day of seeding

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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

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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