sirius wheat simulation model: development and applications mikhail a. semenov rothamsted research,...
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Sirius wheat simulation model: development and applications
Mikhail A. SemenovRothamsted Research, UK
IT in Agriculture & Rural Development, Debrecen, 2006
Sirius: wheat simulation model
Initially developed by Peter Jamieson from Crop & Food Research, NZ; since 1992 development in collaboration with Mikhail Semenov at Rothamsted Research, UK
Intensively tested in different environments and used in many countries (www.rothamsted.bbsrc.ac.uk/mas-models/sirius.php)
Sirius is a part of the GCTE International Wheat Network
Sirius: wheat simulation model
Sirius
Inputs Outputs
Daily Weather
Soil
Management
Cultivar
Grain yield
Grain quality
N leaching
Water and N uptake
Sirius: a process-based model
Radiation use efficiency and biomass accumulation
Phenological development Canopy model Nitrogen uptake and redistribution Evapotranspiration and water limitation Soil model
Modelling growth: Radiation Use Efficiency
Radiation Use Efficiency
0
1
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9
10
0 1 2 3 4
Light intersepted, Mj / m2
Bio
ma
ss
g /
m2
Beer's Law
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10
Leaf Area Index
Lig
ht
Inte
rse
pte
d, %
Biomass = RUE*R
R intercepted radiation
P = 1-exp(-k LAI)
P proportion of light intercepted
Modelling canopy
Phenology is used to predict emergence times of individual leaves
Deal with leaf “layers” avoid consideration of tillers avoid adding extra parameters for calibration
Define genetic potential growth
Modelling Canopy: Leaf Area Index
Leaf Area Index
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0 5 10 15 20 25 30 35 40 45 50
time
LA
I
anth
esi
smax LAI Sirius grows a canopy Sirius grows a canopy
(LAI) according to simple (LAI) according to simple rules involving rules involving temperature, water and temperature, water and N supplyN supply
Modelling phenology
Pre-emergence and after anthesis calculations are based on thermal time
Calculation of anthesis is based on the final leaf number and the value of phyllochron
Calculation of the final leaf number includes vernalization and daylength responses
Em
erg
enc
e
Anthesis
Matu
rit
y
N Limitation
Leaf Area Index
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9
10
0 5 10 15 20 25 30 35 40 45 50
time
LA
I
anth
esi
s
max LAI
Green area contains 1.5 g N/m2 ; “non-green” biomass can store 1% labile N
Daily N-demand is set by the increment of new GA and biomass
Unsatisfied demand limits the GA increment and/or causes N release through premature GA senescence
Calibration and validation
Calibration – measuring (direct) or fitting (indirect) model parameters to observed data
Validation – using independent (not used during calibration) observed data for testing model skills
Validation of Sirius: N experiments
0
1
2
3
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5
6
7
20-Jan 19-Feb 21-Mar 20-Apr 20-May 19-Jun
GAI
Low N High N Obs LoN Obs HiN
FACE, Maricopa, 1996/97
Sirius: soil, evapotranspiration & water limitation
Soil model is based on modified SLIM (UK) and DAISY (DENMARK) models
ET is calculated as the sum of transpiration and soil evaporation after Ritchie (1972). The upper limit is given by the Penman potential ET rate or the Priestley&Taylor equation
Water stress factor reduces leaf expansion and accelerate leaf senescence.
Validation: water-limited grain yield
0
2
4
6
8
10
12
0 2 4 6 8 10 12
Measured yield (t/ha)
Sim
ula
ted
yie
ld (
t/h
a)
Y = X
Canterbury, NZ
Rothamsted, UK
Maricopa, H2O
Free-Air CO2 Enrichment Project (FACE)
RUE = f(CO2)
USDA-ARS U.S. Water Conservation LaboratoryUSDA-ARS U.S. Water Conservation Laboratory, Maricopa, USA, Maricopa, USA
Model complexity
Model complexity is related to a number of model parameters and model equations
Hierarchy of complexity: Meta-model (Brooks et al, 2001); Sirius (Jamieson et al., 1998), AFRCWHEAT
(Porter 1993), CERES-Wheat (Ritchie and Otter, 1985);
Ecosys (Grant, 1998).
Simplifying model
Mimic model output by non-linear regression
0
1
2
3
4 0
1
2
3
4
-1
0
1
0
1
2
3
4
Model response surface
0
1
2
3
40
1
2
3
4
-1-0.5
0
0.5
1
0
1
2
3
4
Fitted approximation
Simplifying model
Simplify a model by analysing model structure, model processes and its interactions
0
1
2
3
40
1
2
3
4
-1-0.5
0
0.5
1
0
1
2
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4
0
1
2
3
4 0
1
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-1
0
1
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4
Comparison between Meta-model and SiriusRothamsted, UK, 1960-1990 (50% precipitation)
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4 5 6 7 8 9 10
Sirius
Meta
Simulation results, Andalucian region, Spain, 1988-1999
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4
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8
9
3 4 5 6 7 8 9
observed
sim
ulat
ed
Meta Sirius
Obs Meta Sirius
Obs 1.00
Meta 0.92 1.00
Sirius 0.63 0.74 1.00
ApplicationPrediction of grain yield in real time
Observedweather
Management
Soil
Sirius
Generatedweather
0
0.05
0.1
0.15
0.2
0.25
0.3
0 4 8 12 16 20
ApplicationPrediction of grain yield in real time
Observedweather
Management
Soil
Sirius
Generatedweather
0
0.05
0.1
0.15
0.2
0.25
0.3
0 4 8 12 16 20
Weather uncertainty in real-time predictions
0
100
200
300
400
500
600
700
800
900
1000
Sep Oct Nov Dec J an Feb Mar Apr May J un J ul Aug Sep
Accumulated Rainfall (mm)
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200
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600
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900
1000
Sep Oct Nov Dec J an Feb Mar Apr May J un J ul Aug Sep
Accumulated Rainfall (mm)
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100
200
300
400
500
600
700
800
900
1000
Sep Oct Nov Dec J an Feb Mar Apr May J un J ul Aug Sep
Accumulated Rainfall (mm)
Accumulated rainfall, mm
Yield prediction using mixture of observed and generated weather at Rothamsted, 1997
4000
4500
5000
5500
6000
6500
7000
7500
8000
8500
9000
0 50 100 150 200 250 300 350
No. observed days
Grain Yield (kg/ha)
Lead-time for predicting wheat growthat Rothamsted
0
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0. 9
1
0306090120150180210240270300330360
No. days bef ore matur i ty
Probability of prediction
Fi nLN
Ant hD
Mat D
Bi omass
Yi el d
Lead-time for predicting grain yield in diverse climates
Yi el d
0
0. 1
0. 2
0. 3
0. 4
0. 5
0. 6
0. 7
0. 8
0. 9
1
0306090120150180210240270300330360
No. days bef or e mat ur i t y
Probability of prediction
Tylstrup Debrecen Toulouse Lincoln Munich RothamstedTylstrup Debrecen Toulouse Lincoln Munich Rothamsted
Grain yield can be predictedwith 0.9 probability:in Toulouse 40 days and in Tylstrup 65 days before maturity
Publications
Jamieson PD, Semenov MA, Brooking IR & Francis GS (1998) Sirius: a mechanistic model of wheat response to environmental variation. Europ. J. Agronomy, 8:161-179
Jamieson PD & Semenov MA (2000) Modelling nitrogen uptake and redistribution in wheat. Field Crops Research, 68: 21-29.
Brooks RJ, Semenov MA & Jamieson PD (2001) Simplifying Sirius: sensitivity analysis and development of a meta-model for yield prediction Europ. J. Agronomy 14:43-60
Lawless C, Semenov MA & Jamieson PD (2005) A wheat canopy model linking leaf area and phenology Europ. J. Agronomy, 22:19-32
Lawless C & Semenov MA (2006) Assessing lead-time for predicting wheat growth using a crop simulation model Agric Forest Meteorology 135:302-313
WWW: www.rothamsted.bbsrc.ac.uk/mas-model/sirius.php