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Sirius wheat simulation model: development and applications
Mikhail A. SemenovRothamsted Research, UK
IT in Agriculture & Rural Development, Debrecen, 2006
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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
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Sirius: wheat simulation model
Sirius
Inputs Outputs
Daily Weather
Soil
Management
Cultivar
Grain yield
Grain quality
N leaching
Water and N uptake
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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
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Modelling growth: Radiation Use Efficiency
Radiation Use Efficiency
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Light intersepted, Mj / m2
Bio
ma
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g /
m2
Beer's Law
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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
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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
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Modelling Canopy: Leaf Area Index
Leaf Area Index
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time
LA
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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
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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
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N Limitation
Leaf Area Index
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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
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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
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Validation of Sirius: N experiments
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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
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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.
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Validation: water-limited grain yield
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Measured yield (t/ha)
Sim
ula
ted
yie
ld (
t/h
a)
Y = X
Canterbury, NZ
Rothamsted, UK
Maricopa, H2O
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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
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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).
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Simplifying model
Mimic model output by non-linear regression
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Model response surface
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Fitted approximation
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Simplifying model
Simplify a model by analysing model structure, model processes and its interactions
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Comparison between Meta-model and SiriusRothamsted, UK, 1960-1990 (50% precipitation)
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Sirius
Meta
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Simulation results, Andalucian region, Spain, 1988-1999
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observed
sim
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Meta Sirius
Obs Meta Sirius
Obs 1.00
Meta 0.92 1.00
Sirius 0.63 0.74 1.00
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ApplicationPrediction of grain yield in real time
Observedweather
Management
Soil
Sirius
Generatedweather
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ApplicationPrediction of grain yield in real time
Observedweather
Management
Soil
Sirius
Generatedweather
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0.25
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Weather uncertainty in real-time predictions
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Sep Oct Nov Dec J an Feb Mar Apr May J un J ul Aug Sep
Accumulated Rainfall (mm)
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Accumulated Rainfall (mm)
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Sep Oct Nov Dec J an Feb Mar Apr May J un J ul Aug Sep
Accumulated Rainfall (mm)
Accumulated rainfall, mm
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Yield prediction using mixture of observed and generated weather at Rothamsted, 1997
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No. observed days
Grain Yield (kg/ha)
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Lead-time for predicting wheat growthat Rothamsted
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0306090120150180210240270300330360
No. days bef ore matur i ty
Probability of prediction
Fi nLN
Ant hD
Mat D
Bi omass
Yi el d
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Lead-time for predicting grain yield in diverse climates
Yi el d
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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
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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