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Sirius wheat simulation model: development and applications Mikhail A. Semenov Rothamsted Research, UK IT in Agriculture & Rural Development, Debrecen,

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Presentation on theme: "Sirius wheat simulation model: development and applications Mikhail A. Semenov Rothamsted Research, UK IT in Agriculture & Rural Development, Debrecen,"— Presentation transcript:

1 Sirius wheat simulation model: development and applications Mikhail A. Semenov Rothamsted Research, UK IT in Agriculture & Rural Development, Debrecen, 2006

2 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 ) www.rothamsted.bbsrc.ac.uk/mas-models/sirius.php Sirius is a part of the GCTE International Wheat Network

3 Sirius: wheat simulation model Sirius Inputs Outputs Daily Weather Soil Management Cultivar Grain yield Grain quality N leaching Water and N uptake

4 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

5 Modelling growth: R adiation Use Efficiency Biomass = RUE*R R intercepted radiation P = 1-exp(-k LAI) P proportion of light intercepted

6 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

7 Modelling Canopy: Leaf Area Index anthesis max LAI Sirius grows a canopy (LAI) according to simple rules involving temperature, water and N supply Sirius grows a canopy (LAI) according to simple rules involving temperature, water and N supply

8 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 Emergence Anthesis Maturity

9 N Limitation anthesis max LAI Green area contains 1.5 g N/m 2 ; “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

10 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

11 Validation of Sirius: N experiments FACE, Maricopa, 1996/97

12 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.

13 Validation: water-limited grain yield

14 Free-Air CO 2 Enrichment Project (FACE) RUE = f(CO 2 ) USDA-ARS U.S. Water Conservation Laboratory, Maricopa, USA

15 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).

16 Simplifying model Mimic model output by non-linear regression Model response surfaceFitted approximation

17 Simplifying model Simplify a model by analysing model structure, model processes and its interactions

18 Comparison between Meta-model and Sirius Rothamsted, UK, 1960-1990 (50% precipitation)

19 Simulation results, Andalucian region, Spain, 1988-1999 ObsMetaSirius Obs1.00 Meta0.921.00 Sirius0.630.741.00

20 Application Prediction of grain yield in real time Observed weather Management Soil Sirius Generated weather

21 Application Prediction of grain yield in real time Observed weather Management Soil Sirius Generated weather

22 Weather uncertainty in real-time predictions Accumulated rainfall, mm

23 Yield prediction using mixture of observed and generated weather at Rothamsted, 1997

24 Lead-time for predicting wheat growth at Rothamsted

25 Lead-time for predicting grain yield in diverse climates Grain yield can be predicted with 0.9 probability: in Toulouse 40 days and in Tylstrup 65 days before maturity

26 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

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