Crop modelling: Sirius wheat simulation model. Mikhail A. Semenov Rothamsted Research, UK

Transcription

1 Crop modelling: Sirius wheat simulation model Mikhail A. Semenov Rothamsted Research, UK AGRIDEMA Workshop, Vienna, 5

2 Sirius: wheat simulation model Initially developed by Peter Jamieson from Crop & Food Research, NZ; since 99 development in collaboration with Mikhail Semenov at Rothamsted Research, UK Intensively tested in different environments and used in many countries ( Sirius is a part of the GCTE International Wheat Network AGRIDEMA Workshop, Vienna, 5

3 Sirius: wheat simulation model Inputs Daily Weather Management Cultivar Soil Sirius Outputs Grain yield Grain quality N leaching Water and N uptake AGRIDEMA Workshop, Vienna, 5

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 AGRIDEMA Workshop, Vienna, 5

5 Modelling growth: Radiation R Use Efficiency Radiation Use Efficiency Beer's Law Biomass g / m Biomass = RUE*R R intercepted radiation Light Intersepted, % P = -exp(-k LAI) P proportion of light intercepted Light intersepted, Mj / m 6 8 Leaf Area Index AGRIDEMA Workshop, Vienna, 5

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 AGRIDEMA Workshop, Vienna, 5

7 Modelling Canopy: Leaf Area Index Leaf Area Index LAI anthesis max LAI Sirius grows a canopy (LAI) according to simple rules involving temperature, water and N supply; parameters (not rules) vary with cultivar time AGRIDEMA Workshop, Vienna, 5

8 Modelling phenology Anthesis Emergence Maturity 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 AGRIDEMA Workshop, Vienna, 5

9 N Limitation LAI Leaf Area Index max LAI Green area contains.5 g N/m ; non-green biomass can store % labile N Daily N-demand is set by the increment of new GA and biomass anthesis Unsatisfied demand limits the GA increment and/or causes N release through premature GA senescence time AGRIDEMA Workshop, Vienna, 5

10 Calibration and validation Calibration measuring (direct) or fitting (indirect) model parameters to observed data Validation using independent (not use during calibration) observed data for testing model skills AGRIDEMA Workshop, Vienna, 5

11 Validation of Sirius: N experiments FACE, Maricopa, 996/97 Reading, 999/ (M.Clarke) GAI Jan 9-Feb -Mar -Apr -May 9-Jun Low N High N Obs LoN Obs HiN GAI Obs kg kg AGRIDEMA Workshop, Vienna, 5

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 (97). 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. AGRIDEMA Workshop, Vienna, 5

13 Validation: water-limited grain yield Simulated yield (t/ha) 8 6 Y = X Canterbury, NZ Rothamsted, UK Maricopa, HO 6 8 Measured yield (t/ha) AGRIDEMA Workshop, Vienna, 5

14 Validation: N uptake 5 Simulated N uptake (kg/ha) Y = X Lincoln UK Arizona 5 Observed N uptake (kg/ha) AGRIDEMA Workshop, Vienna, 5

15 Free-Air CO Enrichment Project (FACE) RUE = f(co ) USDA-ARS ARS U.S. Water Conservation Laboratory,, Maricopa, USA AGRIDEMA Workshop, Vienna, 5

16 Model complexity Model complexity is related to a number of model parameters and model equations Hierarchy of complexity: Meta-model (Brooks et al, ); Sirius (Jamieson et al., 998), AFRCWHEAT (Porter 99), CERES-Wheat (Ritchie and Otter, 985); Ecosys (Grant, 998). AGRIDEMA Workshop, Vienna, 5

17 Simplifying model Mimic model output by non-linear regression Model response surface Fitted approximation AGRIDEMA Workshop, Vienna, 5

18 Simplifying model Simplify a model by analysing model structure, model processes and its interactions AGRIDEMA Workshop, Vienna, 5

19 Comparison between Meta-model and Sirius Rothamsted, UK, (5% precipitation) 9 8 Meta Sirius AGRIDEMA Workshop, Vienna, 5

20 Simulation results, Andalucian region, Spain, Obs Meta Sirius 8 Obs Meta..9. simulated Sirius observed Meta Sirius AGRIDEMA Workshop, Vienna, 5

21 Application Prediction of wheat growth in real time Observed weather Generated weather..5. Sirius.5..5 Management 8 6 Soil AGRIDEMA Workshop, Vienna, 5

22 Application Prediction of wheat growth in real time Observed weather Generated weather..5. Sirius.5..5 Management 8 6 Soil AGRIDEMA Workshop, Vienna, 5

23 Weather uncertainty in real-time predictions Accumulated Rainfall (mm) Accumulated Rainfall (mm) Accumulated Rainfall (mm) Sep Oct NovDec Jan Feb Mar AprMay Jun Jul Aug Sep Sep Oct NovDec Jan Feb Mar AprMay Jun Jul Aug Sep Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Accumulated rainfall, mm AGRIDEMA Workshop, Vienna, 5

24 Grain prediction using mixture of observed and generated weather at Rothamsted, Grain Yield (kg/ha) No. observed days AGRIDEMA Workshop, Vienna, 5

25 Lead-time for predicting wheat growth at Rothamsted.9.8 FinLN Probability of prediction AnthD MatD. Biomass Yield No. days before maturity 9 6 AGRIDEMA Workshop, Vienna, 5

26 Lead-time for predicting grain yield in diverse climates Yield Tylstrup Debrecen Toulouse Lincoln Munich Rothamsted Probability of prediction Grain yield can be predicted with.9 probability: in Toulouse days and in Tylstrup 65 days before maturity No. days before maturity 6 AGRIDEMA Workshop, Vienna, 5

27 Publications Jamieson PD, Semenov MA, Brooking IR & Francis GS (998) Sirius: a mechanistic model of wheat response to environmental variation. Europ. J. Agronomy, 8: Jamieson PD & Semenov MA () Modelling nitrogen uptake and redistribution in wheat. Field Crops Research, 68: Brooks RJ, Semenov MA & Jamieson PD () Simplifying Sirius: sensitivity analysis and development of a meta-model model for yield prediction Europ. J. Agronomy :-6 Lawless C, Semenov MA & Jamieson PD (5) A wheat canopy model linking leaf area and phenology Europ. J. Agronomy, :9- Lawless C & Semenov MA (6) Assessing lead-time for predicting wheat growth using a crop simulation model Agric Forest Meteorology (in press) WWW: AGRIDEMA Workshop, Vienna, 5