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DECISSION SUPPORT SYSTEM DECISSION SUPPORT SYSTEM PERUN PERUN lecture lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna AGRIDEMA – Vienna 2005 2005
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DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

Jan 13, 2016

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Page 1: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

DECISSION SUPPORT SYSTEM DECISSION SUPPORT SYSTEM PERUNPERUNlecturelecture

Miroslav Trnka

Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud

AGRIDEMA – ViennaAGRIDEMA – Vienna20052005

Page 2: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

PERUN based applications:PERUN based applications:

PERUN – decision support system

seasonal analysis (1 location, 1 crop) multi-seasonal analysis at one location

+ multi-site analysis sensitivity analysis – weather, soil, crop etc. probabilistic yield forecasting climate change impact analysis

Page 3: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

PERUN sensitivity analysis:PERUN sensitivity analysis:

Page 4: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

PERUN sensitivity analysis:PERUN sensitivity analysis:

Page 5: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

Sensitivity analysis: 3 parameters are varied: soil - station - RDmax

Sensitivity analysis: 3 parameters are varied: soil - station - RDmax

Page 6: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

PERUNprobabilistic seasonal crop

yield forecasting

PERUNprobabilistic seasonal crop

yield forecasting

Page 7: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

seasonal crop yield forecasting1. construction of weather series

seasonal crop yield forecasting1. construction of weather series

Page 8: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

seasonal crop yield forecasting2. running the crop model

seasonal crop yield forecasting2. running the crop model

Page 9: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

a) expected values valid for the forthcoming days

(e.g., first day/week: 12±2 °C, second day/week: 7±3 °C, …)

a) expected values valid for the forthcoming days

(e.g., first day/week: 12±2 °C, second day/week: 7±3 °C, …)

b) increments with respect to long-term

means (1st day/week/decade: temperature = + 2 C above normal; precipitation = 80% of normal; 2nd day/week/decade: ….., …. )

weather forecast is given in terms of:

Page 10: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

crop yield forecasting at various days of the yearcrop yield forecasting at various days of the year

probabilistic forecast <avg±std> is based on 30 simulationsinput weather data for each simulation =[obs. weather till D−1] + [synt. weather since D ~ mean climatology)

a) the case of good fit between model and observation

crop = spring barleyyear = 1999emergence day = 122maturity day = 225observed yield ≈ 4700 kg/hamodel yield ≈ 4600 kg/ha

(simulated withobs. weather series)

enlarge >>>

Page 11: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

crop yield forecasting at various days of the year a) the case of good fit between model and

observation

crop yield forecasting at various days of the year a) the case of good fit between model and

observation

Page 12: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

task for future research: find indicators of the crop growth/development (measurable during the growing period) which could be used to correct the simulated characteristics, thereby allowing more precise crop yield forecast

indicators

crop yield forecasting at various days of the year b) the case of poor fit between model and

observation

crop yield forecasting at various days of the year b) the case of poor fit between model and

observation

Page 13: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

Spatial assessment – regional level :

Spatial assessment – regional level :

Page 14: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

Regional yield forecastRegional yield forecast

Page 15: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

Climate change impact on crop growth

Climate change impact on crop growth

Page 16: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

Mean yields in the CR:

a) potential yields

b) water-limited yields

Mean yields in the CR:

a) potential yields

b) water-limited yields

Page 17: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

WATER LIMITED YIELD CO2 = present

[indirect effect of CO2]

WATER LIMITED YIELD CO2 = present

[indirect effect of CO2]

present-333CSIRO(hi)-333 ECHAM(hi)-333HadCM(hi)-333 NCAR(hi)-333

Page 18: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

Mean yields in the CR:

a) potential yields

b) water-limited yields

Mean yields in the CR:

a) potential yields

b) water-limited yields

Page 19: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

Water limited yield: combined effect of CO2Water limited yield: combined effect of CO2

now~333L now~535L

A-hi~535L E-hi~535L

H-hi~535L N-hi~535L

Page 20: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

PERUN based applications:PERUN based applications:

Now: description of the PERUN interface (Martin) distribution of the instalation CDs

Afternoon session: seasonal analysis (1 location, 1 crop) multi-seasonal analysis at one location sensitivity analysis – weather, soil, crop etc. probabilistic yield forecasting climate change impact analysis

Page 21: DECISSION SUPPORT SYSTEM PERUN lecture Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud AGRIDEMA – Vienna.

Need help?Need help?

We will be around during lunch…. OR at– [email protected]