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DECISSION SUPPORT SYSTEM PERUN lecture

DECISSION SUPPORT SYSTEM PERUN lecture. AGRIDEMA – Vienna 2005. Miroslav Trnka Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud. PERUN based applications:. PERUN – decision support system seasonal analysis (1 location, 1 crop)

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DECISSION SUPPORT SYSTEM PERUN lecture

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

  2. 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

  3. PERUN sensitivity analysis:

  4. PERUN sensitivity analysis:

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

  6. PERUNprobabilistic seasonal crop yield forecasting

  7. seasonal crop yield forecasting1. construction of weather series

  8. seasonal crop yield forecasting2. running the crop model

  9. a) expected values valid for the forthcoming days(e.g., first day/week: 12±2 °C, second day/week: 7±3 °C, …) weather forecast is given in terms of: 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: ….., …. )

  10. crop yield forecasting at various days of the year probabilistic forecast <avg±std> is based on 30 simulations input 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 barley year = 1999 emergence day = 122 maturity day = 225 observed yield≈ 4700 kg/ha model yield≈ 4600 kg/ha (simulated with obs. weather series) enlarge >>>

  11. crop yield forecasting at various days of the yeara) the case of good fit between model and observation

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

  13. Spatial assessment – regional level :

  14. Regional yield forecast

  15. Climate change impact on crop growth

  16. Mean yields in the CR:a) potential yieldsb) water-limited yields

  17. WATER LIMITED YIELD CO2 = present[indirect effect of CO2] present-333CSIRO(hi)-333 ECHAM(hi)-333HadCM(hi)-333 NCAR(hi)-333

  18. Mean yields in the CR:a) potential yieldsb) water-limited yields

  19. Water limited yield: combined effect of CO2 now~333L now~535L A-hi~535L E-hi~535L H-hi~535L N-hi~535L

  20. 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

  21. Need help? We will be around during lunch…. OR at– dub@ufa.cas.cz

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