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

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Presentation transcript:

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

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

PERUN sensitivity analysis:

Sensitivity analysis: 3 parameters are varied: soil - station - RD max

PERUN probabilistic seasonal crop yield forecasting

seasonal crop yield forecasting 1. construction of weather series

seasonal crop yield forecasting 2. running the crop model

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 (1 st day/week/decade:temperature = + 2 C above normal; precipitation = 80% of normal; 2 nd day/week/decade: ….., …. ) weather forecast is given in terms of:

crop yield forecasting at various days of the year probabilistic forecast 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≈4700kg/ha model yield≈ 4600kg/ha (simulated with obs. weather series) enlarge >>>

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

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

Spatial assessment – regional level :

Regional yield forecast

Climate change impact on crop growth

Mean yields in the CR: a) potential yields b) water-limited yields

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

Mean yields in the CR: a) potential yields b) water-limited yields

Water limited yield: combined effect of CO2 now~333Lnow~535L A-hi~535LE-hi~535L H-hi~535LN-hi~535L

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

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