decission support system perun lecture miroslav trnka contributions from: martin dubrovský, joseph...
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DECISSION SUPPORT SYSTEM DECISSION SUPPORT SYSTEM PERUNPERUNlecturelecture
Miroslav Trnka
Contributions from: Martin Dubrovský, Joseph Eitzinger, Jan Haberle, Zdeněk Žalud
AGRIDEMA – ViennaAGRIDEMA – Vienna20052005
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
PERUN sensitivity analysis:PERUN sensitivity analysis:
PERUN sensitivity analysis:PERUN sensitivity analysis:
Sensitivity analysis: 3 parameters are varied: soil - station - RDmax
Sensitivity analysis: 3 parameters are varied: soil - station - RDmax
PERUNprobabilistic seasonal crop
yield forecasting
PERUNprobabilistic seasonal crop
yield forecasting
seasonal crop yield forecasting1. construction of weather series
seasonal crop yield forecasting1. construction of weather series
seasonal crop yield forecasting2. running the crop model
seasonal crop yield forecasting2. 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, …)
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:
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)
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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
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
Spatial assessment – regional level :
Spatial assessment – regional level :
Regional yield forecastRegional yield forecast
Climate change impact on crop growth
Climate change impact on crop growth
Mean yields in the CR:
a) potential yields
b) water-limited yields
Mean yields in the CR:
a) potential yields
b) water-limited yields
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
Mean yields in the CR:
a) potential yields
b) water-limited yields
Mean yields in the CR:
a) potential yields
b) water-limited yields
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
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
