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)

enlarge >>>

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

Need help?Need help?

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