1. PERUN: THE SYSTEM FOR SEASONAL CROP YIELD FORECASTING BASED ON THE CROP MODEL AND WEATHER GENERATOR Martin Dubrovský (1), Zdeněk Žalud (2), Mirek Trnka (2), Jan Haberle (3) Petr Pešice (1) ( 1) Institute of Atmospheric Physics, Prague, Czech Republic (2) Mendel University of Agriculture and Forestry, Brno, Czech Republic (3) Research Institute of Crop Production, Prague, Czech Republic project QC1316 of NAZV (National Agency for Agricultural Research, Czech Republic)

2. PERUN = system for crop model simulations under various meteorological conditions NAZV project: 2001 - 2002 tasks solved by PERUN:  crop yield forecasting  climate change/sensitivity impact analysis

3. PERUN - components: 1) WOFOST crop model (v. 7.1.1.; provided by Alterra Wageningen) new: Makkink formula for evapotranspiration was implemented (motivation: Makkink does not need WIND and HUMIDITY data) 2) Met&Roll weather generator (Dubrovský, 1997) some modifications had to be made (will be discussed later) 3) user interface - input for WOFOST ( crop, soil and water, weather & climate, start/end of simulation, production levels, fertilisers,...) - launching the process (weather generation, crop model simulation) - statistical and graphical processing of the simulation output

4. seasonal crop yield forecasting 1. construction of weather series

5. seasonal crop yield forecasting 2. running the crop model

6. Modifications of previous version of the 4-variate Met&Roll generator (1) 4-variate  6-variate: To generate all six daily weather characteristics required by WOFOST (PREC, SRAD, TMAX, TMIN, VAP, WIND), the separate module adds values of VAP and WIND to the previously generated four weather characteristics (SRAD, TMAX, TMIN, PREC) using the nearest neighbours resampling from the observed data. (2) The generator may produce series which consistently follow with the observed data at any day of the year. (3) The second additional module allows to modify the synthetic weather series so that it fits the weather forecast

7. A) 4-variate  6-variate: 4-variate series: @DATE SRAD TMAX TMIN RAIN... 99001 1.9 -2.7 -6.3 0.3 99002 2.1 -3.6 -3.7 0.7 99003 1.5 0.1 -1.3 2.4 99004 2.4 0.3 -2.7 0.6 99005 1.4 -1.4 -5.1 0.1... learning sample: @DATE SRAD TMAX TMIN RAIN VAPO WIND... xx001 1.6 1.3 -1.5 3.3 0.63 1.0 xx002 1.6 -0.8 -3.8 0.3 0.53 1.7 xx003 3.9 -2.3 -9.9 0.0 0.23 2.0 xx004 4.5 -2.3 -11.4 0.0 0.38 1.0 xx005 1.6 -6.1 -12.9 0.0 0.33 1.3 xx006 1.6 -1.8 -12.4 1.1 0.23 3.3 xx007 3.8 1.2 -2.3 0.0 0.52 4.7 xx008 1.7 -0.1 -4.3 0.0 0.39 1.3 xx009 1.7 -1.8 -6.7 0.4 0.42 4.0 xx010 1.7 -3.8 -8.0 1.0 0.36 2.0 xx011 1.7 0.0 -3.9 8.3 0.46 2.0 xx012 2.9 3.7 -0.3 2.8 0.57 1.7 xx013 1.8 2.6 -0.8 1.0 0.62 2.0 xx014 4.0 2.9 -3.3 0.0 0.45 2.7 xx015 4.0 2.4 -5.9 0.0 0.37 1.3... 6-variate series: @DATE SRAD TMAX TMIN RAIN VAPO WIND... 99001 1.9 -2.7 -6.3 0.3 0.34 3.0 99002 2.1 -3.6 -3.7 0.7 0.28 3.0 99003 1.5 0.1 -1.3 2.4 0.61 3.0 99004 2.4 0.3 -2.7 0.6 0.57 3.0 99005 1.4 -1.4 -5.1 0.1 0.47 3.0

8. B) series which consistently follow with the observed data (1) generator working in normal-mode (1 st -order generator assumed): X(0) ~ PDF(X) X(t) = f [X(t-1), e][e = vector of random numbers] (2) series which follows with the observed data at day D 0 : (observed weather data are available till D 0 -1) X(D 0 ) = f [X OBS (D 0 -1), e] X(t) = f [X(t-1), e]for t > D 0

9. B) series which consistently follow with the observed data

10. C) modification of the synthetic weather series so that it fits the weather forecast:

11. weather forecast is given in terms of - expected values valid for the forthcoming days (e.g., first week: 12±2 °C, second week: 7±3 °C, …) alternative formats of the weather forecasts: (useful in climate change/sensitivity analysis) - increments with respect to long-term means (first week:temperature = + 2 C above normal; precipitation = 80% of normal; second week: ….., …. - increments to existing series

12. a) weather forecast is given in terms of the absolute values * weather forecast random component METHOD = 1...averages.....std. deviation.. @JD-from JD-to TMAX TMIN PREC TMAX TMIN PREC 99121 99130 17 6 30 2 2 10 99131 99140 14 4 60 3 3 20 99141 99150 21 10 10 4 4 10 @

13. c) increments with respect to the long- term means * weather forecast random component METHOD = 3...averages.....std. deviation.. @JD-from JD-to TMAX TMIN PREC TMAX TMIN PREC 99121 99130 1 1 1.2 2 2 0.1 99131 99140 0 0 1.0 2 2 0.1 99141 99150 -1 -1 0.9 2 2 0.1 @

14. time for software demostration (egs03~wh.exp !!!)

15. 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; without forecast!] (better to say: forecast = mean climatology) a) the case of good fit between model and observation site =Domanínek, Czech Rep. crop=spring barley year=1999 emergency day=122 maturity day=225 observed yield=4739kg/ha model yield=4580kg/ha (simulated with obs. weather series) enlarge >>>

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

17. Crop yield forecasting at various days of the year b) the case of poor fit between model and observation site = Domanínek, Czech Republic crop= spring barley year=1996 emergency day=124 maturity day=232 observed yield=3956kg/ha model yield=5739kg/ha (simulated with obs. weather series) enlarge >>>

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

19. crop yield forecasting at various days of the year b) the case of poor 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

20. E N D Thank you for your attention!