1. ESTIMATING THE CROP YIELD POTENTIAL OF THE CZECH REPUBLIC IN PRESENT AND CHANGED CLIMATES Martin Dubrovsky (1) Mirek Trnka (2), Zdenek Zalud (2), Daniela Semeradova (2) dub@ufa.cas.cz www.ufa.cas.cz/dub/dub.htm www.ufa.cas.cz/dub/crop/crop.htm ( 1) Institute of Atmospheric Physics, Prague, Czech Republic (2) Mendel University of Agriculture and Forestry, Brno, Czech Republic

2. this presentation: –crop yield potential of the republic = total crop yield integrated over its territory –methodology (the main focus) –first results maps of the crop yields (potential, water+nutrient limited) for present and changed climate (2050)

3. PERUN = system for crop model simulations under various meteorological conditions tasks solved by PERUN:  probabilistic seasonal crop yield forecasting  climate change impact analysis  sensitivity analysis  single-site or multiple-site analysis

4. PERUN - components 1) WOFOST crop model (v. 7.1.1.; executable and source code provided by Alterra Wageningen) was calibrated for several crops and several locations in the Cech Republic! 2) Met&Roll weather generator - Met&Roll = stochastic 4/6-variate daily weather generator validated in terms of various climatic characteristics! 3) user interface - input for WOFOST ( crop soil and water weather & climate start/end of simulation production levels fertilisers...) - launching the process (preparing weather series, crop model simulation) - statistical and graphical processing of the simulation output

5. PERUN - user interface

6. input weather series for climate change impact analysis: a) direct modification approach: present climate:observed weather series changed climate:observed weather series modified by climate change scenario b) weather generator approach: present climate:WG with parameters derived from the observed series changed climate:parameters of WG are modified according to the climate change scenario

7. Crop yield potential of the Czech Republic input data: –soil: 25 soil types in approx. (1x1 km) resolution –weather: 45 stations with 40-years observed data

8. Crop yield potential of the Czech Republic = integration of crop model yields over its territory: CYP = SUM X,Y ( ModelYield [ weather[x,y], soil[x,y] ) problems: - necessity to define climate for each grid (may be done by WG with interpolated parameters, but the present version of 6-variate WG is too slow!!!) - too many grids (115553)  number of weather-soil combinations should be reduced

9. Crop yield potential of the Czech Republic reducing number of soil-weather combinations: 1) finding the representative (“nearest”) weather station for each soil grid; dist. = f[(a*  lat] 2 + (b*  long) 2 + (c*  alt) 2 ] - (17 soils) x (45 weather stations)  765 combinations - some combination miss  322 soil-weather combinations 2) crop model is run for all 765 soil-weather combinations, and then the crop yield for each grid is interpolated (using crop model outputs for the given soil and all weather stations)

10. Soil types(“full set” of 25 types)

11. Soil types (reduced set of 19 types)

12. Czech Republic relief + weather stations

13. weather stations: mean temperature

14. weather stations: mean daily preipitation

15. e x p e r i m e n t : crop = spring barley 30-year crop model simulation for each of 322 soil-weather combinations climate change scenario: –ECHAM4, HadCM2 for 2050 weather data for changed climate: –direct modification of observed weather potential & water-limited yields simulated

16. present climate - limited yields (variability from 30 years, 322 soil-weather combinations)

17. HadCM2 climate(2050) - limited yields (variability from 30 years, 322 soil-weather combinations)

18. ECHAM4 climate(2050) - limited yields (variability from 30 years, 322 soil-weather combinations)

19. present climate - potential yields (variability from 30 years, 322 soil-weather combinations)

20. HadCM2 climate(2050) - potential yields (variability from 30 years, 322 soil-weather combinations)

21. ECHAM4 climate(2050) - potential yields (variability from 30 years, 322 soil-weather combinations)

22. - variability of potential yields is lower than the variability of the water-limited yields - crop yields (both potential and water- limited) decrease in changed climate - variability over different climates is larger than the variability over different soilss

23. present climate - mean limited yields

24. HadCM2 climate(2050) - mean limited yields

25. ECHAM4 climate(2050) - mean limited yields

26. present climate - mean potential yields

27. HadCM2 climate(2050) - mean potential yields

28. ECHAM4 climate(2050) - mean potential yields

29. ECHAM4 climate(2050) - potential yields (MIN-AVG-MAX from all 322 soil-weather combinations)

30. conclusion present results are the first results! (to be done: finetune the methodology and input data) crop yield will derease in changed climate...... but: –adaptation responses: other cultivars or other crops shift of the planting date

31. Plans for future more stations with weather data other methods: –model crop yields are spatially interpolated –weather generator with interpolated parameters will require improving the WG!!!! will require suitable interpolation technique sensitivity analysis + uncertainty analysis inclusion of adaptation responses –(e.g. shift of the planting date) other crops (winter wheat,...) other crop model (CERES)

32. e n d dub@ufa.cas.cz www.ufa.cas.cz/dub/dub.htm www.ufa.cas.cz/dub/crop/crop.htm