Transcript ESTIMATING THE CROP YIELD POTENTIAL OF CZECHIA IN …

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)
[email protected]
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
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)
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
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
PERUN - user interface
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
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
Crop yield potential of the Czech Republic
= integration of crop model yields over its territory:
CYP = SUMX,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
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)
Soil types(“full set” of 25 types)
Soil types (reduced set of 19 types)
Czech Republic
relief + weather stations
weather stations:
mean temperature
weather stations:
mean daily preipitation
experiment:
• 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
present climate - limited yields
(variability from 30 years,
322 soil-weather combinations)
HadCM2 climate(2050) - limited yields
(variability from 30 years,
322 soil-weather combinations)
ECHAM4 climate(2050) - limited yields
(variability from 30 years,
322 soil-weather combinations)
present climate - potential yields
(variability from 30 years,
322 soil-weather combinations)
HadCM2 climate(2050) - potential yields
(variability from 30 years,
322 soil-weather combinations)
ECHAM4 climate(2050) - potential yields
(variability from 30 years,
322 soil-weather combinations)
- variability of potential yields is lower than
the variability of the water-limited yields
- crop yields (both potential and waterlimited) decrease in changed climate
- variability over different climates is larger
than the variability over different soilss
present climate
- mean limited yields
HadCM2 climate(2050)
- mean limited yields
ECHAM4 climate(2050)
- mean limited yields
present climate
- mean potential yields
HadCM2 climate(2050)
- mean potential yields
ECHAM4 climate(2050)
- mean potential yields
ECHAM4 climate(2050) - potential yields
(MIN-AVG-MAX from all 322 soil-weather combinations)
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
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)
end
[email protected]
www.ufa.cas.cz/dub/dub.htm
www.ufa.cas.cz/dub/crop/crop.htm