1. Climate impacts on UK wheat yields using regional model output
Jemma Gornall1, Pete Falloon1, Kyungsuk Cho2, Richard Betts1, Robin Clark1
1Met Office Hadley Centre 2Korea Meteorological Administration
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2. Outline Climate impacts on UK winter wheat case study
The Decision Support System for Agrotechnology Transfer (DSSAT)
Methods and UKCP09 climate projections
Results
Validation
Future projections
Case study where regional model ensemble data has been applied to crop modeling
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3. DSSAT http://www.icasa.net/dssat/
Integrates effects of:
Soil
Crop phenotype
Weather
Management options
DSSAT can model 17 different crop types and integrates soil, crop phenotype, weather and management options
Modular structure, main program runs and references a land unit module, which accesses the other primary modules weather, soil, etc And each of those in turn has sub sections
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4. DSSAT CROPGRO Plant Growth Module CERES Plant Growth Module
Grain Legumes - Soybean, peanut, dry bean, chickpea, cowpea, velvet bean, and faba bean
Vegetables - Pepper, cabbage, tomato
Grasses – Bahia, brachiaria
CERES Plant Growth Module
Grain Cereals -Rice, maize, millet, sorghum, wheat, and barley
SUBSTOR Plant Growth Module
Potato
The three different crop models within DSSAT, covering a wide range of species and varieties of crops, this case study uses CERES as we are interested in wheat.

5. DSSAT
Minimum Inputs:
Daily weather (max. & min. temperature, total precipitation, solar radiation)
Soil (albedo, water coefficients, N & P contents, evaporation, drainage and runoff, root growth factor . . .)
Crop genetic inputs (coefficients related to photoperiod sensitivity, duration of grain filling rates and vernalization requirements)
Management options (planting date and seed density)
For management options can also specify irrigation and fertilization regimes, which is especially useful if your interested in looking at whether it is possible to adapt to any changes in crop production due to climate change.
one of the limitations of DSSAT is that it requires quite a lot of soil and cultivar input parameters, you can use generic values, but then you must take this into consideration when analyzing the results.
Vernalization – when a plant acquires the ability to flower or germinate (may require more time before actual flowering begins, sometimes needlow winter temperature to initiate vernalization)
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6. Climate impacts on UK winter wheat – using DSSAT/CERES
CERES-Wheat (Crop Estimation through Resource and Environment Synthesis-Wheat)
Dynamic process-based crop model, widely validated
Used extensively for site and regional climate impact studies.
Temperature - key role in vegetative growth and development
Environmental factors (water, nutrient stress) linked to plant growth and development.
Daily biomass production calculated using solar radiation
Can simulate physiological effects of increased CO2
Management orientated model which can simulate the impact of various environmental factors
CERES wheat simulates phenological development
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7. Approach for regional crop modelling.
Focus on changes over broad regions.
Used generic parameters for cultivar and soil coefficients available from DSSAT
Validate generic DSSAT set-up with UK field data from sites
Site meteorological data used to drive the model for validation.
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8. Validation (Rothamsted Broadbalk, 1999-2009)
Cho et al. 2011, Climate Research (accepted)
Validation (Rothamsted Broadbalk, )
So here we have model verses observation plots of yield and days to physiological maturity, and if the model was perfect, we would expect them to run long this line here. However, of course, the model is not perfect. Firstly looking at yield we can see . . .
These results were considered acceptable for this study
Cultivar – plant selected for desirable characteristics (e.g. yield, flavour)
Model over estimates yield – pest as disease effects excluded, generic cultivar parameters
Model reaches maturity faster – development in model more temperature dependent
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9. Approach for regional crop modelling.
Used generic parameters for cultivar and soil coefficients available from DSSAT
Validate generic DSSAT set-up with UK field data
UKCP climate projections (daily min/max temp., precip, solar radiation)
13 administrative regions, 11 member RCM (SRES A1B)
30 year time slices (2020s, 2050s, 2080s)
25km resolution
Assess uncertainties in future climate impacts using RCM ensemble
Used daily data area averaged as inputs to CERES, no bias correction took place
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10. Current UK winter wheat distribution
Mainly grown in South and East of UK.
Can also achieve reasonable yields in the North.
Important crop for UK

11. UKCP09 Climate Projections
Change in 30 y average ( ), from baseline ( )
Max. temperature (ºC)
Min. temperature (ºC)
Precipitation (%)
Box plots of 25th and 75th percentile of the ensemble members, + is the median, and diamonds are the value of the un perturbed ensemble member
Large increases are seen in min and max temp. in all ensemble members across all 13 regions of the UK
Larger model spread in precip, which is pretty much what is expected, but in most regions there is an increase in precip.
These are annual values, but if I were to show seasonal plots you would see a tendency for increases in winter precip and decreases in summer precip
NE N.E. England NW N.W. England NI N. Ireland NS N. Scotland WS W. Scotland WM W. Midlands Wa Wales EE E. of England ES E. Scotland EM E. Midlands SE S.E. England SW S.W. England YH Yorks & Humber.
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12. Future projections Development rate Yields
Heading date (average date by which a crop has formed seedheads)
Physiological maturity (date of max. kernel dry weight = readiness for harvest)
Yields
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13. Heading date (days after planting)
Change in 30 y average, from baseline
Sowing date 10th October
N application 200kg ha-1yr-1
Increased rate of development in all areas – increases larger in 2080s than 2020s.
Biggest impact in the North
Heading data – period of time from sowing to emergence of the first panicle
This result is robust across all 11 members of the RCM ensemble
NE N.E. England Wa Wales
NW N.W. England EE E. of England
NI N. Ireland ES E. Scotland
NS N. Scotland EM E. Midlands
WS W. Scotland SE S.E. England
WM W. Midlands SW S.W. England
YH Yorks & Humber.
© Crown copyright Met Office 2011
Cho et al. 2011, Climate Research (accepted)

14. Physiological maturity (days after planting)
Change in 30 y average, from baseline
Sowing date 10th October
N application 200kg ha-1yr-1
Increased rate of development in all areas – increases larger in 2080s than 2020s.
Biggest impact in the North
Physiological maturity – point at which crops have reached there maximum yields
NE N.E. England Wa Wales
NW N.W. England EE E. of England
NI N. Ireland ES E. Scotland
NS N. Scotland EM E. Midlands
WS W. Scotland SE S.E. England
WM W. Midlands SW S.W. England
YH Yorks & Humber.
© Crown copyright Met Office 2011
Cho et al. 2011, Climate Research (accepted)

15. Yield (%) Change in 30 y average, from baseline
Sowing date 10th October
N application 200kg ha-1yr-1
Uncertainty increases with time
Gains in the North
Loses in the South
The yield uncertainty is probably related to climate uncertainty. As we approach the end of the century, climate uncertainty across the ensemble increases, and the yield results do the same thing.
Some regions, particularly in the south and over Northern Ireland see a reduction in grain yield, and those in the north anre projected to see increases in yield.
NE N.E. England Wa Wales
NW N.W. England EE E. of England
NI N. Ireland ES E. Scotland
NS N. Scotland EM E. Midlands
WS W. Scotland SE S.E. England
WM W. Midlands SW S.W. England
YH Yorks & Humber.
© Crown copyright Met Office 2011
Cho et al. 2011, Climate Research (accepted)

16. Conclusions to UK wheat study
In all regions, temperature increases accelerate wheat development
Positive impact on yield, particularly further north; some decreases in the South
Uncertainties in yield production increase with time (climate driven)
CO2 fertilisation may compensate yield losses due to temperature and rainfall change
UK production: losses in some regions may be compensated by gains elsewhere
Overall positive impact on yield, some decreases in the south, which is currently the primary area of wheat production
We will need to consider implementing adaptation measures to take advantage of increased yields in the north
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17. Summary
Use of regional model ensembles can provide useful information to climate impacts studies of crops – used to inform adaptation
The DSSAT software is a good framework for these assessments - can access management options (e.g. Irrigation.)
If generic parameterisation is used some level of validation should take place and results should be interpreted accordingly.
DSSAT requires a lot of soil and cultivar parameter inputs