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 © Crown copyright Met Office 2011

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 © Crown copyright Met Office 2011

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 © Crown copyright Met Office 2011 3

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.

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) © Crown copyright Met Office 2011 5

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 © Crown copyright Met Office 2011

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. © Crown copyright Met Office 2011

Validation (Rothamsted Broadbalk, 1999-2009) Cho et al. 2011, Climate Research (accepted) Validation (Rothamsted Broadbalk, 1999-2009) 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 © Crown copyright Met Office 2011 8

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 © Crown copyright Met Office 2011 9

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

UKCP09 Climate Projections Change in 30 y average (2070-2099), from baseline (1971-2000) 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. © Crown copyright Met Office 2011 11

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 © Crown copyright Met Office 2011

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)

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)

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)

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 © Crown copyright Met Office 2011

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