Download 2.1 Climate change scenarios

DEPARTMENT for ENVIRONMENT, FOOD and RURAL AFFAIRS
Research and Development
CSG 15
Final Project Report
(Not to be used for LINK projects)
Two hard copies of this form should be returned to:
Research Policy and International Division, Final Reports Unit
DEFRA, Area 301
Cromwell House, Dean Stanley Street, London, SW1P 3JH.
An electronic version should be e-mailed to [email protected]
Project title
Re-assessing drought risks for UK crops using UKCIP02 climate change
scenarios
DEFRA project code
CC0368
Contractor organisation
and location
Rothamsted Research
West Common
Harpenden
Total DEFRA project costs
Project start date
£ 90,649
01/08/03
Project end date
31/05/04
Executive summary (maximum 2 sides A4)
This project re-assessed the risk of drought-related yield loss for representative arable crops using (a) new
climatic predictions based on UKCIP02 emission scenarios, and (b) more advanced crop modelling approaches.
The aim was to address DEFRA policy on “Climate adaptation, risk, uncertainty and decision-making” with
respect to typical winter-sown crops and spring-sown crops growing throughout the summer (examples chosen:
winter wheat, sugar beet). The results are compared with outputs of the preceding project (CC0336), based on
the UKCIP98 Medium-High scenario.
Methods:
1. Weather scenarios were generated by the LARS-WG stochastic weather generator using the UKCIP02
scenarios and the output from the HadRM3 climate model. We used Low and High emission projections to
generate daily site specific weather data for 17 selected representative sites in the UK. Each scenario
consists of 50 years of daily weather with minimum and maximum temperature, rainfall and radiation.
2. The Sirius2003 wheat model with the new canopy model was used in the project; it was re-calibrated for
two varieties (Mercia, Consort) using ADAS experimental datasets.
3. A more detailed hydrology model was implemented into the Broom’s Barn sugar beet model, calibrated and
evaluated for annual and long-term experimental data.
4. Drought indicators were displayed as relative soil moisture deficit (rSMD) with reference to physiological
thresholds (senescence) and the reduction of potential yields (YR) for different emission scenarios.
5. Probability distributions of future yields were determined, by using the models for temperature-driven and
water-limited crop production for a range of emission scenarios and time-lines.
6. Management options (crop allocation, variety selection) were evaluated to adapt to increased risks if
necessary.
CSG 15 (Rev. 6/02)
1
Project
title
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
DEFRA
project code
CC0368
Results:
In the future, with greater warming, the weather is likely to be wetter in winter, spring and autumn but drier in
summer compared with the past. This is likely to increase the soil moisture deficit in mid summer, which
winter-sown crops are likely to escape, but which will affect spring-sown crops which grow throughout the
summer, like sugar beet. According to our new simulations drought will be more severe and more frequent than
estimated before. Instead of 3-4 % increase of moisture deficit in East Anglia by the 2050s, the new scenarios
predict an increase of 8 to 12 %, for the low and high emissions. The probability of exceeding the physiological
threshold of enhanced senescence could rise to 84 or 90 % by the 2050s, and to more than 95 % by the 2080s
(Table 3). In conclusion, we must anticipate more drought stress in sugar beet production. In comparison with
the earlier scenarios (2050s-MH) based on HadCM2 the drought stress may be doubled.
While the drought-related yield reduction (YRdr) is likely to be small for winter wheat, YRdr for sugar beet will
increase by 30 to 50 % by the 2050s-High, depending on the region (see Fig. 6). By the 2080s drought-related
yield loss in sugar beet could be as high as 40 to 100 %. In conclusion, YRdr of sugar yield may actually double
compared with the current situation, and 20 % more loss is likely to occur within our lifetime.
Future yield distributions
Wheat yields as simulated with the new scenarios and Sirius2003 will increase and have less variation (decrease
in CV %). The overall average increase of wheat yield by the 2050s (1.55 t/ha) is very similar to the yield
increase predicted in the earlier scenarios (Richter et al., 2002). The reasons for this result are the increase in
RUE due to CO2 elevation and the acceleration of development by five to six weeks allowing wheat to escape
the drought stress at the end of the season. Crucially, late sowing dates are to be avoided not to delay anthesis
and shorten grain filling.
For sugar beet, earlier simulations gave a yield increase of 1.9 t/ha sugar by the 2050s. In the new simulations,
the sugar yield increases by between 1.4 and 2 t/ha in the 2050s low and high emission scenarios, respectively.
Unlike the results for wheat, the range of yields becomes much wider in the future. Yield variation increases
from 15-18% (baseline) to about 18-23 % (2050s) and 19-25 % by the end of the century.
The differences between the regions are not very large with respect to yield, on average less than 1 t/ha, but
greater with respect to variation due to soil properties. In comparison to earlier simulations (see Fig. 17 in
Richter et al., 2002), the uncertainty of yields is generally higher now. The response to low soil available water
capacity (AWC) is slightly different with the new hydrology module. Variation of yields decreases with
improving AWC to 7 % but maximum variation increased by 3 to 4 %. Yields increase from 8 t/ha on sand to
about 11 t/ha on good soils (baseline), in the future (2050s), yields will remain low on sands but increase to 15
t/ha on deep soils. Yields of sugar on the best soils may increase by more than 3 t/ha (2050s) and 5 t/ha (2080s).
Management options to mitigate climate impacts
For winter wheat we tested the effects of (a) variety selection, (b) sowing date, and (c) crop allocation to soils:
For the baseline scenario, Consort yielded 1.0 t/ha more than Mercia due to earlier anthesis and longer period
of grain fill. For Consort simulated yields were on average 1.5 t/ha higher than Mercia in all scenarios.
Late sowing results in later anthesis date, smaller final leaf number and reduced yields, compared to the earliest
sowing date, for both Mercia and Consort.
The increase of wheat yields is greatest on shallow and light soils in future. In future, with earlier development
wheat will benefit more from winter and spring rainfall, however, yield variation remaining higher in soils with
low AWC compared with soils of high AWC.
For sugar beet production, further drought stress reduces suitability of shallow and light soils, if irrigation
cannot be secured. Breeding for drought tolerance could improve yields.
CSG 15 (Rev. 6/02)
2
Project
title
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
DEFRA
project code
CC0368
Conclusions
Under the UKCIP02 climatic change scenarios we reach the following conclusions with respect to drought risk:
Risk and uncertainty:




Wheat is simulated to escape drought, which is likely to occur in mid-summer (August), due to accelerated
development of current varieties. This is dependent on a low vernalisation requirement and early anthesis.
All simulated indicators have a smaller variation in the future compared with the baseline scenario.
For summer grown crops like sugar beet drought stress is very likely to be more severe than in the past due
to much drier summers, especially in the High emission scenario.
Rising atmospheric CO2 concentration will compensate for drought-related losses.
Management decisions for adaptation


More intense rainfall events in the winter may have implications for seedling damage and water logging in
poor draining and lowland soils, resulting in substantially reduced yields. On sloping sites late sowing of
winter wheat will not only result in poor yields, but may increase the risk of water erosion.
Winter wheat may become more common on very sandy and shallow soils. These sites may, however, be
more secure for the use of perennial (biofuel) crops or drought resistant crops like Triticale.
Breeding and variety selection
 Unknown response of UK varieties to increased heat stress, which is very like to occur by the 2080s. Heat
stress may become more of an issue as the number of hot days (>30°C) is predicted to increase sharply, thus
reducing grain number and grain yield.
 As winters become warmer, winter wheat varieties will need to be selected with a low vernalisation
requirement, because an early anthesis date is needed to avoid summer drought.
 Winter wheat varieties should be selected for weaker photoperiod sensitivity to make sure they are able to
reach the anticipated earlier flowering date due to future warmer temperatures.
 Winter wheat varieties should be selected for resistance to water logging and heat stress
 Sugar beet varieties will be required which use water still more efficiently and which are more effective at
acquiring water (root traits).
CSG 15 (Rev. 6/02)
3
Project
title
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
DEFRA
project code
CC0368
Scientific report (maximum 20 sides A4)
1 Introduction
In our earlier risk assessment of drought impact on crops in the UK (project CC0336) the climate change
scenarios were derived from the HadCM2 climate model. For the “medium-high” scenario (MH) we concluded
that drought risk is likely to increase until the 2050s reducing potential yields. However, rising atmospheric
CO2 concentrations, [CO2], more than compensated the drought-related yield reduction (Richter and Semenov,
2004; Richter et al., 2002). New scenarios have become available (UKCIP02; Hulme et al., 2002), which
predict summers to be warmer and drier than in the old scenarios. We therefore update for DEFRA the drought
risk assessment to answer the question to what extent crop production will be affected in future.
2
Methods applied in the risk assessment
2.1
Climate change scenarios
The UKCIP02 climate change scenarios are based on the HadRM3 regional climate model with a spatial
resolution of 50km. Using simple spatial interpolation techniques this resolution was increased to 5km, which
is a big improvement compared with the ~350km resolution of the original HadCM3. Nevertheless the
temporal resolution of the climate scenarios remains the same, i.e. monthly mean values. For agricultural
applications daily weather is required. Agricultural extremes are usually recorded on a daily basis, e.g. the
sequence of days with temperature below 0 °C or the sequence of days when total precipitation exceeds a
particular threshold. Agricultural and hydrological models are non-linear, and their responses are different
when monthly or daily data are used (Semenov, 1995; Porter, 1999). A methodology has been developed for
temporal downscaling of GCMs based on a stochastic weather generator (Semenov, 1997). In this project we
used a new version of the LARS-WG stochastic weather generator, version 4, which has been specifically
developed and validated for climate change studies in the UK. The new version includes may new features
compared to the version used in the previous DEFRA study (project CC0336). A methodology for spatial
interpolation of LARS-WG was developed (Semenov & Brooks, 1999), which allows the generation of climate
change scenarios at any site in the UK where observed data is not available. In this project, we have applied a
methodology developed for temporal downscaling using UKCIP02 and HadRM3 projections. Changes in
climatic variability such as duration of dry and wet spells or temperature variability were derived from daily
output from HadRM3 and incorporated into the scenarios. Scenarios were generated for UK-HI and UK-LO
projections for 2020s, 2050s and 2080s time intervals and for the baseline. 50 years of daily weather were
generated with minimum and maximum temperature, rainfall and radiation, and used for the risk assessment.
2.2
Model modifications and validations
The models used in the preceding project were changed by modifying (a) the canopy dynamics in Sirius and (b)
the water balance sub-model in the Brooms Barn sugar beet model. A description of the changes and recent
validation is summarised in the following:
2.2.1 Wheat model
We used the Sirius2003 wheat model (Lawless et al, 2004) which includes a new canopy model compared with
Sirius2000 (Jamieson et al, 1998). The new canopy model was developed as part of the DEFRA funded project
(AR0906) “A Rational Basis for Design of Wheat Canopy Ideotypes for UK Environments”. The new canopy
model replaces the empirical curve representing green area index (GAI) development used in Sirius2000. The
two parameters used in Sirius2000 to describe GAI were replaced by a single parameter, AreaMax in
Sirius2003. AreaMax can easily be measured as the maximum GAI achieved by a crop grown in non-limited
conditions (soil water and nutrition). Calibration was done on observed GAI of the same wheat data sets as used
before (varieties Mercia and Consort) from three sites in England, Boxworth (Cambs.), Sutton Bonington
(Leics.) and Rosemaund (Hereford) (HGCA 1998a; b; 2000). The varieties Mercia and Consort were reparameterised for use with Sirius2003. Combine-harvested yields of the same data set were used to evaluate the
CSG 15 (Rev. 6/02)
4
Project
title
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
DEFRA
project code
CC0368
new simulation of dry matter production and yield as these give a better representation of yields achieved on
real farms. Overall, the best fit to the anthesis dates and observed yields was obtained by:



Adjusting the parameters which model the response to vernalisation (VAI and VBEE) for Mercia and for
Consort. These were finally fixed to 0.004 and 0.04 respectively for both varieties, which is outside their
normal range.
Increasing the phyllochron to 108 for Mercia and to 100 for Consort based on recorded dates between
GS39 and GS61
Adjusting thermal time of grain fill (TTBGEG) to 950 for Mercia and 1050 for Consort, to describe
measured grain yield.
Anthesis can also be delayed by increasing the vernalisation temperature response, VAI. This was obtained
purely by fitting the data; it was not based on any experimental evidence. Unfortunately there were no data
available in this dataset to calibrate the vernalisation parameters (see Section 4). Adjusting the day length
response within the typical range did not improve anthesis date. Reducing thermal time from sowing to
emergence from 250 to 150 improved the simulation of anthesis date. 150 is the typical value for this parameter
(Jamieson et al. 1998).
The variety Consort was grown at the three sites in 1997. This is higher yielding than Mercia, and later
maturing (NIAB 1999). Parameters were adjusted to fit the observed data. Yields were fitted by adjusting
thermal time of grain fill (TTBGEG) to 1050 degree days. As with Mercia, anthesis date could be made to fit
by adjusting either the phyllochron or vernalisation parameters. All other parameters were unchanged from
Mercia. None of these parameter changes were based on any experimental data (except the phyllochron).
Boxworth
01-Sep
LAI
1997
Sutton Bonington
25
20-Mar
06-Oct
01-Sep
20-Mar
06-Oct
01-Sep
20-Mar
06-Oct
9
8
7
6
5
4
3
2
1
0
01-Sep
Biomass & yield for all sites
Simulated yield, biomass (t/ha)
LAI
1993
Rosemaund
9
8
7
6
5
4
3
2
1
0
20
15
10
5
0
0
20-Mar
06-Oct
01-Sep
20-Mar
06-Oct
01-Sep
20-Mar
06-Oct
5
10
15
20
25
Observed yield, biomass (t/ha)
Figure 1: Green leaf area index (GLAI) for Mercia (1993) and Consort (1997) and biomass (closed) and yields
(open symbols) at different sites (■□ Boxworth, ▲∆ Sutton Bonington, ◊ Rosemaund)
The newly parameterised model showed good agreement for most indicators (Figure 1). GLAI dynamics were
best described at the wettest site (Rosemaund, Herefordshire) and in a generally moist year – 1993. In 1997
crop establishment was impaired in Boxworth due to seed bed quality. In Sutton Bonington the experiments
may have been N-limited (185 kgN/ha), which was not accounted for in the simulation. Biomass production
and yields were well described. The anthesis date was difficult to predict in 2 out of 5 years, which was related
to warm winter temperatures (2-4 oC higher than the long-term average). This suggests that the variety trait of
vernalisation and associated parameters need special attention in the future because winters will be warmer and
varieties with lower vernalisation requirement will be needed.
CSG 15 (1/00)
5
Project
title
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
DEFRA
project code
CC0368
2.2.2 Sugar beet model
Two major points were pursued in the modification of the sugar beet model: (a) include a multi-layer soil
hydrology module and (b) re-evaluate the parameters for canopy senescence. Independent referees reviewing
our regional assessment of drought risk for sugar beet (Richter et al. 2003) had criticised the soil hydrology
sub-model to be over-simplified with respect to the soil moisture dynamics. This was one of the possible
reasons to underestimate dry matter production above 21 t/ha in a long-term data set from continental Europe
(Richter et al., 2004). Recent simulations for the UK revealed that simulated yields in high productivity sites
may be too low due to overestimated canopy senescence, which proved to be a crucial parameter to estimate
production in deep continental soils (Qi et al. (2004). The principal changes in the simulation of transpiration
and water stress in the sugar beet model are the following:
 Distinguish two soil horizons with different physical parameters to account for variable available water
capacity
 Implement a simple water redistribution and surface evaporation routine to account for drying and rewetting of the surface horizon and variable soil water content (SWC) in the profile,
 Simulate rooting depth and root elongation rate (RER) according to penetration resistance as a function of
soil water content in the profile, and calculate dry matter accumulation of fine roots and empirical
distribution of root length density (RLD)
 Simulate water uptake from individual layers according to variable RLD and root efficiency (REFF)
dependent on relative SWC
The original FORTRAN code was extensively re-written to implement the above into the model and input
parameters were derived to describe a range of soil profiles. We analysed the sensitivity of the new model to
variation of initial conditions, fractional changes of flux resistance and transpiration coefficients to describe
different hydrological and meteorological boundary conditions.
18
35
(a)
(b)
16
30
Modelled TDM (t/ha)
Modelled suger yield (t/ha)
14
12
10
8
6
4
New (CTc = 1)
Orig. model
New (CTc = 1.25)
2
25
20
15
10
Reduced senescence
5
New Hydrology
Orig. model
0
0
0
2
4
6
8
10
12
14
16
18
Observed sugar yield (t/ha)
0
5
10
15
20
25
30
35
Observed TDM (t/ha)
Figure 2: Graphic evaluation using observed de-trended vs. simulated (a) sugar yield at
Brooms Barn (24 yrs) and (b) total biomass (TDM at Bad Lauchstädt (32 yrs);
representation of different model versions (see text)
We validated the new model against the state variables of the hydrological and the crop growth compartments
for a range of experiments. In summary, the dynamics of soil hydrology are well described under rain-fed
cultivation. Under extreme (imposed) drought, transpiration may be underestimated as shown for the rainshelter experiment; observed yields could be described by reducing potential ET or the crop transpiration
coefficient (CTc 1 vs. 1.25). This suggests that (a) water uptake is beyond the observed and simulated root
distribution and that (b) the canopy and transpiration must fully recover from water stress due to subsequent
irrigation. The uptake of soil water from below the root zone is physically plausible and could be imitated by
CSG 15 (1/00)
6
Project
title
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
DEFRA
project code
CC0368
ignoring rooting constraints. Increasing AWC for this particular experiment resulted in higher yields at higher
transpiration. Further investigation was beyond the scope of the project.
We further tested the new model for two sets of long-term observations (Figure 2). We confirmed that initial
conditions are of little importance for simulating the climatic impact in the UK, unless we include an extreme
year in the data set (e.g. 1976). In the UK it is justified to assume field capacity. In continental locations
variable initial conditions are crucial. In the UK, the new hydrology model conserved the predictive capacity of
the model (Fig 2a; R2 improved) but the Residual Mean Square Error (RMSE) and bias (MD) increased
depending on the crop transpiration coefficients. Varying ETP (CTc = 1.25/1.0) the RMSE increased compared
with the original, simpler model (1.4/ 1.1 t/ha versus 1.0 t/ha). The model under- or over-estimated yields
depending on the evaporation demand (MD = 0.75 and –0.59 t/ha, respectively). For simulations of the dry
matter production at Bad Lauchstädt (Fig. 2b) the new hydrology model reduced the bias significantly (MD 1.6
vs. 3.8 t/ha) but did not improve the simulation of high yielding years. Reducing the senescence parameter to
the value derived by Qi et al. (2004) for high-productivity sites in Germany, however, increased many high
yields towards the 1:1-relationship, further reducing the bias to less that 1 t/ha. However, both modifications of
the model and the parameters resulted in simulated biomass with a high RMSE (3.3 t/ha), especially due to
over-estimated dry matter production below 21 t/ha.
In conclusion, the expansion and improvement of the code for the hydrology sub-model proved to describe the
dynamics of the soil water content within reasonable agreement. However, water uptake is beyond rooting
depth, which is plausible because of capillary rise. The simulation of long-term yields in the UK (Broom’s
Barn data set) is of similar quality as before without changing the parameters of canopy senescence. In highproductivity sites this is not quite satisfactory, and different senescence and partitioning parameters are needed.
The scatter was fairly large and it will be necessary to develop a unified approach describing canopy
senescence as a function of soil water uptake for a wider ranging data set.
2.3
Construction of regional scenario simulations
The procedure for the regional simulation was identical with that chosen in the previous project (CC0336)
except that the simulations were run for each ((crop + variety)*soil* management) with 50 years of weather
instead of 35 years. The regions were grouped according to the former MAFF-classification, except that for
sugar beet all outer western weather stations were pooled in one region (WEST). The allocation of wheat and
sugar beet to the regions and to the soils were following the surveys published in the Soil Bulletins (giving data
for the 1980s) and a more recent compilation published by the Central Science Laboratory in York (MAFF
1999) and more recent publications (http://farmstats.defra.gov.uk/).
CSG 15 (1/00)
7
Project
title
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
DEFRA
project code
CC0368
Table 1: Regional distribution of wheat in England and Wales and allocation to
different classes of available soil water capacity (AWC, mm) – fraction per region
and total area per class of AWC
Class of AWC (mm)
Wheat
Area
(103 ha)
80-120
-160
- 180
North
N West
N East
W Mid
42
13
180
123
0.4
0.4
0.3
0.4
0.3
0.3
0.26
0.3
0.2
0.2
0.14
E Mid
330
0.1
0.45
0.45
E Anglia
S East
S West
Wales
Total
103 ha
305
413
155
9
0.1
0.25
0.35
0.25
0.5
0.55
0.5
0.5
0.4
1570
350
700
350
Region
-220
>220
0.2
0.2
0.2
0.2
0.2
0.15
0.25
130
40
The scenarios for wheat production in England and Wales were represented using single weather stations in
each region. Only in the southwest of the country did we distinguish coastal and inland sites. We think that this
approach is sufficient to represent the national variation of climate, covering a climatic water balance from -40
to +300 mm.
Table 2: Area (ha *1000) in the main growing regions that is in arable
rotations involving sugar beet, grouped by soil texture class and
AWC (mm) ; following Table 3.2.3 in MAFF (1999)
Annual Area
W
N2
AWC
E1
Σ
Soil class
(mm)
Shallow (SH)
70-110
10
10
Sands (S)
80-120
20
5
5
30
Sandy Loam (L)
120-160
40
11
9
60
Clay Loam (CL)
160-200
30
7
7
44
Silt Loam (SiL)
200-220 *20
1
4
25
Organic (O)
>300 *20
0
1
21
Total (*1000 ha)
140
24
26 190
1
East Midlands and Eastern (Anglia); 2 Yorkshire/Humberside
The scenarios for sugar beet production in England and Wales were represented using several weather stations
in each region. Since the 1980s, concentration and reduction in the production of sugar beet has continued as
the number of factories was reduced from 18 to 9 in 2000 and to merely six in 2004. Shallow soils, as given in
Table 2, have been taken out of sugar beet production. The ratio of potentially suitable soils for sugar beet is
about 4 times higher than that actually used, which leaves room for better embedding into more diverse
rotations.
CSG 15 (1/00)
8
Project
title
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
DEFRA
project code
CC0368
The different climate scenarios are sketched in Figure 3: Two different emission scenarios were chosen (LOW,
HIGH), and four time slices (Baseline, 2020, 2050 and 2080) which results in seven different CO2concentrations and temperature/precipitation regimes for the simulation. In Figure 3 the change in total
precipitation and distribution is sketched for the HIGH emission scenario only, the extreme events are
visualised for the 2080s-HIGH.
Management scenarios were selected as described before, selecting three different sowing dates for wheat (21
Sept, 11 Oct, 31 Oct.) and three different harvest dates for sugar beet (30 Sept, 25 Oct, 15 Dec), with a standard
sowing date (01 Apr). Observations on sugar beet length of season were derived from a series of sowing and
harvest date data collected in the East Midlands (ADAS, unpublished data); harvest dates ranged from late
September (DOY 270) until late December (351), with a mean DOY 315. Scenarios were simulated by running
each combination of soil-sowing/harvest date for 50 realisations of the climate variability generated by the
stochastic weather generator. In the simulations for sugar beet the weighting of the soil properties and harvest
date is already reflected in the simulation output and thus in the frequency distributions. For winter wheat
weighting for both factors was done after the simulations. The difference between weighted and non-weighted
means for winter wheat was about 0.3 t/ha (national, weighted according to area grown), and less than 0.2t/ha
for soil distribution and sowing date.
Mitigation options were assessed as (a) sowing (wheat) date (b) variety selection (wheat), and allocation to soil
type.
3
Key findings
3.1
Climatic variability
The new scenarios (UKCIP02; Hulme et al., 2002) include a more comprehensive analysis of extreme weather
events, rainstorms and heat waves and have a native spatial resolution of 50km compared with the 350km
resolution of the previous scenarios (UKCIP98). Compared with the earlier Medium-High (MH98), the new
simulations predict a delayed but stronger increase of [CO2] in the high emission scenario. More warming than
in the HadCM2 Medium-High scenarios is expected to occur by the 2080s. The new high-emission scenario
(High02) predicts temperatures similar to the old medium-high from UKCIP98 (MH98) in the 2050s. In
contrast to the old scenarios, summer will become drier, with only half as much rain in the southeast by the
2080s. The scenarios predict that soil moisture may be reduced by 30 and 40 % over large areas of England by
the 2050s and 2080s. These changes are critical to the drought risk and its impact on key crops. However, the
frequency distributions are discussed in the results of the water balance simulation for wheat and sugar beet.
Statistics of weather for the different scenarios (UKCIP98 MH, UKCIP02 low – high) are displayed in Figure
3. The statistical analysis showed that the frequency of extreme events (high temperature, rainfall intensity) is
markedly increasing in the end of the century. Days with Tair exceeding 30 °C will increase from less than one
per year to more than 10 per year in the high emission scenario, mainly occurring in July and August. High
rainfall intensities are also predicted for the 2080s-High in terms of shifting rainfall to the wet season (winter),
in which the average rainfall may increase by up to 20 % with a small increase in the frequency of very wet
days (>15mm) in winter and spring. The autumn and spring may show some reduction of average rainfall (max
-10%) but the frequency of heavy rainfall is predicted to increase by between 2 and 5 days per decade.
Summers will become substantially drier and receive fewer intensive rainfall events.
CSG 15 (1/00)
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Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
Increase [ o C]
700
600
4
High02
MH98
Low02
3
2
1
o
Days with Tmax > 30 C
15
1961-90
2080s - HI
10
5
0
0
2020s
2050s
2080s
SWest
WMid
EMid
EAng
SEast
500
20
20
10
400
300
Change [% ]
CO 2 concentration [ppmv]
800
CC0368
20
Average temperature
Tmax >30 o C [ d / yr ]
5
900
DEFRA
project code
Change [d/decade]
Project
title
0
-10
2020s
2050s
2080s
Low02
422
489
525
MH98
447
554
697
-30
High02
437
593
810
-40
-20
Precipitation
2020s
2050s
Annual
DJF
2080s
MAM
JJA
SON
15
10
Change in wet days (>15 mm)
MAM
JJA
SON
DJF
5
0
-5
-10
-15
S West
WMid
EMid
EAng
SEast
Figure 3: Climatic variables derived from the UKCIP scenario simulations (Hulme and Jenkins, 1998; Hulme
et al., 2002)
3.2
Risk assessment modelling scenarios
The drought risk for the yields of the two crops winter wheat and sugar beet – were assessed in parallel for
combinations of climate, soil type and management as outlined in the previous report. The emphasis was put on
the new emission scenarios published by the UKCIP in 2002. Some of the old scenario runs were rerun for both
crops with weather generated from parameters given UKCIP98-MH. From the comparison of old and new
climate data we concluded that the old Medium-high emission scenario (2050s) was so close to the new 2050sHigh that - in contrast to the original idea – it was unnecessary to use the new Medium-High. We run the
following climatic scenarios: baseline and UKCIP02-low and UKCIP02-high for 2020s, 2050s and 2080s.
We assessed the drought risks in form of regional probability distributions of the following drought and
crop performance indicators:
(a) soil moisture deficit with reference to the prevailing soil types (available soil water capacities) and
physiological thresholds,
(b) reduction of potential yields, defined as the ratio of water-limited and potential yield (Ywl/Ypot), and
(c) distribution of actual harvestable yields.
3.2.1 Soil moisture deficits under winter wheat and sugar beet
In contrast to the scenarios based on HADCM2 and UKCIP98 the new scenario simulations show that in spite
of reduced rainfall winter wheat will experience less water stress. On average, the soil moisture deficit (SMD)
during the wheat growing season is predicted to diminish by about 4 % (2050s) and by about 9% in the 2080s.
Even in the east (Anglia/ Midlands) and southeast of England the average soil moisture deficit may decrease
during the growing season of winter wheat. However, there could be a slightly greater variation in some areas.
This indicates that wheat may actually escape the summer drought in most instances, due to earlier
development and ripening of the crop. Looking at the spring sown crop, sugar beet, it becomes clear that the
maximum SMD is not reached before the end of August, irrespective of the scenario.
CSG 15 (1/00)
10
Project
title
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
DEFRA
project code
CC0368
Mean rleative SMD
0.7
0.6
0.5
Baseline
0.4
HI2050
0.3
HI2080
0.2
0.1
C
ar
lis
Le le
S
em
B
on ing
ni
ng
C
am ton
br
id
ge
O
xf
Ly ord
ne
h
Pl am
ym
o
Sh uth
aw
bu
ry
R
in
gw
Ab
a
er y
po
r th
0.0
Figure 4: Variation of average maximum relative soil moisture deficit rSMD (SMDmax/AWC) in
the regions (represented by site of weather station) for winter wheat
The probability of exceeding the threshold for leaf senescence (rSMD = 0.7) of winter wheat will be small and
decrease compared to the baseline scenario. This is in contrast to the simulations using Sirus2000 which
predicted that for example in Shawbury (West Midlands) this threshold would be exceeded in 70 % of years
compared with 60 % in the baseline. Simulations with the new model and new climate scenarios are very
different and reduce this estimate to about 7% of years in the baseline, 8 to 6 % by the 2050s and to about 4 to 3
% by the 2080s under LOW and HIGH CO2 emission, respectively.
On the contrary, sugar beet is going to experience a greater degree of senescence enhancing drought stress in
the future. The date of drought occurrence is predicted to be before the end of August, which is well in midseason and likely to have great impact on yields. The question is how long the maximum seasonal SMD will
persist. Even the average of the SMD is predicted to exceed the threshold of 0.7 (Fig. 5a) and by the 2050s the
average stress level is going to increase by 10 %. The change of drought stress is greater in the East Midlands
than in East Anglia. As illustrated for the high emission scenario in East Anglia (Fig 5b) there are only a few
sites left which are not exposed to an increased risk of senescence. The change in maximum drought level is
drastic for all regions under the High emission scenario (Table 3), much less under the Low emission scenario.
A probabilistic view of drought impact needs to be taken with respect to yield realisation and physiological
margins. The threshold for enhanced senescence is set to 0.7 and this is compared to the probability of relative
soil moisture deficit (rSMD) exceeding this threshold.
Table 3: Probability of not exceeding the critical threshold of 0.7 for
senescence simulated for the sugar beet season under two different
emission scenarios (Low, High) for three different time slices.
Region
East Anglia
East
Midlands
West
Midlands
CSG 15 (1/00)
Scenario
base
0.27
0.45
0.37
Low emission
High emission
2020s 2050s 2080s 2020s 2050s 2080s
0.23
0.16
0.14
0.21
0.10
0.04
0.31
0.28
0.24
0.29
0.21
0.07
0.28
0.26
11
0.19
0.26
0.17
0.06
0.9
0.8
16
0.8
0.7
14
0.6
12
0.5
10
0.4
8
0.3
6
0.2
4
0.1
2
0.1
0
0
0
(b)
DEFRA
project code
CC0368
East Anglia - High
0.7
0.6
0.5
0.4
0.3
0.2
0.2
ba
se
20
20
lo
20
20
hi
20
50
lo
20
50
hi
20
80
lo
20
80
hi
(a)
Cumulative probability
1
18
1
0.9
Increase (%)
20
max
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
Relative SMD
Project
title
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Relative SMD
Figure 5: Maximum relative soil moisture deficit under sugar beet (a) in the major growing regions (East
Anglia □; East Midlands ■) and average increase (▬), and (b) cumulative probability to exceed the
threshold of 0.7 (vertical bar) simulated for the high emission scenario; baseline (▬▬), 2020s (——),
2050s (— —) and 2080s (– – –)
In conclusion, - and recalling the information on climatic change, such as increasing temperature and
decreasing summer rainfall - winter wheat is likely to escape the summer drought whereas we must anticipate
more drought stress in sugar beet production. In comparison with the earlier scenarios (2050s-MH) based on
HadCM2 the drought stress may be twice as high. Regional differences are likely to diminish. It is important to
know how long the drought season will last. According to Figure 3, with the beginning of September more
rainfall can be expected.
3.2.2 Reduction of potential yields (YR)
Reduction of Y pot (%)
60
East Anglia
East Midlands
50
40
30
20
10
i
H
Lo
20
80
20
80
i
H
20
50
20
50
Lo
i
H
Lo
20
20
20
20
ba
se
0
Figure 6: Reduction of yield potential Ypot, of sugar beet (Mean ±SD)
predicted for the UKCIP02 scenarios (Lo vs Hi; 2020s to 2080s).
Based on the results of soil moisture, it is likely that the potential yields of winter wheat and sugar beet will be
affected very differently. In contrast to the earlier model predictions, reduction of potential yields in winter
wheat is going to be generally smaller in the future. In the East Midlands the yield loss due to drought will
decrease from 19% (base) to 16 and 12 % by the 2080s, assuming LOW and HIGH emission of CO2,
respectively. In the West Midlands the decrease is even greater (to 12 and 10 %, respectively). Wheat sown at
later dates, however, might respond with later anthesis and will be affected more strongly since delayed grain
filling is affected by accelerated maturation. These results again suggest that earlier development and anthesis
CSG 15 (1/00)
12
Project
title
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
DEFRA
project code
CC0368
of wheat will avoid the impact of greater summer drought. On the other hand, sowing dates become more
critical for wheat production.
For the production of sugar beet, the situation is quite different, and the examples for the main sugar beet
growing area display a large increase in drought-related reduction of potential yields (Figure 6). Especially, by
the 2050s the drought-related reduction of potential sugar yields may increase by 30 to 50 %, depending on the
selected scenario (LOW vs. HIGH). By the end of the century (2080s) drought impact on yields may actually
double. The yield reduction is widely spread, which reflects the great variation of future climate and uncertainty
of yields.
In conclusion, wheat is less likely to be negatively affected by changing temperature and water regime due to
it’s terminate character than sugar beet. Drought effects on sugar beet may actually be up to twice that of the
current situation, as 20 % more drought is likely to happen within our lifetime.
3.2.3 Distribution of winter wheat yields
Indicators for the dynamics of yield formation are (a) the anthesis date, (b) the time of maturity, and (c) the
number of leaves. The dates of anthesis are illustrated for the West Midlands only but are representative for the
general trend:
Anthesis of Mercia is moved forward from the 13 June by three to five weeks due to higher temperatures,
occurring on average by 19 May (2080s-Low emission) or the 6 May (2080s-High emission). Consort is
flowering one week earlier than Mercia and for the HIGH emission scenario flowering is predicted to occur six
weeks earlier than in the past (end of April). It is important to notice that sowing date has an impact on anthesis
date (see below) and will result in change of yield.
Maturation dates of these two varieties are very similar, they are predicted to move from 27 August (baseline)
to the end of July (2080s-Low) or even the middle of July (2080s-High). The increase of yield is very similar
for both varieties: 18 to 38 % till the end of the century using the Low and High emission scenarios. The interregional variation of anthesis date is decreasing because warming in the west and north of the country
accelerates the development but increases the time needed to fulfil vernalisation in those parts of the country
which are warmer. The phenology of wheat in the warmer and cooler regions will become more alike in the
future.
Numbers of leaves are decreasing with delay in sowing date, by two leaves from earliest (21 Sept) to latest of
sowing (31 Oct). Rising temperature will increase the number of leaves but increase the difference between
earliest and latest sowing (five leaves by the 2080s-High). Delayed anthesis will always result in reduced
yields.
Based on the indicators discussed above (rSMD, YR) wheat yields should be unaffected by drought and are
expected to increase. In Table 4a we summarise the regional mean yields for the former reference variety,
Mercia, for the HIGH emission scenario. The range of baseline yields (6.3-8.5 t/ha) simulated with the new
Sirius version and parameterisation is considerably wider than simulated earlier (7.2- 8.1 t/ha). Further, the
overall average of baseline yield is almost 0.9 t/ha smaller than simulated earlier. For East Anglia the difference
is greater but there is no obvious Southwest – Northeast pattern in the deviation from earlier simulations.
The difference between the high and low emission (Table 4b) scenarios is small, and it mostly concerns the
yield level, which is between 0.1 (2020s), 0.5 (2050s) and 1.2 t/ha (2080s). The respective spread of the yield
distributions are very similar (Figure 7), and in general the yield distributions are shifting almost parallel to
each other to higher yields, more strongly in the High emission scenario.
CSG 15 (1/00)
13
Project
title
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
DEFRA
project code
CC0368
Table 4a: Regional mean winter wheat yields (t/ha) and CV (%) and increase in the HIGH emission scenario
compared with the baseline scenario (weighted for soil distribution and sowing date within the region);
* I = inland (Lyneham) and C = coastal (Plymouth)
Region
North
North East
East Midlands
East Anglia
South East
South West I*
South West C*
West Midlands
North West
Wales
Scenario
Weight
3.6
12.9
21.4
17.5
24.9
5
5
7.8
1.2
0.6
English weighted mean
Baseline
Mean
CV (%)
6.92
14.7
6.47
17.2
7.15
15.9
6.29
20.1
6.85
17.6
7.30
17.3
8.45
16.4
6.97
17.5
7.05
17.6
7.81
21.8
6.84
Mean
8.29
8.07
8.55
7.98
8.45
8.73
10.47
8.37
8.39
10.45
8.38
High 2050
CV (%)
Increase
15.9
1.37
17.9
1.60
17.2
1.40
18.7
1.70
15.8
1.60
16.9
1.43
11.7
2.02
16.7
1.40
16.6
1.34
18.1
2.64
1.55
Mean
9.68
9.15
9.48
8.40
9.07
9.76
11.03
9.63
9.54
12.25
9.19
High 2080
CV (%) Increase
13.0
2.8
14.0
2.7
14.1
2.3
20.5
2.1
15.4
2.2
14.4
2.5
11.9
2.6
12.9
2.7
14.1
2.5
13.5
4.4
2.36
Table 4b: Regional mean winter wheat yields (t/ha) and CV (%) and increase in the LOW emission scenario
compared with the baseline scenario (weighted for soil distribution and sowing date within the region);
* I = inland (Lyneham) and C = coastal (Plymouth)
Scenario
Region
Weight
North
3.6
North East
12.9
East Midlands
21.4
East Anglia
17.5
South East
24.9
South West I
5
South West C
5
West Midlands
7.8
North West
1.2
Wales
0.6
English weighted mean
CSG 15 (1/00)
Baseline
Mean
6.92
6.47
7.15
6.29
6.85
7.30
8.45
6.97
7.05
7.81
6.84
CV(%)
14.7
17.2
15.9
20.1
17.6
17.3
16.4
17.5
17.6
21.8
Mean
7.69
7.53
8.01
7.48
7.94
8.14
9.79
7.77
7.93
9.50
7.84
14
LOW 2050
CV(%) Increase
16.7
0.77
18.1
1.05
17.7
0.85
19.1
1.19
17.2
1.10
18.4
0.84
13.3
1.34
17.6
0.79
17.4
0.88
19.3
1.69
1.01
Mean
8.10
7.62
8.03
7.29
8.18
8.56
9.93
8.28
8.20
9.88
7.98
LOW 2080
CV(%) Increase
13.0
1.2
14.0
1.2
14.1
0.9
20.5
1.0
15.4
1.3
14.4
1.3
11.9
1.5
12.9
1.3
14.1
1.1
13.5
2.1
1.14
Project
title
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
Low emission scenario
0.40
0.35
0.30
0.30
0.25
0.25
0.20
0.20
0.15
0.15
0.10
0.10
0.05
0.05
0.00
0.00
0.40
East Midlands Low
West Midlands Low
0.35
0.35
0.30
0.30
0.25
0.25
0.20
0.20
0.15
0.15
0.10
0.10
0.05
0.05
0.00
0.00
0.40
0.40
South East Low
South West Low
CC0368
High emission scenario
0.40
North East Low
0.35
0.40
Probability
East Anglia Low
DEFRA
project code
0.35
0.35
0.30
0.30
0.25
0.25
0.20
0.20
0.15
0.15
0.10
0.10
0.05
0.05
East Anglia High
North East High
West Midlands High
East Midlands High
South West High
South East High
0.00
0.00
3 4 5 6 7 8 9 10 11 12 13
3 4 5 6 7 8 9 10 11 12 13
Yield class (t/ha)
3 4 5 6 7 8 9 10 11 12 13
3 4 5 6 7 8 9 10 11 12 13
Yield class (t/ha)
Figure 7: Distribution of wheat yields in the major regions of England simulated with Sirius2003
for the UKCIP02 emission scenarios (Low and High); baseline (▬▬), 2020s (——), 2050s
(— —) and 2080s (– – –)
In conclusion, wheat yields as simulated with the new scenarios and the new Sirius version indicate increasing
yields with lower uncertainty (decrease in CV %). The overall average increase of wheat yield by the 2050s
(1.55 t/ha) is very similar to the yield increase predicted in the earlier scenarios (Richter et al., 2002). The most
obvious reasons for this result are the increase in RUE due to higher CO2 concentrations and the acceleration of
development by five to six weeks. Consequently the wheat crop will escape from the drought, which reaches its
peak in August thus coinciding with natural maturation and harvest. Crucially, late sowing dates should be
avoided so as not to delay anthesis, which would shorten grain filling.
Further discussion will follow the paragraph on adaptation and mitigation (3.3).
3.2.4 Distribution of sugar beet yields
The distributions of sugar yields displayed in the following were simulated for each region with a range of soil
types and weather stations using a single sowing date only and a selection of harvest dates. Effects of earlier or
later sowing were comprehensively covered in the earlier project (Richter et al., 2002).
All distributions are distinctly peaked under the baseline scenario, and in contrast to the previous project, there
is more homogeneity inside a region than between different weather sites. This is probably due to more
homogenous weather and not due to changes in model algorithms. Earlier, we simulated a yield increase of 1.9
t/ha sugar by the 2050s, in the new simulations, the sugar yield increases by between 1.4 and 2 t/ha in the low
and high emission scenarios, respectively (Table 5). The results are quite different from the results for wheat as
with increasing CO2 emission and temperature in the future the range of yields becomes much wider (Figure
8). The CV increases from 15-18% (baseline) to about 18-23 % (2050s) and 19-25 % by the end of the century.
CSG 15 (1/00)
15
Project
title
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
16 0 0
North low
12 0 0
12 0 0
800
800
400
400
0
0
16 0 0
West Midlands low
North high
16 0 0
East Midlands low
CC0368
East anglia high
East Midlands high
West Midlands high
12 0 0
12 0 0
Frequency
Frequency
16 0 0
East Anglia low
DEFRA
project code
800
400
800
400
0
0
16 0 0
16 0 0
South low
West low
12 0 0
12 0 0
800
800
400
400
0
West high
South high
0
2
6
10
14
18
22
2
6
10
14
18
22
2
6
10
Yield class (t/ha)
14
18
22
2
6
10
14
18
22
Yield class (t/ha)
Figure 8: Regional frequency distributions of sugar yields in the Low (left) and
High (right) emission scenario; baseline (▬▬), 2020s (——), 2050s
(— —) and 2080s (– – –).
Table 5: Mean sugar yields, t/ha (and CV %) in the regions under different climate scenarios (low,
high emission) for different phases of the century; weighted English mean yields (t/ha) for
the scenarios
Region
East Anglia
E. Midlands
W.Midlands
North
South
West
Weighted
Scenario
weight
base
0.36
9.9 (18)
0.36
9.9 (15)
0.144
9.6 (18)
0.133
9.5 (16)
10.3 (17)
0.003
10.6 (15)
1
9.8
Low emission
2020s
2050s
10.9 (20)
11.3 (21)
10.7 (17)
11.2 (19)
10.5 (19)
11.0 (20)
10.3 (17)
11.1 (18)
10.4 (22)
11.3 (23)
11.6 (16)
12.0 (17)
10.7
11.2
2080s
11.5 (21)
11.6 (19)
11.2 (22)
11.5 (18)
11.8 (22)
12.2 (18)
11.5
High emission
2020s
2050s
11.0 (21)
11.8 (23)
10.8 (17)
11.9 (21)
10.6 (19)
11.5 (22)
10.4 (18)
11.8 (19)
10.7 (22)
11.6 (25)
11.7 (16)
12.7 (19)
10.8
11.8
2080s
12.5 (24)
12.9 (21)
12.1 (25)
12.7 (22)
12.4 (26)
13.6 (22)
12.6
The differences between the regions are not very large with respect to yield, on average less than 1 t/ha, but
greater with respect to the expected variation. This is a consequence of the distributions of soils. In comparison
to earlier simulations (see Fig. 17 in Richter et al., 2002), the uncertainty of yields – expressed as %CV per
class of soil AWC – is similar with respect to response to available water capacity, yields increase from 8 t/ha
on sand to about 11 t/ha on good soils in the baseline scenario(Figure 9). In the future, yields will remain low
on sands but increase to 15 t/ha by 2050s on very deep soils. The variation of these yields decreases with
improving AWC (24 down to 7 %) the maximum variation increase by 3 to 4 %. The response to low soil
quality (AWC) is about 5 % higher with the new model using a stratified hydrology module.
CSG 15 (1/00)
16
Project
title
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
DEFRA
project code
CC0368
18
(a)
16
Sugar yield (t/ha)
14
12
10
8
6
4
2080 hi
2050 hi
2
base
0
Yield variation (CV %)
35
(b)
30
25
20
15
10
5
0
0
50
100
150
200
250
300
Available water capacity (mm)
Figure 9: Dependence of (a) sugar yield with soil AWC (mm) and (b) variation of yield
in the Baseline and High emission scenario; (▬) baseline; (—▲) 2050s (—■)
2080s.
In conclusion, it is advisable not to grow sugar beet on shallow or very sandy soils but to keep better soils
reserved to gain most of the productivity increase due to higher CO2 and RUE. Yields of sugar on the best soils
may increase by more than 3 t/ha by the 2050s and 5 t/ha by the end of the century.
3.3
Adaptation strategies and mitigation options
For sugar beet production it is clear that the quality of the soil should be good enough to buffer drought stress.
All shallow and light soils should be taken out of production if irrigation cannot be secured. It is not clear
whether further breeding for drought tolerance can improve yields on such soils.
For winter wheat production we investigated the changes for three different management decisions
(a) Variety selection (breeding for changing dates of anthesis and maturation in wheat).
(b) Crop management such as sowing for winter wheat
(c) Crop-soil allocation
3.3.1 Variety selection
We investigated the effects of using the variety Consort, which is a later maturing variety than Mercia, yielding
around 1t/ha more than Mercia in recent NIAB trials (NIAB 1999). The crop parameters for Consort were
adjusted to give a longer period of grain fill than for Mercia and a slightly earlier anthesis date.
With the baseline scenario, Consort yielded 1.0 t/ha more than Mercia, due to reaching anthesis one week
earlier, and a longer period of grain fill. Both varieties matured on the same date. Consort was simulated to
yield on average 1.5 t/ha more than Mercia with the 2050 and 2080 low and high scenarios, again due to an
earlier anthesis date and longer period of grain fill. Both varieties showed a very similar response to climate
change, with yields predicted to increase by up to 33% by the 2080 High scenario (Table 6).
CSG 15 (1/00)
17
Project
title
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
DEFRA
project code
CC0368
Table 6: Comparison of the wheat varieties Mercia and Consort for the East Midlands
Consort Mercia
Yield t/ha
Scenario
Baseline
2020LO
2050LO
2080LO
2020HI
2050HI
2080HI
8.2
8.6
9.2
9.2
8.7
9.8
10.9
Consort Mercia
StDEV
7.2
7.4
8.0
8.0
7.6
8.5
9.5
1.22
1.38
1.41
1.39
1.38
1.46
1.30
1.14
1.31
1.42
1.39
1.32
1.47
1.34
Consort Mercia
Increase from
baseline (%)
4.4
11.6
12.3
5.6
18.6
32.5
Consort
Mercia
Anthesis date
4.1
11.9
12.3
5.7
19.5
32.6
02-Jun
22-May
15-May
10-May
20-May
08-May
28-Apr
10-Jun
31-May
23-May
19-May
29-May
17-May
06-May
Such yields are dependent on the varieties having a relatively low vernalisation requirement. Anthesis is
predicted to occur progressively earlier, occurring on April 27th in the West Midlands by 2080 High scenario
with Consort. Mercia is predicted to reach anthesis one week later.
3.3.2 Sowing date
Three sowing dates were compared, early (21 Sept) mid (11 Oct) and late (31 Oct). Late sowing results in later
anthesis date, smaller final leaf number and reduced yields, compared to the earliest sowing date, for both
Mercia and Consort. Yields were predicted to be around 1.5 t/ha less, except in the 2080 (High scenario) when
the anthesis date of the early sow date was later than the mid sow date, due to a large increase in the number of
leaves (Figure 10).
Final leaf number
Anthesis date
28-Jun
20
08-Jun
15
19-M ay
10
Yield t/ha
12
10
8
6
Y
4
2
50
20
80
LO
LO
I8
0
LO
I5
0
H
I2
0
H
H
Ba
se
I8
0
LO
20
LO
50
LO
80
I5
0
H
H
I2
0
0
H
Ba
se
H
H
H
I8
0
LO
20
LO
50
LO
80
0
I5
0
09-Apr
I2
0
5
Ba
se
29-Apr
Figure 10: Effect of sowing date on anthesis date, final leaf number and yield for wheat
variety Mercia in East Anglia, East Midlands and South East England. ■ 21
Sept, ■ 11 Oct, □ 31 Oct.
3.3.3 Soil quality – available water
From Figure 11 it is clear that the increase of wheat yields is greatest on the shallow and light soils in the
future. This is difficult to interpret but it could mean that future climate will accelerate development so that
wheat can benefit from winter and spring rainfall. Yield variation is clearly higher in soils with low AWC
compared with soils of high AWC, which affects yields more evenly by the mid-century. Later, regional
differences in the climate may affect yields differently either de- or increasing variation.
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Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
40
30
25
20
15
y = -0.06x + 30.47
R2 = 0.32
10
5
0
100
DEFRA
project code
CC0368
25
y = -0.08x + 41.39
R2 = 0.68
35
Yield variation ( CV % )
Yield increase (% over base)
Project
title
120
140
160
180
200
Soil Available Water (mm)
20
y = -0.09x + 29.63
R2 = 0.48
15
10
y = -0.09x + 29.54
R2 = 0.88
HI2050
5
0
100
HI2080
120
140
160
180
200
Soil Available Water (mm)
Figure 11: Effect of soil available water on (a) wheat yield increase compared with
baseline and (b) wheat yield variation (CV %); scenario specific for ∆2050s and
■ 2080s, high emission; soil specific means; data pooled from East Anglia, East
Midlands and the Southeast.
4 Discussion and conclusions
For both models certain improvements were implemented and have enabled us to test these against existing data
and scenario simulations. In both cases we could test it against a wide range of existing field data, and we can
assume that the new models describe the response to temperature, radiation, CO2-concentration and water
uptake and transpiration (finally arriving at better estimates for water use efficiency (WUE).
Wheat model Sirius:
The recalibration of Sirius2003 left some questions about the phenology and especially the vernalisation
parameters for Mercia and Consort. The difficulty with the calibration of vernalisation parameters is that it
requires experiments with multiple sowing dates and multiple years. The experimental data used for the project
was not suitable for the correct calibration of vernalisation parameters. An overestimated vernalisation
requirement may result in an artificially late future anthesis date for different weather conditions. This could be
a possible reason for underestimating yields in the baseline scenario. The weighted average yield for winter
wheat simulated with the new parameters was 7 t/ha instead of 7.7 t/ha as simulated and validated against
regional observations earlier (Richter and Semenov, 2004).
In comparison with the earlier simulations the overall yield increase for the 2050s was very similar between the
MH-98 and High-02. This is plausible because temperature and CO2 are very similar for these scenarios
(Figure 3). Yield increase till the end of the century is more than 2 t/ha. This estimate, however, is ignoring the
fact that high maximum temperatures (> 30 oC) will reduce the grain number and thus sink. Hot days are going
to increase in the High emission scenario by the 2080s and some years with heat stress related yield reduction
may occur.
Sugar beet model:
The hydrology model was improved, which creates a wider range of yield response due to higher yields. The
effect of the new model is shown in Figure 12, which shows a distinct shift toward higher yields in the forecast
(MH 2050s) purely due to the change in model. On average, yields change only marginally for the baseline
scenario (< 0.1 t/ha) but by more than 1 t/ha in the MH2050s scenario with an increase in variation as well (see
Table 7). The new hydrology model causes future yields to be more variable and generally to be higher than
with the simple – 1-compartment – soil profile delivering a single average water content to control
transpiration.
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Project
title
Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
DEFRA
project code
CC0368
Table 7: Statistical parameters for the distributions in Figure 12
East Midlands
Old
8.9
1.45
16.3
Mean
SD
CV
Baseline (0)
New
9
1.54
17.1
Old (0)
1000
Medium High 2050s
Old
New
11.1
12.2
1.75
2.24
15.8
18.4
New (0)
Old (MH50s)
New (MH50s)
900
800
Frequency
700
600
500
400
300
200
100
0
0
2
4
6
8
10
12
14
16
18
20
Sugar yield class (t/ha)
Figure 12: Distribution of sugar yield simulated for the East Midlands using the old and
new hydrology model for sugar beet, baseline (0) and medium high scenario for
the 2050s (MH50s)
The parameter for reduced senescence in high fertility (available water, AWC) soils is not yet regarded in this
distribution of yields. Canopy senescence still lacks a unified response which is essential for climate change.
We did not change the related parameters, which would have been necessary because the deep organic and silty
soils represent about 22 % of the total area. The highest sugar yields may therefore be another 2 t/ha higher than
predicted now on the basis of a more sophisticated hydrology model. Overall, it is important to realise that there
is a complex interaction between water stress and canopy expansion, which needs further evaluation and a more
mechanistic approach than proposed here.
Validity of the impact assessment
It is obvious from the field data that simulations of wheat and sugar beet are in good agreement. Regional
comparison needs to allow for loss processes along harvest and delivery. It is also very difficult to assign
factory data from sugar beet to certain soil-management in the regions. We can also only compare data for the
past, and differences in the model complexity may be more important in the future than in the past (Figure 12).
For the past, the shapes of the distribution of sugar yields are very similar to what we have simulated (Figure
13). The average yield over all of England was about 8.1 t/ha (~ 90 % of simulated), and the variation was very
similar to the result in the baseline simulation (CV 19 to 21 %), which is larger than simulated using either old
or new model (Table 6). Some of the recorded variation is due to variable losses and errors in the delivery/area
ratio.
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Re-assessing drought risks for UK crops using UKCIP02
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9.0
2000
Mean yield (t/ha)
4
1600
5
1400
12
1200
17
1000
18
800
600
22.5
22.0
8.5
21.5
21.0
8.0
20.5
20.0
19.5
7.5
19.0
18.5
400
7.0
200
0
0
2
4
6
8
10
12
14
16
Sugar yield class (t/ha)
ls
Al
CC0368
23.0
1
1800
Frequency
DEFRA
project code
Variation (CV %)
Project
title
18.0
tt
co
)
(1
ry
Bu
)
(4
C
y
tl e
an
)
(5
N
ew
k
ar
(
)
12
n
to
ng
si
is
W
(
)
17
rk
Yo
8)
(1
Figure 13: Distribution of sugar yield recorded in the factories across England (2000-2003); mean and CV (%)
Benefits for the public
From this risk assessment the public can be reassured of its food supply. One benefit of this project is that plant
breeders should give appropriate priority to drought resistance as a target when breeding for the UK market.
This is still true for sugar beet. For winter wheat there seems to be a need to be aware of varieties with a strong
vernalisation requirement, and those sensitive to heat stress.
Action required by policy makers
Sugar beet: In contrast to earlier forecasts (UEA for DEFRA; CC0358) that sugar beet would exceed its lower
economic margin in England by the 2080s, we can conclude from the above results that the compensatory
effects of rising [CO2] and varietal progress may ensure high yields for sugar beet on some soils. This allows
policy makers to consider sugar beet as a crop for biofuels, due to its high yield potential and adaptation to
drought, at some sites, even were it to be no longer economic for sugar production. As is currently the case, all
shallow and light soils should continue to remain out of sugar beet production, due to the high uncertainty of
yield on these soils.
Winter wheat: From these results it is hard to see any further problem in wheat production related to drought
stress. More work might need to be done on high temperature effects and disease resistance.
There may be a necessity to analyse lowland soil water regimes because heavy winter rains may cause flooding
and anoxia in wheat.
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Project
title
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Re-assessing drought risks for UK crops using UKCIP02
climate change scenarios
DEFRA
project code
CC0368
References
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Home Grown Cereals Authority. Project Report No. 151, Vol. 3.
HGCA 1998b Home Grown Cereals Authority. Project Report No. 174, Vol. 2.
HGCA 2000 Reducing winter wheat production costs through crop intelligence information on variety and
sowing date, rotational position and canopy management in relation to drought and disease control.
Home Grown Cereals Authority. Project Report No. 235.
Jamieson P D, Semenov M A, Brooking I R and Francis G S 1998 Sirius: a mechanistic model of wheat
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Lawless C, Semenov M A and Jamieson P D 2004 A wheat canopy model linking leaf area and phenology.
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Qi A, Kenter C., Hoffmann C., and Jaggard, K. W., 2004. The Broom’s Barn sugar beet growth model and its
adaptation to soils with varied available water content. European Journal of Agronomy (accepted)
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climate change. DEFRA Project CC0336, Final Report. 22pp.
Richter G M, Semenov M A and Mitchell D 2001 Testing Sirius predictions against wheat data sets in the UK.
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Richter G M and Semenov M A, 2004. Modelling impact of climate change on wheat yield in England and
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Forest Meteorology 73: 265-283.
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