Transcript Slide 1

TSEC-Biosys: Yield and spatial supply of
bioenergy poplar and willow short
rotation coppice in the UK
M.J. Aylott, G. Taylor
University of Southampton, UK
E. Casella
Forest Research, UK
P. Smith
University of Aberdeen, UK
Biomass role in the UK energy futures
The Royal Society, London: 28th & 29th July 2009
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Contents
 Introduction
 Aims
 Empirical Modelling
– Method
– Results
 Process Modelling
– Method
– Results
 General Conclusions
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Introduction.
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Introduction
 Short rotation coppice
(SRC) poplar and willow
are two widely planted
bioenergy crops
 Both species are fast
growing and found
across a wide range of
environments
 Climate change presents
challenges but also
opportunities for
bioenergy
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How much bioenergy do we have?
18.5M
hectares
(ha) UK
agric. land
310,000 ha oilseed rape (biodiesel)1
125,000 ha sugar beet (bioethanol)1
9,800 ha Miscanthus1
5,700 ha poplar and willow1
Renewable energy
production in 20072
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1. (NNFCC, 2008), 2. (BERR, 2008)
How much bioenergy do we need?
UK Renewable Energy Strategy
= 15% renewable (2020)
= 200,000 ha dedicated
energy crops1
Renew. Transport Fuel Obligation
= 2.5-5% biofuel (2014)
= 215,0002-870,0003 ha oilseed
rape (biodiesel)
= 500,0003-525,0002 ha wheat
(bioethanol)
Up to 5%
of agric.
land may
be needed
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1. (Britt et al. 2002), 2. (DTI & DEFRA, 2007), 3. (NFFCC, 2009)
Aims.
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Aims
1. Predict current spatial
productivity of SRC
poplar and willow using
measured data from UK
field trials (empirical)
2. Predict future spatial
productivity of SRC
poplar and willow by
adapting the
ForestGrowth model for
a coppice system in the
UK (process)
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Empirical Modelling.
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Empirical modelling: Method
 Measurements taken from
national SRC field trials
network
 Largest field trial network
in the UK (49 sites)
 16 poplar and 16 willow
varieties grown (6 yrs)
 Extensive measurements
taken at each site including
plant productivity, soil
profiles and daily climatic
records
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 Plot data for each
genotype was
modelled using Partial
Least Squares
regression (Simca-P)
 Existing spatial data
was used to upscale
model outputs
 Climate
 Topography
 Soil
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Empirical modelling: Results
Species
Genotype
Rotation
Observed
Mean Yield
Predicted
Mean Yield
Poplar
Beaupré
First
7.34 (2.33)
7.42 (1.25)
Poplar
Ghoy
First
6.45 (2.47)
6.50 (1.38)
Poplar
Trichobel
First
9.08 (2.67)
9.31 (1.37)
Willow
Germany
First
7.14 (2.94)
7.05 (1.83)
Willow
Jorunn
First
9.09 (3.01)
9.29 (2.09)
Willow
Q83
First
8.03 (3.23)
8.21 (2.09)
Poplar
Beaupré
Second
4.87 (2.43)
4.90 (1.38)
Poplar
Ghoy
Second
5.77 (2.46)
5.85 (1.24)
Poplar
Trichobel
Second
9.59 (2.78)
9.70 (1.38)
Willow
Germany
Second
7.46 (4.00)
7.49 (2.46)
Willow
Jorunn
Second
9.15 (2.70)
9.30 (1.77)
Willow
Q83
Second
10.71 (3.74)
10.72 (1.38)
* standard error in brackets
 The model
describes 5175% of the
variation in
yield
 Willow yields
were higher
than poplar,
esp. in the 2nd
rotation
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Empirical modelling: Results
 Mean poplar yield = 7.3 odt ha-1 yr-1
 Mean willow yield = 8.7 odt ha-1 yr-1
 Potential to supply >28 TW h-1 of electricity
(a) Poplar var. Trichobel
(b) Willow var. Jorunn
(c) Willow var. Q83
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Willow var. Jorunn
 Spring/summer precipitation highly correlates to yield,
indicating both species were limited by water availability
 Other factors (i.e. soil pH) gave localised yield disparity
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Excluded areas:
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Areas of Outstanding
Natural Beauty
•
National Park
•
Forest Park
•
Planted Ancient
Woodland Site
•
RSPB Reserve
•
Inland water, town and
road
•
National Trust land
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Lowland
Heath/Bogs/Fens/Mire
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Ancient woodland
•
Coastal sand dune
•
RAMSAR site
•
SSSI
•
Special Protected Area
•
Local or National Nature
Reserve
•
Countryside Right of Way
•
Registered Common Land
•
Country Park
•
Listed building, World
Heritage Site or
Monument
Yield
in millions of odt/yr15
Greenhouse Gas Emission Modelling
 Yield data used
to produce
greenhouse gas
maps
 20-year average
using RothC
 Replacing
arable or
grassland with
SRC reduces
GHG emissions
Gross CO2 emissions (tonnes/ha/yr)
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Process Modelling.
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Process modelling: Method
 Process-based models help us
explore interactions between
yield and climate
 ForestGrowth1,2 is a yield
model for mature forest
species, which has been
parameterised for SRC3,4,5
 The model uses UKCIP climate
change predictions
1. (Evans et al., 2004), 2. (Deckmyn et al., 2004)
3. (Casella & Sinoquet, 2003), 4. (Gielen et al., 2003), 5. (Casella & Aylott, unpublished)
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SRC-MOD: Method
 Phase 1: Root
carbon used to
grow leaves on
existing stem
 Phase 2: If layer
doesn’t have
enough light, stems
grow and new
leaves are added
 Phase 3: Carbon
stored for the next
years growth
 Phase 4: Leaves fall
 Phase 5: Dormancy
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Process modelling: Current Climate
 Parameterised for Populus
trichocarpa (black
cottonwood)
 Yields predicted by the
model are within ± 20% of
measured yields (seven
sites)
 Average annual yield = 9.4
odt ha-1 yr-1
Productivity map of P.
trichocarpa, second rotation
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Process modelling: Future Climate
 Currently, SRC-MOD uses arbitrary increases in CO2,
temperature and precipitation
– UKCIP02 2050 medium emission scenario
– One site (Alice Holt, clay loam soil)
– One species (P. trichocarpa)
 In future, SRC-MOD will use complete UKCIP09
weather datasets
– Different emission scenarios for 2020’s, 2050’s & 2080’s
– UK wide
– Multiple species
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Carbon Dioxide Effect on Yield
 CO2 set to increase to
550 ppm by 2050
 Leads to increase in
photosynthetic activity
 Ten years of CO2
experiments on poplar
found:
– 500-700 ppm leads to
mean increase in above
ground productivity of
+34 %
Source: NOAA, 2008
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Carbon Dioxide Effect on Yield
 Atmospheric CO2
predicted to increase
from 370 to 550 ppm
– Increased photosynthesis
– UK yields +29%
– Parts of S. England & N.
Scotland +50%
– Calfapietra et al. (2003),
found an increase of up to
27% in poplar yields
Carbon Dioxide vs. Yield map for
P. trichocarpa, second rotation
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Temperature Effect on Yield
 Futures temperatures are
likely to rise
– Summer temperatures
increasing faster than
those in winter
 Higher temperatures
– Advance budburst
– Increase photosynthesis
– But increase transpiration
and respiration rates
Source: UKCIP02 Climate
Change Scenarios
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Temperature Effect on Yield
Yield (odt/ha)
40
35
30
25
20
Yield (Baseline)
Yield (Inc. temp)
15
10
5
0
0
500
1000
1500
2000
Simulation Day
 Temperature increase of +2.5oC (Summer) and +0.5oC
(Autumn to Spring)
– Yield increased by 0.5 odt/ha/yr (+4%) by end of second rotation
at Alice Holt site  respiration costs also increase over time
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Precipitation Effect on Yield
 Future climate predictions
(Hulme et al., 2002)
– Decreased summer precipitation
 increased soil moisture deficit
– Increased winter precipitation 
higher risk of flooding
 Souch & Stephens (1998)
showed poplar yield decreased
60-75% in drought conditions
 Water used in many leaf
biochemical processes, by
decreasing its availability
photosynthesis will decrease
Source: UKCIP02 Climate
Change Scenarios
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Precipitation Effect on Yield
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Yield (odt/ha)
30
25
20
Yield (Baseline)
Yield (Dec. ppt)
15
10
5
0
0
500
1000
1500
2000
Simulation Day
 Precipitation decreased by 10%
– Yield decreased by 1.3 odt/ha/yr (-12%) by end of second rotation
at Alice Holt site  increased soil moisture deficit
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Predicted Yield in 2050
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Yield (odt/ha)
40
35
30
25
Yield (Baseline)
Yield (2050 med)
20
15
10
5
0
0
500
1000
1500
2000
Simulation Day
 CO2 x temperature x water
– Yield increased by 2.1 odt/ha/yr (+19%) by end of second rotation
at the Alice Holt site
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General Conclusions.
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General Conclusions
Empirical model
– Current yields of the three extensively grown poplar
varieties was 7.3, and for willow was 8.7 odt ha-1 yr-1
– Water availability was largest limiting factor
Process model
– By 2050, SRC-MOD predicts P. trichocarpa will be 19%
more productive (Alice Holt site)
– Longer growing season and more photosynthesis BUT
plants respire and loose water more quickly
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2007: 12,000 tonnes = >0.01% of
electricity
o Current potential = 13 Modt
(6.7% electricity)
2014: 2.5-5% fuel from biofuel
2020: 15% electricity from
renewables
2050: +19% yield (med. emissions)
= 8.0% electricity
o Less agricultural land needed
o Breeding/technology expand
potential
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This research was funded by NERC as part of the Towards a
Sustainable Energy Economy (TSEC) initiative and through a PhD
studentship to Matthew Aylott (NER/S/J/2005/13986). Thanks to
Forest Research for the provision of the site data.
Contact M Aylott for more information: [email protected]
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Thank you for your attention!
TSEC Biosys
TSEC Biosys
www.tsec-biosys.ac.uk
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