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 1 Biomass role in the UK energy futures The Royal Society, London: 28 th & 29 th July 2009

2. Contents 2  Introduction  Aims  Empirical Modelling – Method – Results  Process Modelling – Method – Results  General Conclusions

3. Introduction. 3

4.  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 Introduction 4

5. Renewable energy production in 2007 2 310,000 ha oilseed rape (biodiesel) 1 125,000 ha sugar beet (bioethanol) 1 9,800 ha Miscanthus 1 5,700 ha poplar and willow 1 18.5M hectares (ha) UK agric. land How much bioenergy do we have? 1. (NNFCC, 2008), 2. (BERR, 2008) 5

6. UK Renewable Energy Strategy = 15% renewable (2020) = 200,000 ha dedicated energy crops 1 Renew. Transport Fuel Obligation = 2.5-5% biofuel (2014) = 215,000 2 -870,000 3 ha oilseed rape (biodiesel) = 500,000 3 -525,000 2 ha wheat (bioethanol) Up to 5% of agric. land may be needed How much bioenergy do we need? 6 1. (Britt et al. 2002), 2. (DTI & DEFRA, 2007), 3. (NFFCC, 2009) 6

7. Aims. 7

8. 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) Aims 8

9. Empirical Modelling. 9

10.  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 Empirical modelling: Method 10

11.  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 11

12.  The model describes 51- 75% of the variation in yield  Willow yields were higher than poplar, esp. in the 2 nd rotation SpeciesGenotypeRotation Observed Mean Yield Predicted Mean Yield PoplarBeaupréFirst7.34 (2.33)7.42 (1.25) PoplarGhoyFirst6.45 (2.47)6.50 (1.38) PoplarTrichobelFirst9.08 (2.67)9.31 (1.37) WillowGermanyFirst7.14 (2.94)7.05 (1.83) WillowJorunnFirst9.09 (3.01)9.29 (2.09) WillowQ83First8.03 (3.23)8.21 (2.09) PoplarBeaupréSecond4.87 (2.43)4.90 (1.38) PoplarGhoySecond5.77 (2.46)5.85 (1.24) PoplarTrichobelSecond9.59 (2.78)9.70 (1.38) WillowGermanySecond7.46 (4.00)7.49 (2.46) WillowJorunnSecond9.15 (2.70)9.30 (1.77) WillowQ83Second10.71 (3.74)10.72 (1.38) Empirical modelling: Results 12 * standard error in brackets

13. (c) Willow var. Q83 Empirical modelling: Results 13 (b) Willow var. Jorunn(a) Poplar var. Trichobel  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

14.  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 14 Willow var. Jorunn

15. Excluded areas: Areas of Outstanding Natural Beauty National Park Forest Park Planted Ancient Woodland Site RSPB Reserve Inland water, town and road National Trust land Lowland Heath/Bogs/Fens/Mire 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 15 Yield in millions of odt/yr

16. 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 CO 2 emissions (tonnes/ha/yr) 16

17. Process Modelling. 17

18.  Process-based models help us explore interactions between yield and climate  ForestGrowth 1,2 is a yield model for mature forest species, which has been parameterised for SRC 3,4,5  The model uses UKCIP climate change predictions Process modelling: Method 1. (Evans et al., 2004), 2. (Deckmyn et al., 2004) 3. (Casella & Sinoquet, 2003), 4. (Gielen et al., 2003), 5. (Casella & Aylott, unpublished) 18

19.  Phase 2: If layer doesn’t have enough light, stems grow and new leaves are added  Phase 1: Root carbon used to grow leaves on existing stem  Phase 3: Carbon stored for the next years growth  Phase 4: Leaves fall  Phase 5: Dormancy SRC-MOD: Method 19

20. 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 20

21.  Currently, SRC-MOD uses arbitrary increases in CO 2, 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 Process modelling: Future Climate 21

22. Carbon Dioxide Effect on Yield 22  CO 2 set to increase to 550 ppm by 2050  Leads to increase in photosynthetic activity  Ten years of CO 2 experiments on poplar found: – 500-700 ppm leads to mean increase in above ground productivity of +34 % Source: NOAA, 2008

23.  Atmospheric CO 2 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 Effect on Yield Carbon Dioxide vs. Yield map for P. trichocarpa, second rotation 23

24. 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 24

25. Temperature Effect on Yield  Temperature increase of +2.5 oC (Summer) and +0.5 oC (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 25

26. 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 26

27. Precipitation Effect on Yield  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 27

28. Predicted Yield in 2050  CO 2 x temperature x water – Yield increased by 2.1 odt/ha/yr (+19%) by end of second rotation at the Alice Holt site 28

29. General Conclusions. 29

30. 30  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 General Conclusions

31. 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 31

32. 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: mja13@soton.ac.uk 32

33. 33 Thank you for your attention! www.tsec-biosys.ac.uk

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