Contributor:

1
TSEC-BIOSYSTheme 2 - Topic 2.2 Modelling
biomass supply

• Contributor
• Rothamsted Research
• 3rd Annual Meeting Month 40 of 42
• November 2008

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TSEC outputs

• Grass and woody biomass species agronomic
  requirements learnt from 15 years field
  experimentation A Riche, A Karp, M Pei, I Shield,
  N Yates. International Plant Protection Congress,
  Glasgow, UK 15-18 Oct 2007.
• Over-winter yield decline in Switchgrass and
  Miscanthus S A Gezan and A B Riche. Aspects of
  applied biology 90, 2008 in press.
• An empirical model for switchgrass to predict
  yield from site and climatic variables A B Riche,
  S A Gezan N Yates. Aspects of applied biology
  90, 2008 in press.
• Performance of 15 different Miscanthus species
  and genotypes over 11 years A B Riche, N E Yates,
  D G Christian. Aspects of applied biology 90,
  2008 in press.

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Progress since in 2008

• Publishing paper for empirical model in
  peer-reviewed journal (Soil Use Management, 2008)
  
• Paper on land availability for biomass crops and
  trade-offs submitted/being revised for
  resubmission to BioEnergy Research
• Conference paper on biomass production from
  switchgrass for AAB Bioenergy III in Dec 2008
• Process model for Miscanthus implemented in water
  and energy balance model was calibrated and
  evaluated for 15 year yield series
• Presented process-model (sink-source balance) for
  Miscanthus at SEB conference, Marseille, July
  2008
• Prepared paper on process model and sensitivity
  analysis for submission

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Modelling bioenergy crops - key objectives

• Purpose I is to assess
• Production potential of bioenergy (BE) at the
  sub-regional scale,
• Trade-offs of BE vs. Food within land use change
  (LUC),
• Cost-based supply as an option within the UK
  energy mix, and
• Environmental implications, like GHG-balance and
  hydrology
• Purpose II is to
• Describe, quantify and predict system behaviour
• Underpin processes in aide of crop
  selection/breeding (G x E)
• Identify the most important genotypic traits and
• Locate crucial control points of yield formation

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Task within TSEC-BIOSYS

• Theme 2 Evolution of UK biomass supply
• Topic 2.2 Bioenergy Models resources
• Biofuel from arable crops models _at_ RRES
• Winter wheat, sugar beet,
• Oilseed rape, maize
• Biomass from grasses, mainly Miscanthus
• Empirical model for Miscanthus ( switchgrass)
• Maps of yield under current climate
• Process model for Miscanthus is available
  parameterized, calibrated and evaluated
• Ready to be used for predictive purposes

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Empirical yield model for MiscanthusRichter, G.
M. et al. (2008) Soil Use and Management 24 (3),
235
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Application of empirical yield maps
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Land use trade-offs - Methods

• Incorporated a range of constraints on energy
  crops
• environmental, physical
• agricultural, agronomic
• socio-economic
• Accounted for currently grown food crops
• Used Miscanthus yield map for England

Lovett, A. A. et al., BioEnergy Research (u. rev.)
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Land use trade-offs Results

• Regional contrasts occur in the importance of
  different constraints
• Between 80 and 20 of are below an economic
  threshold of 9 t/ha
• Areas with highest yields co-locate with
  important food producing areas

Lovett, A. A. et al., BioEnergy Research (u.rev.)
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Supply Demand Modelling

• Majority of land would yield between 10 - 14 t
  odm/ha/yr
• Cost map gives annual cost of 20 to 60 /t odm
• Switch from yield to cost optimal crop affects
  only a small fraction of land
• Preference map shows 4.4 Mha of Miscanthus and 6
  Mha of SRC

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Supply Demand Modelling

• Allocation based on opportunity costs (ALC) show
  that
• Grade 3 and 4 land is preferred
• With higher demand (30)
• More marginal than high quality land is assigned
• About 2 Mha are needed (corresp. to 20 Mt odm/ha)

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Conclusions for integration (Theme 4) - based on
working paper between IC, UoSo, RRes, FR

• Yield maps are available for Miscanthus, willow
  and poplar
• Overlay of yield maps implied some exclusion
  criteria (slope gt 15, organic soils)
• Yield and cost advantage maps have been created
• Potential availability of 10 Mha preferably used
  for willow and Miscanthus (ratio 64)
• Suitability and constraint maps reduced area to
  about 3 Mha (preference of food production given
  to high grade land) cooperation with UEA
  (Lovett)
• Simulations of biomass crop allocation based on
  opportunity costs confirmed expansion of lower
  grade land being used under higher BE-demand
• Paper is based on empirical models describing
  current (past) yields only future scenarios
  (2050) are excluded up to now
• Future scenarios must be based on process-based
  models

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Modelling Purpose II

• Describe, quantify and predict system behaviour
  at process-level
• Underpin the processes in aide of crop selection
  and breeding (G x E interaction)
• Identify the most important genotypic traits that
  can be easily quantified and
• Locate crucial control points of yield formation

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Experimental basis for Process Model

• Long-term, highly resolved data at Rothamsted
• Light interception (LAI)
• Dry matter
• Leaf senescence, loss (litter)
• Morphological data
• Stem number, height diameter
• Leaf length, width
• Growth dynamics of belowground biomass (rhizomes)

Christian, D. G. et al., Biomass Bioenergy 30,
125 (2006) Christian, D. G., Riche, A. B., Yates,
N. E., Industrial Crops and Products 28, 109
(2008)
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A sink-source interaction model
Physiology Asat, f rs, ksen,,fW, fT rdr,
halflife
rad, P, T,..
Photo- synthesis
Inter- ception
ksen
kfrost
fT(A)
kext
Energy Balance
PER
Ta
Phenology Phyllochron, nL Tb, TS(e, x,
a), cv2g
Carbohydrates
fw
cL/P
fsht
Morphology WD(L), SLA, nV, nG MaxHt, SSW(d)

Tillering
Water Balance
crf
Reserves 10-20
Roots
?fc, ?pw, depth, ...
Source Formation
Sink Formation
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Sensitivity of model parameters
?yield/?parameter
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Parameter sensitivity for Miscanthus

• Grouped according to
• Initial establishment
• Phenology
• Physiology
• Morphology

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Model evaluation Sensitivity Analysis
Toptv2g
DMrhz
cv2g
TS(x)
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Sink Source Balance
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Leaf DM GLAI dynamics
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Model evaluation shoots

• Shoot Generative Tiller
• Initially fixed No. of VegTiller
• cv2g is an important factor
• Tiller dynamics linked to height
• Height dynamics
• Increases with GY
• PER function of T CHORes
• Partitioning PER using cL/P
• Stem weight evaluation
• Discrepancy is consequence of height estimate,
  tiller dynamics
• Loss of stem weight at harvest is due to stubble

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Leaf area dynamics and water stress
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Yield prediction over 14 years
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Conclusions for Process-based Model

• A generic grass model was successfully adopted to
  simulate dry matter production of Miscanthus x
  giganteus
• Identified important morphological traits
• Calibrated evaluated for one site, one variety
• Ranked parameter using OAT sensitivity analysis
• Exploring sink-source balance, tillering dynamics
  
• Future applications of this model are needed
• For different species varieties to identify
  optimal grass ideotypes
• In different environments (G x E interaction)

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Thank you for questions !
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T-scale function, photosynthesis
Asat, f f(Ta)
Naidu, S. L. et al., Plant Physiology 132 (3),
1688 (2003). Farage, P. K., Blowers, D., Long, S.
P., and Baker, N. R., Plant Cell and Environment
29 (4), 720 (2006).
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Water stress function
late response
early response
Sinclair, T. R., Field Crops Res. 15, 125
(1986). Richter, G. M., Jaggard, K. W., Mitchell,
R. A. C., Agric For Meteorol 109, 13 (2001).
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Morphological Parameters Leaf

• Leaf extension rates (L/PER)
• A priori parameters from Clifton-Brown Jones
  (1997)
• Simplified either as linear model or Arrhenius
  function (Q10)
• Compared to in situ measurements
• Specific area (SLA)
• Unchanged principle from LinGra giving a min-max
  range
• Range adjusted to observed SLA
• Dynamic components
• Number of leaves growing simultaneously (nL 2.7 ?
  gt 3)
• Senescence rates (age, shading, drought)
  determine tiller density

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Morphological parameters Shoot/Stem

• Stem extension rate
• Related to leaf extension rate e.g. le 0.83
  0.07
• (Clifton-Brown Jones 1997)
• Shoot density m-2
• Initially 100 to 140 m-2 (Danalatos et al. 2007
  Bullard et al. 1995)
• 50 to 80 m-2 at equilibrium (Clifton-Brown
  Jones 1997 Danalatos et al. 2007)
• Specific stem weight
• 10 to 11 g m-2
• (acc. to Danalatos et al., 2007)
• Changes with height and plant age (unpublished)

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Sensitivity Analysis

• Morris-method varies parameters as one-at-a-time
  at discrete levels (4 to 8)
• Parameters given as mean variation, randomly
  generated within 5-95
• change is defined as ?yield/?parameter
• µ / µ are means of distribution of the global
  parameter effect
• s is an estimate of second- and higher order
  effects of parameter (interactions with other
  factors, non-linearity)
• Simultaneous display of µ and s allows to check
  for non-monotonic models (negative elements in
  distribution)
• References
• Morris (1991) as described in Saltelli et al.
  (2004)
• Morris M.D. Technometrics 33(2) 161-174 Saltelli
  A., et al.. Sensitivity analysis in practice.
  WILEY