Contributor:
Rothamsted Research3rd Annual Meeting Month 40 of 42
November 2008
TSEC-BIOSYSTheme 2 - Topic 2.2
Modelling biomass supply
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
Task within TSEC-BIOSYS
Theme 2: Evolution of UK biomass supply• Topic 2.2: Bioenergy Models resources
Biofuel from arable crops – models @ 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
0
5
10
15
20
Arthur
Rick
wood
Boxwor
th
Bridge
ts
Brooms B
arn
TG
Buckf
ast A
bbey
Gleadth
orpe
High M
owth
orpe
Rosem
aund
Rosewar
ne
Rotham
sted
408
Rotham
sted
480
Rotham
sted
TGSCRI
Wobu
rn m
ain T
G
Wobu
rn m
icro
TG
Yie
ld (
dry
mat
ter
- t
ha-1
)
0
2
4
6
8
10
12
14
16
18
20
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Yie
ld (
dry
ma
tte
r -
t h
a-1 )
RES 408
RES 480
Empirical yield model for MiscanthusRichter, G. M. et al. (2008) Soil Use and Management 24 (3), 235
Application of empirical yield maps
Aide to
Producers &
LUC planners
BE Allocation
Trade-offs
Economic of BE Supply & Demand
Assess environmental impact/benefit
Richter et al., Soil Use Manage 24, 235 (2008)
GHG H20
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.)
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.)
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
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 > 15%, organic soils)
• Yield and cost advantage maps have been created
• Potential availability of 10 Mha preferably used for willow and Miscanthus (ratio 6:4)
• 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
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
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)
0
5
10
15
20
25
01/05/96 26/06/96 21/08/96 16/10/96 11/12/96 05/02/97 02/04/97
Dry
ma
tte
r [
t h
a-1 ]
Total
Stems
Leaves
Dead Leaves
0
2
4
6
8
10
12
14
16
18
20
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Yie
ld (
dry
ma
tte
r -
t h
a-1
)
RES 408
RES 480
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)
kfrost
Carbohydrates
Reserves10-20%
StemsDensity (n),
Ht, Wt
RhizomesRGR(T), SRWT,
[RhDR(t)]
fT(A)
rad, P, T,..
θfc, θpw, depth, ...
crf
fsht
cL/P
Source Formation Sink Formation
MorphologyWD(L), SLA,
nV, nGMaxHt, SSW(d)
PER
Photo-synthesis
Flowers
fw Phenology
Phyllochron, nLTb, TΣ(e, x, a),
cv2g
Energy Balance
Water Balance
Ta
Leaves
LAI
Inter-ception
kext
Roots
PhysiologyAsat, φ
rs, ksen,,fW, fT
rdr, halflife
ksen
A sink-source interaction model
Tillering
Parameter sensitivity for Miscanthus
• Grouped according to
– Initial establishment
– Phenology
– Physiology
– Morphology
0
100
200
300
400
500
0 500 1000 1500 2000 2500 3000
μ_Change
σ_
Ch
an
ge
physio- pheno-
morpho- initial
Model evaluation – Sensitivity Analysis
cv2g
Toptv2g
TΣ(x)
φ
kext
Tb(A)
Asat
Tn(A)
Tx(A)
WDL
cL/P
fsht
SLAx
cSSW
Tb(sht)DMrhz
Sink – Source Balance
0
10
20
30
40
50
60
70
80
1 91 181 271 361 451 541 631
Day after start of simulation (1/1/94)
Car
bo
hyd
rate
S&
D [
g m
-2 d
-1 ]
ShootGrowthPotn AGGrowthSourceLimited
Leaf DM & GLAI dynamics
0
1
2
3
4
5
6
01/01/94 01/01/95 01/01/96 31/12/96 01/01/98 01/01/99
Le
af
dry
ma
tte
r [
t h
a-1 ]
0
1
2
3
4
5
6
7
Jan 94 May 94 Sep 94 Jan 95 May 95 Sep 95
GL
AI [
m2 m
-2 ]
Model evaluation – shoots
0
50
100
150
200
01/01/94 01/01/95 01/01/96 31/12/96 01/01/98 01/01/99
Sh
oo
t n
um
ber
0
50
100
150
200
250
300
01/01/94 01/01/95 01/01/96 31/12/96 01/01/98 01/01/99
Hei
gh
t [
cm ]
• 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
0
5
10
15
20
25
01/01/94 01/01/95 01/01/96 31/12/96 01/01/98 01/01/99
Ste
m d
ry m
atte
r [
t h
a-1 ] Harvested
Leaf area dynamics and water stress
0
1
2
3
4
5
6
7
8
9
10
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
LA
I
-1.5
-1
-0.5
0
0.5
1
Wat
er s
tres
s fa
cto
r, k
w
LAI [-] k_w
Yield prediction over 14 years
9495
96
9798
9900
01
02
03
0405
06
07y = 1.03x
6
10
14
18
22
6 10 14 18 22
Observed yield [ t ha-1 ]
Sim
ula
ted
yie
ld [
t h
a-1 ]
0
5
10
15
20
25
Jan94
Jan95
Jan96
Jan97
Jan98
Jan99
Jan00
Jan01
Jan02
Jan03
Jan04
Jan05
Jan06
Jan07
Jan08
Ste
m d
ry m
att
er
[ t
ha-1
]
Harvested
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)
0
0.2
0.4
0.6
0.8
1
1.2
0 10 20 30 40
T air [ oC ]
f so(
Tai
r)
Naidu rel(Asat)
Farage rel(Asat)
Naidu rel(φ)
Farage rel(φ)
Asat, φ = 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).
T-scale function, photosynthesis
Water stress function
0.0
0.2
0.4
0.6
0.8
1.0
0 0.2 0.4 0.6 0.8 1
Relative soil water content
Ra
te r
ed
uc
tio
n
ws-factor = 12
ws-factor = 6
kws = 2 / ( 1 + exp (-Ws-factor * relSWC))
early response
late 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).
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 → > 3)– Senescence rates (age, shading,
drought) determine tiller density
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0 5 10 15 20 25
Temperature
PE
R [
mm
hr-1
]
Measured (C-B&J)
3-polyn (C-B&J)
Linear
Arrhenius
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)
Maximum specific stem weight
0
5
10
15
20
25
0 0.5 1 1.5 2 2.5 3
Stem height [ m ]
Sp
ec
ific
ste
m w
eig
ht
[ g
/m ]
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 Δyield/Δparameter
• μ / μ* are means of distribution of the “global” parameter effect
• “σ” is an estimate of second- and higher order effects of parameter (interactions with other factors, non-linearity)
• Simultaneous display of μ* and σ allows to check for non-monotonic models (negative elements in distribution)
ReferencesMorris (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
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