Transcript Powerpoint version - TSEC

TSEC-BIOSYS
Theme 2 - Topic 2.2
Modelling biomass supply
Contributor:
Rothamsted Research
3rd Annual Meeting Month 40 of 42
November 2008
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
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Yield ( dry matter - t ha-1 )
-1
Yield ( dry matter - t ha )
Empirical yield model for Miscanthus
Richter, G. M. et al. (2008) Soil Use and Management 24 (3), 235
20
18
16
RES 408
14
RES 480
12
10
8
6
4
2
0
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
20
15
10
5
0
Application of empirical yield maps
BE Allocation
Trade-offs
Aide to
Producers
&
LUC
planners
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 BEdemand
•
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
RES 408
18
RES 480
16
14
12
10
8
6
4
2
0
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
25
20
Total
Stems
Leaves
Dead Leaves
-1
Dry matter [ t ha ]
-1
Yield ( dry matter - t ha )
20
15
10
5
0
01/05/96 26/06/96 21/08/96 16/10/96 11/12/96 05/02/97 02/04/97
– 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)
A sink-source interaction model
Photosynthesis
rad,
P, T,..
Physiology
Interception
kext
fT(A)
Asat, φ
rs, ksen,,fW, fT
rdr, halflife
ksen
kfrost
Flowers
Energy
Balance
Ta
PER
Leaves
Carbohydrates
fw
Water
Balance
LAI
cL/P
Stems
fsht
Phenology
Phyllochron, nL
Tb, TΣ(e, x, a),
cv2g
Density (n),
Ht, Wt
Morphology
Tillering
crf
Reserves
10-20%
WD(L), SLA,
Rhizomes
RGR(T), SRWT,
[RhDR(t)]
nV, nG
MaxHt, SSW(d)
Roots
θfc, θpw,
depth, ...
Source Formation
Sink Formation
Sensitivity of model parameters
Δyield/Δparameter
Parameter sensitivity for Miscanthus
• Grouped according to
– Initial establishment
– Phenology
– Physiology
– Morphology
Model evaluation – Sensitivity Analysis
500
cL/P
400
σ_Change
kext
Asat
300
φ
fsht
cSSW
200
WDL
SLAx
Tn(A)
100
Tb(sht)
Tx(A)
TΣ(x)
Tb(A)
Toptv2g
cv2g
DMrhz
physio-
pheno-
morpho-
initial
0
0
500
1000
1500
μ_Change
2000
2500
3000
Sink – Source Balance
80
ShootGrowthPotn
AGGrowthSourceLimited
-2
-1
Carbohydrate S&D [ g m d ]
70
60
50
40
30
20
10
0
1
91
181
271
361
451
Day after start of simulation (1/1/94)
541
631
6
7
5
6
GLAI [ m m ]
3
2
1
0
01/01/94
5
-2
4
2
Leaf dry matter [ t ha-1 ]
Leaf DM & GLAI dynamics
4
3
2
1
0
01/01/95
01/01/96
31/12/96
01/01/98
01/01/99
Jan 94
May 94
Sep 94
Jan 95
May 95
Sep 95
Model evaluation – shoots
Shoot number
200
• Shoot ≡ Generative Tiller
150
100
50
0
01/01/94
01/01/95
01/01/96
31/12/96
01/01/98
01/01/99
• Height dynamics
300
Height [ cm ]
250
– Increases with GY
– PER function of T & CHORes
– Partitioning PER using cL/P
200
150
100
50
0
01/01/94
01/01/95
01/01/96
31/12/96
01/01/98
01/01/99
• Stem weight evaluation
Harvested
-1
Stem dry matter [ t ha ]
25
– Discrepancy is consequence of
height estimate, tiller dynamics
– Loss of stem weight at harvest
is due to stubble
20
15
10
5
0
01/01/94
– Initially fixed No. of VegTiller
– cv2g is an important factor
– Tiller dynamics linked to height
01/01/95
01/01/96
31/12/96
01/01/98
01/01/99
Leaf area dynamics and water stress
k_w
10
9
8
7
6
5
4
3
2
1
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
1
0.5
0
-0.5
-1
-1.5
Water stress factor, k w
LAI
LAI [-]
Yield prediction over 14 years
22
y = 1.03x
07
Harvested
15
10
5
-1
20
Simulated yield [ t ha ]
Stem dry matter [ t ha-1 ]
25
18
99 05
04
00 97
03 98
14
96
94
02
01
95
06
10
0
Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan
94 95 96 97 98 99 00 01 02 03 04 05 06 07 08
6
6
10
14
18
Observed yield [ t ha-1 ]
22
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)
Thank you for questions !
T-scale function, photosynthesis
Asat, φ = f(Ta)
1.2
1
fso(Tair)
0.8
0.6
Naidu rel(Asat)
0.4
Farage rel(Asat)
Naidu rel(φ)
0.2
Farage rel(φ)
0
0
10
30
20
40
o
T air [ C ]
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).
Water stress function
1.0
late
response
Rate reduction
0.8
early
response
0.6
ws-factor = 12
ws-factor = 6
0.4
kws = 2 / ( 1 + exp (-Ws-factor * relSWC))
0.2
0.0
0
0.2
0.4
0.6
0.8
1
Relative soil water content
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
•
3.0
– A priori parameters from CliftonBrown & Jones (1997)
– Simplified either as linear model
or Arrhenius function (Q10)
– Compared to in situ
measurements
Measured (C-B&J)
3-polyn (C-B&J)
Linear
Arrhenius
-1
PER [ mm hr ]
2.5
Leaf extension rates (L/PER)
2.0
•
1.5
Specific area (SLA)
– Unchanged principle from LinGra
giving a min-max range
– Range adjusted to observed SLA
1.0
•
0.5
0.0
0
5
10
15
Temperature
20
25
Dynamic components
– Number of leaves growing
simultaneously (nL 2.7 → > 3)
– Senescence rates (age, shading,
drought) determine tiller density
Morphological parameters – Shoot/Stem
• Stem extension rate
Specific stem weight [ g/m ]
25
Maximum specific stem weight
– Related to leaf extension rate
e.g. le ~ 0.83 ±0.07;
20
(Clifton-Brown & Jones 1997)
• Shoot density [ m-2 ]
15
– Initially 100 to 140 m-2
(Danalatos et al. 2007;
Bullard et al. 1995)
10
– 50 to 80 m-2 at equilibrium
(Clifton-Brown & Jones 1997;
Danalatos et al. 2007)
5
• Specific stem weight
0
0
0.5
1
1.5
2
Stem height [ m ]
2.5
3
– 10 to 11 g m-2
(acc. to Danalatos et al., 2007)
– Changes with height and plant
age (unpublished)
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)
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