Transcript Document

TSEC Biosys
TSEC Biosys
How much biomass do we have? –
Is UK supply from Miscanthus water-limited?
www.tsec-biosys.ac.uk
Dr. Goetz M Richter
Rothamsted Research
Biomass role in the UK energy futures
The Royal Society, London: 28th & 29th July 2009
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Contents
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 What were the hypotheses?
 Objectives and Approaches
 Regional estimates using a simple empirical model based
on soil and climatic data
 Uncertainties of estimates and optimising crop allocation
 What can we learn from detailed process analysis?
 How can we improve crop productivity?
 What is the way forward?
What were the hypotheses?
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• Miscanthus has a higher productivity under lower
water consumption than other local herbaceous
crops due to its C4-photosynthetic pathway
• Miscanthus is yielding robustly in areas with lower
precipitation and particularly useful for eastern
England
• Miscanthus x Giganteus, is potentially a bioenergy
crop ideally suited for marginal land, especially
considering its low nutrient demand
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Objectives and approaches
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Objective 1:
Quantify yield effect of soil and agro-meteorological
variables
Approach
• Evaluate harvestable Miscanthus yields (litter-free, 15 Feb; 3+
year) from local long-term experiment and a UK-wide series of
experiments
• Derive a universal empirical model for UK conditions
• Up-scale empirical model to the agricultural landscape (yield
maps) using spatially distributed input data (soil, weather)
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Effect of soil water availability on yield
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y = 0.053x + 2.97
R² = 0.69
Average yield [ t ha -1 ]
16
14
12
10
8
6
4
2
0
0
50
100
150
200
250
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• Available water capacity (AWC)
in top 1.5 m from soil survey
data base (NSRI) can be
underestimated by up to 50%
• Best estimate accounts for
hydrological character of site
(water from porous rock;
depth to ground water;
management)
• AWC can be estimated using
pedotransfer functions and
applying first principles
Best estimate of AWC [ mm ]
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Effect of potential soil moisture deficit
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Yield [ t DM ha-1 ]
18
16
14
12
10
8
6
y = -11.1x + 19
R² = 0.47
4
2
0
0.0
0.2
0.4
0.6
0.8
Average seasonal rel. PSMD
1.0
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• Potential soil moisture deficit
(PMSD) is the cumulative
difference between
precipitation and potential ET
• PSMD is averaged over the
main growing season (AprilAug) and scaled in proportion
to the AWC
• For all 21 observations in 3
experiments at Rothamsted
rPSMD explained about 50% of
the observed yield variability
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Empirical grass yield model (EGM)
TESTED INPUT DATA
300
•
•
•
•
250
1.0
50
0
0
0.8
y = 0.70x
R2 = 0.89
0.6
0.4
100
200
300
400
•
•
max 20
PSMD [ mm ]
0.2
0.0
0
-1
100
(b)
Modelled yields [ t ha ]
150
Rel Mean PSMD (Apr-Aug)
Mean PSMD (Apr-Aug)
(a)
200
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1550
100
150
200
250
Mean PSMD (Apr-Aug) [ mm ]
Seasonal air temperature (Ta)
Global radiation (Rg)
Rainfall (P)
Average seasonal potential soil
moisture deficit (PSMD)
available water capacity (AWC)
year planted (GY) for individual
observations (year, a, location, l)
FINAL MODEL
10
5
0
0
5
10
15
-1
Observed yields [ t ha ]
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Y(local) = f(AWC, rPSMD);
r2 ~ 0.7; RMSE 1.4 t ha-1
Y(regional) = f (AWC, P, Ta);
r2 ~ 0.5; RMSE 2.1 t ha-1
Spatial implementation of EGM
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• Transform soil map into database
of input variables
– Extract NATMAP variables:
• Available Water Capacity
(arable, grass) or primary soil
variables for PTF
– Make use of Hydrology of Soil
Types (HOST classes)
• Build database of weather
– Inputs: precipitation and
temperature
– Local weather stations
– Interpolated weather data (1
km2; Hijmans et al., 2005;
http://www.worldclim.org
Revisiting the soil input data (AWC_PTF)
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• Expanded HYPRES pedotransfer function (Woesten et al., 1999) to E&W
• Estimated AWC (PTF) from soil texture, bulk density and organic matter
• Set of rules considered four different soil groups:
– non-gley shallow soil overlying porous rock and other non-gleysol, and
– deep gleysol and shallow gleysol above hard rock and sediments.
• AWC is water retention between FC and WP (-1500 kPa), water at FC was
estimated at -10kPa for gleysols, and -33 kPa for any other soil
• For shallow soils over porous rock water was approximated for those soils
classified as HOST classes 1 to 3 (Boorman et al., 1995)
– AWC of porous rock was assumed to be between 10 vol% (chalk) and
– 5 vol% (oolitic limestone, sandstone), estimated for the layers
exceeding depth of rock to the maximum profile depth.
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Estimating soil-series specific AWC
500
AWC - AP_PTF [ mm ]
400
300
200
NG_PR
100
NG
G
G_HR
0
0
100
200
300
AWC - AP_SB [ mm ]
400
500
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• Only for the deep NonGley
soils both estimates of AWC
were similar
• Non-gley soils over porous
rock (NG_PR) could provide
on average an additional
17% of water
• Gleyic soils (G, G_HR) can
provide an additional 40 to
50% of water
• Hydromorphic soils cover
large areas of the UK
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Impacts of BE expansion on land-use
• Yield map for all soils except
organic (~ 11 M ha)
• Yield map for 9 (primary)
constraints (<8 M ha)
• Yield map 11 (secondary)
constraints (<5 M ha)
• Yield map for all constraints
plus ALC 3 & 4 (~ 3 M ha)
Lovett et al. 2009 Bioenergy Research 2, 17-28
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Conclusions for Regional Scale Estimates
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• Improved our understanding of the control factors at the
landscape scale
• In spite of its high WUE yields of Miscanthus are clearly
related to and limited by water supply
• Estimates of the most limiting factor, soil AWC, are subjected
to a rather large uncertainty
• Mapped data need being replaced by more physically and
hydrologically founded estimates (e.g groundwater depth)
• There are no independent, regionally distributed yield data
from on-farm trials or commercial fields to prove our
estimates
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Objectives and approaches
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Objective 2:
Adapt a process-based crop growth model describing
above / belowground carbon partitioning and yield
Approach:
• Parameterise model from literature and calibrate using initial
growth curves from a local long-term experiment
• Conduct a sensitivity analysis to identify most growth limiting
parameters
• Evaluate model using various indicators 14 years of the same
experiment
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Experimental basis for Process Model
• Long-term, highly resolved
data at Rothamsted
RES 408
18
RES 480
16
14
– Light interception (LAI)
– Dry matter
– Leaf senescence, loss (litter)
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
• Morphological data
– Stem number, height &
diameter
– Leaf length, width
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Dry matter [ t ha ]
-1
Yield ( dry matter - t ha )
20
15
10
• Growth dynamics of
belowground biomass
(rhizomes)
5
0
01/05/96 26/06/96 21/08/96 16/10/96 11/12/96 05/02/97 02/04/97
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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
Ta
Water
Balance
Flowers
Leaves
cL/P
LAI
Stems
fsht
crf
10-20%
Source Formation
Phenology
Phyllochron, nL
Tb, TΣ(e, x, a),
cv2g
Density (n),
Ht, Wt
Morphology
Tillering
Reserves
θfc, θpw,
depth, ...
kfrost
PER
Carbohydrates
fw
Asat, φ
rs, ksen,,fW, fT
rdr, halflife
ksen
fT(A)
Energy
Balance
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Rhizomes
RGR(T), SRWT,
[RhDR(t)]
WD(L), SLA,
nV, nG
MaxHt, SSW(d)
Roots
Sink Formation
Sensitivity analysis (SA) for Miscanthus model
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• One-at-a-time SA (Morris, 1991) ranks parameters acc to the strength (μ)
and variance (σ) of their yield effect (Δy/Δp)
• Parameter contribution for different process traits
– Phenology (e.g. transformation of vegetative to generative tillers, cv2g)
– Morphology (e.g. partitioning to leaf, cL/P; shoot fsht; leaf width WDL etc.)
– Physiology (photosynthesis at light saturation, Amax; quantum efficiency, φ;
and their temperature dependence)
• We explored the balance between parameters characterising the sink
(morphological traits) and source size (physiological traits)
• Model will be used to explore the traits of different species & varieties in
aid of identifying optimal grass ideotypes
Sensitivity analysis to rank parameters of
Miscanthus yield model
σ - Spread of model response
500
• Parameter effects on yield
vary across and between
process traits
cL/P
400
kext
Asat
300
cSSW
200
Tn(A)
T100
b(sht)
Tx(A)
TΣ(x)
0
0
SLAx
Toptv2g
DMrhz
cv2g
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φ
fsht
WDL
Tb(A)
physio-
pheno-
morpho-
initial
– Initial conditions (e.g. DMrhz)
– Phenology (e.g.
transformation of vegetative
to generative tillers, cv2g)
– Morphology (e.g. partitioning
to leaf, cL/P; shoot fsht; leaf
width WDL etc.)
– Physiology (photosynthesis at
light saturation, Amax;
quantum efficiency, φ; and
their temperature
dependence)
• Balance between size of sinks
and sources (morphological
500
1000
1500
2000
2500
3000
and physiological traits) is
μ - Strength of model response
dynamic
Preparing Submission for Global Change Biology- Bioenergy
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Sink – Source Balance
80
ShootGrowthPotn
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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
What about water stress ?
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high stress
1.0
tolerance
Rate reduction
0.8
low stress
tolerance
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).
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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
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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
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 [-]
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Yield prediction over 14 years
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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 ]
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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
–
–
–
–
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Identified important morphological traits
Calibrated & evaluated for one site, one variety
Ranked parameter using OAT sensitivity analysis
Explored 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)
Finally – where do we go from here?
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• We need feedback from the growers!
• We need strengthening of the agronomy of these
crops, SRC and Miscanthus
• Regionally distributed on-farm trials and
demonstrations on different soil types are needed
• Research needs focus to improve our understanding
(e.g. water use) and the varieties to be grown
• Get on with the work!
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Thank you for your attention!
TSEC Biosys
TSEC Biosys
TSEC Biosys
TSEC Biosys
www.tsec-biosys.ac.uk
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