1 How much biomass do we have? – Is UK supply from Miscanthus water-limited? Dr. Goetz M Richter Rothamsted Research Biomass role

1

How much biomass do we have? – Is UK supply from Miscanthus water-limited?

www.tsec-biosys.ac.uk

Dr. Goetz M RichterRothamsted Research

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

TSEC BiosysTSEC Biosys


http://www.tsec-biosys.ac.uk/


TSEC BiosysTSEC BiosysContents

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?


TSEC BiosysTSEC BiosysWhat were the hypotheses?

• 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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TSEC BiosysTSEC BiosysObjectives and approaches

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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TSEC BiosysTSEC BiosysEffect of soil water availability on yield

• 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

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80 100 120 140 160 180 200 220 2400

2

4

6

8

10

12

14

16

18

f(x) = 0.0529631755484206 x + 2.97467130634428R² = 0.694335926656229

Best estimate of AWC [ mm ]

Aver

age

yie

ld [

t ha

-1 ]


TSEC BiosysTSEC BiosysEffect of potential soil moisture deficit

• Potential soil moisture deficit (PMSD) is the cumulative difference between precipitation and potential ET

• PSMD is averaged over the main growing season (April-Aug) 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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0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.90

2

4

6

8

10

12

14

16

18

20

f(x) = − 11.0965666753058 x + 19.0112845092129R² = 0.468818341394072

Average seasonal rel. PSMD

Yiel

d [

t D

M h

a-1

]


TSEC BiosysTSEC BiosysEmpirical grass yield model (EGM)

TESTED INPUT DATA• 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 MODELY(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

(a)

y = 0.70x

R2 = 0.89

0

50

100

150

200

250

300

0 100 200 300 400

max PSMD [ mm ]

Mea

n P

SM

D (

Ap

r-A

ug

)

(b)

0.0

0.2

0.4

0.6

0.8

1.0

0 50 100 150 200 250

Mean PSMD (Apr-Aug) [ mm ]

Rel

Mea

n P

SM

D (

Ap

r-A

ug

)

0

5

10

15

20

0 5 10 15 20

Observed yields [ t ha-1 ]

Mo

del

led

yie

lds

[ t

ha

-1 ]


TSEC BiosysTSEC BiosysSpatial implementation of EGM

• 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

http://www.worldclim.org/


TSEC BiosysTSEC BiosysRevisiting the soil input data (AWC_PTF)

• 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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TSEC BiosysTSEC BiosysEstimating soil-series specific AWC

• 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

10

0

100

200

300

400

500

0 100 200 300 400 500

AW

C -

AP

_PT

F

[ m

m ]

AWC - AP_SB [ mm ]

NG_PR

NG

G

G_HR



• 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

Impacts of BE expansion on land-use


TSEC BiosysTSEC BiosysConclusions for Regional Scale Estimates

• 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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TSEC BiosysTSEC BiosysObjectives and approaches

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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TSEC BiosysTSEC BiosysExperimental 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., 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 PhenologyPhyllochron, 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



Sensitivity analysis (SA) for Miscanthus model

• 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

0

100

200

300

400

500

0 500 1000 1500 2000 2500 3000

μ - Strength of model response

σ -

Sp

rea

d o

f m

od

el r

es

po

ns

e

physio- pheno-

morpho- initial

• Parameter effects on yield vary across and between process traits – 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 and physiological traits) is dynamic

cv2g

Toptv2g

TΣ(x)

φ

kext

Tb(A)

Asat

Tn(A)

Tx(A)

WDL

cL/P

fsht

SLAx

cSSW

Tb(sht) DMrhz

Preparing Submission for Global Change Biology- Bioenergy


TSEC BiosysTSEC BiosysSink – 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


TSEC BiosysTSEC BiosysWhat about water stress ?

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))

low stress tolerance

high stress tolerance

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).


TSEC BiosysTSEC BiosysLeaf 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 ]


TSEC BiosysTSEC BiosysLeaf 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, kw

LAI [-] k_w


TSEC BiosysTSEC BiosysYield 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– 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)


TSEC BiosysTSEC BiosysFinally – where do we go from here?

• 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!



www.tsec-biosys.ac.uk

http://www.tsec-biosys.ac.uk/

Documents

1 How much biomass do we have? – Is UK supply from Miscanthus water-limited? Dr. Goetz M Richter Rothamsted Research Biomass role