Copyright 2007, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Europec/EAGE Annual Conference and Exhibition held in London, United Kingdom, 11–14 June 2007. This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers presented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of where and by whom the paper was presented. Write Librarian, SPE, P.O. Box 833836, Richardson, Texas 75083-3836 U.S.A., fax 01-972-952-9435.

Abstract

Global warming caused by anthropological emissions of

greenhouse gas (GHG) is now an inconvenient reality. CO2,

the largest contributor, was emitted at the rate of 6 Gt C y-1 by

burning fossil fuels in 1990, which are projected to rise to

around 10 Gt C y-1 by 2020. Using bio-fuels, such as bio-

ethanol or bio-diesel in transportation, or biomass in power

generation reduces CO2 emissions as the carbon is fixed by the

plants from the atmosphere and saves the equivalent fossil

fuel.

The biospheric flux of carbon from the soil and terrestrial

biota to the atmosphere is about 120 Gt C y-1 and is roughly

balanced by the fixation of carbon by photosynthesis.

However, anthropological land use change, through increased

agriculture and forestry, resulted in atmospheric emissions of

1.1 Gt C in 1990, projected to rise to 1.5 Gt C in 2020, so the

production of biofuels is not GHG emission free if land use

change is involved.

This paper explores the GHG emission cost of the

production of bio-fuels derived from energy crops and

compares them to fossil fuels used in transport and electricity

generation. The bio-fuels emission cost are presented for

several land use scenarios showing that highest sequestration

can be achieved by using existing arable land for bio-fuel

production and not land with a currently undisturbed

ecosystem.

Considering these drivers and the GHG emissions, we

model the future potential of Europe to produce bio-fuels with

four different future land use and climate change scenarios and

conclude that up to 20% of Europe’s current primary energy

consumption could be provided by bio-fuels by the year 2080

with a corresponding reduction in carbon emissions, taking

into account the GHG cost of production.

Introduction

The global pattern of energy use is changing with the

successive industrialization of the economies of South East

Asia and Brazil, and more recently with the increasing pace of

the industrialization of China and India. This has driven an

increase in the demand for energy, and hence for fossil fuel, at

the rate of 2-3% per year1. The rate at which conventional oil

production can be increased has been reduced by the lack of

refining capacity, and the fact that nearly 50% of the world’s

proven and probable conventional light crude oil reserves have

already been consumed2. This flat-topping in the availability

of oil has been compensated for by the increased availability

of natural gas and new reserves of cheap coal. Natural gas has

been increasing its share of the energy supply mix as the

infrastructure and technology of its transportation is put into

place both by pipelines, liquefaction and conversion to

methanol. In developed economies, gas has displaced both oil

and coal, whilst coal use has increased in developing

economies, particularly in China. At the same time the use of

nuclear energy has stagnated due to public concerns about

waste storage and disposal.

Globally, biomass currently provides around 46 EJ of

bio-energy in the form of combustible biomass and wastes,

liquid bio-fuels, renewable municipal solid waste, solid

biomass/charcoal, and gaseous fuels. This share is estimated to

be 13.4% of global primary energy supply3 but this is mainly

from “traditional biomass” estimated to provide 32EJ in 2002

of non-commercial firewood, charcoal and dung used for

cooking and heating in developing countries4. Such low-grade

biomass provides around 35% of primary energy in many

developing countries, but more than 70% in Africa5.

Concerns about global warming and the international

discussions ending in the Kyoto Protocol and medium to long

term concerns about energy security have led many countries

to ambitious, near term policy objectives for bio-energy6.

Increasing bio-energy use will require changes to agricultural

and forestry production and the active growth of dedicated

energy crops. Hoogwijk7 analyzed the use of biomass for 17

different scenarios and showed its “research focus” potential

by 2025 to 2050 was between 67 EJ and 450 EJ whereas the

“demand driven” potential was between 28EJ to 220 EJ. The

global technical potential of bio-energy is therefore large and

could provide around 200-400 EJ yr-1 at competitive costs by

SPE 107495

Sequestering Carbon Dioxide by the Use of the Energy Crop Miscanthus: Quantifying the Energy Production and Sequestration Potential of Europe A. Hastings, SPE, U. of Aberdeen; J. Clifton-Brown, IGER; and M. Wattenbach, C.P. Mitchell and P. Smith, U. of Aberdeen

2 SPE 107495

20508. Hoogwijk et al9 had analyzed the ranges of several bio-

fuel crops for future scenarios to 2100 and demonstrated the

wide geographical range of suitability of Miscanthus, a C4

elephant grass native to Eastern Asia, in Europe.

Tuck et al.10 have evaluated the suitability of European

historical and future climates for various bio-fuel crops. These

can be divided by their end bio-fuel product as ethanol, bio-

diesel, woody and non woody biomass. In the European range

of climatic conditions, oilseed rape and linseed have the

widest application for bio-diesel, wheat, sugar beet and

potatoes for ethanol, Miscanthus for non woody bio-mass and

short rotation coppice (SRC) willow for woody biomass11. For

all crops, yields are a function of temperature and solar

radiation but are limited in many areas by water availability 12,13.

Sims et al.14 demonstrated that in Europe Miscanthus

provided one of the highest yields of energy when expressed

in MJ/ha and also has one of the lowest energy costs of

production. If estimates of the potential to produce bio-energy

in Europe are to be made, then considering Miscanthus would

provide an idea of the maximum energy available from this

resource.

The technology to use biomass derived from Miscanthus

and SRC willow coppicing in stand alone or co-fired power,

CHP (combined heat and power) or CCP (Combined Cycle

Power) or CCHP (Combined Cycle Heat and Power)

generation stations has been proved in pilot tests and plants,

and is in commercial use in the USA15 and Sweden16,

countries that have built on their many years experience of

power generation using by-products from the forestry industry.

Technology has also been developed to co-fire coal power

stations using up to 10% biomass mixed with the coal15.

Use of bio-energy is generally considered to be carbon

neutral in that the energy from the sun is used to convert

atmospheric CO2 into plant material which is then released

back to the atmosphere when the bio-fuel or bio-mass is burnt

to produce thermal energy. However, there are carbon and

other greenhouse gas costs associated with the growth of

biomass for fuel. Direct carbon costs are related to the use of

fertilizers, planting and establishing the crop, and harvesting

drying, transporting and preparing the biomass as a fuel. This

carbon is associated with the energy cost of production and for

Miscanthus in Europe it is 22% 14.

The management of soils for plant production can result

in increases in soil respiration (CO2) and the emission of other

greenhouse gasses (GHG) such as methane (CH4) and nitrous

oxide (N2O). Methane and nitrous oxide are more potent as

GHG’s with a global warming potential equivalent to 21 and

296 time more than CO217, respectively. The amount of

respiration is driven by many factors; the initial SOC (soil

organic carbon) and its nitrogen content, any fertilizer input,

plant debris input, land use change and climatic changes.

Changes in these factors affect the oxygen, moisture content

and acidity of the soil which in turn change the relative

activity and abundance or aerobic, methanogenic, nitrifying

and denitrifying bacteria, which in turn controls the amount of

soil respiration of GHG emissions. Soil respiration must be

considered when evaluating the benefits of using biomass

derived fuels to mitigate GHG emissions.

To estimate the total energy that can be produced from

the biofuel crop Miscanthus in Europe it is important to be

able to predict how the crop will grow at particular

geographical locations with current and future climatic

conditions. It is therefore important to be able to model crop

yields and also consider soil and climatic conditions, so that

energy yields for the crop can be predicted for future climate

scenarios.

Clifton-Brown et al.18 developed a spreadsheet-based

model, MISCANMOD, using parameterized growth equations

developed from field scale experiments of Miscanthus

gigantus in Ireland and other European countries to predict

Miscanthus crop yields in Ireland over the same time period as

the crop experiments 1984-1993. Clifton-Brown et al.19

developed this model further to include water stress and

modeled Miscanthus yield over the European area. This model

(Miscanmod) was used to look at the potential yields of

Miscanthus with current climatic conditions for both water

limited and irrigated conditions.

This model used the average temperature and

precipitation over the period 1960-1990 which have been

extracted from the CRU meteorological data set 20. The model

results were compared to the yields from 32 European field

scale crop growth experiments. Stampfl et al.21 calculated

theoretical Miscanthus production for EU 25 and estimated the

percentage of the renewable obligation that this would satisfy

based upon Miscanmod using the average climate for the

period 1960-1990 and the varying percentages of the arable

land available as mapped in 1997. Here we extend this work

by looking at the annual variation of the yields, climatic

limitations of the establishment of the crop and future climate

and land availability scenarios.

To calculate the energy yield for Miscanthus at a

European level the quantity and geographical distribution of

available land is required. The quantity of land available for

the growth of bio-fuels is limited by suitability and competing

demands for use of the land, for example for food production

(Note: population is stable in EU25 and demand for food land

is declining). The ever increasing yields made possible by

modern intensive farming techniques have been achieved

using new the technology to select high yield hybrids, the

extensive use of fertilizers and pesticides and intensive tillage

but without careful timing of fertilizer application the

increased yield comes at the cost of increased greenhouse gas

emissions22. The impact of bio-fuel crop farming techniques

on greenhouse gas emissions will affect the choice of crop, as

well as its energy intensity. This means there is an optimum

balance between the fuel energy that can be created per ha of

land, and the emissions saved from fossil fuel use when

considering the emissions cost of production of the bio-fuel.

An estimation of the land available for bioenergy crops and

spare land has recently been calculated for several IPCC

scenarios 23, 24, 25.

Miscanthus x giganteus, the sterile triploid hybrid for

Miscanthus, is a non native species and its range is limited by

climatic conditions due to frost and extreme draught killing

the plant and its rhizome. Clifton-Brown and Lewandowski26

conducted field and laboratory experiments to determine that

Miscanthus giganteus and Miscanthus Sinensis rhizomes were

SPE 107495 3

killed at a temperature of -3.4 and -6.4 degrees Celsius

respectively so winter conditions will limit the range of each

hybrid. In addition Clifton-Brown et al.27, 28 determined the

limit of water stress that the plants and rhizomes could

withstand and determined that 30 days below the wilt point

would kill the shoot and 60 days below the wilt point would

kill the rhizome this limits the range of the plant without

irrigation. These criteria enable the geographical limits of each

genotype to be established for each climate scenario.

Stampfl et al.21 reported that 33% dry matter is lost from

peak yield until spring harvest. Here we use this information

to derive the practical harvest yield from the peak yield by

applying a linear DM reduction from peak yield representing

plant senescence.

Biogenic GHG fluxes are often estimated using numerical

soil / ecosystem models. Models, such as the De-Nitrification

De-Composition Model29,30 for calculating soil and crop CO2,

soil N2O and soil CH4 fluxes, contain sequential modules to

calculate soil water content, soil organic carbon

decomposition, nitrification, de-nitrification and crop growth

processes. The algorithms for each of the modules were

developed by fitting equations to multiple field and laboratory

data. The soil water calculation uses precipitation,

temperature, evapo-transpiration, soil clay content or texture,

and run-off to calculate the soil capillary pressure curves. The

SOC module divides the SOC into pools with different

decomposition rates depending on temperature and soil

moisture, and calculates both anaerobic and aerobic

decomposition depending on the redox potential. This mimics

the effect of zymogenous and autochthonous bacteria

degrading simple and complex carbon compounds, and

different soil depths. The nitrification / de-nitrification module

also divides the soil nitrogen into pools and the processes are

driven by temperature and water content. The crop growth

modules divide the growth into root, shoot and fruit pools and

consider sunlight radiation for photosynthesis and the

availability of nutrients30. DNDC has been parameterized for

crop production, forestry and grasslands, but not for

Miscanthus. However, it has the facility to add new crop

types.

This paper describes the further development of the

mechanistic Miscanthus growth model developed by Clifton-

Brown et al.12, that uses the soil parameters from the FAO soil

map, the land areas available from previous studies and

predicted climatic conditions form the CRU data base to

predict the dry matter yield of Miscanthus for the various

future climate scenarios (driven by the four IPCC emission

scenarios), to calculate the potential primary energy yield for

Europe under current conditions, and in the future. It further

describes the use of the DNDC model to predict the soil GHG

emissions associated with the production of Miscanthus.

Methods and Materials. Miscanthus is neither a native species to Europe nor a crop

and there are a limited number of crop experiments and

limited modeling experience. MISCANMOD was based upon

crop experiments in Ireland12,18. Van der Werf31 parameterized

a similar model using Miscanthus crop experiments in the

Netherlands. Both were based upon the Monteith32 method for

photosynthesis and leaf expansion. MISCANMOD was

rewritten in FORTRAN and further developments were made

to improve the water stress calculation, add plant physiology

stages including leaf and plant senescence. The block diagram

for this program, called MiscanFor, is shown in figure 1.

Figure 1 Block diagram of MiscanFor plant growth module showing the major data input, calculation stages and outputs.

The MiscanFor plant growth module is encapsulated in a

model that runs spatially at 0.5 degree resolution for which

historical meteorological data is available on a 0.5 degree grid

worldwide20. This is interpolated in the European area from

500 to 1,200 weather station data at varying geographical

locations from 1901-2002. FAO soil data33 is available

worldwide on a 0.1 degree grid. The meteorological data

therefore defined the grid block size to be modeled without

extending the project to include climate modeling. To model

the bio-fuel crop production for Europe on a 0.5 degree grid

results in a 5,273 grid-block model.

The monthly maximum and minimum temperature, frost

days, rain days, average cloud cover and precipitation are read

at 0.5 degree resolution. The global solar radiation is

calculated using latitude, day of year, water vapour pressure

and average cloud cover using the SWAT34 method. The PET

(potential evapo-transpiration) are calculated according to

Thornthwaite35 and modified to match the Penman –Monteith

PET using the UEA/CRU method36 to correct Thornthwaite in

dry climates.

Soil water parameters from the FAO 0.1 degree series soil

parameter rasters: field capacity, wilt point and plant available

water were interpolated and extracted at a 0.5 degree

resolution. These soil parameters were compared with those

acquired during some of the crop growth experiments to

understand difference between the 0.5 degree grid predictions

and the field scale experiments.

MiscanFor calculates the AET (actual evapo-transpiration)

using a three component model that modifies the PET first by

surface water evaporation then plant transpiration based upon

the soil moisture content and the leaf area index, and finally

evaporation from the soil based upon the soil capillary

pressure. This AET and precipitation is then used to determine

the daily soil water status for each year and grid point. The

daily soil water deficit is used to calculate the down-regulation

factor for leaf index expansion and photosynthesis. The

temperature is used to calculate the beginning and end of the

growing season18. A leaf expansion and photosynthesis model

4 SPE 107495

following Monteith32, with parameters determined by Clifton-

Brown et al12, is then run to calculate the leaf index. This is

then combined with solar radiation and the photosynthesis rate

to calculate the above ground dry matter production for each

day, which is summed for each year and grid point. The results

can be output as daily or annual time series. The complete

MiscanFor system block diagram is shown in figure 2.

Figure 2. Block diagram of the MiscanFor model system showing the methodology for the meteorological and soil data input, the function of the plant growth model in providing the Miscanthus yield estimates, the use of the land use ARC/GIS data from Corinne and the A-team land use for each CRU climate scenario.

MiscanFor was calibrated using daily growth experiments

in Ireland18, Germany37, Denmark38, and the Netherlands31

with monthly measurements of yields and the actual site

specific daily meteorological time series and using annual

yields form other crop experiments in Sweden12, Portugal12,

Greece39, Italy40 and England41 using monthly meteorological

time series from the (1901-2000) CRU 0.5 degree data base20.

To validate the model the outputs were also compared to

field experiments in two ways. The MiscanFor module was

run using the site specific metrological data and the measured

soil parameters and the daily incremental crop yield was

compared to incremental harvests made during the crop

experiments. Then the total model was run using the CRU 0.5

degree grid meteorological data for the year of the experiment

and using the FAO soil data the harvestable yield was modeled

for each of the grid blocks that contained an experimental site

for each year of the experiment and the results compared.

MiscanFor was designed to create model outputs that could

be converted to rasters to be visualized as maps. MiscanFor

could also output the mean and standard deviation of any

parameter such as temperature or precipitation or results such

as leaf index or dry-matter yield over specified time intervals.

Historically the mean 1960-90 climate is used as the base

case for all future climate change scenarios and this mean

climate had been used to calculate Miscanthus yields for the

base case but as this gives no idea of annual variation in yields

MiscanFor was used to calculate Miscanthus yields for the

base case differently by calculating the dry matter yield for

each year from 1960 to 1990 using the actual annual

temperature and precipitation time series and then calculating

the mean and standard deviation of the yields for each grid

point. This standard deviation for each grid block was used to

calculate the potential yield range of yields that could be

expected for each future time slice/climate scenario/grid block.

MiscanFor was run using the 0.5 degree grid of future

climate scenario data from Mitchell et al.20 which provides

climate projections for the IPCC A1F1, A2, B1 and B2

emission scenarios for the period 2000 to 2100. Yield outputs

are compared at 2020, 2050 and 2080.

The Corine42 land use map (1 minute grid resolution) was

used to define the arable crop land in each cell, which was

then transformed in ArcGis43 to a 0.5 degree grid of the

percentage of arable land per cell.

Miscanthus range is limited by climatic conditions due to

frost and extreme draught killing the plant and its rhizome.

Algorithms were added to the program to calculate rhizome

kill flags for both frost and draught conditions for the M.

giganteus (at a soil temperature of -3.4 degrees Celsius) and

M. sinensis genotypes (at a soil temperature of -6.4 degrees

Celsius). This kill flag was then used as a mask to reset the

yield to zero in each grid cell year that a kill event occurred.

This also enabled the geographic range of Miscanthus

genotypes to be determined for each climate scenario.

Using ARC-GIS the grids of dry-matter yield for each year

and scenario were then multiplied by the arable land area grids

to calculate the average yield for Europe for each scenario.

The average yield for each year and scenario was then

combined with the area available for bio-fuel crop production

determined by Rounsevell et al.23.24 to calculate the amount of

dry matter that could be produced in a given year for a given

scenario. This dry-matter was corrected for losses between the

peak yield and harvest and was then converted to primary

energy yield, using energy densities derived from Sims et al.5

(2006) and expressed as a percentage of the total European

primary energy requirement for that year and scenario.

Using the Irish site meteorological data set, soil data and

the experimental yields the crop growth parameters for

Miscanthus were determined to match the DNDC model to the

experimental Miscanthus giganteus crop yields. The model

was then used to match the changes in soil organic carbon

observed at the site and hence estimate the soil GHG

emissions. The model was then run on this data set for

different initial SOC values over 1000 years to determine the

CO2, CH4 and N2O emissions for each year and the annual

change in SOC.

The results were used to fit an exponential decay to the

SOC endpoint for Miscanthus cultivation in mid-latitude

Europe and polynomial regressions were fitted to obtain a

relationship between annual CO2, CH4 and N2O emissions for

given start of year SOC. These relationships were used at each

grid point to calculate soil emissions for that year, grid point

and climate scenario.

Using the yields and soil GHG emissions at each grid point

the GHG emissions in CO2 equivalent were calculated in

terms of the kg CO2 MJ-1 and compared to emissions from

fossil fuels: coal, oil and gas. In this way a map of Europe was

generated showing locations where Miscanthus could be

grown producing less emissions than fossil fuels.

SPE 107495 5

Results

MiscanFor was initially written to clone the XL spreadsheet

model and run on the same data set and the Miscanthus dry

yield output compared for all grid points output compared and

produces a linear match with R2=0.9812.

The new water use algorithm was implemented and

produces a response curve that fitted the data on soil water

published by Aslyng44. The soil water capillary pressure

method of evaporation down regulation was implemented and

checked to experimental data sets from Portugal, Ireland,

England and Spain that were known to have been grown in

water limiting conditions. Statistically significant matches to

the experimental monthly time series of LAI (leaf index),

height and DM (dry matter yield) were obtained. An example

of the comparison of experimental and MiscanFor LAI and

DM predictions is shown in figure 3 for Cashel in 1995.

1995 Cashel Ireland

0

2

4

6

8

10

12

14

16

18

0 100 200 300 400

journal day

DM

t/h

a

MiscanFor DM

Exp DM

physiostat

Exp LAI

MiscanFor LAI

Figure 3. Comparison of the MiscanFor modeled LAI and DM is shown for the water limited 1995 Cashel, Ireland. The Physiostat (Physiological plant growth stage is also shown.

The MiscanFor water stress down regulation results in a

linear match P<0.001 match of predicted yields with field

experiment yields when run using the meteorological data and

soil parameters measured at each of the experimental sites.

Data was available from sites in Portugal, France, Italy,

Germany, Greece, Netherlands, Denmark, Sweden, Great

Britain and Ireland. This match is shown graphically in figure

4. and demonstrated the applicability of the modified program.

The dry matter yields were then calculated using

MiscanFor for each experimental site using the 0.5 degree grid

average CRU meteorological data time series and FAO soil

parameters for the grid block that contained the site. A linear

regression was used to compare the results to the experimental

yields and found a statistically significant linear relationship

P<0.005.

Comparison of MiscanFor predictions to experimental

measurements of Miscanthus drymatter yields in

tonnes.ha-1

y = 1.0326x

R2 = 0.8676

0

5

10

15

20

25

30

35

40

45

0 10 20 30 40 50

Experimenal site measured yield

Mis

ca

nfo

r p

red

icte

d y

ield

Figure 4. Comparison of MiscanFor predictions of Miscanthus dry matter yield at winter harvest with the measured harvests at the experimental sites, MiscanFor is run using actual site meteorological and soil data and the error bar on the experimental measurements shows the 95% confidence interval for all of the experimental plot measurements.

The full model was run for each year for the period 1960

to 1990 and the mean and standard deviation of the Dry matter

yields of each grid block calculated. The mean peak yield for

the entire Europe 25 was 16.3 t.ha-1 with a standard deviation

of 2 t.ha-1 . When the mean monthly meteorological

conditions for each grid block were calculated from the CRU

time series for this 30 year time period and then MiscanFor

was run the to predict the Miscanthus yield for each grid block

using the average meteorological conditions the mean

European peak yield was found to be 18 t.ha-1.

Using the CRU predicted meteorological time series of

maximum and minimum temperatures, precipitation and cloud

cover for the IPCC A1F1, A2, B1 and B2 climate scenarios for

the period 2002 to 2100, MiscanFor was run for each year and

each grid-block to predict the Miscanthus dry matter yield.

The results were transferred into ArcGis to make maps of

Miscanthus dry matter yield over the European area. for each

scenario year. Time slices of 2020, 2050 and 2080 were

selected to compare to the 1960-90 average yields for each

climate change scenario. The example for the A1F1 scenario is

shown in figure 5.

6 SPE 107495

Figure 5. MiscanFor predicted yields of Miscanthus dry matter yield for the A1F1 IPCC scenario at time slices 2020, 2050 and 2080 compared to baseline case, which is the average of the period 1960-1990. The color scale is green 40 tonnes.ha

-1 red is 0

tonnes.ha-1

of Miscanthus dry matter.

Predictions of land available to grow bio-fuel crops were

available for four IPCC scenarios A1F1 (Global economic,

fossil fuel intensive), A2 (Regional economic), B1 (Global

environmental) and B2 (Regional environmental), for time

slices of 2020, 2050 and 2080. These include the area that

would be used to grow bio-fuel crops in the scenario and

unused cropland that is not used for any other purposes

(Rounsevell et al. 2005; 2006). The European area is split

between EU 15 plus Norway and Switzerland and Eastern

Europe, which includes Lithuania, Latvia and Estonia but

excludes FSU (former Soviet Union) states. These areas are

expressed as a percentage of the arable land available in

Europe for the baseline year of 1990. Shown in figure 6.

Figure 6. Area predicted by the ATEAM study to be used for Bio-energy crops for four IPCC scenarios: A1F1, A2, B1 and B2, expressed as a percentage of 1990 arable land for time slices of 2020, 2050 and 2080.

The spatial distribution of arable land was obtained from

the Corine land use map. This data was re-gridded using

ArcGis from the original 250m grid size to produce a 0.5

degree grid of the percent of arable land in each grid block,

which was then used to calculate the land available in each

grid block for biomass for bio-energy production. The

resulting coarse grid arable land map is shown in figure 6.

Figure 6. Grid of percentage of arable land in 1990 on a 0.5 degree grid block size extracted from the Corrine 250m gridded land use map. Scale is green is 80-96%, to red 0-4%.

This map excluded Norway Switzerland and Serbia as

they are not in the EU. This enabled the average EU25 yield to

be calculated. The arable land percentage in Norway,

Switzerland and Serbia is small due to the topography and will

not contribute much to the overall yield.

From the MiscanFor prediction runs, the time slices 2020,

2050 and 2080 for each of the four IPCC scenarios were

extracted and used to calculate the average European yield

using the area per grid block extracted from the Corine land

use map. The results are shown graphically in figure 7.

Figure 8 Predictions of the average EU25 yield of Miscanthus dry matter in tones.ha

-1 for EU25 for each IPCC scenario and the time

slice 2020,2050 and 2080.

This average yield was applies to the land available to

grow bio-fuel crops for each scenario to calculate the

maximum EU25 yield of Miscanthus for each year and then

this yield is converted to energy yield by using an energy

intensity for Miscanthus of 15MJ kg-1. The total energy

production per scenario and time slice is expressed as a

percentage of the energy use in 2000 and displayed in

SPE 107495 7

graphical from in figure 10. The 95% confidence interval for

annual yield in each grid block for the mean of the years 1960-

90 is used to calculate the range of energy yields that could be

expected for each time slice and scenario.

Figure 10. Prediction of the potential Miscanthus energy yield using the land area available from the A-team study and the MiscanFor simulated yields, expressed as a percentage of the year 2000 energy use for the A1F1, A2, B1 and B2 climate change scenario and 2020, 2050 and 2080 time slices.

Miscanthus parameters were developed for the DNDC model

so that the crop yields matched and the predicted change in

SOC (soil organic carbon) over the 10 year crop experiment

matched the experimental observations at the Cashel site of an

increase from 59 to 64 tonnes.ha-1.

The matched model was then used to run for a 100 year

scenario with different initial SOC values and the resulting

soil GHG emission time series calculated for nitrous oxide,

methane and carbon dioxide. The emissions were restated in

terms of total GHG emission in CO2 C equivalent emitted per

GJ of energy liberated by burning Miscanthus, averaged over

15 years for 10 initial SOC conditions. A second order

polynomial was fitted to the data with an R2 of 0.982, shown

in figure 11.

Miscanthus GHG emission/MJ versus soil organic

carbon(SOC)

y = -8.8648x2 + 5.0557x - 0.1752

R2 = 0.9816

-0.100

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.000 0.050 0.100 0.150 0.200 0.250 0.300 0.350

SOC kg C/kg Soil

Mis

can

thu

s G

HG

CO

2

C e

qu

ivale

nt

kg

/M

J

Gas

Oil

coal

Figure 11. DNDC derived relationship between initial SOC for the Miscanthus crop site and the average GHG emissions per MJ of energy produced over the 15 year crop life. The emissions are calculated as the CO2 C equivalent of N2O, CH4 & CO2 soil emissions minus the harvested carbon.

This relationship was then used to compare the total GHG

emissions for Miscanthus fuel compared to coal, diesel and

natural gas, which have emissions of 0.12, 0.082 and 0.059 kg

C MJ-1 respectively. Using the FAO soil organic carbon map

for SOC each grid block was assigned a condition based upon

the expected emissions of the Miscanthus fuel. The conditions

were:- “sequestration” - carbon is stored in the soil/biomass,

“better than gas” – emits less GHG than natural gas but no

sequestration of C takes place, “better than oil” - GHG

emissions between gas and oil, “better than coal” – GHG

emissions between oil and coal and “worse than coal” –

produces more GHG gas emissions than burning coal. The

resulting ArcGis map is shown in figure 12.

Figure 12. Map of Europe with the area classified by the GHG emitted from Miscanthus fuel grown in that area compared to fossil fuels. Colour codes reflect the following classifications:- “sequestration” - carbon is stored in the soil/biomass, “better than gas” – emits less GHG than natural gas but no sequestration of C takes place, “better than oil” - GHG emissions between gas and oil, “better than coal” – GHG emissions between oil and coal and “worse than coal” – produces more GHG gas emissions than burning coal.

Discussion

This development of MISCANMOD into the FORTRAN

MiscanFor program has added functionality to the crop growth

model to predict Miscanthus yields under water limited

condition. This model matches many diverse sets of

experimental data with a P<0.001. Previous

authors18,31,37,38,39,40&41 had suggested using different radiation

use efficiencies in order to match model growth rates to the

observed crop growth but the new water down-regulation

algorithm eliminates this. MiscanFor calculates the plant

senescence between peak growth yield and winter harvest so

that the dry-matter yields can be used without correction for

energy yield calculations.

The standard deviation of mean of the dry-matter yields

calculated for the period 1960 to 1990 is 3.3 tonnes ha-1 of the

mean European yield of 16.5 tonnes ha-1. The 95% confidence

interval of yields ranges from 9.9 to 23.1 tonnes ha-1. This

indicated the standard deviation of energy yields calculated for

future scenarios is 20% and the 95% confidence interval will

be +- 40%. Design of Miscanthus and other bio-fuel projects

will need to consider the variation in annual yields due to

inter-annual weather patterns, both from the point of view of

the minimum level of harvest and the requirement to be able to

handle the larger ones.

8 SPE 107495

Europe’s ability to produce crops for use in as a bio-fuel is

limited by the available land. St.Clair & Smith45 demonstrated

that the total GHG emissions from the growing of crops for

the production of biofuels is not zero and it depends the

previous use of the land. In this paper we further show that

each crop system/land use has an equilibrium state of SOC for

any particular climatic condition and changing either the land

use or crop management or climatic conditions will cause this

equilibrium SOC value to change. This change causes GHG

emissions or sequestration. This means that if the objective of

producing biofuel is to reduce GHG emissions then land use

change has to be managed so that GHG emissions are avoided.

To achieve a GHG mitigating or neutral crop, the only land

that can be used for biomass/bio-fuel production is land that

was previously used for producing food crops, preferably

using intensive farm management techniques. If land that

currently has natural ecosystems, grassland or woodland is

converted to grow biomass and bio-fuel crops it will results in

emission that are higher than burning coal. Miscanthus

giganteus experiments show that after 10 year of cultivation

the equilibrium SOC is between 3 and 3.5% with a starting

point of between 1 and 2.5% SOC in the original arable land.

DNDC runs show close agreement to the experimental data

and show that the time constant for the change of SOC to the

Miscanthus equilibrium is 31 years. This is the similar to the

time constant of the dwell time of atmospheric CO2.

The IPCC-ATEAM study to estimate the land that would

be used for crops for bio-fuel and biomass was based upon the

economic forces that drive the four representative IPCC

climate scenarios. These vary from 35% for the global market

and fossil fuel intensive A1F1 scenario to 20% for the local

market environmentally driven B2 scenario for the 2080 time

slice. Market forces in the A1F1 scenario produce the largest

area for energy crops. In this study, only land that was used for

arable farming in 1990 was considered for energy crops as it

was assumed that crop yields in general will increase as the

farming industry become more efficient and homogenized

across Europe and make land available for bioenergy crops. In

this study we have used these estimates of available land for

each scenario and time slice. The distribution of arable land in

Europe was taken from the Corine 250m resolution study and

re-gridded as the percentage of arable land per 0.5 degree grid

block. This loses resolution, especially in the coastal area but

is a necessary compromise to enable the model to run in less

than 20 minutes for the yield predictions. In future the model

could be run with smaller grid blocks for more detail over

specific areas.

The spatial area distribution of arable land was used to

calculate the average European Miscanthus yield for

comparison purposed between the time slices and scenarios

and clearly shows the increased yield in the Northern latitudes

due to warming and the reduction in yields in the south due to

less rainfall. The contribution of bio-energy crops to Europe’s

primary energy needs in terms of the percent of energy is

presented as the percent of energy use in the year 2000 rather

than the actual energy predicted to be used in the IPCC

scenario. This has been done for comparison purposes. It

shows that there is the potential for Miscanthus to contribute

up to 26% of Europe’s primary energy by 2080 in the A1F1

scenario, but due to inter-annual meteorological variations,

planners will have to have contingencies for alternative energy

for the minimum of the 95% confidence interval of 15% of

European primary energy needs.

Although the work on energy yield only considered the use

of arable land, we also considered the impact of using

grasslands or woodlands for energy crops to look at the impact

on GHG emissions. The relationship between GHG emissions

of Miscanthus as an energy crop for various initial SOC values

was derived from the DNDC runs that only considered current

meteorological conditions and did not consider global

warming that will increase soil respiration with increasing

temperature and potential changes in Miscanthus yields. This

relationship shows the threshold of SOC at which GHG

emissions from the Miscanthus energy crop are less than gas,

oil and coal respectively. This is mapped showing that there

are many land areas with an initial SOC above 5% that cannot

be used for bio-energy crops without producing more GHG

emissions than caused by burning coal.

This paper has considered only Miscanthus as an energy

crop as Europe wide it has the highest energy yield per ha of

land, so the study represents the maximum possible energy

yield with current plant and conversion technology. Future

developments in crop yield and genotype improvement could

increase this value. In addition if other bio-fuel crops are used

in areas where their potential yield is higher than Miscanthus

then the total production could be marginally higher. We have

highlighted that in order to achieve maximum energy yield

with minimum GHG emissions, only the arable land that is

surplus for food production should be used for bio-fuel

production and that natural ecosystems such as grasslands,

heath lands and woodlands should not be used for this

purpose.

Acknowledgements

This work was funded by a Sixth Century Scholarship from

the University of Aberdeen as a joint project between the

College of Physical Sciences and the College of Life Sciences

and Medicine. Irish Meteorological Service (Met Eireann)

provided Kilkenny daily meteorological data.

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