environmental impact and economic assessment for uk sugar beet production systems

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Agriculture, Ecosystems and Environment 107 (2005) 341–358

A

0

d

Environmental impact and economic assessment for UK

sugar beet production systems

J. Tzilivakis a,*, K. Jaggard b, K.A. Lewis a, M. May b, D.J. Warner a

a Agriculture and Environment Research Unit (AERU), Science and Technology Research Institute,

University of Hertfordshire, College Lane Campus, Hatfield, Hertfordshire AL10 9AB, UKb Broom’s Barn Research Station, Higham, Bury St Edmunds, Suffolk IP28 6NP, UK

Received 20 April 2004; received in revised form 30 November 2004; accepted 8 December 2004

bstract

It is important to understand and evaluate the environmental impacts of all crops if we are to develop more sustainable

production systems. Understanding the impact of sugar beet (Beta vulgaris) is particularly important as there are alternative

crops that can be used for sugar production and also because there is growing interest in its potential as a source for biofuel. This

paper presents the findings of a research project to evaluate the environmental impact and economic viability of the range of

sugar beet production systems in the UK. The study used 13 sugar beet production scenarios that represent those used throughout

the UK. These scenarios differed in soil type, nutrients applied (inorganic and organic), crop protection (chemical and cultural)

and use of irrigation. The assessment included an evaluation of inputs (nutrients, pesticides and energy) and their impact on the

environment. A net margin for each scenario was also calculated to provide an economic assessment. An average of the

assessment results for the different parameters across the scenarios was calculated (weighted by the sugar beet area each scenario

represented) to provide a mean figure for sugar beet production in the UK. The results for this mean on a per ha basis were a yield

of 52 t, a net margin of £560, consumption of 21.4 GJ of energy, emission of 1.4 equiv. t of carbon dioxide, 3.3 kg nitrogen

leached, 15.2 kg nitrogen lost to denitrification and a pesticide ecotoxicity score of 26 (low). A sustainability profile, on a per ha

and a per tonne basis, for each of the 13 scenarios was constructed enabling all scenarios to be compared in terms of their overall

environmental and economic performance. This comparison showed that the most profitable scenario also had the best overall

environmental performance. This scenario represented 18% of UK sugar beet area. Three other scenarios that represented 57%

of the total area closely followed this performance. The overall performance of the organic scenario was equal to the best

conventional scenario on a per ha basis, but on a per t basis its performance was lower (similar to the mean for sugar beet in the

UK) due to its significantly lower yield. This study illustrates that a significant proportion of the UK crop is being grown in an

economically efficient way whilst minimising environmental damage.

# 2005 Elsevier B.V. All rights reserved.

Keywords: Sugar beet; Environmental impact; Energy; Pesticides; Nutrients; Economics; Sustainability

* Corresponding author. Tel.: +44 1707 285259; fax: +44 1707 284185.

E-mail address: [email protected] (J. Tzilivakis).

167-8809/$ – see front matter # 2005 Elsevier B.V. All rights reserved.

oi:10.1016/j.agee.2004.12.016

J. Tzilivakis et al. / Agriculture, Ecosystems and Environment 107 (2005) 341–358342

1. Introduction

Agriculture has a complex relationship with the

environment because of its dependence on natural

resources and processes. It can have both positive and

negative impacts on the environment. The negative

effects tend to arise from the use of farm inputs

(fertilisers, pesticides and fossil fuels) and land

management practices that can result in pollution,

loss of wildlife habitats and reduced biodiversity. On

the other hand agricultural land management can

create a diverse range of habitats and ameliorate

pollution. The challenge is to enhance the positive and

minimise the negative effects within the framework of

economic viability. This is the balance of sustain-

ability, optimising the production system to meet

evolving economic, environmental and social objec-

tives. In order to achieve this it is important to

understand and evaluate the impacts of all crops.

Sugar beet (Beta vulgaris) is an important crop

within many arable rotations in England and it is

commonly grown in conjunction with wheat (Triticum

spp.) and barley (Hordeum spp.) and sometimes with

potato (Solanum spp.). It is a valuable break crop,

preventing the build up of disease and reducing the

need for pesticides in the following crops. In 2002,

169 000 ha was devoted to sugar beet in the UK

(DEFRA, 2003a). This represents about 3.7% of the

total cropped area and is similar to the area of potato.

This area supplies about 55% of the sugar consumed in

the UK and a surplus (exports less imports) of about

200 000 t for export. It also supplies approximately

600 000 t of dried animal feed each year. The beet

crop is also important for the wider rural economy,

in particular employment. Although agricultural

employment has been in decline, farms still provide

significant employment for those living in the

countryside. This is not just through direct employ-

ment on farms and in beet sugar factories, but also in

the service and ancillary industries that support farms

and processors. The industry estimates that it supports

over 20 000 jobs in the UK (NFU/British Sugar, 2003).

This paper presents the findings of a study in which

the objective was to evaluate the environmental impact

and economic performance of sugar beet production

systems in the UK. Understanding the impact of sugar

beet is particularly important because there are alterna-

tive sources for sugar, e.g. sucrose from sugar cane

(Saccharum officinarum L.) and high fructose syrup

from maize (Zea mays L.). From a sustainability pers-

pective, a broader assessment of the impacts of sugar

production (e.g. processing, packing, transport and

consumption) is required to evaluate fully which sys-

tems present the most sustainable solution for society.

This wider analysis must also acknowledge social

considerations, which are a significant part of the

Government’s sustainable development policy and its

aim of achieving ‘‘a better quality of life for everyone’’

(DEFRA, 2002). It can be difficult to relate social

objectives to the growing of a crop, but links do exist.

For example, transporting sugar beet during winter can

result in soil from fields being deposited on public roads.

This may be trivial in terms of soil erosion, but

important for road safety, which is a serious issue for

many communities. These wider issues have not been

included within the objectives of this study due to their

scale (beyond the farm gate) and difficulties with

objective assessment and quantification (with respect to

social aspects). However, it is important to acknowledge

their existence in order to maintain a holistic perspective

and understand the contribution this study offers. This

study focuses on the production of sugar beet at the farm

level and delivery to the factory gate. This includes the

impacts that arise from the production of farm inputs,

such as fertilisers and pesticides, their use, energy

consumption and emissions of pollutants. An eco-

nomic analysis was also undertaken as part of the study.

2. Methodology

2.1. Sugar beet production scenarios

Thirteen theoretical sugar beet production scenarios

were constructed to represent the range of current

production systems in the UK. Table 1 lists some details

of the scenarios and the area they represent today. The

proportion of the UK beet area represented by each

scenario was estimated from recent surveys of

production practices carried out by staff from British

Sugar on a random selection of approximately 500

fields each year. Scott and Jaggard (2000) also used this

survey and Turner (1992) gives details of the survey

structure. Table 2 shows the cultivation operations for

each scenario, Table 3 the crop nutrition and Table 4

the crop protection regimes.

J. Tzilivakis et al. / Agriculture, Ecosystems and Environment 107 (2005) 341–358 343

Tab

le2

Cu

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III

III

IVV

VI

VII

VII

IIX

XX

IX

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1–

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21

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5S

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15

Sep

tem

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15

Sep

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15

Sep

tem

ber

15

Sep

tem

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15

Sep

tem

ber

––

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15

Sep

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31

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ary

15

Feb

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Feb

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ber

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Apri

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Apri

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63

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May

30

May

30

May

30

May

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May

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May

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May

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May

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A:

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ne

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10)

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ves

t.

Table 1

Sugar beet production scenarios

Scenario % Area Soil type Notes

I 3 Sand FYMa applied, irrigated

II 2 Sand Irrigated

III 1 Sand Cover crop, irrigated

IV 4 Sand Poultry manure applied,

irrigated

V 7 Sand Rainfed

VI 2 Sand Minimal tillage, rainfed

VII 12 Sandy loam FYMa applied, rainfed

VIII 24 Sandy loam Rainfed

IX 21 Clay loam Rainfed

X 18 Silt Rainfed

XI 2 Peat Cover crop, rainfed

XII 4 Peat Rainfed

XIII 0.2 Sandy loam Organic production, rainfed

a FYM: farm yard manure.

2.2. Energy and global warming potential

The amount of energy put into each scenario was

calculated and is presented in gigajoules (GJ). Each

activity, from seedbed preparation and planting

through to transport of the beet to the factory, was

examined and the energy requirement calculated.

This includes the energy required to manufacture and

apply inputs and for manufacturing and maintaining

machinery. The energy balance has been calculated

based on a technique described in Hulsbergen and Kalk

(2001). Full details of the energy assessments have

been published separately (Tzilivakis et al., in press).

Global warming potential (GWP) was calculated

based on the emissions of greenhouse gases that arise

from the various production activities in each of the

scenarios. These emissions are largely from energy

use but also include other gaseous emissions such as

those from denitrification (see Section 2.3). Each

greenhouse gas, i.e. carbon dioxide (CO2), methane

(CH4) and nitrous oxide (N2O), has a GWP, which is

the warming influence relative to that of carbon

dioxide. The GWP is expressed as equivalent tonnes of

carbon dioxide (equiv. t CO2).

2.3. Nutrients

The dynamics of available N in the soil, including

the addition of fertiliser (Table 3) for each scenario,

except XI and XII on peat soil, were modelled using


Table 3

Crop nutrition regime

Scenario Soil type Inorganic N

(kg/ha)

Manure

(t/ha)

P2O5

(kg/ha)

K2O

(kg/ha)

Lime

(t/ha)

MgO

(kg/ha)

Na

(kg/ha)

MnSO3

(kg/ha)

Borona

(kg/ha)

I Sand 80 30b – – 2 – – – –

II Sand 120 – 50 100d 2 85 150 10 7

III Sand 120 – 50 100d 2 85 150 10 7

IV Sand 40 10c – – 2 – – – –

V Sand 120 – 50 100d 2 85 150 – 7

VI Sand 120 – 50 100d 2 85 150 – 7

VII Sandy loam 80 30b – – 2 – – – –

VIII Sandy loam 120 – 50 100d 2 85 150 – –

IX Clay loam 120 – 50 50d – – 150 – –

X Silt 120 – 50 50d – – – – –

XI Peat 30 – 50 50d – 85 – 20 10

XII Peat 30 – 50 50d – 85 – 20 10

XIII Sandy loam – – – 100e 2 – – – –

a Boron: boron spray, typically sodium borate (Na2B4O7).b FYM: farm yard manure, 30 t containing 200 kg N/ha, 50 kg P/ha and 200 kg K/ha.c PM: poultry manure, 10 t containing 240 kg N/ha, 110 kg P/ha and 150 kg K/ha.d kg of K2O applied as muriate of potash (60% K2O).e kg of K2O applied as sylvinite (24% K2O).

SUNDIAL (Smith et al., 1996) to estimate losses of N

via leaching and denitrification. SUNDIAL has been

applied and validated in numerous studies (Bradbury

et al., 1993; Gabrielle et al., 2002; Smith et al., 2003;

Gibbons et al., 2005) and performs well for common

arable crops. Three sets of weather data were used

(30-year average, 1976 [dry] and 2000 [wet]) to

produce a range of losses. These were then averaged

to provide overall figures for leaching and denitri-

fication for each scenario. The figures for organic

beet (scenario XIII) were modelled in a slightly

different way to account for the different crop

nutrition regime. Organic sugar beet would usually be

grown after a cereal crop that probably had a grass ley

before it (Lampkin, 1994) so soil nitrate is not high

when beet is sown. Based on this rotation (grass 1–4

years, winter wheat, sugar beet) SUNDIAL was used

to model the losses in the same way for scenarios I–

X. The effect on nitrate and ammonia are difficult to

predict on peat soils due to its complicated anion

cation exchange mechanisms, thus SUNDIAL could

not be used for scenarios XI and XII. However, an

estimate has been made of losses to leaching and

denitrification for scenarios XI and XII, for the

purposes of this study, based on a range of literature

(Kirkham and Wilkins, 1993; Koops et al., 1997;

Davidsson et al., 2002; Juntunen et al., 2002).

Some of the N that is denitrified will be lost as

nitrous oxide (N2O). The proportion lost as N2O varies

depending on specific climate and soil conditions and

is still far from being fully understood. Some

researchers estimate this fraction to be only 1–3%

of the total N applied (Skiba et al., 1996), others

suggest a fraction of between 3 and 12% of the

denitrified N (de Vries et al., 2003). In this instance,

following de Vries et al. (2003), we have assumed 7%

of denitrified N was released as N2O for peat soil and

3.5% for other soils. Although these amounts of N are

small compared to the total amount applied, even

small amounts of N2O have a significant environ-

mental effect because its GWP is 310 times greater

than that of CO2 (Houghton et al., 1996).

2.4. Ecotoxicity from pesticides

The crop protection programme for each scenario

(Table 4) was assessed to identify any potential risks to

non-target fauna and flora using the p-EMA software

(Brown et al., 2003; Hart et al., 2003; Lewis et al.,

2003a). The potential ecotoxicity of pesticides is

dependant on the environmental concentration taxa

are exposed to and the toxicity of the pesticide active

substance with respect to the specific taxa. The

location and environment are important aspects to

J.T

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Table 4

Crop protection programmes for scenarios (dates, products and application rates)

A Scenarios

I II III IV V VI VII VIII IX XI XII XIII

1 – – – – – – – – – 03 June 04 June –

2 – – – – – – – – – 2 May – – –

3 – – 23 April – – – – – – 05 May – –

4 – – – – – – 17 March 17 March 17 March 7 March 17 March 17 March –

5 – – – – – – – – – – 03 April –

6 06 August 06 August 06 August 06 August 06 August 06 August 06 August 06 August 06 August 6 August 06 August 06 August –

7 – – 15 Marcha – – – 18 Marchb 18 Marchb 10 Aprilc 0 Aprilc – – –

8 17 March 17 March 01 February 17 March 17 March 17 March 01 November 01 November – – – –

9 15 March 15 March 15 March 15 March 15 March 15 March – – – – – –

10 – – – – – – – – – – – 06 August

11 – – – – – – – 09 April 23 April 3 April 14 April 14 April –

12 – – – 21 April 21 April 21 April 23 April 23 April 08 May 8 May – – –

13 06 April 06 April 06 April 06 April 06 April 06 April 09 April – – – – –

14 – – – – – – – – – 25 May 24 May –

15 – – – – – – – – – 24 April 24 April

15 May 14 May

16 – – – 05 May 05 May 05 May – – – – – –

17 21 April 21 April 21 April – – – – – – 8 May 03 May 04 May –

18 05 May 05 May – – – – – – – – – –

19 – – 05 May – – – – – – – – –

A: applications – (1) Betanal tandem 3.0 l/ha, (2) Dow shield 0.5 l/ha, (3) fusilade 0.5 l/ha, (4) Gaucho 0.099 kg/ha, (5) PDQ 3.0 l/h (6) Punch C 0.625 l/ha, (7) Pyramin DF a1.7 kg/

ha, b2.5 kg/ha, c3.3 kg/ha, (8) Sting Eco 3.0 l/ha, (9) Temik 10.0 kg/ha, (10) Thiovit 10.0 kg/ha, (11) Mix: Goltix WG (1.25 kg/ha) Betanal Flo (1.7 l/ha) + oil (1.0 l/ha), (12) Mix:

Betanal Progress (0.75 l/ha) + Debut (30.0 g/ha) + Venzar (0.4 l/ha) + oil (1.0 l/ha), (13) Mix: Betanal Flo (1.7 l/ha) + Venzar (0. l/ha), (14) Mix: Betanal Flo (1.5 l/ha) + Venzar

(0.4 l/ha) + Pyramin DF (0.25 kg/ha) + Debut (30 g/ha) + oil (1 l/ha) + Dow Shield (0.5 l/ha), (15) Mix: Betanal Flo (1.7 l/ha) + nzar (0.4 l/ha) + Pyramin DF (0.25 kg/ha) + oil

(1 l/ha), (16) Mix: Betanal Tandem (3 l/ha) + Goltix WG (1 kg/ha) + oil (1 l/ha), (17) Mix: Betanal Progress (0.75 l/ha) + Debut 0 g/ha) + Venzar (0.4 l/ha) + oil (1 l/ha) + Dow

Shield (0.5 l/ha), (18) Mix: Betanal Tandem (3 l/ha) + Goltix WG (1 kg/ha) + oil (1 l/ha) + Dow Shield (0.5 l/ha), (19) Mix: Goltix G (1.7 kg/ha) + oil (1 l/ha) + Dow Shield (0.5 l/

ha).

Active substances: Betanal Flo = 160 g/l phenmedipham as suspension concentrate, Betanal Progress = 25 g/l desmedipham + 51 g/l ethofumesate + 76 g/l phenmedipham as

emulsifiable concentrate, Betanal Tandem = 100 g/l ethofumesate + 80 g/l phenmedipham as emulsifiable concentrate, Debut = 50% (w/w) triflusulfuron-methyl as wettable granules,

Dow Shield = 200 g/l clopyralid as soluble concentrate, Fusilade 250 EW = 250 g/l fluazifop-P-butyl as oil-in-water emulsion, Gauc o = 70% (w/w) imidacloprid as water dispersible

powder, Goltix WG = 70% (w/w) metamitron as wettable granules, PDQ = 80 g/l diquat + 120 g/l paraquat as soluble conce trate, Punch C = 125 g/l carbendazim + 250 g/l

flusilazole as suspension concentrate, Pyramin DF = 65% (w/w) chloridazon as wettable granules, Sting Eco = 120 g/l glyphosate as oluble concentrate, Tachigaren 70WP = 70% (w/

w) hymexazol as water dispersible powder, Temik 10G = 10% (w/w) aldicarb as granules, Thiovit = 80% (w/w) sulphur as wetta e granules, Venzar Flowable = 440 g/l lenacil as

suspension concentrate.

X

–

2

–

1

–

0

1

–

–

–

2

0

–

–

–

–

0

–

–

a,

+

4

Ve

(3

W

1

h

n

s

bl


Table 5

Sugar beet growing locations in the UK

A B C

Location East Anglia Eastern England Upland

(Lincolnshire, Nottinghamshire,

Yorkshire)

West Midlands

Location represented by: Higham, Suffolk Cranwell, Lincolnshire Pershore, Worcester

Soil typea Varied with scenario Varied with scenario Varied with scenario

Annual rainfall (mm) �590 �650 �670

Excess winter rainfall (mm) <125 126–200 126–200

Groundwater vulnerability Highly vulnerable,

major chalk aquifer

Low vulnerability Low vulnerability

a As each of the selected locations is large, soil type could potentially vary significantly. As a result crop management varies significantly

depending on soil type. Therefore a single soil type was not fixed for each location but varied depending on the Management Scenario (see Table 1).

consider in this assessment, as they will influence

exposure. For example, soil type, rainfall, ground-

water vulnerability and drainage can influence

hydrological processes and thus concentrations in

groundwater and surface water. Habitats surrounding

fields will also have an influence on exposure and what

taxa are exposed. To account for this variability a

number of locations and environments were assessed.

Each scenario was modelled using the p-EMA

software for three different sugar beet growing

locations in the UK (Table 5) with three different

environments (A–C in Table 6). A fourth environment

was also constructed (D in Table 6) based on an

analysis of an aerial survey (video) of 582 fields that

Table 6

Environment/habitat descriptions

Feature Environment

A B

Surface water None None

Field margins Bare Vegetated

Field boundary Sparse, gappy

hedgerow only on

15% of boundary

Moderately good

hedges along 50%

of boundary

Conservation

headlands

None None

Woodlands None None

Beetlebanks None None

Drainage No artificial

drainage

No artificial drainag

General practices Legal practice Best practice

grew sugar beet in 2002. The survey provides

information for a national sugar yield forecast

(Jaggard, 1992), so is designed to be representative

of the beet crop in the UK. A helicopter is flown along

prearranged flight lines in all the beet growing regions,

and all the beet fields that intersect these lines are

filmed. The film from July 2002 was analysed by

categorising the type of wildlife habitat at the point

where the flight line crossed the boundary of each end

of each field. Fig. 1 shows results of the survey for the

three sugar beet growing regions and the UK as a

whole. Environment D was constructed from the

analysis of this survey. It differs from environment C

by having no surface water, but having some

C D

River on one side of

field 3 m away from

spray area, 3 m wide,

0.5 m deep flowing

None

Vegetated Vegetated

Sound hedgerow,

75% of boundary

Sound hedgerow,

75% of boundary

None None

None 5% of field boundary

Present Present

e Sandy soil fields – no

artificial drainage; other

fields artificial drainage

plus mole drains

Sandy soil fields – no

artificial drainage; other

fields artificial drainage

plus mole drains

Best practice Best practice


Fig. 1. Results of habitat survey of sugar beet fields in the three sugar beet growing regions and for the UK as a whole (expressed as percent of

field boundary).

woodland. This survey provided a more realistic

description and appropriate analysis of the risks from

pesticide use.

The p-EMA software generates a number of

outputs, including a summary where the risks to each

organism are placed into ‘risk bands’ of acceptable,

moderate (orange) and high (red) risk alerts. These

summaries draw attention to any specific pesticide

application within the crop protection programme

that has been identified as an orange or red risk to one

or more taxonomic groups. The taxonomic groups are

mammals, birds, bees, fish, earthworms, daphnia and

algae. For this study a scoring system was devised for

pesticide ecotoxicity based on the orange and red risk

alerts for each of the scenarios. Each orange alert

received a score of 5 and each red alert a score of 10

(to reflect the significance of the risk). These were

summed to give a score that is a surrogate for

pesticide ecotoxicity potential, allowing the ecotoxi-

city effects of the scenarios to be compared on a

numerical scale. The scores for environments A, B

and C were averaged to provide a score applicable

across a range of habitats. This score is presented

separately from the score for environment D, which

represents the habitats typically found around sugar

beet fields in the UK.

2.5. Economics

The economic assessment of the sugar beet

production scenarios was based on estimates of the

crop’s financial output, less both the variable costs of

purchasing seed, agrochemicals, contractor’s services

and the costs of carrying out the full range of cultural

operations. The difference between income and costs

is defined as the net margin in this study. This net

margin is not directly comparable to the gross margin

calculations published by Lang (2002), for example,

as it includes items such as the costs of ploughing and

tractor hoeing.

Yield data are not collected on an individual field

basis in the UK, so yield results cannot be associated

with cultural practices in the crop production survey

carried out by British Sugar. Instead we have had to

assume an average yield for each scenario. The

weighted average of these yields is 52 adjusted t ha�1


Table 7

Assessment results

Per ha

Scenario Yield (t) Net

margin (£)

Energy

(GJ)

GWPa

(equiv. t CO2)

Nitrate leaching

(kg N)

Denitrification

(kg N)

Ecotox

(score)

Ecotox

(SBH)b (score)

I 50 415 25.4 2.0 3.5 42.7 67 60

II 50 310 25.6 1.6 3.5 7.6 67 60

III 50 256 26.8 1.6 3.2 7.1 63 55

IV 55 621 21.9 1.9 3.4 55.7 70 25

V 45 322 22.2 1.4 3.5 9.4 65 15

VI 45 356 21.3 1.3 3.5 10.1 77 25

VII 50 563 23.8 1.8 0.9 31.0 55 15

VIII 50 489 23.9 1.5 1.0 9.3 67 25

IX 50 545 19.1 1.2 7.0 10.0 63 25

X 60 784 19.0 1.2 2.4 5.9 70 25

XI 60 701 16.0 1.5 7c 25c 95 25

XII 60 725 15.6 1.4 7c 25c 72 45

XIII 34 674 19.0 1.3 3.0d 11.0d 0 0

Mean e 52 560 21.4 1.4 3.3 15.2 67 26

Per t of yield

t £/t GJ/t equiv. t CO2/t kg N/t kg N/t score/t score/t

I 50 8.31 0.51 0.04 0.07 0.85 1.33 1.20

II 50 6.21 0.51 0.03 0.07 0.15 1.33 1.20

III 50 5.12 0.54 0.03 0.06 0.14 1.27 1.10

IV 55 11.30 0.40 0.03 0.06 1.01 1.27 0.45

V 45 7.15 0.49 0.03 0.08 0.21 1.44 0.33

VI 45 7.92 0.47 0.03 0.08 0.23 1.70 0.56

VII 50 11.25 0.48 0.04 0.02 0.62 1.10 0.30

VIII 50 9.78 0.48 0.03 0.02 0.19 1.33 0.50

IX 50 10.91 0.38 0.02 0.14 0.20 1.27 0.50

X 60 13.06 0.32 0.02 0.04 0.10 1.17 0.42

XI 60 11.69 0.27 0.02 0.12 0.42 1.58 0.42

XII 60 12.09 0.26 0.02 0.12 0.42 1.19 0.75

XIII 34 19.82 0.56 0.04 0.09 0.32 0.00 0.00

Meane 52 10.61 0.42 0.03 0.06 0.29 1.28 0.50

a GWP: global warming potential.b SBH: sugar beet habitat. The pesticide ecotoxicity scores in this column are based on the ecotoxicological risk assessment using the habitat

description from the survey of habitats surrounding beet fields in the UK (D in Table 6).c These are estimated figures based on a range of literature (Kirkham and Wilkins, 1993; Koops et al., 1997; Davidsson et al., 2002; Juntunen

et al., 2002).d The figures for organic beet have been modelled slightly differently to scenarios I–XII to reflect the different crop nutrition regime (i.e. a

rotation where the crops prior to sugar beet are grass 1–4 years then winter wheat, to reflect the nutrient building stage of the organic rotation).e The mean value for each parameter is based on an average of the scenarios weighted by the area they represent (see Table 1).

(see Table 7), close to the 52.5) t ha�1 average for the

UK crop during 2000 to 2002.

The values for agricultural products and field

operations were obtained from Nix (2002) and ABC

(2002). Additional costs for agrochemicals were

obtained from quotes from Brown-Butlin Ltd., ACT

Ltd. and May (2003, personal communication). The

value of crop output was calculated as follows:

� 8
9% of the conventional crop sold for £33.25/t
(£30/t plus £3.25/t average transport allowance).

Early or late delivery allowances (up to £4.90/t) for

deliveries before October and after December were

not included.

� 1
1% of the conventional crop was treated as surplus
and sold for £11/t (£5/t plus £6/t late delivery and

haulage allowance).


� 1
00% of the organic crop sold for £51.5/t
(excluding transport allowance). This assumes that

the transport costs from the farm to the Newark

factory (the only factory to process organic beet) are

paid in full by the processor (ABC, 2002).

The average value of the conventional crop was £3-

0.80/t, very close to the average value of £31/t achieved

by growers in Eastern England in 2001 (Lang, 2002).

2.6. Performance measures and index

The assessments generate a range of data by which

to assess performance including yield, net margin,

Table 8

Normalised indices of performance

Scenario Net margin GWPa Energy

Per ha

I 0.53 0.00 0.09

II 0.40 0.21 0.08

III 0.33 0.18 0.04

IV 0.79 0.05 0.28

V 0.41 0.30 0.11

VI 0.45 0.32 0.15

VII 0.72 0.10 0.15

VIII 0.62 0.24 0.14

IX 0.70 0.39 0.31

X 1.00 0.41 0.43

XI 0.89 0.25 0.52

XII 0.93 0.26 0.53

XIII 0.86 0.35 0.00

Mean 0.71 0.28 0.26

Per t of yield

I 0.42 0.02 0.09

II 0.31 0.22 0.08

III 0.26 0.19 0.04

IV 0.57 0.15 0.28

V 0.36 0.23 0.11

VI 0.40 0.26 0.15

VII 0.57 0.12 0.15

VIII 0.49 0.26 0.14

IX 0.55 0.40 0.31

X 0.66 0.52 0.43

XI 0.59 0.39 0.52

XII 0.61 0.40 0.53

XIII 1.00 0.06 0.00

Mean 0.71 0.28 0.26

a GWP: global warming potential.b SBH: sugar beet habitat. The pesticide ecotoxicity scores in this colum

found around sugar beet fields.

energy input, energy efficiency, GWP, nitrate leach-

ing, denitrification and pesticide ecotoxicity (environ-

ments A–C) and pesticide ecotoxicity (environment

D). These have been calculated and expressed as both

amount per ha and amount per t (see Table 7). Six of

these measures have been selected as key performance

measures. These are net margin, GWP, energy use,

nitrate leaching, denitrification and pesticide ecotoxi-

city (environment D) (Ecotox (SBH) in Table 7).

Each of these six measures were then ‘performance

indexed’ by expressing the value for each scenario as a

fraction (0–1) of the maximum value across all

scenarios. For example, the net margin for scenario V

Nitrate

leaching

Denitrification Pesticide

ecotoxicity (SBH)b

0.50 0.23 0.00

0.50 0.86 0.00

0.54 0.87 0.08

0.51 0.00 0.58

0.50 0.83 0.75

0.50 0.82 0.58

0.87 0.44 0.75

0.86 0.83 0.58

0.00 0.82 0.58

0.66 0.89 0.58

0.00 0.55 0.58

0.00 0.55 0.25

0.57 0.80 1.00

0.52 0.73 0.57

0.50 0.15 0.00

0.50 0.85 0.00

0.54 0.86 0.08

0.55 0.00 0.62

0.44 0.79 0.72

0.44 0.78 0.54

0.87 0.39 0.75

0.86 0.82 0.58

0.00 0.80 0.58

0.72 0.90 0.65

0.17 0.59 0.65

0.17 0.59 0.38

0.37 0.68 1.00

0.52 0.73 0.57

n are based on the ecotoxicological risk assessment using the habitat


would score 0.41 (i.e. 322/784). For the parameters

where the smallest figure is desirable (GWP, energy

efficiency, leaching, denitrification and pesticide

ecotoxicity) the inverse is calculated (i.e. 1 minus

the fraction of the maximum). This process generates a

normalised set of performance scores as shown in

Table 8. Summing and then plotting all the scores

identifies the best performing scenario overall, i.e. the

scenario with the highest index, on a per ha and per t

basis (Fig. 2(a and b), respectively).

A mean value was calculated for all the assessment

results (Table 7) and performance measures (Table 8).

This is an average of the values in the scenarios

weighted by the area of the sugar beet production they

represent (see Table 1). This indicates how national

sugar beet production compares with the best and

worst scenarios in this study.

2.7. Comparison with other crops

The performance index is a mechanism for

comparing sugar beet production scenarios, but it

cannot be used to compare sugar beet with other crops.

Instead, these comparisons have to be made for each of

the performance measures in turn and in most

instances on a per ha basis. The figures for the other

crops were obtained from those calculated for another

study (DEFRA, 2003b) using the same techniques

described here, but using fewer scenarios. Where

appropriate, figures from other studies were also

included in the comparison.

3. Results

3.1. Overview

Table 7 shows the results of the assessments made

on the 13 scenarios. The mean values (and variation

between the scenarios) on a per ha basis were a yield

of 52 t (45–60 t), net margin of £560 (£256–784),

consumption of 21.4 GJ of energy (15.6–26.8 GJ),

emission of 1.4 equiv. t CO2 (1.2–2.0 equiv. t CO2),

3.3 kg N leached (0.9–7.0 kg N), 15.2 kg N lost to

denitrification (5.9–55.7 kg N) and a pesticide

ecotoxicity score of 26 (0–60). Table 8 shows the

normalised performance scores for the key measures,

which have been plotted in Fig. 2(a and b).

Scenario X is the best all round performer on both a

per t and a per ha basis. It is one of the highest yielding,

with the best net margin, smallest GWP and least

denitrification. Its energy efficiency (GJ/t) is also good,

nitrate leaching losses are small and the ecotoxicolo-

gical effects from the pesticides are minimal. Other

scenarios perform better in relation to pesticide

ecotoxicity, energy input efficiency and leaching, but

they perform less well in other areas. Scenario XIII, the

organic scenario, performed well on a per ha basis,

comparable to scenario X. However, on a per t basis it is

penalised by its low yield (34 t ha�1), particularly with

respect to energy use and GWP. Scenario I performed

the least well overall. It is not the worst in all the

categories, only in pesticide ecotoxicity and GWP, but

it does not do well in any category. The net margin is

slightly below average, the energy efficiency is low,

nitrate leaching is average and denitrification is one of

the highest (second only to scenario IV).

3.2. Interpretation of results and comparison with

other crops

3.2.1. Net margin

The net margin calculated for UK sugar beet

production (£560 ha�1, Table 7) compares favourably

with the enterprise margins for growers with near-

average yields in British Sugar’s crop profitability

study in 2002 (£508 ha�1) (Bee and Limb, 2003). Net

margins or enterprise margins are seldom published,

so it is not possible to compare these sugar beet values

to those for other crops. However, in our case it is more

important to note that the scenarios that we have

constructed do reflect closely the economics of real

sugar beet production in the UK. A surprising feature

was the profitability of the organic scenario (XIII),

despite its small yield. The profitability of real organic

crops depends heavily on the weed control costs. The

cultivation and weed control programme in this study

cost approximately £600 ha�1.

3.2.2. Energy and GWP

Energy input for the sugar beet crop, averaged

across all scenarios, was 21.4 or 19.8 GJ/ha if

transport to the factory is excluded. Table 9 compares

energy inputs and GWP for a range of crops grown in

the UK, including sugar beet. Sugar beet and winter

wheat require similar amounts of energy but both


require much less than potato, which have the highest

energy input and the largest GWP. These energy inputs

are similar to those calculated by Hulsbergen and Kalk

Fig. 2. (a) Sustainability profile comparing the performance of the sce

performance of the scenarios on a per t of yield basis.

(2001) for sugar beet, potato, wheat and barley. It can

be concluded that energy use and GWP are significant

issues for sugar beet, as they are for wheat and potato,

narios on a per ha basis, (b) sustainability profile comparing the


Table 10

Estimated losses of N2O from crops

Crop kg N2O/ha year

Potato l.1a–2.9b

Sugar beet 0.5 (1.75 onpeat)c–2b

Winter wheat 0.3a–0.9b

Oilseed rape (Brasseca spp.) 0.7a–0.8b

Spring barley 0.5a–0.8b

Pea (Pisum spp.) 0.2a

a These figures were determined using the same techniques as

used this study, but are based on a limited number of scenarios

(typically four per crop) in comparison to the 13 used for sugar beet.b Calculated from Skiba et al. (1996).c Calculated in this study.

Table 9

Comparison of energy inputs and GWP for producing 1 ha of a range

of crops in the UK

Crop Energy

input (GJ/ha)

Equiv. t CO2/ha

GWP

Potatoa 31.3 3.0

Sugar beet 19.8 1.0–1.8

Winter wheata 20.8 1.7

Oilseed rapea 15.5 1.2

Spring barleya 9.3 0.7

Peaa 6.7 0.7

a The figures for these crops were determined using the same

techniques as for this study but are based on a limited number of

scenarios (typically four per crop) in comparison to the 13 used for

sugar beet.

and any opportunities to save energy should be

considered, e.g. using minimal tillage. These three

crops could be seriously compromised if energy prices

rise significantly.

3.2.3. Nitrate leaching

The mean loss via leaching of 3.3 kg N/ha (and the

7 kg N/ha lost in the worst scenario) is small (Table 7).

Other studies for a range of crops estimate that nitrate

leaching losses from farms can range from 19 to

65 kg N/ha or more (Hansen et al., 2000; Pacini et al.,

2003). Thus nitrate leaching is not a significant issue

for sugar beet crops.

3.2.4. Denitrification

Modelled losses of N via denitrification were much

larger than the losses to leaching, up to 55.7 kg N/ha

for scenario IV and a mean of 15.2 kg N/ha (Table 7).

Much more N was lost via denitrification in those

scenarios where organic manures were a source of

nutrients. This loss is likely to occur whenever the

manures are applied, irrespective of the arable crop on

which they are used. The actual amounts lost as N2O

are a fraction of the denitrified N. In this study it is

assumed that for a peat soil that 7% of the denitrified N

was lost as N2O, and that for other soils the fraction

was 3.5% (de Vries et al., 2003). For the two peat

scenarios (6% of the area) this gives a weighted mean

of 1.75 kg N2O/ha, for the remainder of the scenarios

the weighted mean is 0.5 kg N2O/ha. These figures

demonstrate that sugar beet production systems

generate amounts of N2O similar to other arable

crops except potato (Table 10), despite being the

recipient of more organic manure. This is also

reflected in the findings of Skiba et al. (1996). The

contribution that N2O makes towards the GWP for

each scenario ranges from 5 to 37% of the total (Fig. 3)

indicating that the levels of denitrification, though

small, are significant. Thus the loss of N and N2O via

denitrification is an important issue for sugar beet

production, as it is for other crops.

3.2.5. Pesticide ecotoxicity

The pesticide ecotoxicity scores (Table 7) are low,

especially for the scenarios with the environment

based on the survey of sugar beet field margins in the

UK (D in Table 6). From the results (Table 7) it can be

seen that in scenarios IV–XII the risk from pesticides

in the sugar beet habitat is less than in the standard

habitat, although this differential is not greatly

significant. The overall drop in risk alerts is mainly

due to the absence of surface water, so there are no

aquatic ecosystems to disturb. In addition, the

inclusion of a small amount of woodland increases

the availability of alternative, uncontaminated feeding

areas for some species and this also decreases the risk

to birds and mammals. For scenarios I–III there is a

smaller differential between the standard and surveyed

environments. The cause of this is the compound

effect of different chemicals with variable loadings of

pesticides potentially harmful to different species. For

example, Pyramin DF (containing chloridazon that is

harmful to fish and other aquatic life) is applied in

scenarios VII–XII. Consequently the absence of water

in these scenarios decreases their ecotoxicity score for

environment D. It is also applied in scenario III but at a


Fig. 3. Contribution of denitrification to overall global warming potential of each scenario.

lower application rate so only marginally reduces the

ecotoxicity score in this scenario. The differences

between scenarios are complicated to interpret, but the

important aspect is that all the scenarios have a

relatively low pesticide ecotoxicity.

The average pesticide ecotoxicity score, for all the

environments compared very favourably to other

crops (Table 11). Pesticide ecotoxicity is not a

significant issue for sugar beet crops, and there could

be adverse consequences should beet be replaced by

other crops in mainly cereal rotations in the future.

Table 11

Comparison of pesticide ecotoxicity scores for a range of crops

Crop Average pesticide

ecotoxicity score

Potatoa 230

Sugar beet (D in Table 6) 26

Sugar beet (A–C in Table 6) 67

Winter wheata 35

Oilseed rapea 85

Spring barleya 30

Peaa 75

a The figures for these crops were determined using the same

techniques as this study but are based on a limited number of

scenarios (typically four per crop) in comparison to the 13 used

for sugar beet.

4. Discussion

A fundamental aim of sustainable agriculture is to

balance economic, environmental and social objec-

tives. A key aspect of achieving this balance is to

understand the trade-offs that exist, not only between

environment and economy but also between different

environmental parameters. For example, the applica-

tion of manure can increase net margins and reduce

energy inputs slightly but at the cost of significantly

increasing denitrification and GWP. Consequently an

optimal balance is not easy to achieve, even when the

manures are considered as a resource and not a

disposal problem. However, scenario X (representing

18% of sugar beet production) clearly demonstrates

that good economic and environmental performance

can be achieved simultaneously.

The excellent performance of scenario X is mostly

a consequence of the large water holding capacity of

the silt soil. This means the plants seldom suffer water

stress, even without irrigation, and large yields are

produced as a consequence. Scenarios with less

fertile soils usually produce smaller yields and have

a poorer economic performance and energy efficiency.

Growers using these soils tend to use organic manure

in an effort to maintain or improve fertility, and they

suffer the N2O losses as a consequence.


The balance of economic, environmental and social

objectives is dynamic in sustainable agriculture, the

‘goal posts’ are always moving. This means there is

always scope to improve the environmental perfor-

mance of a production system, essentially optimising

the production process to meet the current objectives.

This is discussed below with respect to the scenarios

examined in this study and to sugar beet production as

whole.

4.1. Energy and GWP

There is a range in energy efficiency amongst the

scenarios from a very efficient 0.26 GJ/t in scenario

XII to 0.56 GJ/t in scenario XIII. However, these two

scenarios represent only a small fraction of the total

area of sugar beet. If we limit the scenarios to those

that are more representative (10% or more of the sugar

beet area) the range is limited to 0.32–0.49 GJ/t. This

mostly reflects the variation in yield (45–60 t) but also

the amount of energy input.

One of the largest single energy inputs is crop

nutrition, particularly the manufacture of inorganic

fertilisers. Crop nutrition accounts for 18–50% of the

total energy input across the scenarios, and accounts

for 12–43% of GWP. Fertiliser manufacture is

becoming more energy efficient (Jenssen and Kong-

shaug, 2003) but this process is beyond the control of

farmer. However, 28–58% of energy inputs are

directly within the farmer’s control and account for

30–45% of GWP. If denitrification is also included

within the control of the farm then the figures rise to

38–69% of GWP. Clearly any scope to improve the

efficiency of farm and field activities could be

beneficial for energy efficiency and GWP. This is

illustrated in the minimal tillage scenario (VI), which

had the smallest energy input. Although scenario VI

only represents 2% of the sugar beet area, its overall

performance is roughly comparable with scenarios V,

VII, VIII and IX, which represent 64% of the area.

Irrigation is a large energy consumer (10% of total

energy inputs for scenarios I, III and IV). However, it

is not widely used for beet in the UK, representing less

than 10% of the area.

This study does not include calculations for each

scenario of the C balance (the amount of CO2 released

while growing the crop compared to the amount fixed

in the crop by photosynthesis). However, if we assume

that the amount of C fixed per t of beet is the same for

all the scenarios then we can use the GWP per t of

yield (see Table 7) as an indicator of the C balance.

This does not show the net C balance, but does

highlight the relative GWP burden of each scenario

(i.e. the equiv. t of CO2 emitted per t of yield).

Scenario X performs well with the largest yield and

lowest GWP. The manure applied in scenarios I, IV

and VII, consequent denitrification and the impact on

GWP can also be seen (Table 7), as these scenarios

have the highest GWP burden per t of yield. The low

yield (34 t/ha) of the organic crop, scenario XIII,

results in a high GWP burden per t.

4.2. Nitrate leaching and denitrification

The study showed that the risk of nitrate leaching

from sugar beet crops is minimal in comparison to

other crops and farming systems, and there is little

scope to reduce it further.

Denitrification was estimated to be a larger route

for the loss of nitrogen than leaching. In most

scenarios losses were still small (5.9–25 kg N/ha).

However, when organic manures were applied, losses

were significantly increased to the extent that the

fraction lost as nitrous oxide was making a significant

contribution to the total GWP of the scenario (up 37%

of GWP). Of the organic manure scenarios, scenario

VII is the most significant as it represents 12% of the

sugar beet area: it lost 31 kg N/ha. Scenarios I and IV,

representing 7% of the sugar beet area lost 42.7 and

55.7 kg N/ha, respectively. Additionally, about one-

third of beet crops on clay loam soils also received

manure and this is likely to result in a similar amount

of denitrification. Because the land destined to grow

sugar beet is fallow for a long period after cereal

harvest, it represents what appears to be an ideal

opportunity to apply the manure. This manure is a

cheap source of nutrients and can help improve soil

organic matter content and structure and reduce

erosion in vulnerable areas, but clearly there are some

negative environmental side effects, in terms of

denitrification and GWP. There may be scope to

reduce N losses through improved application tech-

niques or through application to other crops in the

rotation, but these will have to be assessed for their

cost and energy consumption to evaluate if any overall

improvements can be made.


4.3. Pesticide ecotoxicity

There are some aspects of environmental perfor-

mance that could be improved with regard to pesticide

use, although the benefits are likely to be marginal. Of

the many active substances applied to the scenarios

(Table 4), aldicarb is a key cause of concern in

scenarios I–VI. However, an on-going EU review,

which was established by Council Directive 91/414/

EEC and subsequent regulations, failed to grant Annex

1 status to aldicarb (except for some essential uses).

Consequently the approval of aldicarb for use on sugar

beet in the UK was revoked on September 18, 2003.

However, aldicarb is likely to be replaced by other

granular carbamate pesticides whose environmental

profile is similar but less toxic. Imadacloprid raises

orange alerts (but no red alerts) across scenarios VII–

XII (81% of the area). To avoid its use would bring a

broad marginal benefit, but this benefit would have to

be traded against a probable loss in net margin and

greatly increased use of insecticide sprays: these sprays

could represent a significant risk. Metamitron raises a

concern in scenarios I, II, III, XI and XII, which

represent 12% of the beet area. A similar situation

exists for chloridazon, which raises orange alerts in

scenarios VII–X (75% of the area). A reduction in the

use of metamitron would tend to increase the use of

chloridazon. The use of paraquat and diquat (PDQ) to

control large weeds in the seed bed raised red and

orange alerts for mammals and birds, but in a random

survey of farms in 2002, paraquat and diquat was used

on less than 1% of the crop (Garthwaite et al., 2003).

Additionally, PDQ is used in March and April when

there is almost no foliage in the field and this reduces

the risk of poisoning hares (which are particularly

susceptible to paraquat). The use of PDQ could be

replaced by use of glyphosate, and this would improve

the pesticide ecotoxicity risk.

4.4. Economics

This study shows that, even with near-average

yields, there can be a wide range in economic returns

from growing sugar beet, with net margins ranging

from £256 up to £784/ha. Clearly to be sustainable

good environmental performance must be achieved

within the constraints of economic viability. If the crop

does not perform well economically then it is unlikely

to be grown and the land may be put to an alternative

use that could have a worse environmental perfor-

mance. If, as a consequence of change to the EU Sugar

Regime (Council Regulation (EC) No 1260/2001),

sugar beet becomes less profitable, it is likely to be

replaced in the UK by either another break crop or by

more winter wheat. The replacement crop will

inevitably have its own set of environmental impacts,

which may or may not be better than those from sugar

beet (as discussed in Section 3.2). Additionally, if beet

is replaced by wheat, the proportion of first wheats in

arable rotations could fall and consequently increase

the N fertiliser and pesticide usage of that crop,

worsening its ecotoxicity profile and energy efficiency.

4.5. Limitations of the study

This study has compared different sugar beet

production systems and, to a limited extent, different

crops. However, it has not addressed the issue of what

level of environmental burden is sustainable. Except in

extreme catastrophic circumstances, e.g. massive soil

loss, the sustainability thresholds are not known

(Tzilivakis et al., 1999). Consequently in this study we

were limited to analysing impacts on the basis of what

is desirable, i.e. we know that it is desirable to reduce

greenhouse gas emissions, losses of nutrients and

energy inputs and undesirable to expose benign and

beneficial species to toxic substances. Methods to

determine the sustainability of human activities are

needed and are still the subject of research (Pacini

et al., 2003; Haberl et al., 2004; Pope et al., 2004).

The study used the most up-to-date techniques to

assess impacts of energy, pesticide and nutrient use by

the crop on the environment. However, some important

topics were excluded, i.e. impacts on biodiversity and

on soil quality. Topics like these still lack objective

assessment and/or modelling frameworks (Perry et al.,

2004; Tzilivakis et al., 2005), hence their exclusion

from this study. Loss of nutrients via surface run-off was

another aspect not included within this study due to the

lack of a suitable modelling framework. Some

modelling frameworks (Lewis and McGechan, 1999;

Lewis et al., 2003b) are under development and these

may offer practical assessment tools to address this

issue in the future. Loss of N as a consequence of soil

erosion from sugar beet fields is anticipated to be very

small. About 15% of beet fields, mostly on sandy soils,


suffer from a small amount of water erosion (median

rate about 1 m3/ha), but movement out of the field is

very rare (Evans, 2002).

The assessment of genetically modified (GM)

herbicide tolerant sugar beet would have been a useful

addition to this study to compare its relative

environmental performance with the conventional

crop. However, a GM scenario was not included as the

farm scale evaluations (Firbank et al., 2003) were still

in progress and important environmental impact data

were not available. The possible impact of GM beet on

crop economics was described by May (2003) and its

impact on weed flora and indirect effects on

arthropods have been explored (Squire et al., 2003).

Since this study, Bennett et al. (2004) have published

work in which they used a life cycle assessment (LCA)

approach to compare GM herbicide tolerant sugar beet

with conventionally grown sugar beet. They suggest

that growing the GM crop would be less harmful to the

environment and human health than growing the

conventional crop, largely due to smaller emissions

from herbicide manufacture and field operations. The

techniques Bennett et al. (2004) used for calculating

energy and GWP were similar to those presented in

this paper and consequently the results are compar-

able. However, the techniques used for ecotoxicity and

nutrient loss were completely different and so are not

comparable. For example, their ecotoxicity calcula-

tion is a generic method within the PEMS LCA

software (PIRA, 1998) based on emissions of the

active substances and their mammalian toxicity and

expressed as kg of chromium (Cr) equivalent.

Whereas in this study, p-EMA is specifically designed

for agriculture and makes use of site-specific

information (such as habitats and soil type) to

calculate exposure and then ecotoxicity to a range

of taxa (Hart et al., 2003).

5. Conclusions

The sugar beet crop is grown in the UK using a

variety of techniques in different locations. Economic

returns from different systems of production can vary,

as can the impacts on the environment. This study has

shown that the detrimental impacts on the environment

from sugar beet production are limited, especially when

the poorest overall performances (see Figs. 2(a and b))

(scenarios I–IV) can be discounted as they represent

only 10% of the area of sugar beet production. However,

most importantly, the study has illustrated that good

economic performance can go hand in hand with good

environmental performance. In the case of sugar beet,

under the assumptions made within the study, a large

yield could be obtained whilst minimising environ-

mental impact. This is clearly demonstrated in scenario

X which represents 18% of the sugar beet area and

which achieved the best economic and environmental

performance. This was closely followed by scenarios

IX, VII and VIII (representing 57%) whose overall

performance was similar to the organic scenario (XIII)

on a per t basis (see Fig. 2(b)). On a per ha basis the

organic scenario (XIII) was equal to scenario X (see

Fig. 2(a)). Although the organic scenario had slightly

lower economic performance than scenario X, its

overall environmental burden on a per ha basis was the

smallest of all the scenarios. However, it is salutary to

note that organic beet production in the UK has now

ceased because it was not economically viable for the

sugar processor.

There is scope to undertake a more holistic study.

The boundaries of the study could be expanded to

include a life cycle assessment (LCA) of sugar produc-

tion. It would then be possible to compare the impacts of

producing sugar from beet and cane. Similarly, sugar

beet could be compared to other crops, such as wheat

and oilseed rape, for the production of biofuels (ethanol

from sugar beet and wheat and biodiesel from oilseed

rape). Both these issues are important for sustainable

agriculture and for the sustainability of society as

whole. Consequently, studies such as this one provide

an important foundation for assessing the sustainability

of an important commodity and renewable fuel.

Acknowledgement

This work was funded by the British Beet Research

Organisation (BBRO).

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