Implications of oil depletion for biodiversity Rowan Eisner

B Sc., Grad Cert Cog Sci, MSWAP

A thesis submitted for the degree of Doctor of Philosophy at

The University of Queensland in 2016

School of Geography, Planning and Environmental Management

Abstract

Since the 1950s the growth of the human population and per capita consumption has accelerated,

raising concerns about the limits to growth due to resource constraints. The oil supply has been of

particular concern, with production predicted to peak in the first decades of the 21st century. This is

a problem because modern society is highly dependent on the supply of petrochemicals for its

energy, including modern industrialised agriculture. One of the key inputs to agriculture that is

dependent on petrochemicals is mineral nitrogen (N), which has increased agricultural yields since

1960s. Constraints to the supply of petrochemicals increase the risk that agriculture will become

less productive, requiring more land to maintain food production and threatening biodiversity.

This thesis investigates the relationship between the oil supply, food security, global deforestation

and biodiversity loss, assessing the worst- and best-case scenarios for agriculture’s footprint without

petrochemicals. It also examines the spatial footprint of alternatives to mineral N globally.

I used a constraint to the oil supply during the 2007-8 global financial crisis to investigate the

dynamics between the oil supply, agriculture’s spatial footprint and the impact on biodiversity. I

found that the rate of forest loss increased 29% during this period, and that as a result an additional

area of forest the size of Italy was lost. This loss tended to occur in areas of remnant forest with

higher biodiversity. I investigated the likely drivers of forest conversion and found that agricultural

extensification and the production of renewable energy were probable contributors, but land

grabbing by foreign countries to secure food supplies was not. I also found examples of successful

policy implementation in Amazonian Brazil and in Australia which had resisted these changes.

To investigate the potential threat from agricultural expansion associated with constraints to

petrochemical supply, I looked at worst- and best-case scenarios. For the worst-case, I estimated the

area that would be required for crop production without petrochemical-based nitrogen fertiliser

using N-use efficiency data and current yields. I then spatially modelled cropland expansion

globally and the impact this would have on biodiversity and food security. Without mineral N there

was insufficient cropland to meet global food needs with many regions experiencing food

insecurity. Cropland would expand onto the remaining fertile land leaving largely poor quality

habitat for biodiversity.

In the best-case scenario, I identified the N source with the smallest footprint by comparing the

most land-efficient renewable energy sources to power the Haber-Bosch process and organic

sources of N. Solar power was significantly more land efficient than the alternatives. The worst-

case option of using no mineral N fertiliser would require about 2000 times the land area and would

have about 81,000 times the impact on biodiversity. Although solar energy is the most land-efficient

way of powering renewable N, there are constraints on its use. I prioritised alternative N sources

globally taking into account impacts and resources available. Some regions would have access to a

range of renewable sources of N without major impacts, but Europe has limited options.

In conclusion, the global energy supply has the potential to influence the land area required for

agriculture through land-fertiliser substitution, and this process impacts on biodiversity. Currently,

N-use decisions are made by landholders for largely economic reasons. Conservation science needs

to take an interest in the N supply in order to mitigate these impacts, particularly in regions of high

biodiversity.

Declaration by author

This thesis is composed of my original work, and contains no material previously published or

written by another person except where due reference has been made in the text. I have clearly

stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical

assistance, survey design, data analysis, significant technical procedures, professional editorial

advice, and any other original research work used or reported in my thesis. The content of my thesis

is the result of work I have carried out since the commencement of my research higher degree

candidature and does not include a substantial part of work that has been submitted to qualify for

the award of any other degree or diploma in any university or other tertiary institution. I have

clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library and,

subject to the policy and procedures of The University of Queensland, the thesis be made available

for research and study in accordance with the Copyright Act 1968 unless a period of embargo has

been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the copyright

holder(s) of that material. Where appropriate I have obtained copyright permission from the

copyright holder to reproduce material in this thesis.

Publications during candidature

Peer-reviewed conference proceedings

Eisner, R, Seabrook, L & McAlpine, CA 2016a, 'Minimising the land area used by agriculture

without petrochemical nitrogen ', paper presented to Proceedings of the International Nitrogen

Initiative 2016, in press, <http://www.ini2016.com/1234>.

Peer-reviewed paper

Eisner, R, Seabrook, LM & McAlpine, CA 2016b, 'Are changes in global oil production influencing

the rate of deforestation and biodiversity loss?', Biological Conservation, vol. 196, pp. 147-55,

DOI 10.1016/j.biocon.2016.02.017

Conference presentations

Eisner, R 2016, Post carbon alternatives to mineral N fertiliser which minimise impact on

biodiversity, Society for Conservation Biology 4th Oceania Congress, 6 July 2016, Brisbane

Eisner, R 2016, Minimising agriculture's post-carbon biodiversity footprint, THECA Forum

Barriers to Biodiversity Conservation, 15 October 2016, Brisbane

Eisner R, Seabrook L, McAlpine C, 2016, Minimising agriculture’s post-carbon footprint, Global

Land Project 3rd Open Science Meeting, Beijing, China, 24-27 October 2016

Eisner, R, Seabrook L, McAlpine C, 2015, Do limits to the global oil supply increase the rate of

deforestation and biodiversity loss? International Congress for Conservation Biology,

Montpellier, August 2015

Seminar series presentations

Eisner, R 2016, How does change in the global oil supply effect biodiversity?, Landscape ecology

seminar series, 1/2/2016, available https://www.youtube.com/watch?v=c75BVEN7KFY

Eisner, R, 2013, Oil depletion and biodiversity: is feeding humanity coming at the expense of

nature?, Global Change Institute Food Security Seminar Series, November 2013.

Conference posters

Eisner, R, Seabrook L, McAlpine C, 2014, Limits to oil production and the increased threat to

biodiversity, Fenner conference on the environment, Sydney, October 2014

Eisner, R, Seabrook L, McAlpine C, 2014, Agriculture’s threat to biodiversity with oil depletion,

Pathways for the Sustainable Intensification of Agriculture Workshop, Nov 2014

Eisner, R 2015, 12th International Permaculture Convergence and Conference, 8-9 September 2015,

London

Eisner, R, Seabrook L, McAlpine C, 2015, Agricultural dependence on petrochemicals and the

threat to biodiversity of post peak agricultural extensification, TropAg15, November 2015, Brisbane

Publications included in this thesis

Chapter 2: Eisner, R, Seabrook, LM & McAlpine, CA 2016b, 'Are changes in global oil production

influencing the rate of deforestation and biodiversity loss?', Biological Conservation, vol. 196, pp.

147-55, DOI 10.1016/j.biocon.2016.02.017

Contributor Statement of contribution

Rowan Eisner (candidate) Designed study 100%

Analysis 100%

Writing 100%

Editing 40%

Proof-reading 30%

Leonie Seabrook Review 50%

Editing 30%

Proof-reading 30%

Clive McAlpine Review 50%

Editing 30%

Proof-reading 40%

Contributions by others to the thesis

Four jointly authored papers form part of this thesis, chapter 2 detailed above, chapters 3 and 4

which have been submitted for publication, and chapter 5 which will be submitted. The authorship

of subsequent chapters is as given in the table above, except for chapter 4 and 5 where Leonie

Seabrook contributed more to reviewing and editing the manuscripts than did Clive McAlpine.

Statement of parts of the thesis submitted to qualify for the award of another degree

None

Acknowledgements

The main people I owe a big thank you to are my supervisors, Clive McAlpine and Leonie

Seabrook. I am very grateful to Clive for accepting me as a student and providing support for the

last 3½ years. Both Clive and Leonie understand the PhD process well and have shepherded me

through it. Clive has very good knowledge of the publishing process, which I was unfamiliar with,

and has patiently guided me through the numerous revisions needed to reach the standard which is

expected. He also steered me through journal selection and choosing examiners. He encouraged me

to take on Leonie as an advisor, and he was right. So many people have told me how lucky I have

been to have Leonie as a supervisor. She has always been there, offering advice, support and editing

my writing into submission, even between contracts. And thanks for sharing your room in Beijing. I

hope we can continue to collaborate and be friends in the future.

Thank you to my family for being so encouraging. To my Mum and brother James for having faith

in me and taking an interest in what I’ve been doing along the way, often over VoIP from the other

side of the world. And to my partner, Willy, for being there every day of the journey, always caring

and confident in me, and making sure the house kept running, even though he had his own thesis to

do.

Thank you to my fellow students for all the conversations along the way, creating a lively

intellectual environment. And particular thanks to the inhabitants of 327D for the good-natured

humour that’s made it enjoyable coming in to work every day. And I’d especially like to thank

Alvaro Salazar for showing how to use ArcGIS when I knew nothing and for forgiving my foibles.

In terms of getting the work done, thank you to the researchers who shared their data and made this

research possible. In particular Sanneke van Asselen and Kalifi Ferretti-Gallon for your data on

drivers of land-use change, Keith Bradby, Amanda Keesing, Justin Jonson, Carl Gosper for your

information on fire management in the Great Western Woodlands, Silvia Forno, Christof Althoff,

Martin Ostermeier; Markus Giger, Tin Geber and Devlin Kuyek for your data and information on

land grabs, to Ralph Trancoso de Silva for helpful discussions on Brazilian deforestation patterns,

Thierry Brunelle and Patrice Dumas for your input on my visit to your lab. Thanks also to Holger

Kreft, Clinton Jenkins and Gerold Kier for your biodiversity indices and advice, and to Achim

Dobermann for his nitrogen use efficiency data. You have to rely on a lot of other people when you

don’t collect your own data.

I’d like to thank the reviewers of my papers for helping getting them up to publication standard –

you know who you are!

I owe thanks to The University of Queensland for their scholarship, which enabled me to focus for

three and a half years on a project which is of particular importance to me – what a luxury! And to

the School of Geography, Planning and Environmental Management for the research grant which

enabled me to attempt to spread the word at international conferences.

Keywords

global, land-cover change, underlying drivers, land–fertiliser substitution, land-grab, food security,

mineral nitrogen, biofixation, renewable nitrogen, spatial prioritisation

Australian and New Zealand Standard Research Classifications (ANZSRC)

ANZSRC code: 069902 Global Change Biology 60%

ANZSRC code: 070108 Sustainable Agricultural Development 25%

ANZSRC code: 090608 Renewable Power and Energy Systems Engineering (excl. Solar Cells)

15%

Fields of Research (FoR) Classification

FoR code: 0502 Environmental Science and Management 60%

FoR code: 0701 Agriculture, Land and Farm Management 20%

FoR code: 0909 Geomatic Engineering 20%

Contents

Chapter 1: Introduction .............................................................................................................. 1

1.1 Problem statement ............................................................................................................ 2

1.2 Aim and objectives........................................................................................................... 3

1.3 Literature Review ............................................................................................................. 3

1.3.1 The limits to growth .............................................................................................................. 4

1.32 Petroleum in agriculture ......................................................................................................... 6

1.2.3 Agriculture and biodiversity ................................................................................................ 10

1.3.4 Relationship to land change science ................................................................................... 12

1.3.5 Global interventions which may influence land-fertiliser dynamics ................................... 12

1.3.6 Knowledge gaps ................................................................................................................... 13

2.0 Thesis structure .............................................................................................................. 13

Chapter 2: Are changes in global oil production influencing the rate of deforestation and

biodiversity loss?...................................................................................................................... 16

Abstract: ............................................................................................................................... 16

2.0 Introduction .................................................................................................................... 16

2.1 Data and methods ........................................................................................................... 18

2.1.1 Data sources and methods .................................................................................................. 20

2.2.2 Analysis of drivers of change ............................................................................................... 23

2.3 Results and discussion ................................................................................................... 23

2.3.1 Patterns and rates of global forest loss and threat to biodiversity ..................................... 23

2.3.2 Regions of decreasing threat to biodiversity ....................................................................... 26

2.3.3 Underlying drivers of change............................................................................................... 30

2.3.4 The connection between oil, the economy and land-use ................................................... 30

3.0 Conclusion ..................................................................................................................... 32

Chapter 3: Global land-use requirements and impacts of crop production without petrochemical

fertiliser .................................................................................................................................... 34

Abstract ................................................................................................................................ 34

3.1 Introduction .................................................................................................................... 34

3.2 Data and methods ........................................................................................................... 35

3.2.1 Conceptual model ................................................................................................................ 35

3.2.2 Data sources ........................................................................................................................ 36

3.2.3 Calculating land requirement without mineral N fertiliser ................................................. 36

3.2.4 N price sensitivity effect on cropland demand.................................................................... 38

3.2.5 Biodiversity impact of cropland expansion ......................................................................... 38

3.2.6 Food security ....................................................................................................................... 38

3.3 Results ............................................................................................................................ 39

3.3.1 Land requirement without mineral N fertiliser ................................................................... 39

3.3.2 N price sensitivity effect on cropland demand.................................................................... 39

3.3.3 Biodiversity impact of cropland expansion ......................................................................... 40

3.3.4 Food security ....................................................................................................................... 40

3.4 Discussion ...................................................................................................................... 44

3.4.2 N price-sensitivity effect on cropland demand ................................................................... 46

3.4.3 Biodiversity impact of cropland expansion ......................................................................... 46

3.4.4 Food security ....................................................................................................................... 48

3.4.5 How realistic is the ‘no N’ scenario? ................................................................................... 48

3.5 Conclusion ..................................................................................................................... 49

Chapter 4: Minimising the footprint of post-carbon agriculture .............................................. 50

Abstract ................................................................................................................................ 50

4.1 Introduction .................................................................................................................... 50

4.2 Methods .......................................................................................................................... 51

4.2.1 N sources for agriculture ..................................................................................................... 51

4.2.2 Habitat – cropland – N production land-use dynamics ....................................................... 52

4.2.3 Data sources ........................................................................................................................ 52

4.2.4 Footprint calculation ........................................................................................................... 53

4.2.5 Mapping minimum footprint ............................................................................................... 53

4.2.6 Biodiversity impact .............................................................................................................. 53

4.3 Results & discussion ...................................................................................................... 53

4.3.1 Footprints ............................................................................................................................ 54

4.3.2 Biodiversity impact .............................................................................................................. 56

4.3.3 Solar power site distribution ............................................................................................... 57

4.4 Conclusion ..................................................................................................................... 59

Chapter 5: Global prioritisation of renewable nitrogen for biodiversity conservation and food

security ..................................................................................................................................... 60

Abstract ................................................................................................................................ 60

5.1 Introduction .................................................................................................................... 60

5.2 Methods and data ........................................................................................................... 61

5.2.1 Data sources ........................................................................................................................ 62

5.2.2 Decision process for selecting alternative sources of N ...................................................... 63

5.3 Results and discussion ................................................................................................... 64

53.1 Solar ...................................................................................................................................... 64

5.3.2 Wind .................................................................................................................................... 66

5.3.3 Organic sources of N ............................................................................................................ 66

5.3.4 Cropland and high biodiversity regions ............................................................................... 67

5.3.5 Regions with no suitable options ........................................................................................ 68

5.3.6 Prioritisation of N sources ................................................................................................... 68

5.3.7 Regions of interest ............................................................................................................... 70

5.3.8 Significance and limitations ................................................................................................. 71

5.4 Conclusion ..................................................................................................................... 72

Chapter 6: Conclusion .............................................................................................................. 73

6.1 Introduction .................................................................................................................... 73

6.2 Major findings ................................................................................................................ 73

6.3 Contributions to conservation science ........................................................................... 75

6.3.1 The oil-fertiliser-biodiversity connection ............................................................................ 75

6.3.2 Agricultural expansion targets biodiverse land ................................................................... 76

6.4 Policy implications ......................................................................................................... 77

6.4.1 Nitrogen supply to agriculture is a conservation issue ....................................................... 77

6.4.2 The risk to conservation of pollution abatement measures ............................................... 78

6.4.3 Incorporation of global scale factors in local decisions ....................................................... 78

6.4.4 Land grabbing as a conservation opportunity ..................................................................... 79

6.5 Limitations of this study ................................................................................................ 79

6.6 Recommendations for future research ........................................................................... 79

6.6.1 Land-fertiliser substitution .................................................................................................. 80

6.6.2 Systems dynamics of the oil-agriculture-biodiversity system ............................................. 80

6.6.3 Land sparing as a conservation strategy ............................................................................. 80

6.6.4 Agricultural Intensification as a conservation strategy ....................................................... 80

6.6.5 Land-grabbing as a future conservation threat ................................................................... 80

6.7 Conclusion ..................................................................................................................... 81

References ................................................................................................................................. 78

Appendix 1 Drivers of land cover change in deforestation acceleration hotspots .................... 94

Appendix 2 Input data layers for selection N production site selection ................................... 98

Figures

Figure 1 The pathways by which oil supply influences habitat loss.................................................. 1

Figure 2 The process by which food production displaces land for terrestrial native ecosystems. ... 3

Figure 3 Energy return on energy invested for US oil discoveries. ................................................... 5

Figure 4 Two ways of comparing Energy Return on Investment for alternative fuels. ..................... 6

Figure 5 World food price correlates strongly with world oil prices ................................................. 7

Figure 6 Factors which influence the price of food. .......................................................................... 7

Figure 7 Riots appear to be triggered by food price increasing above a threshold ............................ 8

Figure 8 Relationship among land, food and population ................................................................... 9

Figure 9 Drivers of forest decline .................................................................................................... 11

Figure 10 Conceptual model of the links between changes in oil production and demand for land . 19

Figure 11 Crude oil production Fertiliser, food and oil prices .......................................................... 19

Figure 12 Forest cover loss for the period 2000-2012, biodiversity index ........................................ 22

Figure 13 Change in deforestation rate between 2000-2012, impact on biodiversity, statistically

significant hotspots and coldspots of the change in biodiversity threat ............................ 25

Figure 14 Change in the rate of forest loss ........................................................................................ 26

Figure 15 Countries where land acquisition is larger than the area of arable land ............................ 28

Figure 16 Change in fertiliser consumption by income groups ......................................................... 29

Figure 17 Drivers of land conversion ................................................................................................ 30

Figure 18 Conceptual model of the sensitivity of the area occupied by cropland to N-use .............. 35

Figure 19 Nitrogen use efficiency for cereal production for available countries .............................. 37

Figure 20 The approach used for cropland expansion modelling. ..................................................... 38

Figure 21 Projected increase in global cropland area to meet food production requirements without

mineral N ........................................................................................................................... 42

Figure 22 The impact of minimal additional land requirements on biodiversity .............................. 43

Figure 23 Availability of unused arable land to meet the need for future cropland expansion ......... 44

Figure 24 Land supply, food imports and N price ............................................................................. 45

Figure 25 Major sources of N for agriculture .................................................................................... 51

Figure 26 How habitat is lost when cropland expands ...................................................................... 52

Figure 27 The minimum and maximum footprint of replacing mineral N with renewable sources . 55

Figure 28 Biodiversity impact of cropland expansion without mineral N compared to solar power

required to power industrial N production and total energy production ........................... 58

Figure 29 Process for selecting sources of N production most suitable at each location. ................. 62

Figure 30 Decision matrix and decision trees for siting N sources. .................................................. 64

Figure 31 Sites most suitable for solar power, and the location of existing solar power stations. ... 66

Figure 32 Sites most suitable for wind power. .................................................................................. 66

Figure 33 Locations suitable for organic nitrogen sources. ............................................................... 67

Figure 34 Regions where it is preferable to import N, or where N production is unsuitable ............ 68

Figure 35 Sources of N for cropping prioritised for biodiversity and cropland conservation. .......... 69

Figure 36 Regions with a wide range of options for sourcing N,

and Europe which has a paucity of options.. ..................................................................... 71

Tables

Table 1 Potential contribution to replacing fossil fuels. ...................................................................... 6

Table 2 Unexpected results. .............................................................................................................. 44

Table 3 Footprint of renewal sources of N, expressed as N yields ................................................... 55

Table 4 Data sources for renewable N suitability .............................................................................. 62

Table 5 Findings from this thesis which make a contribution to conservation science ..................... 76

Table 6 Drivers of land conversion .................................................................................................... 97

Abbreviations used in the thesis

N Nitrogen

MW Megawatt

EROI Energy return on investment

GFC Global financial crisis

DNI Direct normal insolation

REDD+ reducing emissions from deforestation and forest degradation in developing countries

NH3 ammonia

FAO Food and agriculture organization

WWF World Wildlife Fund

OECD Organisation for Economic Co-operation and Development

GDP Gross domestic product

NASA National Aeronautics and Space Administration (USA)

M Ha Million hectares

PV Photovoltaic

Ed Price elasticity of demand

1

Chapter 1: Introduction

Biodiversity is under threat to the extent that humans and domestic species already constitute in

excess of 97% of terrestrial vertebrate biomass (Smil 2011). This threat has recently been

exacerbated by an increase in the demand for land to compensate for oil depletion (Scheidel &

Sorman 2012). Most biodiversity loss comes about through the spread of agriculture and associated

land clearing for crop production and grazing (Geist & Lambin 2002). Over recent decades, the

expansion of agricultural land has not kept pace with human population growth because of

increased productivity due mainly to artificial fertilisers (Ramankutty et al. 2006). This process of

agricultural intensification is under threat of reversing as oil production passes its peak and its

products, including nitrogen fertiliser, become increasingly inaccessible (Arizpe et al. 2011).

Growth in human productivity is reaching several limiting factors, through constraints to resources

such as land, water, materials and energy. (Heinberg & Fridley 2010; Ragnarsdottir 2008; Scholz et

al. 2013), as predicted in the renowned 1970s modelling by the Club of Rome, The Limits to

Growth (Meadows et al. 2004). The model predicted that these limits would be reached in the first

two decades of the 21st century. Their prediction has been confirmed by comparing standard runs of

their model with 40 years of real world data (Turner 2012). This analysis suggests that growth is

indeed being limited by resource constraints to the economy.

One such limit which has been predicted, and which may being reached, is the peak in global oil

production (Hall & Day 2009; Hubbert 1956). Petrochemicals are linked to land clearing though

two main pathways: fuel and food (Figure 1). Restrictions in fuel availability result in the need for

alternatives, such as unconventional sources of fossil fuels, biomass, biofuels and renewables. This

relationship is well described (Giampietro & Mayumi 2009; Mediavilla et al. 2013; Scheidel &

Sorman 2012). However, the food pathway from oil to land clearing has received less attention, in

particular the implications of reduced fertiliser use, the global interest in acquiring farmland and the

transition away from fossil fuels. These topics are the subject of this study.

Figure 1 The pathways by which oil supply influences habitat loss. The route under investigation is that through food

production.

Oil

supply

Biofuels

Food

crop

yields

Habitat loss

2

Modern agriculture relies heavily on oil for fuel, for farm machinery and transport, and especially

for agricultural inputs such as fertiliser and pesticides. Nitrogen fertiliser represents the largest

petrochemical consumption in agriculture, consuming the equivalent of 1.5 litres for each kg (Kelly

2009). Fertiliser is responsible for increasing yields, enabling food production to occupy less land.

Constraints to the oil supply increase the price of fertiliser, making it inaccessible to low-income

farmers, thereby reducing agricultural productivity and creating pressure to extend farm land to

meet demand for food. This move towards extensification has implications for biodiversity loss.

An additional form of farmland expansion strongly linked to oil transition and the perceived need

for food security is what has become known as ‘the global land grab’(Zoomers 2010). In 2010, The

World Bank conducted a study into the dramatic increase in the acquisition of agricultural land

arising out of the 2007-8 global food crisis (Deininger et al. 2011). The land was mostly acquired

where the cost of agricultural land was low, and targeted countries right across the economic

spectrum. Around 100 million ha of these acquisitions have been documented since the levelling off

of global oil production, and the rate of reported acquisitions rose by a factor of forty between the

early and late 2000s (Anseeuw 2012 ). These land acquisitions have the potential to accelerate the

conversion of previously intact native ecosystems to cropping or pastures. The literature focusing

on land acquisitions are mostly case studies investigating the social justice implications and the

financial processes involved (eg, Borras Jr & Franco 2012; Zoomers 2010). However, there has

been little published and no systematic study of the impact on native ecosystems and biodiversity.

The dynamics of the links between oil supply, food security and biodiversity loss are occurring at a

global scale, mediated by global commodity markets, but the effects are seen at a local level. This

study will assess the potential impact of constraints on petrochemical nitrogen fertilisers on the

conversion of native forests to agriculture, and the consequences for biodiversity. It will develop a

spatial prioritisation framework to mitigating those losses while maintaining food security.

1.1 Problem statement

This study investigates the threat to biodiversity from land-use change for food production due to

reaching limits to the world’s oil supply. Since the Green Revolution, the growth in the human

population has outstripped expansion of land under agricultural production because of increased

cropping intensity due to petrochemical fertilisers. This increased productivity has been enabled

through a 12.8 x 1018J energy subsidy from fossil fuels (Smil 2008). As oil production becomes

limited, and prices increase, the use of artificial fertilisers will become increasingly constrained, and

the intensification process could potentially reverse. This extensification could result in an

expansion in agricultural land to compensate for lost productivity, and an increased threat to

3

biodiversity (Figure 2). The resulting increase in food price also poses a threat to food security, and

an increase in agricultural land acquisition, as occurred after 2003 (Arizpe et al. 2011).

1.2 Aim and objectives

My overall aim is to prioritise interventions to protect biodiversity from the pressure of expanding

food production as a result of the effect of oil supply constraints on the land-fertiliser substitution

problem. This will require the production of global biodiversity threat mapping to be able to assess

the impact of the alternatives, and of a decision framework which can help to identify solutions.

To achieve this aim, I have four specific objectives:

1. Assess the impact and drivers of land cover change on biodiversity during the global financial

crisis when fuel/food prices began escalating in 2005.

2. Put an upper bound on the threat from land-fertiliser substitution by mapping and assess the

impact on biodiversity and food security if petrochemical fertiliser were not used.

3. Identify a best-case intervention for the problem and quantify the difference this would make to

habitat area lost and to biodiversity.

4. Spatially prioritise solutions based on a decision framework considering resource availability,

land-use competition with food production, impact on biodiversity, affordability and effect on

albedo.

1.3 Literature Review

The problem identified in this study links three main thematic areas: limits to growth, with peak oil

as an exemplar of these limits; the role of petrochemicals, particularly petrochemical fertiliser, in

food production and hence agricultural land expansion; and the impacts of agriculture on

biodiversity.

Land for food

Lan

d f

or

nat

ure

Figure 2. The process by which food

production displaces land for terrestrial

native ecosystems. The total land area, once

occupied by terrestrial ecosystems, has been

gradually replaced by agriculture. This

conversion process is predicted to increase

through agricultural extensification.

4

1.3.1 The limits to growth

In1972, Meadows and colleagues modelled global population, resources and pollution through a

series of scenarios reflecting possible courses of action (Meadows et al. 1972). They came to the

conclusion that, unless we significantly change our behaviour (their ‘standard run’), resource

constraints would create a limit to growth in the first two decades of the 21st century (Meadows et

al. 1972). Since this initial modelling was conducted, various researchers have compared the

model’s output with the real world data (Bardi 2011; Hall & Day 2009; Meadows et al. 2004;

Turner 2008). Most recently, Turner (2012) compared 40 years of real-world data, with the Limits

to Growth standard run and with two of their other scenarios: the ‘comprehensive technology’ run

which attempts technological solutions to the problem, and the ‘stabilised world’ run which also

uses social policies such as agricultural land conservation and availability of contraception. Turner

(2012) found that the path we have been following until 2010 is close to the standard run. This

would imply that we are likely to experience economic contraction starting around 2015 with food

per capita peaking in the following decade and global population beginning to fall around 2030. In

the modelling, growth is constrained by limits to resources.

One such resource limit which has received much attention is ‘peak oil’: the point in time when

global oil production reaches its maximum. Finite resource extraction follows a bell curve which

can be modelled and predicted, but the peak can only be observed empirically after it has happened.

Hubbert (1956), who first proposed this global peak, predicted that it would occur somewhere

around 2006. He also correctly predicted peak production would occur for the USA in 1970. Many

observers believe that oil did begin to peak around the middle of the 2000s, and it has been

proposed that this was a major driver behind the global financial crisis (Hamilton 2009). Once oil

production slowed, rising demand pushed up prices, putting pressure on all aspects of our heavily

oil-dependent economy, especially those at the margins – the poor on the urban fringe where

housing is cheaper. In these ‘VAMPIRE’ suburbs (Vulnerability Assessment for Mortgage, Petrol

and Inflation Risks and Expenditure), any increase in petrol price can mean an inability to pay the

mortgage, since fuel is obligatory for getting to work, as was seen in the sub-prime suburbs across

the USA (Dodson & Sipe 2008). This was postulated as trigger for the global financial crisis and

provides a link between oil depletion and the GFC (Mishkin 2010).

In general, once the peak of oil production is reached, the net rate of production is believed to

decline from that point onwards on the Hubbert curve, as it takes an ever increasing amount of

energy to extract the remaining oil (Czúcz et al. 2010). This is known as Energy Return on (Energy)

Invested or EROI. Initially, oil reserves were under pressure and required little energy to extract,

5

Figure 3 Energy return on energy invested for US oil

discoveries. The proportion of energy required to

extract oil from reserves increased rapidly until

production peaked in 1970 (Guilford et al. 2011).

but the EROI declines over time as can be seen in Figure 3. We are increasingly relying on

‘unconventional oil’ which is unconventional because it is difficult to extract and has low EROI

(Figure 4). This can be due to the depth of the reserve, especially under water (eg Deepwater

Horizon at 2 km), due to its inaccessibility or fragmentation, as in coal seam gas, or being under ice,

or in an extreme climate and remote locations, as in the Arctic reserves, or in highly dilute, impure

or unrefined forms such as the Canadian tar sands and shale oil (Kerschner et al. 2013). The

difficult nature of these operations also increases the environmental costs, the risk of political

opposition and the need for military involvement to secure the resources against competing

interests. But it also means that an ever increasing proportion of the energy extracted is used up in

the extraction process.

It is thought that a minimum EROI of about 10 is needed to run an industrial society (Hall 2009).

New discoveries of oil are already below that level, as are most of the renewable sources we might

use to replace oil (Figure 4). For example, shale oil has an EROI of about 3-4. There are two current

renewable energy sources which have a sufficiently high EROI: hydro and wind power. Most of the

large-scale highly efficient hydro sources in the world have already been exploited, and the

remaining options are in politically unstable regions, or would come at human or environmental

costs which have been considered unacceptable in democratic societies (Scudder 2005). Wind

power has a theoretical global maximum beyond

which energy can no longer be extracted efficiently

from the atmosphere. This maximum would only

provide about 6% current energy usage (de Castro et

al. 2011). Nuclear energy has a marginally sufficient

EROI, which excludes the energy needed for long-

term waste management (100,000s years) since this

process is yet to be devised, and uranium stocks

would provide sufficient fuel for about 9 years of

global energy usage with current technology.

Since decreasing EROI is making oil production returns more marginal over time, and replacements

facing higher resource constraints (Table 1), we can predict a corresponding contraction of the

global economy as fuel costs rise, as predicted by The Limits to Growth.

6

Table 1 Potential contribution to replacing fossil fuels. The alternatives to fossil fuels are limited in their potential as

replacements with current technology and current reserves. There is limited capacity of wind to become a viable

replacement energy source, current biomass is tiny compared to the ancient biomass preserved as fossil fuel and

uranium has potential for short-term transitioning. Solar is the only replacement with realistic quantities, but puts

further pressure on land- and material-use (compiled from Scudder 2005 Smil 2011,Stervrup 2013, Schiedel & Sorman

2012).

Energy source Limits to the potential

Total wind in the atmosphere Could supply 6% of energy needs

Total biomass on Earth Would last 17 minutes, if supplying total energy needs

Total uranium reserves Would last 9 years, if supplying total energy needs

Solar 1.5-3 million km2 required

Rooftop PV Could supply 6% of energy needs

1.32 Petroleum in agriculture

Food prices are strongly correlated with oil prices (figure 5). This is unsurprising since fuel

comprises about 30% of the cost of producing food, with nitrogen fertiliser contributing an

additional 30-40% of the cost. Natural gas comprises about 80% of the cost of fertiliser production

(International Energy Agency 2007). Nitrogen fertiliser is highly energy intensive to produce

(requiring 5x1018J/ha, (Smil 2008)) with both the energy and the hydrogen required usually sourced

from fossil fuels. About a fifth of fertiliser production in the USA and Canada was suspended

during the period of high fuel prices in the 2000s, and natural gas is highly substitutable with other

fuels in industrial processes (International Energy Agency 2007).

A recent World Bank Policy Research Working Paper calculated the relative contribution of various

drivers to the increase in basic food commodity prices (Baffes & Dennis 2013). Baffes and Dennis’

model accounts for most of the traditional explanations of food price increases through their

Figure 4 Two ways of comparing EROI for alternative fuels. Those sources with an EROI less than 10 may be

insufficiently efficient to produce enough energy to maintain an industrial society (Hall & Day 2009; Murphy & Hall

2010).

[amalgamate graphs?]

7

-ve

Food stock Commodity

demand

Biofuels

-ve

-ve

Extreme

weather

Food

production

Income

Food

price

Investment

funds

-ve

Trade

embargos

US$ depreciation

-ve

-ve (relatively inelastic)

Oil

price

Figure 6 Factors which influence the price of food. More than half of the increase since the global

financial crisis is attributable to the increased price of oil. Modified from (Baffes & Dennis 2013)

impacts on commodity stock-to-use ratios (figure 6). Biofuels, for example, affect fuel prices by

creating additional demand for basic commodities, and exchange rates for the US$ affects demand

and production outside the USA. The model showed that since 2004, when the recent precipitous

grain price rise began, oil price accounted for more than half of the grain price increases. Prior to

2004, when oil prices were more stable, food storage was a more important factor, but since then,

storage and currency fluctuations have each made up about 15% of food price increases. In the

model, food price was not linked to demand, which is fairly price inelastic. Also not shown is the

link to urbanisation, which reduced during the GFC, reducing sprawl onto agricultural land.

Figure 5 World food price correlates strongly with world oil prices (Chefurka, 2011), which might be

expected since energy costs contribute a large proportion of the cost of producing food.

8

These rises in the cost of basic food stuffs are a serious problem for the half of the world’s

population who live on about US$3/day or less. Largi et al (2011) showed that once the FAO food

price index goes above a critical threshold, believed to be around 210, there is a marked increase in

food-related unrest, as shown in figure 7.

Figure 7. Riots appear to be triggered by food price increasing above a threshold (Lagi et al. 2011).

In 2008, within months of the oil price spike, the world entered a global food crisis (Conceicao &

Mendoza 2009), marked by a doubling of food prices in a year and food riots in approximately 50

countries (Mueller et al. 2011). The resulting response of many countries which are dependent on

food importation for their food security was to secure agricultural land though direct or indirect

investment (Hallam 2011). Speculators, noting the resultant increase in agricultural land values,

increased their land holdings, exacerbating the price escalation (Headey & Fan 2008). Together

these investments represented a more than ten-fold increase in such land acquisitions in a single

year following the onset of the crisis.

Land-fertiliser substitution as a land change mechanism has been studied by a number of authors,

mostly using economic modelling, and the substitution was often an input to the model. A common

theme was the importance of the price of land (or rent) in determining the degree of substitution.

Brunelle and colleagues (2014) modelled land fertiliser substitution and found that global dietary

convergence to a USA style diet was not feasible. They found that under a dietary scenario

suggested by FAO, an additional 600 million hectares would be required between 2005 and 2050

9

mainly due to fertiliser-land substitution with rising fertiliser price. Woltjer (2013) found that land

shortage drives up land and crop prices, increasing the pressure to intensify. Bartelings and

colleagues (2014) found that fertiliser subsidies improve food security but that there was a lack of

empirical evidence for the elasticity of land-fertiliser substitution. Minear (2015) pointed out that

expansion onto more marginal land requires more fertiliser to maintain productivity, and in ‘The

Cropland Crisis’, Crosson (2013) made the point that when combined with other technologies such

as pesticides and plant varieties, rates of substitution are higher than with fertilisers alone. He

reported that, in developing countries, a ton of fertiliser substitutes for 19.9 acres of land.

There have been attempts by climate charge researchers to define future possible scenarios known

as Shared Socioeconomic Pathways (Hertel et al 2016, Baldos et al 2016). These use approaches

such as partial equilibrium models to look at the future demand for food and land (fig 8). This

model indicates the role of land rent response in mediating the elasticity of input/land substitution,

but in many areas of subsistence agriculture there is no land rent, so land expansion is always

preferable to increased input costs.

Figure 8 Schematic of the relationship among land, food and population combined from Hertel et al (2016)

and Baldos et al (2016).

They found that income and population were the main drivers of food demand with income the

more important factor. They predict lower long term prices for food, but that this is critically

Land

rent

response

10

dependent on agricultural productivity. They found that diet related consumption is growing slower

than the reduction in population growth (Baldos et al 2016). Another model, built on a literature

review, predicts that cropland will continue to grow at the same rate in the future as it has in the

past, but that this is critically dependent on elasticity of non-land inputs such as fertiliser, and

doesn’t consider energy constraints on fertiliser production (Hertel 2016). These methods have also

been applied to the effect of a carbon tax and were unable to consider the effects of land use change

and renewable energy on outcomes. They called for research into the effect of these and energy

prices on food security (Ringler et al 2016).

1.2.3 Agriculture and biodiversity

When examining biodiversity loss, the drivers are typically characterised as either proximate or

underlying causes of change. The ultimate underlying cause is human activity, since none of the

proximate drivers are natural phenomena, and the rate of species loss is around 300 times the

natural background rate (Rockström et al. 2009). There is general agreement on how underlying

drivers are expressed as proximate causes of biodiversity loss.

These are habitat alteration and loss, over-harvesting, species and disease introduction, and pollution and

climate change. Of these, habitat alteration is clearly the predominant cause… (Wood et al. 2000, p5)

The United Nations Framework Convention on Climate Change estimates that agricultural

expansion has led to around 80% of deforestation, with most of the remainder caused by logging

and 5% for wood fuel (UNFCCC 2007). With a decline in fossil fuels and a return to biofuels, as

were used prior to dependence on fossil fuels, this sector could greatly increase (Fernandes et al.

2007). Lambin and colleagues estimate 97% of deforestation is attributable to agricultural

expansion (2001). Geist and Lambin (2002) also provide a framework for classifying these causes

and illustrating how they relate (Figure 9).

It is not clear how resource constraints would fit in to this framework. Scheidel and Sorman (2012)

have identified ‘moving away from fossil energy stocks’ as an ultimate driver of the land rush. They

quantified the additional land required by the switching to alternative energy sources such as wind,

solar, hydropower, biomass and nuclear due to their power densities (power per area of land) as

being 1-5 orders of magnitude lower than conventional oil fields. While they regard this as

sufficient to provoke the global land rush, they do not include in their assessment the increased land

demand from the move to unconventional fossil fuels such as coal seam gas, shale oil and tar sands

which are more land intensive than conventional sources. Neither do they include the impact of

11

reduction in fossil fuels on land use for food agricultural production, which is the subject of the

current research.

Figure 9 Drivers of forest decline, after Geist and Lambin (2002)

Arizpe and colleagues (2011) note that these pressures on agricultural systems differ depending on

the level of development and the demographics of the society. Poorer countries need to maximise

their agricultural return on land and keep costs down as they lack the economies to support high

cost food, whereas wealthier countries need to maximise their return on labour. The application of

green revolution technology in less developed regions is expanding agriculture onto marginal land

with resulting habitat loss. They call for research into alternative systems which will allow the

selection of practices suitable to their context, which is the aim of this research. This also feeds into

the better targeting of solutions to the land sharing vs land sparing debate, which has also been

called for (Phalan et al. 2011; Tscharntke et al. 2012).

The world was already experiencing a crisis in biodiversity loss prior to these recent additional

threats (Barnosky et al. 2011). Understanding the biodiversity impact of oil-constrained agricultural

expansion; including the global rush to acquire agricultural land, will be critical to designing

interventions to mitigate further loss in the case of future oil constraints. The large-scale acquisition

of land for industrial agriculture is being documented through various international collaborations

(GRAIN, Land Matrix). These approaches come from a social justice perspective and ecological

information such as prior land cover is not collected. In addition to these large-scale acquisitions, it

is likely that there may be additional expansion and intensification of agriculture, for example by

subsistence farmers and pastoralists displaced by these acquisitions (Borras Jr & Franco 2012), and

through more extensive food production to compensate for productivity loss caused by reduction in

fertiliser use, due to the five-fold increase in fertiliser price during the global financial crisis (The

Infrastructure

extension

Agricultura

l expansion

Wood

extraction

Demographics

factors

Economic

factors

Technological

factors

Policy

factors

Cultural

factors

Other

factors

12

Government Office for Science 2011). This trend has been acknowledged by The Future

Agricultures Consortium, who noted in The Global Fertiliser Crisis and Africa,

African fertiliser importing countries… face increased fertiliser import costs and difficult choices. Unless fertilisers

are subsidised, use is likely to fall, reducing food and export crop production, with increased food import bills and

reduced export earnings. High food prices, likely food shortages and low export crop production would have very

damaging effects on welfare, balance of payments and economic growth in some countries. There will also be high

environmental costs of reduced fertiliser use. (Dorward & Poulton 2008)

The move to bioenergy with rising fuel costs can also contribute to lost agricultural productivity

because burned crop residues and manures are not returned to the field, reducing fertility

(International Energy Agency 2007).

1.3.4 Relationship to land change science

This study is interdisciplinary, with land change science (LCS) one of several disciplines upon

which it draws. Other discipline areas include resource economics, agricultural science, energy

systems and conservation science. The principal ways in which the research relates to LCS is its

subject matter and its methods. The problem under investigation is a problem of land cover change,

which is an aspect of land change. Land change science studies how land changes, along with the

rates, causes, and impacts (U.S. Geological Survey 2013). This requires methods for characterising

spatial relationships, distributions and dynamics. These typically include the use of remote sensing,

modelling and decision-support tools. Advances in remote sensing have enabled global monitoring

of land change, and are useful for assessing biodiversity change (Turner et al. 2007). Recent

advances in the processing of remote sensed data will allow the analysis of forest loss at higher

resolution than has hitherto been feasible. Combining this mapping with modelling will enable

spatial and quantitative characterisation of the societal-biophysical linkages, resulting in a decision

analysis to bring together complex information in a form which can be more readily utilised in

decision-making.

1.3.5 Global interventions which may influence land-fertiliser dynamics

This study concerns reactive nitrogen and biodiversity, which are also two of the planetary

boundaries thought to have been dangerously exceeded (Rockström et al. 2009). Interventions to

reduce nitrogen pollution of the biosphere, such as the International Nitrogen Initiative and global

agreements to abate greenhouse gas emissions may feed into the land-fertiliser substitution

problem, unless care is taken to prevent this.

13

1.3.6 Knowledge gaps

Although there is a rich literature in each of the individual discipline areas of agriculture,

conservation and resource depletion, as previously discussed, there is a lack of connection in the

literature between the oil supply, food production and biodiversity loss. The effect on biodiversity

of changing oil supplies is rarely considered. The potential magnitude and spatial dynamics of the

impacts need to be assessed so that interventions can be devised that will address biodiversity loss.

Research into this has been called for by Czucz and colleagues (2010):

… peak oil is also a fundamental concern as it pertains to ecological systems and conservation… it is crucially

important to wisely manage our ecosystems during the transition period to an economy based on little or no

use of fossil fuels. The development of resource-constrained scenarios should be addressed immediately.

Ecologists and conservation biologists are in an important position to analyze the situation and provide

guidance, yet the topic is noticeably absent from ecological discussions (page 948).

In particular, they note the potential de-intensification of agriculture as top of their list of potential

mechanisms. Foley and colleagues (2011) also call for solutions to these conflicts, in particular:

We need better data and decision support tools to improve management decisions, productivity and

environmental stewardship (page 341).

And in Food Security: The Challenge of Feeding 9 Billion People, Godfray and colleagues say

(2010),

…we must avoid the temptation to further sacrifice Earth’s already hugely depleted biodiversity for easy gains

in food production... Navigating the storm will require a revolution in the social and natural sciences concerned

with food production, as well as a breaking down of barriers between fields. The goal is no longer simply to

maximize productivity, but to optimize across a far more complex landscape of production, environmental, and

social justice outcomes (page 817).

This research project takes an interdisciplinary approach to addressing the impact of oil depletion

on the food supply, developing a decision framework to conserve biodiversity.

2.0 Thesis structure

In this thesis, I have examined the problem that constraints to the supply of petrochemicals might

pose for biodiversity through the effect on agriculture’s land footprint, with the aim of determining

the potential scale of the problem and spatially explicit interventions to minimise the impacts.

Throughout this thesis, I use the term ‘footprint’ to refer to a land area which can be measured in

spatial units.

14

My first objective (Chapter 2) was to identify empirical evidence that such a relationship might

exist by examining what happened using the natural experiment of the global financial crisis (GFC)

of the late 2000s. During this period, the oil supply did not keep pace with demand, and prices

doubled providing an opportunity to test the idea that agriculture’s footprint might be affected.

Using the Hansen forest loss/year dataset (Hansen et al. 2013a), I compared the rate of forest loss

during the GFC with the background rate, and globally mapped the impact on biodiversity. I found

that forest loss increased greatly during this period and that the changes aligned spatially with

concentrations of biodiversity. A meta-analysis of quantitative analysis of drivers of the changes in

the statistically significant areas showed expansion of commercial agriculture as the dominant

driver with these areas also being the most sensitive to the price of nitrogen fertiliser. These results

were consistent with land being substituted for fertiliser, as fertiliser prices soared. Large scale land

acquisitions, which also increased during this period, were not associated with deforestation,

implying that agricultural production has largely taken place on existing agricultural land. There

were areas where policy appears to have been successful in resisting accelerating deforestation, in

the Brazilian Amazon, and in The Great Western Woodlands and south west Queensland in

Australia.

My second objective (Chapter 3) was to put an upper bound on the scale of the potential problem

for biodiversity and food security in a business-as-usual scenario. I used a global dataset of

nitrogen-use efficiency to derive the global average yield with no mineral N use, and from this

calculated the minimum and maximum land footprint of global cropland. From a map of current

cropland, I modelled cropland expansion finding that cropland would be occupying extremely

marginal land even with the minimum requirements, leaving little for biodiversity, and there was

insufficient land for the maximum requirements. Even at the minimum level, most of the world

would become food insecure, exhausting remaining potential arable land.

To find a best-case alternative, my third objective (Chapter 4), I found the minimum land footprint

which would be required to replace petrochemical-derived fertiliser using renewable sources. I used

the N yields of organic sources of N and compared these with the most land-efficient sources of

renewable energy for powering the existing industrial nitrogen production plants. The most land-

efficient option was using solar power, which was of the order of 1000 times more land-efficient

than using the most efficient organic source. Without intervention, the business-as-usual scenario

would require about 2000 times the area and result in about 80,000 times the impact on biodiversity.

However, the existing solar infrastructure is not well positioned to maximise access to insolation or

to minimise impact on biodiversity.

15

In Chapter 5 which addresses Objective 4, I produce a global spatial prioritisation for sourcing

nitrogen based on resource availability, biodiversity and other impacts. This shows that relatively

little of the land area is highly suitable for solar power when insolation, conflict with biodiversity

and cropping and albedo are considered, but there is sufficient highly suitable area to meet total

global energy needs twice over from solar alone. Other sources of N are more suitable in some areas

because they have very high yield gaps, lack access to solar resources or have high albedo.

In Chapter 6, I conclude that agricultural nitrogen use is of importance to conservation science

because of its potential to influence agriculture’s land footprint and that intervention is needed in

the case of constraints to the supply of petrochemicals. Moves to curb nitrogen pollution of the

biosphere and greenhouse gas emissions also need care so as not to inadvertently put upward

pressure on agricultural land-use.

16

Chapter 2: Are changes in global oil production influencing the rate of deforestation and

biodiversity loss?

Abstract:

Global biodiversity loss is driven principally by the expansion of agriculture. This expansion has

slowed over the last 50 years as agricultural production has intensified, largely through the use of

petrochemical-based fertilisers. The mid-2000s saw a transition where oil production became

unresponsive to the increased demand for petrochemicals, pushing up their price and that of their

end-products, including fertilisers. Such oil supply constraints threaten to reverse previous

agricultural intensification gains and increase pressure for the conversion of native ecosystems.

Price-driven land and food speculation and the search for alternative energy sources also have

the potential to increase the demand for land. This chapter aimed to measure the change in the

rate of deforestation and to map the resultant impact on biodiversity as oil production became

inelastic in 2005. Globally, an additional 290,000 km2 of forests was cleared in the period 2007-

12 compared with 2000-2006, which is a net increase of 29% between the two periods. The areas

of increased forest loss broadly corresponded with the areas of highest biodiversity. We tested

for, but found little correspondence with large-scale, corporate land acquisitions. Statistically

significant hotspots of increased threat to biodiversity generally lie in a band through the tropics,

particularly in south-east Asia, Africa and Central America, with fertiliser consumption affected

in hotspot areas. A review of the drivers in these hotspots indicated that non-subsistence growth

factors underpin most land-cover change. We conclude that conservation efforts need to mitigate

pressures from growth and agricultural extensification, and be aware that the rate of loss

increased in tropical and sub-tropical regions, coinciding with the areas of highest biodiversity.

2.0 Introduction

Expansion of agricultural land is the main driver of global biodiversity loss (Ferretti-Gallon &

Busch 2014; Wood et al. 2000). The increase in agricultural intensity associated with the Green

Revolution has resulted in an agricultural footprint less than half of the area predicted under pre-

Green Revolution yields (Borlaug 2007). However, this trend may now be reversing, accelerating

the threat to biodiversity (Haberl et al. 2011). Modelling has predicted that resource constraints,

especially restrictions to the supply of oil, would result in an economic downturn in the early 21st

century because an increasing proportion of global capital would be required to extract ever more

expensive oil (Meadows et al. 2004; Meadows et al. 1972; Turner 2012). In 2005, global oil

production became inelastic whereby the supply became unresponsive to increased demand

(Murray & King 2012). The world has experienced three global phenomena since 2006 which have

17

been linked to limits to the global oil supply: the global financial crisis, the global land grab, and a

series of global food crises (Baffes & Dennis 2013; Demissie 2014; McMichael 2009; Murray &

King 2012; Neff et al. 2011; Turner 2012). These have all been associated with growing demand for

land (Friis & Reenberg 2010).

Food and oil prices are closely coupled because petrochemicals constitute a major component of the

cost of producing food (Arshad & Hameed 2009; Baffes & Dennis 2013). For example, limits to the

supply of oil were an underlying cause of the escalating price of food during 2007-8, which became

known as ‘the global food crisis’ (Headey & Fan 2008). Insecurity in the supply of food and fuel

associated with the food crisis and the transition to oil supply inelasticity led countries reliant on oil

and/or food imports to acquire agricultural land for food and biofuel production (Deininger et al.

2011). This included the production of ‘flex-crops’ which can be used to produce either food or

biofuel (Anseeuw et al. 2012). In addition, the insecurity of traditional investments during the

global financial crisis combined with rising commodity prices caused capital flight into agricultural

land and food commodities (Friis & Reenberg 2010). These investments have become known as

‘the global land grab’.

Land-fertiliser substitution driven by the rising cost of fertiliser is another important agricultural

land expansion mechanism which occurs because any increase in the cost of fuel creates an increase

in the cost of fertiliser, which is extremely fuel-intensive to produce (Brunelle 2012 ). For example,

between 2002 and 2012, the oil price increased about 4.5 times and the cost of fertiliser increased

fivefold. As fertiliser becomes increasingly unaffordable to marginal farmers, the expansion of

cultivated land is substituted for fertilisers in order to produce sufficient food (The Future

Agricultures Consortium 2008). Also, constraints to the oil supply increase the demand for land

because energy alternatives, such as wind and solar power and coal seam gas, require multiple

orders of magnitude more land than conventional oil per unit of energy produced (Scheidel &

Sorman 2012). As increased fuel prices drive up agricultural input costs and commodity prices,

production choices will depend on the relative price increases (Rane & Deorukhkar 2007). This is

further complicated by government incentives which can be motivated by balance of trade

considerations, as has been the case with biofuels in the USA and the EU (Zilberman et al. 2012),

and by the depressive effect of high oil prices on economies, leading to price cycling (International

Energy Agency 2007). It might also be expected that increase in crop prices would also lead to

expansion of cropland and intensification, each of which reduces the pressure for the other (Hertel

et al. 2013). Urban sprawl has had a significant impact on biodiversity (Czech 2004), a form of

development which could become unaffordable with increasing oil prices as the outer suburbs are

18

the most vulnerable to oil prices (Dodson & Sipe 2008). Although the global economic downturn

has since resulted in recent decline in demand and oversupply of oil, resulting in falling prices

(Tverberg 2012), the effect of these changes on associated changes in deforestation rates may not

yet be evident, and the net oil supply is predicted to decline over the coming decades. Pressure on

the supply of land linked to oil supply constraints may change the threat to biodiversity due to land

conversion between natural ecosystems and agricultural production, indicating that the oil transition

of the mid-2000s is a critical period for the investigation of any potential repercussions of the

increased demand for land on biodiversity (Czucz et al. 2010). However, the energy supply is only

one of many factors contributing to agricultural land expansion, including population growth and

growth in consumer demand for products, especially grain-fed meat (Foley et al. 2011).

Hertel and colleagues (2013) used an equilibrium model to estimate the potential land required for

biofuels over 30 years under various intensity and policy assumptions and found that 44 - 124 Mha

of additional cropland would be required. Econometric modelling of land use change due to price

fluctuations during the same period as this study estimated that a doubling of fertiliser price results

in a 1-7% reduction in crop productivity and that the acreage response to price was 0.0325, 0.025

and 0.010 for wheat, maize and rice respectively (Haile et al 2016).

This chapter aimed to investigate if the rate of global forest loss and the resulting loss of

biodiversity have altered in association with inelastic oil supplies since 2005. It assessed the change

in the rate of global deforestation, the extent of the recent forest loss, and the probable impact on

biodiversity after 2005. We used a global forest loss dataset to map the change in deforestation rate

globally since these crises began. We considered the consequences for biodiversity based on indices

of endemism richness. We spatially analysed the distribution of deforestation change in relation to

the location of international land acquisitions and sensitivity to the price of fertiliser to see whether

these factors were contributing to the changes in the rate of deforestation. We then reviewed the

literature of the underlying drivers of change in the regions with the highest changes in the impact

on biodiversity.

2.1 Data and methods

The potential links between changes in oil production and biodiversity loss are illustrated

graphically in the conceptual model (Figure 10). As oil prices increase or decrease, alternative

sources of energy go in and out of production depending on their cost of production. These energy

alternatives are more land intensive than conventional oil. Fertiliser prices rise with the oil price

(see Figure 11) resulting in reduced usage and a larger area requirement to meet the demand for

19

food. The insecurity in the food supply leads to investment in both land and food, such as the

phenomenon known as ‘land grabbing’ or investment the broader food production industry. Rising

oil prices increase the demand for land through all of these pathways, and hence increase the

pressure on natural ecosystems and their biodiversity.

Figure 10 Conceptual model of the links between changes in oil production and demand for land. Alternative

energy sources, food production extensification or intensification and level of investments in food production

and agricultural land all change the demand for land and the pressure on biodiversity.

Figure 11 a Crude oil production (million barrels per day).The transition in 2005 in the oil supply from

elastic (where supply can expand to meet demand) to inelastic (where supply fails to keep up with demand).

Once the supply does not match demand, buyers compete, pushing the price up. Figure 11b Fertiliser, food

and oil prices (index points, FAO) tend to magnify changes in oil price (EIA, dollars per barrel, nominal

US$, World Bank).

Alternative energy

development

Changes in oil

supply and price

Fertiliser-land

substitution

Changes in demand

for land

Rate of deforestation

and biodiversity loss

Land and food

investment

66.0

68.0

70.0

72.0

74.0

76.0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

0

50

100

150

200

250

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Energy price

FAO food price index

Fertiliser price

a

b

Crude oil

Elastic production

Inelastic production

20

2.1.1 Data sources and methods

The following datasets were used: the Hansen forest loss by year dataset (Hansen et al. 2013b); the

Land Matrix and GRAIN databases provided records of land acquisitions (GRAIN 2012; The Land

Matrix Global Observatory 2013) and the Kier biodiversity indices of vertebrate and plant

endemism richness (Kier et al. 2009). The Hansen dataset is a 30 m resolution dataset of tree loss by

year between 2000 and 2012. The Land Matrix and GRAIN databases are collections of records of

land acquisition transactions where a large tract of land (>200ha) has been acquired by an outside

interest for commercial production, threatening traditional land usage. The Land Matrix is compiled

for developing countries into an online database from a variety of sources, including crowd-sourced

entries. Records are verified before entry into the database. GRAIN data are compiled mainly from

media reports. The Land Matrix records were used, where available, with gaps filled with records

from the GRAIN database, with each record representing an acquisition. The Kier biodiversity

indices are compiled from the known ranges of vascular plants, mammals, reptiles, amphibians and

birds, creating a biodiversity measure that incorporates richness and endemism and is mapped by

867 biogeographically similar ecoregions (Kier et al. 2009). Although tree loss could be detected at

a fine scale perhaps that of an individual tree, the biodiversity impact of this was only coarsely

assessed. While a biodiversity measure which included abundance would have improved sensitivity

to threat from deforestation, no such global dataset was available.

Using the Hansen 2014 ‘lossyear’ dataset (Figure 12a) and a global equal area projection to allow

for area calculations, we mapped the difference in the amount of deforestation between the period

2000-2006 and the period 2007-2012 and calculated the change in the deforestation rate between

the two periods (Hansen et al. 2013b). This dataset may under-detect forest loss when compared to

datasets which have been ground-truthed, possibly due to rapid change in ground level, as are found

in riparian zones, leading to under-detection of tree height change.

We then compared the distribution of changes in the rate of deforestation with land acquisition data

from the Land Matrix and GRAIN databases to investigate whether land acquisitions were a

possible explanatory factor of deforestation changes (GRAIN 2012; The Land Matrix Global

Observatory 2013). Deforestation and land acquisitions records (2000-2012) were compared by

extracting the deforestation rates for the countries for which the land acquisition data were

available, and conducting a Pearson’s correlation of the deforestation areas with the area of land

acquired (ha). The Land Matrix includes records for acquisitions under negotiation, which although

not yet finalised, were included in the analysis, since these lands are at risk from future

21

deforestation. Pearson’s correlations were also conducted for the restricted set of land deals which

have been concluded and where production has been implemented for comparison.

We combined the Kier vertebrates’ index and the plants’ index into a single index. The plants index

is of more variable quality and the records have a quality score ranging from 1 (very poor) to 4

(very good). The two indices were combined with the plant data weighted by its data quality rating

so that the weights were equal to vertebrate data for the most certain plant data, whereas the most

uncertain records were given a weighting of 0.25 (Figure 12b). The combined index was normalised

between 0 and 1 and factored into the deforestation change map on a pixel by pixel basis, to create a

map of change in biodiversity threat. In this way, the biodiversity of non-forest ecosystems was

incorporated in the impact estimation. These are known to be lost at higher rates and have a lesser

proportion remaining than do forests (Groombridge & Jenkins 2002; Henry & Rae 2012). The

impact of the loss of grassland and shrublands can be taken into account in the impact on

biodiversity by using their relative abundance, rates of loss and biodiversity to estimate the global

impact, but this could not be mapped because the loss of grassland and shrubland is not available in

a global dataset

We then calculated the globally statistically significant ‘hotspots’ and ‘coldspots’ of increasing and

decreasing impact on biodiversity using ArcMap Getis-Ord Gi* which identifies local clusters

which are significant in relation to the rest of the dataset, by comparing the local sum for a feature

with those nearby with all of the sum of other features and testing the probability of this occurring

randomly (ArcGIS v10.2, ESRI, Redlands, CA, USA).

To gauge the potential for land-fertiliser substitution as an explanatory pathway, the average

nitrogen fertiliser price elasticity was calculated for the study period using the formula

e(p) = d(Q/Q) / (dP/P), where the Q is the quantity consumed and P is the price. We then compared

between hotspot countries and all countries where fertiliser use data are available, using FAO

fertiliser-use data, Fertilizerworks nitrogen price information and World Bank country income

classifications (FAO 2012; Fertilizeworks 2011). Although this was calculated across the whole

study period, because the figures are global in a global fertiliser market, comparison between

countries is useful as an indicator of the relative sensitivity to price or production constraints, but

because marginal elasticity was not calculated, causation cannot be attributed.

22

Four sizable ‘coldspot’ areas were identified. These areas had all seen the implementation of new

regulatory instruments controlling forest loss. An f-test and t-test were performed to measure the

significance of the reduction in the rate of deforestation after regulation began.

The units used for reporting in the results include continents, regions and countries. Hotspot, driver,

land acquisition analysis was by country because this is the unit which is named and in common

between datasets. However, we also used continents for consistency with Forest Resources

Assessment (FRA) regions used in previous FAO deforestation reporting. We separated Russia

from Europe and Central America from North America at the continental scale. Russia covers a vast

area and includes European and Asian regions, and both Russia and Central America had

deforestation rate changes in the opposite direction from the rest of their FRA region. Hotspot

regions were generated by the analysis and did not necessarily align with political boundaries, but

we discussed them in terms of countries when data or factors such as policy made this relevant.

Figure 12 a) Forest cover loss at 30 m resolution for the period 2000-2012 (Hansen et al

2014); b) biodiversity index derived from Kier et al (2009) combining the known ranges of

vascular plants and vertebrates by ecoregion.

a

2013

⋮ 2000

No loss

Water or no data

a

b

Biodiversity

index

High

Low

23

2.2.2 Analysis of drivers of change

The literature was reviewed to identify drivers of land cover change for the statistically significant

areas of increased biodiversity threat (‘hotspots’). This included papers that referred to the Hansen

et al. (2013b) data and a meta-analysis of the data contained in two recent global reviews of

econometric studies of drivers of land-cover change (Ferretti-Gallon & Busch 2014; van Asselen et

al. 2013), with gaps filled (seven countries) using a REDD+ assessment (Kissinger 2012). The data

for the countries with threat hotspot areas were extracted from the reviews and information on the

published drivers compiled, excluding areas identified as predominantly forestry operations in the

USA (Hansen et al. 2013b). The broad categories identified were: ‘non-subsistence’ for commercial

activities, ‘subsistence’ for non-commercial growth factors, ‘climate’, ‘social/technical’ and

‘landscape’ factors. The ‘climate’ category included climate change factors such as temperature,

precipitation, sea-level rise and extreme climate events. Social, technical and landscape factors

include aspects such as laws, policies, technologies and accessibility which may enable or inhibit

other drivers. Unique drivers identified for a unique location were counted to gauge the relative

frequency of these factors. The non-subsistence category is probably the main area of interest in this

chapter, particularly where it relates to low- to middle-income groups who earn money from

agricultural or buy their food.

2.3 Results and discussion

The study aimed to assess whether the rate of global forest loss and the resulting loss of biodiversity

have changed following the period of oil supply inelasticity in 2005. Below, we first present and

discuss the changes in deforestation rate and biodiversity loss. We then look in more detail at the

underlying drivers of these changes, in areas where the threat to biodiversity is increasing (hotspots)

or decreasing (coldspots).

2.3.1 Patterns and rates of global forest loss and threat to biodiversity

Globally, an additional 290,000 km2 of forests was cleared in the period 2007-12 compared with

2000-2006, which is a net increase of 29% between the two periods, based on Hansen et al’s total of

2.3 million km2 of forest loss for the entire period (Hansen et al. 2013b). This is approximately 24

times higher per year than the annual rate of increase in forest loss of 2,000 km2 for the period

2000-2006 and the equivalent period immediately preceding it, although the latter used different

data and methods. The areas with the highest rates of increased deforestation were broadly co-

located with the areas of highest biodiversity threat (Figure 13 a and b). This correspondence is

highlighted by the hotspot regions with the highest rate of forest loss, which largely occur in a band

through the tropical areas of south-east Asia, southern Africa and Central America (Figure 13 c).

The countries with significant hotspots are:

24

Asia: Indonesia, Malaysia, Myanmar, Thailand, Vietnam, Cambodia, Laos and China;

Africa: Angola, Zambia, Mozambique, Madagascar Tanzania, DR Congo, Benin, Nigeria,

Sierra Leone, and Liberia;

The Americas: Argentina, Paraguay, Peru , Ecuador, Panama, Costa Rica, Nicaragua,

Honduras, Guatemala and Mexico.

Forest loss was highest in Asia where over 30,000 km2 of additional forest was lost annually in the

2007-2012 period (Figure 14). Indonesia’s loss alone equates to an increase of nearly 10,000 km2

per year between the two periods, which contrasts with the decline in Indonesia’s deforestation rate

recorded in the previous decade (Hansen et al. 2010). This acceleration, together with its high

biodiversity endemism richness, makes Indonesia the country of the greatest increased threat to

biodiversity. Margono and colleagues (2014) noted that Indonesia’s increase in the rate of forest

loss was the highest globally, with 38% of the loss occurring in native forests, and the remainder

occurring in forestry and regrowth areas which were of relatively low biodiversity value.

25

Three regions experienced a slowing of forest loss over the study period: North America, Oceania

(due to the reduction in deforestation in Australia) and Russia (Figure 13 and 14). Russia

experienced an overall reduction in forest loss (nearly 5,000 km2), but this masks the substantial

areas of increased loss in European Russia, Siberia and the Far East, where timber harvesting has

b Biodiversity impact from change in deforestation rate

Reduced impact

Increased impact

c

Hotspot of increase in impact on biodiversity Coldspot of decrease in impact on biodiversity 99% confidence

Change in deforestation 2000-2012 (ha) a

Change in deforestation (km2)

-29.5 - -4.1

-4.1 - -2.5

-2.5 - -0.9

-0.9 – 0.7

0.7 – 2.3

2.3 – 3.8

3.8 – 29.5

Figure 13 a) the change in deforestation rate between 2000-2012 (from Hansen et al. 2013);

b) change in impact on biodiversity from the change in forest loss 2000-2012; and c) the

globally statistically significant hotspots and coldspots of the change in biodiversity threat

of map 4b).

26

expanded considerably. Timber is being exported to China to provide flooring and furniture for the

North American, European and Japanese markets (Newell & Simeone 2014). Although hotspot

areas in Russia include forestry operations and are relatively low in biodiversity, the very rapid

increase in deforestation during the 2007-2012 period in these areas results in significant increase in

threat to biodiversity.

Figure 14 Results show the change in the rate of forest loss (2007-2012 minus 2000-2006) in km2

per year. Deforestation has slowed in three areas (Russian, North America and Oceania). This is

outweighed by the speeding up of forest loss in the rest of the world, most notably in Asia, where an

extra 30,000 km2 per year were lost in the second period (data extracted from map in figure 13a).

2.3.2 Regions of decreasing threat to biodiversity

Although the trend globally has been towards a rapid acceleration in biodiversity impact, some

regions have seen a marked deceleration, including the Brazilian Amazon, El Salvador and two

parts of Australia. Three of these areas experienced reduced deforestation after the introduction of

new policy-driven controls aimed at retaining native vegetation: the Plano de Prevenção e Controle

do Desmatamento na Amazônia Legal (PPCDAm) in Brazil, and in Australia the Vegetation

Management Act in Queensland and the implementation of a new fire management plan in the

Great Western Woodlands in Western Australia. For these coldspots, there was a significant

reduction in forest loss after the introduction of the new measures (t-test results: p=0.001 (Amazon),

0.00001 (Qld), 0.003 (WA)). It is not known whether regions without coldspots may also have

introduced regulatory instruments, but if they have factors such as weak enforcement or conflicts of

interests with economic development this may negate the policies. El Salvador’s reduction in

deforestation has previously been noted and international remittances have been posited as a

contributory factor (Hecht & Saatchi 2007). However, the decrease in deforestation threat to

biodiversity during this period was not associated with a significant change in remittances or per

capita GDP, so the reasons remain unexplained.

-7341

-8815

-4616

30946

1601

9149

17480

9542

-15000 -10000 -5000 0 5000 10000 15000 20000 25000 30000 35000

Oceania

Russia

Nth America

Asia

Central America

Sth America

Africa

Europe

27

The clearest example of the effectiveness of government controls is in Amazonian Brazil where the

PPCDAm began targeting illegal logging in 2005, and which coincides with the most contiguous

and largest coldspot of deceleration in threat to biodiversity globally (Arima et al. 2014). The

Cerrado region has a similar plan, but its implementation did not begin till 2010 (Höhne et al.

2012), so the effects were not evident by 2012. The area along the Amazon deforestation front is a

part of the coldspot, consistent with the slowing of deforestation in that area (WWF Living Amazon

Initiative 2014). Enforcement was weakest where there were strongest economic drivers, including:

the construction of large hydroelectric dams on the Xingu and Madeira Rivers, charcoal production

in the poorest area in the northeast Cerrado and the development on prime agricultural land

southeast of that (May et al. 2010). Although this policy has been successful, relatively few of the

high biodiversity loss countries would have the capacity to implement such a policy, which

involved satellites, remote sensing analysts and helicopters (Börner et al. 2014), and it has triggered

policy resistance, with the areas being logged becoming smaller than the remote sensing detection

threshold. The hotspot to the east of the Legal Amazon region might indicate leakage to areas

outside the Legal Amazon. These three regions in Brazil and Australia experienced pre-emptive

clearing in anticipation of introduction of the new controls, with the highest level of deforestation

occurring in the year of their introduction, and the three have moved to overturn the protection

policies with subsequent administrations.

2.3.2 Relationship with the conceptual model

The conceptual model of the influence of oil depletion on land-cover change proposed the main

biodiversity loss pathways as alternative energy development, food supply extensification, and land

and food investment (Figure 10). The additional land requirement for energy alternatives has been

estimated to be up to 3 million km2 by 2020 (Scheidel & Sorman 2012), so this is likely to have

been a contributory factor. The remaining pathways of land and food investment and agricultural

extensification due to fertiliser substitution are now considered.

2.3.2.1 Land and food investment

There was no significant correlation between the change in rate of deforestation and the locations

for land acquisitions under negotiation (r2 = 0.07). The correlation with deforestation increased to

0.11 when only Land Matrix data were used, though they accounted for 85 out of 90 country totals.

The Land Matrix only includes less developed countries, so it may be that the increase in

correlation was related to the tendency for both deforestation and land acquisition for occur in these

countries. Land and food investment can manifest as the commercial acquisition of agricultural or

potential agricultural land, and is driven by underlying economic motives and food security

28

concerns. Such land grabbing increased markedly between 2007 and 2012 and the scale of land

acquisitions world-wide is of the order of hundreds of thousands of square kilometres, which makes

land acquisition potentially a future major contributor to biodiversity loss. Several African and

southeast Asian countries have proposed land-grab areas larger than their total current area of arable

land (Figure 14). This will inevitably result in the conversion of natural land and the subsequent

loss of biodiversity if the total area under negotiation were put into production.

Based on 2013 data, restricting the correlation to include only land deals where the land is known to

be under production reduced the correlation by a factor of 10. This may be due to preferential use of

existing cropping land, and is consistent with the common complaint that such deals displace

traditional uses of the land (Ambalam 2014; Glazebrook & Kola-Olusanya 2013; Pearce 2012;

Rosset 2013). Perhaps it has been discovered that increased commodity price is more than made up

for by the costs of land clearing and transport.

Although the low correlation between land-grabbing and increased deforestation indicates that land-

grabbing is not currently a major contributory factor to deforestation, its contribution could increase

significantly as more of the acquired lands are put into production or displaced landholders develop

new agricultural lands. It can be argued that land-grabbing has the potential to improve land-use

efficiency by improving use of productive land (Lambin & Meyfroidt 2011), and any deforestation

in the place where the land is acquired may be offset by a reduction in deforestation in the country

acquiring the land, possible reducing deforestation, if the acquired land were more productive. Land

Figure 14 Countries where the area of land under negotiation for acquisition is larger than

the area of arable land in the country. If developed, these land concessions would

inevitably result in the conversion of natural vegetation to farmland.

1381%

112%

353%

2135%

101%

498%

182%

> 100%

50 - 100%

30 - 50%

5 - 50%

0 - 5%

29

-0.35

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

High:OECD

High:nonOECD

Uppermiddle

Lowermiddle

Low Hotspotcountries

Elas

tici

ty

grabbing might increase deforestation if the price of land made extensification more cost effective

than fertiliser use. In either case there could be a net impact on biodiversity as target countries tend

to be more biodiverse. What matters is the total land used for agriculture, driven by demand for

food and energy, and the biodiversity of the land which is being used.

2.3.2.2 Cropland extensification through fertiliser substitution

To investigate land-fertiliser substitution as a contributory factor to deforestation, the price

elasticity of demand for fertiliser in the hotspot countries was compared with other countries,

grouped by their World Bank income classification (Figure 16). The OECD countries continued to

slightly increase their fertiliser consumption, despite the 4-fold price increase. The other income

groups decreased their usage by between 15% and 29%. The hotspot countries decreased their

usage by 29%, on a par with low income countries, despite being comprised of a fairly even mix of

upper-middle, lower-middle and low income countries (8, 6 and 6 countries respectively). If land

were substituted to compensate for yield losses associated with these reductions in fertiliser use, this

would contribute to agricultural land expansion and potentially deforestation. The non-subsistence

groups of low- to middle-income

agricultural landholders may be affected

by both price rises for agricultural

inputs, particularly fertiliser, and by land

acquisitions of existing agricultural land,

and hence drive deforestation for new

agricultural land. Other processes may

account for the observed LUC, and

marginal elasticity analysis would be

necessary to determine causality. Locally

prices can vary considerably but local

price data was not available globally for

this study. We would have liked to

compare the hotspots’ agricultural yield

changes during this period, but, while

this information is being collected by FAOSTAT and the Global Yield Gap Atlas, hotspot regions

are currently not covered. This would be a useful future research topic.

Figure 16 Change in fertiliser consumption with price for countries of different income groups. The

hotspot countries where increased deforestation had increasing impact reduced their consumption of

30

fertiliser by a similar amount to the poorest countries. This reduction in use might be expected to

impact yields and hence land requirements to meet food needs.

2.3.3 Underlying drivers of change

A meta-analysis of data from three recent reviews of land-change drivers (Ferretti-Gallon & Busch

2014; Kissinger 2012; van Asselen et al. 2013) was undertaken to identify the drivers of change in

the hotspot areas (appendix 1). The reviews did not contain drivers for Angola, Benin, Nigeria,

Sierra Leone, Paraguay and Nicaragua. Our analysis indicates that non-subsistence growth factors

dominate (the combined consumption impact of economic growth and population growth among

non-subsistence populations), with subsistence population growth and climate change playing more

minor roles (Figure 17). Social and institutional factors such as laws and enforcement contributed in

an enhancing or deterring role. Increased use of land is predominantly for profitable pursuits, with

the provisioning of the local population, ecosystems services and the increased impacts of fire,

flood and other extreme events less frequently identified.

Figure 17 Number of times that drivers of land conversion were identified in reviews of

deforestation and wetland conversion in the hotspot areas. The non-subsistence commercial drivers

dominate subsistence and climate factors, while social, technological and landscape factors such as

policy or slope play an enabling or hindering role.

2.3.4 The connection between oil, the economy and land-use

Our conceptual model posited that changes in oil supply and price feed into four pathways that in

part lead to land conversion and deforestation, namely: energy development, land-fertiliser

substitution, land investment and food investment (Figure 10). Our analysis of the drivers of

deforestation found that the vast majority were linked to economic opportunity for non-subsistence

population groups. The interaction between the oil supply and economic activity is complex,

because while supply constraints have an overall inhibiting effect on the economy, more cost

effective energy, food and investments are then sought, especially from the most fundamental of

natural resources: land (Murphy 2014).

The overall increase in deforestation rate observed in this chapter is consistent with the prediction

that constraints to the oil supply have created an increased demand for land, due to increases in

fertiliser prices, use of land for alternative energy crops rather than food, and potentially land grabs

66

16

5

22

15

0 10 20 30 40 50 60 70

Non-subsistence

Subsistence

Climate

Social/technical

Landscape

31

to secure food supplies. We recognise that determining a direct cause-effect relationship from the

correlation shown here is difficult. There are many other factors influencing changes in

deforestation rates such as climate change with increased incidence of fire, population increase and

increased consumption and waste of meat and other land-intensive products, but these would be

expected to change more slowly and may not be detectable over a 6 year time period. This period

also coincided with the spike in oil, food and fertiliser prices. Monitoring over a longer time period

would be required to definitively attribute cause to the observed change (Czech 2014; Smil 2011).

However, Brunelle and colleagues (2015) modelled the expected expansion of cropland due to

increases in fertiliser price and resultant fertiliser-land substitution for the period in question, and

predicted that the area of cropland would increase by 0.4% per year, with fertiliser prices increasing

as during the decade 2001-2010. At 4.8 Mha/year, the increased rate of deforestation shown in this

chapter represents about 76% of this additional land requirement. Not all new cropland is gained

though forest conversion (eg some is converted from grazing land), so the current findings are

consistent with Brunelle and colleagues’ predicted land-use change.

Per capita food production has declined for 30 years through a combination of population growth

and energy and water constraints (Pimentel & Pimentel 2007). This poses the risk of growing food

insecurity and land requirements for food production in the future with further energy constraints.

The drivers outlined above indicate that economics of the non-subsistence sector are largely driving

land cover change. The effect of oil prices are embedded in these economic factors , with oil shocks

(supply disruptions) causing recessions, and impacting on exchange rates, foreign reserves,

inflation, credit availability, materials, food, heating and transport costs, which disproportionately

affect the poor and the agricultural sector (Butler 2009; Headey & Fan 2008; Neff et al. 2011). The

2008 oil price rise was different from previous oil supply shocks in that it was driven by an inability

of production to keep up with demand, especially from India and China. This resulted in a slowing

global economy which then led to lower oil prices making more expensive sources unviable to

extract (Childs & Kiawu 2009; Deininger et al. 2011; Hamilton 2009; Piesse & Thirtle 2009). Food

production costs are highly susceptible to oil price, with production, processing marketing and

transportation affected (Baffes & Dennis 2013; Childs & Kiawu 2009; Headey & Fan 2008). The

cost of oil contributed more than 50% to the rise in cost of maize, wheat, rice and soybeans in the

period to 2012 (Baffles 2013). As cropping extensifies, the energy required per tonne of produce for

fuel would also increase.

32

Much of the increase in food price can be attributed to the cost of fertiliser which accounts for as

much as 20% of the price. Fertilisers are the commodity most sensitive to oil price, as they are

highly energy intensive to make, and price rises are exacerbated by limits to production capacity

(Baffes 2007; Headey & Fan 2008; Piesse & Thirtle 2009). Rises in fertiliser price impact on farm

yields, especially in developing countries where small holders may lose previous production gains

(Brunelle et al. 2015; Conceicao & Mendoza 2009; Piesse & Thirtle 2009). A peak in the oil supply

is expected to be associated with increased competition between energy and food supplies with

inherent environmental impacts (Scheidel & Sorman 2012).

In addition to yield loss, the food supply is impacted by the diversion of food crops into biofuels. In

2008, for example, 30% of US maize was used for ethanol production (Baffes & Dennis 2013). Oil

prices drive biofuel development, and have resulted in US wheat and rice areas being converting to

maize (Anseeuw et al. 2012; Coyle 2007; Miranowski 2014). Much of the biofuel expansion has

been driven by US government subsidies and European Union government mandates, motivated by

the effect of high prices on balance of trade, with expansion occurring in the tropics where the

climate is most productive and costs are lowest (Demissie 2014). This policy resulted in a sudden

increase in the demand for biofuels which is likely to have been influential in the increase in maize

prices, and has resulted in the expansion of US production by over 120% per year during the 2005-

2008 period despite the economic downturn (Baffes & Dennis 2013; Headey 2011; Holt-Giménez

2009). The loss of food production to biofuel production creates pressure for agricultural expansion

(Pretty et al. 2010). Forest clearing rates are sensitive to product prices and demand with cropping

area expanding by up to 25% for each doubling of crop price, and land demand is likely to continue

to grow in the future, with increasing demand for all alternative energy sources including wood and

hydroelectricity, and biofuels predicted increase to 44 million hectares by 2030 (Haile et al. 2013;

Kissinger 2012; Wheeler et al. 2013).

3.0 Conclusion

In this chapter, we investigated whether the rate of global forest loss and the resulting loss of

biodiversity have increased in association with inelastic oil supplies after 2005. Providing

conclusive evidence that changes in oil supply and price cause changes in forest cover is

challenging, as there are many other factors influencing change in the deforestation rate. The recent

rapid increase in deforestation, particularly in south-eastern Asia and central America, and its

colocation with high biodiversity mean that the threats to biodiversity are greater than previously

thought. While deforestation is primarily being driven by growth in demand for food and energy,

33

future constraints to the oil supply could drive agricultural extensification or intensification with

varying consequences for biodiversity.

Conservation efforts need to consider how changes in oil supply constraints and oil price affect the

footprint of agriculture, and how this could affect biodiversity. Conservation strategies need to take

into account these economic drivers, and investigate options for less land-intensive energy and

fertiliser sources. Research is needed to monitor future trends for a possible causal link between oil

price and supply and land conversion and biodiversity loss. However, a precautionary approach is

necessary, as much biodiversity could be lost waiting to gain sufficient data to demonstrate

causality.

34

Chapter 3: Global land-use requirements and impacts of crop production without

petrochemical fertiliser

Abstract

Modern agriculture is dependent on nitrogen fertilisers, but petrochemicals supply limitations may

lead to agricultural extensification. We examine potential changes to cropland area and impacts on

biodiversity and food security without these fertilisers. We used global nitrogen-use efficiency data

to estimate cropland requirements, finding that 2.4–5.4 times as much land as current cropland

would be required. Without nitrogen fertiliser we cannot meet crop production requirements from

available arable land, resulting in food insecurity and loss of biodiversity. Global cropland

expansion was mapped by iteratively extending and intensifying existing cropland. Food insecurity

would most affect regions from Central Asia to the Middle East, and biodiversity loss would most

affect East and South-east Asia and Central America. Price sensitivity tended to increase the

difference between wealthier countries and the rest, increasing both biodiversity loss and food

insecurity. Affordable replacements for mineral nitrogen are needed to retain global food security

and biodiversity.

3.1 Introduction

The mid-2000s were marked by a period when the global oil supply did not keep up with demand

resulting in high prices for petroleum and petroleum-based products such as fertilisers (Murray &

King 2012). This period was associated with an increase in deforestation, especially in areas of high

biodiversity (Eisner et al. 2016b). Globally, the conversion of native ecosystems to agriculture is the

primary driver of biodiversity loss (Ferretti-Gallon & Busch 2014; Foley et al. 2011). Modern

commercial agriculture is dependent on fertilisers which require petrochemicals to supply the

energy necessary to fix nitrogen from the atmosphere, and as the source of hydrogen to create the

compounds used in nitrogen fertiliser, such as ammonia (NH3). Farmers make decisions about the

relative cost-effectiveness of using fertilisers and expanding their cropland in a process known as

land-fertiliser substitution, often in response to price variations in fertilisers driven by oil prices

(Brunelle et al. 2015). Since mineral fertilisers are petrochemical-intensive and petrochemicals are a

finite resource, the world will be required to increasingly use non-petroleum derived nitrogen as

petroleum resources decline. This poses the question of the potential impact of these changes in

arable land use on biodiversity.

Nitrogen is the key limiting factor in many agricultural systems (Bhattacharjee et al. 2008), with

more cropland required to achieve the same level of crop production if mineral nitrogen became a

scarce resource. For example, if green manures were used to replace nitrogen fertiliser, more than a

third of cropland would be required for this purpose, and that without nitrogen fertiliser crop yields

35

would drop to about a third of current yields (Fischer et al. 2012). If this occurred, the Earth could

support approximately 4 billion people (Bardi et al. 2013). To compensate for this lost productivity,

farmers may extensify by converting currently unused land to cropping (Brunelle et al. 2015).

Based on evidence of the change in the rate and distribution of deforestation during the 2007–2008

Global Financial Crisis (GFC), and because of the productivity-biodiversity relationship at the

global scale (Chase & Leibold 2002; Currie & Paquin 1987), we would expect these land-use

changes to be concentrated in the tropical and subtropical regions, which still have a high

proportion of intact ecosystems and biodiversity.

This chapter aims to estimate the impact of the cessation of mineral N use in agriculture on: 1) the

biodiversity that would be replaced by cropland expansion, and 2) food security due to reduced

yields. We used the predicted area required to compensate for a lack of mineral N, to expand the

existing cropland footprint, and compared the biodiversity implications with the changes which

occurred during the global financial crisis. Because fertiliser use is mediated by the price of

fertiliser, the influence of price was also investigated. We explored the implications for food

security by comparing the additional cropland requirements with the unused arable land area of

countries, and examined the relationship this had with their nitrogen price elasticity and dependence

on food imports.

3.2 Data and methods

3.2.1 Conceptual model

In our conceptual model for N-use and cropland-habitat dynamics (Figure 18), constraints to the

supply of N fertiliser and resulting increases in price reduce its use in agriculture and increase the

amount of land required to maintain food production, thereby encroaching on native ecosystems.

Conversely, if N price decreases and supply increases, this reduces the footprint of the cropland

required, increasing potential habitat, albeit secondary forests, for biodiversity. Land which has

been used for crops may have lower biodiversity for hundreds of years (Catterall et al. 2012).

Changes in the price of nitrogen and greenhouse abatement policies could have similar effects.

Area of

Habitat Area of

Cropland

N-use

N supply constraints

N price

Greenhouse abatement

Figure 18 Conceptual model of the sensitivity of the area occupied by cropland to N-use. The area required to

produce the same quantity of food increases with decreased N-use which may result from supply constraints,

price increases or greenhouse abatement policy.

36

3.2.2 Data sources

To assess the future cropland area requirements, we used a nitrogen-use efficiency dataset for 80

countries over a 42 year period. This dataset included the regional production of a broad range of

grains (rice, wheat, maize, barley, sorghum, millet, oats and others), for a range of soil and climatic

conditions (Dobermann 2006; Dobermann & Cassman 2005). We also used a digital map,

developed by Fritz et al. (2015) of current cropland at a 1 km scale where each pixel represents the

percentage of cropland in that pixel. This was compiled from a number of regional and global maps,

including MODIS v5, and validated with high-resolution satellite imagery using Geo-Wiki and

crowdsourcing. A classification accuracy of 82.4% was achieved (Fritz et al 2015). For the

biodiversity impact, we used an index of endemism richness compiled from indices of the known

ranges of plants and vertebrates and mapped by ecoregion (Eisner et al. 2016b; Kier et al. 2009).

The food security component was based on the data for potential arable land from a World Soil

Resources Report which combined the Soil Map of the World, climate data and crop soil and crop

climate requirements for 160 countries (Bot et al. 2000). This produced a suitability rating on a 5-

point scale from ‘very suitable’ to ‘not suitable’. All of the suitability categories for the 21 crop

types, except for the ‘not suitable’, were considered to be potential arable land. The Fritz et al.

(2015) dataset also was used for current arable land. Net food importation was calculated using raw

food import and export data from a World Bank Policy Research Working Paper which were based

on United Nations COMTRADE Statistics (Ng 2008).

The N price elasticity was calculated using nitrogen fertiliser price and use. The price information

was sourced from Fertilizerworks, which is a supplier of information to the agricultural industry and

provides a ‘basket price’ for N-based fertiliser (Fertilizeworks 2011). The data on N-use was

sourced from the FAOstat ‘Nitrogen Fertilizers (N total nutrients)’, for the period 2002–2012 in

order to capture the change in price which occurred during the global financial crisis (FAO 2014).

3.2.3 Calculating land requirement without mineral N fertiliser

A yield range for cereal crops without mineral N was extrapolated using a linear model of nitrogen-

use efficiency data from 80 countries (Figure 19). The yield response to nitrogen fertiliser was

found to be linear (Dobermann 2006), and so it was possible to estimate a range of values for zero

N from the global range of application rates using the function, Yield = 0.03 kg N + 1.05, giving the

value for yield of 1.05 at a value for N application of 0. We used this to estimate a range of yield

reductions using a current global average N application rate of 75 kg/ha/yr (Alexandratos &

Bruinsma 2012) minus the yield without mineral N and 95% confidence limits, which represents the

uncertainty in the regression line. I used these limits from the model to project minimum and

maximum future land requirements. The intercept was used to calculate the additional land

37

requirements to maintain current food production without mineral N, by multiplying the current

cropland area by the yield reduction. The energy required for land clearing was not included in the

calculation.

The area required for cropland without nitrogen fertiliser was modelled by iteratively expanding

and then intensifying current cropland by equal areas to achieve the minimum and maximum area

required (Figure 20). Equal expansion and intensification were chosen because the relationship

between intensification and expansion is not well understood, and this method produced cropland

expansion which had the same relative intensity as current cropland use. Intensification involved

increasing the proportion of cropland in each cell, rather than increasing crop yield. The current

cropland map is a 1 km grid with the percentage cropland, or intensity, in each cell. Cropland was

expanded by spreading into new, unused areas if those areas had cropland in a neighbouring cell. It

then took the value of the lowest intensity neighbour which had any cropland. When this had been

done globally, the additional area which had been gained was calculated and the global cropland

area was increased by the same amount by increasing the percentage of cropland which occurs in

each cell. In this way, the area was increased by equal expansion and intensification. A maximum

cell intensity limit of 73% was set, which reflected the intensity which was rarely exceeded in the

dataset. This process was repeated until minimal additional cropland area was found.

Algeria

Ethiopia

Myanmar

HondurasAustralia

El Salvador

Saudi ArabiaSpain

SwitzerlandEgyptUK

France

USANew Zealand

0

1

2

3

4

5

6

7

0 20 40 60 80 100 120 140 160

Me

an c

ere

al y

ield

(t/

ha)

N fertilizer rate (kg N/ha harvested)

Figure 19 Nitrogen use efficiency for cereal production for available countries (Dobermann 2006). These data

were used to derive a mean global yield for an N application rate of 0 based on the equation y = 0.03 x + 1.05,

with R2 = 0.68. Minimum and maximum yields were derived from the 95% confidence limits.

38

Cropland was expanded and intensified iteratively in this way without taking into account current

land use or agricultural suitability because we were interested in the amount of land required rather

than whether expansion was feasible.

3.2.4 N price sensitivity effect on cropland demand

The difference in the future distribution of cropland due to an increase in the price of nitrogen

fertiliser was estimated using the change in price and the changes in nitrogen usage patterns.

Several countries, mostly in Africa, lacked N-use data so price sensitivity could not be calculated.

The nitrogen price information and FAO nitrogen-use data were used to calculate N price elasticity

given by δ(nitrogen use)/δ(nitrogen price) for the years 2002–2012. This price elasticity was

factored into the minimal increased cropland requirement to indicate how a price change would tend

to influence N-use and therefore cropland expansion.

3.2.5 Biodiversity impact of cropland expansion

In order to gauge the impact of future cropland expansion on biodiversity, an index of endemism

richness was factored into the minimum cropland expansion map. This method was applied to the

map of price-mediated cropland extent to compare the biodiversity impact of even distribution and

price-mediated distribution of cropland expansion.

3.2.6 Food security

Food security was estimated by the availability of unused land which is suitable for crop production

to meet future cropland expansion requirements. This was calculated by subtracting the area

required for cropland expansion from the area of potential cropland (based on land suitability), by

Expand

Intensify

Reached target?

1km

Figure 20 The approach used for cropland expansion modelling. The additional area required is distributed

by iteratively expanding into new areas and infilling existing areas. At (a) there are two existing cells with

cropland (one low, say 10%, and one medium, say 35%). In (b) expansion has occurred to bring each

neighbouring cell up to the same percentage (the lowest of 10% or 35%) of cropland as the two original

cells. In (c) the amount of cropland in the all the cells is increased by the same proportion of agriculture (so

an additional 10% or 35%) up to a maximum of 73%. If this does not meet the target then the process is

repeated from the new stating state. A grid cell size of 1km was used.

0%

% cropland

Low %

Medium %

High %

Steps 1) Expand cropland one

pixel out into non-cropland

areas at lowest neighbouring

cropland %

2) Calculate area gained and

increase the area by the

same amount by increasing

the % cropland of each pixel

3) If the target has not been

reached, repeat

(a)

(b)

(c)

39

country. The results were classified into four categories: countries which have no unused arable

land, countries with insufficient land to support the minimum expansion required, countries with

insufficient land to accommodate the maximum potential expansion, and fourthly, countries with

sufficient arable land. Major food importing countries and countries which are sensitive to the price

of N fertiliser were also identified. Food security was assessed on a national basis because the data

for potential arable land was available by country, because most food is consumed locally with

relatively little exported and because of lack of freedom of movement across borders to obtain food.

3.3 Results

We present four main results.

3.3.1 Land requirement without mineral N fertiliser

In order to meet the current total global crop production without mineral N, between 2.4 and 5.4

times today’s cropland area would be required (Figure 21). This range is due to the variability in N

response globally and reflects the 95% confidence levels in the linear model of crop response

(Figure 19). The minimum additional cropland requirement (2.4 times current cropland) is shown in

Figure 21b. The amount of available land globally was exceeded when an area 3.9 times the current

cropland area was reached (Fig. 21c). The target of 5.4 times the current cropland – the maximum

amount of land required to match today’s crop production without nitrogen fertilizer - could not be

reached. Even at a lower end of the range, biodiversity loss would be substantially increased, since

essential agricultural expansion would use all the available productive land and still be expanding

onto ever more marginal land, leaving little for native ecosystems and their biodiversity. This is

based on country-level N-use data, and there is considerable within-country variation. There is the

opportunity to reduce N use in regions with the highest application rates with little impact on yield.

3.3.2 N price sensitivity effect on cropland demand

The change in N-use due to the price of N which occurred during the GFC was factored into the

projected cropland requirements to gauge the change to future cropland taking price into account.

With price elasticity factored in, some regions increased their cropland expansion rate to keep up

with reduced N use while other areas continued to intensify. The regions which continued to

increase their N use included western Europe, southern Central Asia, parts of the eastern Asia

(Vietnam, Japan and South Korea), North Africa (Morocco and Libya), the Pacific Islands (Vanuatu

and Fiji) and most of the Caribbean, except Cuba. The regions which would require more land for

cropping included Mexico, non-Amazonian Brazil and neighbouring South American countries;

eastern Europe and Russia; Southeast Asia; and western Africa and Angola (Figure 22b). Typically,

countries in the wealthiest regions (OECD countries) were affected less by price increases and all

40

the other countries were affected more, even those categorised as ‘high income’ by the World Bank,

although we found that there is no association between price elasticity and per capita GDP (r2 = -

0.04).

3.3.3 Biodiversity impact of cropland expansion

To gauge the impact of this cropland expansion on biodiversity, an index of endemism richness was

factored into the minimum additional area required for cropland. The regions where biodiversity

was most impacted were eastern and Southeast Asia, northern sub-Saharan and eastern Africa,

Central America and non-Amazonian Brazil. Without mineral N, eventually most intact ecosystems

and their biodiversity would be lost on productive land. Even the minimum area required would

have a significant global impact (Figure 22a). Price mediation reduced the relative impact across

Western Europe, parts of central and Eastern Asia, Africa and New Zealand and increased the

impact in Central America, and parts of northern and South East Asia, western and southern Africa

and South America (Figure 22b). These changes tended to concentrate the cropland expansion in

areas of higher biodiversity.

3.3.4 Food security

The geographic extent to which countries’ future cropland expansion requirements can be met by

unused arable land (Figure 23) shows the countries which are most dependent on food imports and

which are most sensitive to the price of fertiliser. Twenty-six countries have no unused arable land,

including most of the Arabian Peninsula and central Asia. Saudi Arabia was the only country to

have all three factors: no remaining arable land, N price-sensitivity and is a major food importer.

Six countries have both no unused arable land and N price sensitivity Mongolia, Oman,

Kazakhstan, Kyrgyzstan, El Salvador and Rwanda. A further four countries have no unused arable

land and are also import dependent: UAE, Egypt, Iraq and Kuwait.

A further 50 countries would have insufficient cropland with the minimum additional land which

would be required without mineral N and Algeria and Greece would join Saudi Arabia as both

import-dependent and price sensitive while running short of arable land. Five countries would have

insufficient land and are N price sensitive including Ukraine, Uganda, Honduras, Guatemala and

Myanmar. Several countries are import-dependent and would be short of land. These include Italy,

Lithuania, Malaysia, South Korea, Bangladesh, Indonesia and Nigeria.

Forty-three countries would have sufficient arable land even in a worst-case scenario, notably

northern South America, southern Africa (excluding South Africa), Nepal, Laos, Papua New

Guinea, New Zealand, Germany, Sweden and Finland. There appears to be a negative association

between N price elasticity and import-dependence, which rarely coincide, especially in countries

41

with little spare cropland capacity. The potential relationships between land shortage, N price

sensitivity and food importation are given in Figure 24, based on a decision tree for selecting the

most cost-effective viable solution. Lack of local land for food production could be resolved by

increasing imports, provided that the population has the financial capacity to pay for it.

42

Figure 21. Projected increase in global cropland area to meet food production requirements without mineral

N: (a) current cropland; (b) minimum additional cropland expansion without mineral N (2.4 x current

cropland); (c) maximum cropland expansion achieved by the algorithm (3.9 x current cropland). The area

required ranged between 2.4 and 5.4 times the current cropland area, but the model ran out of additional land

at 3.9 times the current area. Land suitability was not taken into account.

a

b

c

Percentage

cropland 0 – 12% 12 – 38% 38 - 64% 64 – 72% > 72%

43

N Price elasticity

> 0%

0 – 10%

10 - 20%

20 - 30%

> 30%

Increased impact

a

c

b

Figure 22The impact of minimal additional land requirements on biodiversity. (a) Even with the minimum

requirements biodiversity is impacted globally, but especially in biodiverse areas such as Central America,

Brazil, West Africa and East Asia. (b) The proportional effect of fertiliser price elasticity on different

countries (the grey areas in b), and c) have no data). (c) The impact on biodiversity of price elasticity and

minimal land requirements. The impact is reduced in western Europe and through central Asia and

intensified in parts of the Far East, Central and South America and West and southern Africa.

44

All unexpected results are summarised in table 2.

Table 2: Most often, the results were as we expected. These are the unexpected results relating to N price

sensitivity, income level, biodiversity loss, biodiversity level and comparison with biodiversity impact during

the GFC

Unexpected factor Regions affected

Lower income & N price insensitive Central Asian countries, Vietnam, Fiji, Morocco and Libya

Wealthier and N price sensitive Russia (Ed = -0.22) and Canada (Ed = -0.11)

Medium biodiversity areas with high

biodiversity impact

Parts of Brazil and West Africa, East Africa and southern Africa,

parts of eastern and Southeast Asia and Myanmar, and Cuba,

Hispanola and Jamaica in the Caribbean

Lower biodiversity but high biodiversity

impact

Peru, Bolivia Argentina and parts of Brazil, Chad, Sudan, Ethiopia,

parts of Tanzania and the Atlantic coast of Madagascar, Vanuatu

Reduced biodiversity impact of N price

in lower income countries

Most of Central Asia, India, Vietnam, Thailand, China, Uganda,

Nigeria, Uruguay, Cuba

Biodiversity impact of cropland

expansion, not hotspots during the GFC

The Caribbean, the Mexican central valley, southern Pacific coast

of Mexico, Colombia and the Brazilian Cerrado

Biodiversity loss during the GFC, but

not associated with cropland expansion

Myanmar (mainly due to timber and resource extraction)

3.4 Discussion

We projected future cropland needs without mineral N and found that these needs would exhaust

the land available. This would seriously impact biodiversity globally, and especially in high

biodiversity areas. There was a tendency for wealthier areas to expand less and poorer areas to

expand more in response to changes in the price of N, which increased the impact on biodiversity as

Figure 23. Availability of unused arable land to meet the need for future cropland expansion. In decreasing order

of food security: 1) no unused arable land so already insecure, 2) insufficient land to meet minimum expansion

needs (would become insecure), 3) insufficient land to meet maximum expansion needs (might become

insecure), 4) sufficient land to meet food needs without petrochemical fertiliser (sufficient potential cropland).

45

poorer areas often have higher biodiversity. Many countries lack sufficient surplus arable land to

accommodate the expansion needed which would result in food insecurity.

The world would be unlikely to be able to meet the minimum additional cropland requirement to

maintain current levels of food production without N. This would require 2.4 times the current

cropping area. This minimum requirement is considerably greater than estimates of remaining

cropland. These range between 1.22–2.0 times current cropland (Alexandratos & Bruinsma 2012;

Fischer et al. 2012; Lambin & Meyfroidt 2011) or 2.6 times the land used by the mid-1990s, much

of which was grazing land or forested at that time (Bot et al. 2000). The extent of potential cropland

expansion without mineral N makes future constraints to the supply of petrochemicals a serious

threat to both biodiversity and food security. In particular, the Indian sub-continent is already

approaching saturation, which means that there is limited opportunity for further expansion in this

region.

The application of N to livestock pastures was not included in this study. Although a few countries

apply most of their N to pasture (e.g. United Kingdom), these countries tend not to be major food

producers, with pasture having a less crucial role in food security than grain production. However,

in a food crisis there would be some capacity to redeploy N from pasture to cropland to improve

food security and reduce the tendency to extensification. Similarly, grains used for animal feed

could be more efficiently used for direct human consumption (Foley et al. 2011). The linearity of

the country level N response masks a levelling off of yield response as higher application rates

which are used by a proportion of farmers in some countries, notably China and India. These cases

No

Wait Enough

food? Yes

Crisis

Yes

Import Intensify Extensify

Implement

No

Afford

it?

Cheapest viable

Figure 24 (a) Land supply, food imports and N price.

Countries may be less sensitive to N price if they are

short of land (so have less choice) or if they import their

food, so are not so dependent on N-use. But if they are

not short of land they may be less N price sensitive,

preferring to expand. (b) Countries would likely choose

the most cost effective viable solution, as long as they

can afford it.

a) b)

46

present an opportunity to target N reduction with reduced land cost, which have been explored by

Foley and colleagues (2011).

Papua New Guinea appears to have the largest proportion of available cropland (Figure 21c). This is

likely to be an artefact of the original cropland data which had poor correspondence between

datasets for PNG. Fritz (pers. comm.) attempted to compensate for this by weighting by the

FAOstat data for temporary and permanent crops, but the data may still be unreliable for this

country. There is also a possibility that the difference may be real as others have found very low

cropping densities in PNG (Deininger 2011; Ramankutty et al. 2002). This requires further

investigation.

3.4.2 N price-sensitivity effect on cropland demand

We predicted that an increase in the price of N fertiliser on cropland expansion would tend to

increase the cropland required in poorer nations and have a weak effect in wealthier nations where

the price change is a relatively small proportion of their budget and the spending is non-

discretionary (Heakal 2015). This was generally the case, although there were some exceptions

(Table 2). These included Libya, Morocco, Vietnam and Fiji which are countries where subsidies or

international aid in the form of fertilisers may distort markets and wealthier countries which are

major agricultural producers (Duflo et al. 2009; Fattouh & El-Katiri 2012; Guixia 2015; Orden et al.

2007; Wanzala 2010). Such subsidies can be extremely expensive for poor countries. For example,

it cost 16% of the national budget for Malawi during the 2008/9 surge in the price of N, and several

countries stopped such subsidies during this period (Dorward & Chirwa 2011; Druilhe & Barreiro-

Hurlé 2012; The Future Agricultures Consortium 2008).

3.4.3 Biodiversity impact of cropland expansion

In this study, we focused on the expansion of cropland in response to reduction in use of N

fertiliser. Land in regions with higher biodiversity is more likely to be replaced through agricultural

expansion than low biodiversity regions since both natural ecosystems and agriculture need similar

resources: water, the sunlight, nutrients (Currie & Paquin 1987). While some areas of high

biodiversity, such as Central America and parts of the Indonesian archipelago, are also areas of high

impact, the most intense impact includes areas with medium and lower levels of biodiversity (Table

2). These differences between current biodiversity levels and expected biodiversity impact from

cropland expansion occur because the expected cropland expansion is so great that it creates a large

impact even though there is less biodiversity. India was not identified as having high biodiversity

loss since most potential for land conversion has already been exhausted.

47

The effect of price on fertiliser use (Figure 22b) may be expected to increase the impact on

biodiversity in poorer, less developed areas where a large component of global biodiversity

remains. This is generally the observed pattern, although there are some surprises (Table 2). Apart

from the impact of subsidies, the indirect impact of N price on biodiversity might be accentuated by

the proximity of existing croplands to lower cost but higher biodiversity areas.

There are similarities between the biodiversity impact of projected cropland expansion (Figure 22a)

with that which occurred during the GFC when fertiliser price increased 5-fold (Eisner et al.

2016b), with the impact concentrated in Central America and South East and eastern Asia. Some

regions are impacted in the cropland expansion map that were not hotspots on the deforestation map

(Table 2). This may be because in these areas the expanding cropland would sometimes replace

ecosystems other than forests, and all of these regions have areas with less than 25% tree cover

(Hansen et al. 2013b). The reverse is less common, but an example is possibly northern Myanmar

where deforestation has other drivers than cropland expansion. Myanmar is experiencing forest loss

at a rate of 1.4% per year, and in the northern border area this is largely driven by timber exports to

China (Matthews et al. 2010).

We did not address either the biodiversity occurring within the cropping land itself (land-sharing) or

the dynamic between cropland and pasture land, with one third of cropland expansion occurring on

existing pasture (Morton et al. 2006). However, intensification spared 17.6 M km2 between 1961–

2005, exceeding available land reserves (Lambin & Meyfroidt 2011). Newly acquired cropland is

increasingly unproductive as the best land is used first, requiring more area and inputs to maintain

production (Fischer et al. 2012). In particular, maintaining production in developing countries will

require intensification, but the use of N fertiliser has decoupled agricultural expansion and

population growth (Niedertscheider et al. 2016). Intensification for cash crops can drive cropland

expansion by increasing demand through reduced price (a process known as Jevon’s paradox),

which is exemplified with soybeans in Brazil, and oil palm in Indonesia and Malaysia (Lambin &

Meyfroidt 2011). Overall, cropland expansion exceeds contraction, and biodiversity remains lower

in recovering land (Hobbs 2012).

Available datasets for potential cropland often exclude areas of forest and protected areas on the

grounds that these should not be converted to cropland (Alexandratos & Bruinsma 2012; FAO

2014; Fischer et al. 2012, p. 17). However, to assess the threat to biodiversity, data on land

suitability within these high biodiversity areas needs to be analysed and it would be helpful if data

providers (such as FAO) would include it in their datasets.

48

3.4.4 Food security

Most of the world would have insufficient arable land to accommodate the expected need for

additional cropland without mineral N. In this case, we could only support half the population on a

very basic, vegetarian diet (Smil 2004). Legumes are a promising protein source which reduce

reliance on N inputs, but they are underdeveloped as crops and increasingly unpopular (Fischer et

al. 2012). Not all current cropland is on land which is considered suitable. Over 220 M ha or about

14% is unsuitable (Alexandratos & Bruinsma 2012), and maybe unsustainable in the long term. Our

analysis includes all land categories except ‘unsuitable’, so maybe overoptimistic.

Our study indicates a negative association between the capacity to expand cropland, dependence on

imports and N price sensitivity. A shortage of cropland would lead to higher food import

dependence. This land supply/importation relationship has been modelled using systems dynamics

(Gerber 2014). It has also been shown that fertiliser application rates decline with increased land

area, which implies that restricted land supply would increase cropping intensity and reduce N price

elasticity (Abedin 1985). It might also be expected that being short of cropland might mean less

sensitivity to the price of N since spending on food is a necessity and necessities tend to be

insensitive to price (Heakal 2015). However, several countries are price sensitive and have a limited

availability of land which may reflect a lack of capacity to pay higher prices.

India’s current level of cropland saturation and N price sensitivity suggest a risk of future food

insecurity, but India is currently a net food exporter, allowing room for some productivity loss. A

potential threat to food insecure countries is lack of foreign exchange with which to import food.

Many of the oil-rich countries depend on oil sales to fund food imports. Low oil prices, dwindling

oil supplies or increased domestic oil consumption can threaten the ability to import food. Egypt has

been an example of this. Some countries, including Egypt, much of the Arabian Peninsula and

Rwanda already have no unused arable land and have already experienced unrest which has been

associated with food insecurity (Diamond 2005; Lagi et al. 2011).

3.4.5 How realistic is the ‘no N’ scenario?

Although it is unlikely we will ever have to manage without any nitrogen fertiliser because the

wealthy, at least, would be likely to switch to renewably-powered N, future wealth is not assured

without fossil fuels as currently at least 100 kg of oil equivalent is required for every $1000 of GDP

(Fattouh & El-Katiri 2012). This is an known problem and researchers are looking for solutions

(Jones 2013). The ‘no N’ case is a ‘worst case’ scenario. However, the renewable power for making

N would also consume considerable land area. For example, it has been estimated that replacing our

fossil fuel use with wind power would require an additional 160 M ha, or an additional 122 M ha

for concentrated solar power (Scheidel & Sorman 2012). At higher N application rates, the yield

49

response is no longer linear and levels off, which is masked by country averages (Mueller et al.

2014). For these high application rates, which are common in China, the yield reduction from

reduced N application would be lower than modelled (Fischer et al. 2012).

The N-use efficiency statistics on which this study is based compare mineral N with the alternatives

which are used now, such as animal manures and composts. These alternatives cannot be scaled up

to global usage because of the lack of addition space for their production. At the global scale, we

could expect lower yields without mineral N than in the current situation, requiring more land. Land

would also be required to produce the additional animal manures and green manures, further adding

to the land requirements, although there is some capacity to grow these in the off season. New

cropland also requires high rates of phosphate application initially, and P is also a resource

approaching global constraints, so such extensive expansion into virgin territories may be

unrealistic (Fischer et al. 2012; Ragnarsdóttir et al. 2012). However, although yields are reduced

with lower N application, it has not been found to affect overall profitability (Glassey et al. 2013).

3.5 Conclusion

The potential threats to native ecosystems and biodiversity from land-fertiliser substitution

occurring with global oil depletion could result in global levels of biodiversity typical of countries

where people use nearly all the land, although the biodiversity outcomes from human appropriation

vary widely. Once all available suitable land has been appropriated, increased population,

consumption or loss of productivity results in food insecurity, which would affect most of the

world’s population even with the minimum requirements for land without petrochemical fertiliser.

The scenario considered here is a worst-case scenario which is not likely to occur, but in order to

make good decisions about preferred alternative courses of action, the alternatives need to be

assessed for their ability to address the problem of land-fertiliser substitution so as to minimise the

human agricultural footprint. For this reason, it would be useful to consider a best case land-use

scenario for most promising mineral N replacements which themselves minimise land-use, and also

consider how land suitability and non-reversible land-uses such as urban areas and mining affect

these land requirement projections.

50

Chapter 4: Minimising the footprint of post-carbon agriculture

Abstract

Biodiversity is threatened in a post-carbon future due to the expansion of agriculture resulting from a

reduction in the use of petrochemical-based fertilisers. Here we evaluate alternative forms of

fertilisers for their potential to minimise future expansion of agricultural land, and present a global

map of the threat to biodiversity for the best-case scenario for replacing mineral nitrogen (N). To

consider diverse low land-intensity approaches, we calculated the footprint for three green manures

(azolla, algae and alfalfa), and three options for mineral N production using renewable energy to

power the Haber-Bosch process (wind, photovoltaics and thermal solar power). Using solar power

for the Haber-Bosch process would provide the minimum global footprint. The biodiversity impact

of expanding the area currently under solar power to be sufficient to power the production of mineral

N was 1/2000 of the area required to maintain the food supply without mineral N, and resulted in 1/81,000

of the impact on biodiversity. We conclude a proactive approach is required in selecting and siting

replacements for mineral N in order to limit the impact of agriculture’s post-carbon footprint on

biodiversity.

4.1 Introduction

Agriculture is dependent on petrochemicals to maintain productivity: every food calorie produced

requires nearly a third of a calorie from fossil fuels, with the major component being nitrogen

fertiliser (Pimentel & Pimentel 2007). Currently, inorganic nitrogen fertiliser comes mainly from

ammonia, which is fixed from atmospheric nitrogen using the Haber-Bosch process (Bardi et al.

2013). About 100 million tonnes per year of nitrogen are applied in agriculture, with about four

percent of the world's natural gas production being consumed in the Haber-Bosch process (Gilland

2014), which is around 1–2% of the world's annual energy consumption (Matassa et al. 2015; Reay

2015). Continued petrochemical use is unsustainable in the long-term because: 1) human

populations and per capita consumption are growing, 2) petrochemicals are a finite resource, 3) they

are becoming increasingly expensive, risky and inefficient to extract, and 4) they are a major source

of greenhouse gases (Bardi et al. 2013). Alternatives to the use of petrochemical fertilisers in

agriculture have generally been less productive and therefore require more land to maintain the food

supply (de Ponti et al. 2012). The resulting land expansion poses a further threat to biodiversity,

which is already being lost largely due to agricultural expansion (Niedertscheider et al. 2016). Food

security is also a risk, particularly in countries where there is little unused fertile land (Lambin &

Meyfroidt 2011).

51

This chapter aims to compare the global footprint of alternatives to N fertiliser. We show the

difference in footprint and impact on biodiversity between the most land-efficient option and

currently used alternatives to mineral N.

We compared the footprints of green manures require to meet the global N supply with the footprint

of producing the same amount of N using renewable energy to run the existing industrial N

production. We mapped the footprint of the most land-efficient option and compared the

biodiversity impact of this with the cropland expansion which would be required to meet food needs

using current farming methods but without mineral N.

4.2 Methods

4.2.1 N sources for agriculture

Figure 25 shows the main alternative sources of N for agricultural production. N for agriculture can

be fixed from the atmosphere, mined from the soils or potentially recovered from waste streams. Of

these, the bulk of N needs must be met from the air because soil production is slow and cannot be

harvested commercially (Smil 2004), and the N in waste streams is largely lost back to the

atmosphere, particularly through tertiary waste treatment (Brands 2014). N can be fixed from the

atmosphere industrially, principally using the Haber-Bosch process, or biologically, for example by

using legumes.

Figure 25 Major sources of N for agriculture. Most N is fixed from the atmosphere, since soil N is not renewable at

commercial cropping rates and in waste streams most of the N is lost back to the atmosphere. N fixation can be

biological or industrial (the Haber-Bosch process).

52

4.2.2 Habitat – cropland – N production land-use dynamics

Figure 26 shows the interaction between N-production, cropping land-use efficiency and habitat

preservation. N production enables more intensive crop production, reducing the expansion of

cropland into natural habitats. N production uses either biological N fixation or the Haber-Bosch

process which is dependent on large quantities of energy and hydrogen, currently supplied using

petrochemicals. Biofixation requires large land areas for green manure production, perhaps an

additional 50% of the cropland area to be supplied with N (Smil 2004). If renewable energy were

used to power the Haber-Bosch process it would also require large land areas (Scheidel & Sorman

2012). The land required for N production competes with natural habitat. All forms of N fixation

contribute to N pollution of the biosphere, a planetary boundary which is being dangerously

exceeded (Rockström et al. 2009). It seems that systems using organic fertilisers are less N efficient

and result in increased N pollution than synthetic fertiliser which can more precisely target plant

needs (Triberti et al 2008).

Figure 26 Habitat is lost when cropland expands. More cropland is required when N-use decreases,

but renewable sources of N also require land. N production influences N use and N-fixation

contributes to N pollution of the biosphere.

4.2.3 Data sources

The data used for the footprint calculations included FAO’s projected global N demand by 2019

(Heffer & Prud’homme 2015) and the N yields for green manures (Anderson et al. 1981;

Dommergues & Ganry 2012; Fairlie 2007; LaRue 2013; NIIR Board 2004; Smil 2004). Nutrient

recovery was also considered, but we chose not to use this because of the low levels of N present in

waste streams after treatment processes (Brands 2014; Dosta et al. 2007; Matassa et al. 2015;

Mulder 2003; Smil 2004). The footprints of renewable power sources were calculated from their

power densities (Scheidel & Sorman 2012; Smil 2008). Lists of solar power stations were sourced

from Wikipedia photovoltaic (PV) and solar thermal entries and checked in Google Earth (Google

US 2016; Wikipedia 2016a, 2016b). A biodiversity index of endemism richness was based on the

known ranges of vertebrates and vascular plants and mapped globally by ecoregion (Eisner et al.

Area of

cropland

N-use

Haber-Bosch Natural gas feedstock

Greenhouse emissions

Non-renewable

Minimal land footprint

Area of

habitat

N land

footprint

green manure renewable

N

N pollution

of the

biosphere

53

2016b; Kier et al. 2009).

4.2.4 Footprint calculation

The footprint was calculated for various sources of N to replace the input of petrochemicals.

Alternatives to petrochemical-based fertilisers were sought from the literature. N yields were found

for those which were renewable and a good source of N. N footprint ranges were calculated for the

three most land-efficient biofixers (green manures) based on FAO’s projected global N demand by

2019 (Heffer & Prud’homme 2015) and the N yields were calculated. Footprint ranges were also

calculated for the three most land efficient sources of renewable energy (wind, solar thermal, PVs)

based on their power density (Scheidel & Sorman 2012).

4.2.5 Mapping minimum footprint

The footprint for the most land-efficient option was calculated by buffering existing locations of

solar power plants globally by the area required to produce the energy needed to power the current

global mineral N production using solar power. The footprint of the total global total energy

consumption for producing N was mapped in the same way. The sites included solar thermal plants

and PV plants over 100 MW, and included plants which are currently operational, are under

construction or are planned for construction. Coordinates from site-details on Wikipedia were used,

where available, and checked for accuracy by visiting the location on Google Earth. Where

coordinates were not available, locality information was used to find the site on Google Earth. If the

site was not visible in satellite imagery then a nearby, undeveloped site was chosen. The area

required for total N production was distributed among the solar power station sites according to the

area of the country and divided by the number of power plants within the country. The same process

was used to map total energy consumption.

4.2.6 Biodiversity impact

The biodiversity impact of the footprint of the energy required to manufacture N using solar power

was mapped by overlaying the solar power footprint with a biodiversity index of endemism

richness. The resulting biodiversity impact was compared with the minimum impact which would

occur due to the cropland expansion which would be required to maintain food production if

mineral N fertiliser were not available and were not replaced.

4.3 Results & discussion

Three biofixers and three sources of renewable N were selected for footprint calculation. Of the

green manures, azolla, algae and alfalfa were chosen. Alfalfa was selected because it was the most

land efficient and is commonly used as a green manure. Algae was also considered because it is

increasingly being produced hydroponically which reduces its competition for arable land. N yield

54

data for azolla were sparse and very variable. At the lower end of productivity, azolla is a land-

inefficient source of N, but very high rates have been achieved although the results have not been

published. Azolla can also be grown hydroponically, with low inputs and without direct sunlight

(Ali et al. 1998; Wagner 1997). Azolla and its cyanobacterial symbionts synergistically share the

electromagnetic spectrum for photosynthesis and enable it to fix atmospheric N (Wagner 1997).

These unique traits and its ability to sequester CO2from the atmosphere (the azolla event), make it

of future interest as a source of N, and a staple source among subsistence producers in India and

China (Speelman et al. 2009; Wagner 1997). The genome of the azolla superorganism was recently

sequenced using crowd funding, and it is likely that improvements in N production efficiency can

be achieved (Li & Pryer 2014).

N recovery from waste water could not be readily compared as a large-scale industrial process

because current treatment systems denitrify the waste stream making it of little use as nitrogen

fertiliser, although a considerable proportion on the N could be recovered by switching to anaerobic

treatment systems or other N recovery technologies (Carey et al. 2016). To recover the N, the

processes would have to be kept anaerobic, or the components with high N concentrations (eg

urine) would have to be kept separate and anaerobic, which is not possible with current

infrastructure. These processes are more accessible to subsistence farmers who have direct access to

the land used for food production.

The most land-efficient renewable energy sources are solar and wind. Solar thermal could produce

49,000-121,000 kg/ha/yr N and PVs 49,000-109,000 kg/ha/yr N. Wind could produce about a tenth

of the yield of solar power, producing 6,000-18,000 kg/ha/yr with current technology. By

comparison natural gas is estimated to use about 2.5 times the land area per unit of energy which is

required for solar power (Jones et al. 2015a). However, most conventional gas is already in

production, so the land required to move to renewables would be additional land.

4.3.1 Footprints

The minimum and maximum footprints of these renewable N sources are shown in Figure 27, with

an outline of the world land-masses shown for scale. The comparative yields from renewable

sources are shown in Table 3.The area required to produce N using green manures is very large

compared to powering N manufacturing plants with renewable energy. The difference is hundreds

of times greater for green manures compared to wind energy and thousands of times greater

compared to solar power. These results are similar to those produced by Smil and Schiedel &

Sorman for power densities of alternative energy sources. This is not surprising since the main

55

difference in the footprint required for N production is the energy component. Figure 27 illustrates

the impracticality of deriving a large proportion of the N required for agriculture from biofixation

because of the area required. However, the

Figure 27 The minimum and maximum footprint (inner and outer circles) of replacing mineral N with renewable

sources of N, with the world map shown for scale. The footprint of green manures is 100s to 1000s of times greater than

renewably powered industrial N production using solar and wind.

Table 3: Footprint of renewal sources of N, expressed as N yields for comparison.

Renewable N Source Kg/ha/yr

Azolla 1.4 - 60

Algae 15 - 65

Alfalfa 150 - 250

Wind 6,000 – 18,000

PV 49,000 – 109,000

Solar 49,000 – 121,000

Wind power

Solar thermal Photovoltaics

56

most efficient biofixation may represent an improvement in land-use efficiency for the world’s most

inefficient crop production, which tends to be in subsistence agriculture which produces no income

with which to buy fertilisers. The energy required for N production is about 2% of total global

energy use, so about 50 times this area would be required to meet all our energy needs in these

ways, which illustrates the problem of using biofuels to meet our energy needs (Bardi et al. 2013).

4.3.2 Biodiversity impact

Figure 28 compares the footprint of the most land-efficient method of obtaining N (solar power)

with the land used by cropland to maintain yields without mineral N. The cropland expansion

required without mineral N is shown in Figure 28a, with the colour representing the area of

biodiversity which would be lost represented by an index of endemism richness. The additional land

requirements for replacing the N required using solar power are shown in Figure 28b. The areas

(whose locations are based on current or planned locations of solar power plants) are very small at a

global scale, but are visible in China, USA and Australia. More than 2000 times the land area is

required to produce crops without mineral N than the area required to manufacture N fertiliser using

solar power. Scheidel and Sorman (2012) found the difference in power density between biofuels

and solar power to be of the order of 1000 times, also based on Smil’s data (Smil 2006, 2008).

Since the change in land requirements is due to the change in the energy source to produce N, the

similarity between land efficiency of different energy sources and of different N sources of the

same materials would be expected.

Because cropland uses land with climate and soils which are suited to life whereas solar power is

best situated in hot, dry areas, this 2000 times greater area results in an 81,000 times greater impact

on biodiversity, using the biodiversity index as a measure of biodiversity (Gaston 2000). That is, the

land that would be occupied by agriculture tends to have about 40 times the biodiversity of land

occupied by solar power. The inset illustrates the USA-Mexico border area and the variation in

biodiversity impact between the different solar power sites, which would need to be taken into

account in local planning. The area required to provide total global energy use using solar power is

shown in Figure 28c for comparison and visibility. This is about 50 times the area required for N

production (Bardi et al. 2013). From this we can see that the impact varies greatly between sites,

with some high biodiversity areas currently being used for solar power, particularly in eastern and

southern China, Thailand and the Philippines.

57

4.3.3 Solar power site distribution

There are many parts of the world where it would be possible to site solar power which currently

have no plants, and the 170 power plants included here represent the early adopters. There may be a

variety of reasons why physically suitable areas may not have solar power stations, such as political

instability, alternative energy sources, lack of financial capital or distance from energy markets.

Libya, for instance, is notable among North African countries for not yet having solar power

stations, although targets have been announced. Their later adoption may have been influenced by

the 2011 war and their abundant oil supply (Energypedia 2015; Montgomery 2014). War can be a

major threat to solar infrastructure as was experienced by the Ukraine, whose solar power station, at

one stage the world’s largest, was lost when Russia annexed the Crimean Peninsula (Kurbatova &

Khlyap 2015).

The current distribution of solar power stations does not reflect the distribution of solar resources.

Only about 5% of sites are in the tropics, and most of the sites in the northern hemisphere are north

of the Tropic of Cancer. However, there are relatively few countries (11) which have no territory

within the latitude range of current power stations. It appears that solar power is not optimally sited

for solar access or for minimising biodiversity loss. It would be beneficial to have an international

agreement, or perhaps international guidelines for solar power station siting which takes into

account factors which are significant at the international level, such as biodiversity conservation and

effect on global warming through change in albedo (Nemet 2009). Situating solar power at sea may

prove preferable in the long-term from both a land-use and an albedo perspective, and technology is

becoming available to make this feasible (Haider et al. 2015; Szondy 2016).

58

Figure 28(a) The biodiversity impact of cropland expansion without mineral N compared to (b) the biodiversity lost due

to the footprint of the solar power required to power industrial N production. (c) Total energy production using solar

power shows how the biodiversity footprint varies with site selection, with high biodiversity areas being used for solar

power particularly in eastern and Southeast Asia. (Inset) In the California-Mexico border area, biodiversity impact

varies between sites.

Solar power has the potential to reduce competition for land between energy production, agriculture

and biodiversity by sharing land in a variety of ways. PVs can be deployed on rooftops, which

generate small quantities of distributed power which can be fed into the grid and can, cumulatively,

generate considerable power (Wiginton et al. 2010). The potential for PV to be installed in small

quantities and their tolerance of rain has made it easier for them to occupy farmland, replacing

crops (Jones et al. 2015b). However, PVs have the potential to share farmland. With suitable

spacing, crops can grow under solar panels, and this may increase their yields since the spacing can

be designed to optimise insolation to the plants’ requirements (Bachev 2015). PVs can also be

designed to make use of parts of the spectrum unused by crops (wavelength selective PVs),

potentially increasing cropping efficiency (Carlini et al. 2010). Deployed in these ways PVs could

possibly have a zero footprint, or perhaps even a negative net footprint.

Solar powered N footprint

Higher biodiversity impact

Lower biodiversity impact

a

b c c b inset c

59

There has been some resistance to solar and wind power because of the variability of their output

(Sovacool 2009). Use of these forms of energy to manufacture fertilisers could potentially act as a

form of energy storage, and ammonia production has been specifically suggested for this purpose,

and for use as a fuel (Müller & Arlt 2013). However, the components of the renewably powered

ammonia production process, electrolysis of water to produce hydrogen and the Haber-Bosch

process do not currently lend themselves to intermittent power supplies, nor does the Haber-Bosch

process scale readily. Alternative technologies have been suggested which address these limitations

(Renner et al. 2015), but these have efficiency costs which would need to be compensated for with

increased energy production, which in turn would have a larger footprint. There are efficiency gains

from using the sun’s heat directly in the N production process, and gains of about 30% from using

solar and wind powered electricity compared to efficiency losses incurred for electricity generated

using fossil fuel combustion (Jacobson & Delucchi 2011).

Optimal N provision may result from combining mineral fertilisers with organic fertilisers which

increase the water-holding capacity of soils and reduce the levels of mineral N required, reducing

cost and pollution and increasing yields (Halweil 2006; Kelly 2009). Industrial farming has been

moving in this direction in recent decades, but currently ammonia-based fertilisers, even if

produced renewably, are not permitted in certified organic systems (Camin et al. 2011).

4.4 Conclusion

The sources of nitrogen chosen for agriculture as the world transitions away from petrochemicals

make a very large difference to land-use and to the loss of biodiversity. If N fertilisers are not

replaced by other sources, then cropland would expand to use all the potential agricultural land in

order to maintain food production. This potential cropland is the land where most of the remaining

biodiversity resides. Choosing the most land-efficient source of N can reduce the land expansion

required by a factor of 2000, and the biodiversity loss by an additional 40 times. This prioritisation

requires choosing to use the most land-efficient options for N production, which are currently solar

power technologies, and siting them in the least damaging locations. To achieve this would require

international coordination and cooperation. Not all countries have suitable land when solar access

and biodiversity are taken into account. Currently, countries make unilateral decisions about these

developments, often at the local level, where awareness, concern and capacity related to the

international implications may be limited. Assistance may be necessary for the poorest countries

with high levels of biodiversity that may see renewable energy as a chance to leapfrog costly fossil

fuels and to provide power to their populations, sometimes for the first time.

60

Chapter 5: Global prioritisation of renewable nitrogen for biodiversity conservation and food

security

Abstract

The continuing use of petrochemicals in mineral nitrogen (N) production may be affected by supply

or cost issues and climate agreements. Without mineral N, a larger area of cropland is required to

produce the same amount of food, impacting biodiversity. Alternative N sources include solar and

wind to power the Haber-Bosch process, currently powered by petrochemicals, and the organic

options such as green manures, marine algae and aquatic azolla and on-farm recycling. However,

renewable sources will use additional land to produce the same amount of N. In this chapter, we

developed a decision tree to locate these different sources of N at a global scale, based on

minimising their spatial footprint and the impact on terrestrial biodiversity. Solar power was the

most land-efficient renewable source of N. However, criteria including using land with low

biodiversity, low albedo and not displacing current cropland, meant relatively few areas in the

western Americas, central southern Africa, eastern Asia and southern Australia were suitable for

solar power. Only about 1% of existing solar power stations are in very suitable locations mostly

because of the high albedo or the biodiversity constraints of the land they occupy. In regions such as

coastal north Africa and central Asia where solar power is not likely to be adopted because of lack of

solar access or lack of farm income, or because land has high biodiversity or high albedo, alternative

sources of N could be used, however, the very large spatial footprint of green manures means that

only a small area of low productivity and low biodiversity were suitable for this option. Europe in

particular faces a challenge because it has access to a relatively small area which is suitable for solar

or wind power. If we are to make informed decisions about the sourcing of alternative N supplies in

the future, and our energy supply more generally, a decision-making mechanism is needed to take

global considerations into account in regional land-use planning.

5.1 Introduction

Modern agriculture is highly dependent on petrochemicals, especially for nitrogen (N) fertiliser

which is made using natural gas. The use of petrochemicals to produce fertiliser is unsustainable for

two main reasons. First, they are non-renewable and consumption is growing faster than the supply

due to both growth in human populations and per capita consumption with increased living

standards (Kruger 2006). Second, their use emits greenhouse gases and aggressive mitigation

measures such as committed to in the Paris Agreement may constrain their use (Thomas et al.

2016). If N use were to be constrained, either through access or through price, then agriculture

productivity would fall and more land would be required to maintain food production. This

agricultural extensification threatens global biodiversity, since the conversion of native ecosystems

to agriculture has long been the major threat to biodiversity.

61

Nitrogen fertiliser can be produced from other sources (Dunn et al. 2012). These include replacing

the existing petrochemical power supply for the Haber-Bosch process with renewable energy

supplies from solar or wind power, and using organic sources of nitrogen. However, these sources

would all use some additional land, which again would potentially impact of biodiversity. An

assessment of alternative sources of renewable N suggested that using solar energy to power the

existing Haber-Bosch industrial process was the most land-efficient option, with a footprint one

tenth that of wind energy and one thousandth that of green manures (Eisner et al. 2016a). A cost-

effectiveness prioritisation would be unable to differentiate between options because the difference

in footprint is so great that this aspect would dominate footprint-to-cost ratios globally, meaning

that solar would always be selected over other options, at any land price and any solar resource

availability. However, there are factors other than land use which determine the choice of N source.

These factors include the resource availability and the affordability to the landholder (Chianu &

Tsujii 2004). There are also factors which influence the desirability of the source of N such as the

competition for agricultural land and biodiversity conservation and the impact on radiative forcing

(Nemet 2009; Rosenthal 2010; Turney & Fthenakis 2011). The extreme variations in the area of

land needed to produce alternative sources of nitrogen make it essential that we understand the

implications of renewable N fertilisers for regional land use planning.

This chapter aims to prioritise renewable sources of nitrogen with the goal of minimising the impact

on biodiversity through agricultural extensification and the competition for arable land, given the

distribution of practical resource constraints. The N sources considered include the most land-

efficient sources of renewable energy to power the existing Haber-Bosch infrastructure (solar and

wind); terrestrial, freshwater and marine organic fertilisers (alfalfa, azolla and seaweed); and the use

of crop residues.

5.2 Methods and data

The steps used to map and prioritise N sources are given in figure 29. Firstly the most important

factors in siting each source of N and suitability thresholds were identifies from the literature, where

available. Then the data needed to map these factors globally was sourced. An algorithm was

developed which mapped the highly suitable regions for each N source. These were then mapped to

produce a map of the most suitable regions for each source of N. Areas of major overlap were

combined into a collective category, and a global map of most suitable N sources was created.

62

Figure 29 The process for developing maps for selecting sources of N production most suitable at each location.

5.2.1 Data sources

The data sources and references for the suitability thresholds used are given in Table 4, and were

rasterised based on 1 km cropland mapping (Monfreda et al. 2008).

Table 4 Data sources for resource availability, constraints and suitability thresholds used for mapping N source

prioritisation (see supplementary data for source maps). The reference is provided for the thresholds applicable to the

variable and for the datasets used.

Variable Reason for

inclusion

Data Threshold Reference

Biodiversity To assess impact Ecoregional

biodiversity indices

0.1064 (Kier et al. 2009)

Commercial

cropland

Space constraint for

N production

Cropland-yield gap >20% (Monfreda et al. 2008)

Green manure Farm income to

purchase fertilisers.

Yield gap > area

required to grow N

Yield gap 0.513 (Monfreda et al. 2008)

Sun Most land efficient DNI for concentrated

solar NASA SWERE

4.93 (NASA 2011)

(Deign 2012)

Wind Second most land

efficient

NASA SSE 5.5ms-1 (NASA 2005)

(Blankenhorn & Resch

2014)

Albedo Solar power can

contribute to global

warming at high

albedo sites

Albedo (1 month) lowest reflectance

values

(lowest 20%)

albedo 35

(NASA Earth

Observations 2016)

(Nemet 2009)

Wetland rice Azolla valuable N

source, no land cost

Pres/abs (Salmon et al. 2015)

Aquaculture Data not found N.A.

Seaweed No land cost Coastal zone y/n (Natural Earth 2016)

Identify criteria and

thresholds from literature

Identify mapping data

Develop selection algorithm

Map most suitable regions for

each source of N

Create combined category for

major regions overlap

Map most suitable

N sources globally

63

5.2.2 Decision process for selecting alternative sources of N

Using solar energy to power N production is most land-efficient renewable method and would allow

the ‘sparing’ of land for other purposes such as growing food and protecting biodiversity (Eisner et

al. 2016a; Scheidel & Sorman 2012). Solar is at least ten times more land efficient than the

alternatives for the production of N. But there are other factors which might constrain its use. Not

everywhere has sufficient sunshine, but may have wind resources, and in some regions the land

would be better used for agriculture or biodiversity conservation. Also, some farming produces

insufficient income to purchase N produced using solar power making it less accessible to

subsistence farmers (Chianu & Tsujii 2004). These farmers may choose other options they can

access without cost, including green manures, waste recycling and, where located near the coast,

marine algae, especially in areas with high marine N (Cavagnaro 2015). In biodiverse regions,

subsistence farmers impact native ecosystems when they use land for N production, so importing N

for these farmers has the potential to limit their impacts (Matthews & De Pinto 2012). For these

reasons, it is necessary to consider how to prioritise the location of each alternative source of N.

Figure 30 shows the logical process for deciding between sources of N for each location which

combines a decision matrix and a decision tree. First, in 1a), in the decision matrix options are

selected on the basis of cropland use and the biodiversity level. Cropland is better used for food

production than for N production to maintain food supply, so in those areas N should be imported,

as is currently practiced. Very unproductive agricultural land produces insufficient income to

purchase N and so farmers need to produce their own organic fertiliser on-farm (Crucefix 1998). In

areas of high biodiversity, the land used for N production competes with biodiversity, so N should

be imported, and farm and household residues recycled, where feasible. If there is no cropping

currently present and low biodiversity then the land can be used for renewable energy production

with low impact. If such land has high biodiversity then the land should be prioritised to preserve

this, and not used for renewable energy production.

Figure 30 b) shows a decision tree for selecting organic fertilisers and the renewable energy source

for powering N production. Organics are best suited for subsistence farmers in low biodiversity

areas. Recycling organic matter is generally beneficial, where feasible. If there is a very large yield

gap then green manures can increase overall productivity, azolla is a significant N source in rice

production and coastal areas have access to seaweed.

Renewables are suitable for use in low biodiversity sites unsuitable for cropping. Sites with low

insolation and high wind are suitable for wind power. Sites with adequate insolation and low albedo

are suitable for solar. Otherwise, none of these options are suitable, but there may be suitable

possibilities in the future or solutions not considered here.

64

5.3 Results and discussion

First we present global spatial analysis of the sites that were most suitable for each individual way

of sourcing N, based on the criteria shown in the decision matrix and trees. These were, for solar,

competition with biodiversity, cropping, solar resource and albedo; for wind, locations where solar

would be suitable but there is insufficient sun but sufficient wind; for green manure, cropping has a

large yield gap; azolla is suitable in wetland rice and seaweed in coastal subsistence farming. Then

we combine the individual means of sourcing N into a global map.

53.1 Solar

Areas were selected as suitable for solar power is they have sufficiently high insolation to

efficiently power concentrated solar power stations (Deign 2012). Solar thermal power is chosen

over PVs because they perform best in low rainfall areas and so tend to compete less with

Bio

div

ersi

ty

Yes Import N No N

No Import N Organics Renewable

energy

Commercial Subsistence None

Cropping

B

iod

iver

sity

Yes Import N No N

No Import N Organics Renewable

energy

Commercial Subsistence None

Cropping

Large

yield gap?

yes

yes Coastal?

Rice/

aquaculture?

Recycle

Azolla

Seaweed

Organics

Green manure

no yes

yes

low

Solar

No

suitable

solutions

Wind

Albedo?

Sun? Wind?

Renewable

energy

high

b)

a)

Indicates a solution

to be mapped

Bio

div

ersi

ty

Yes Import N No N

No Import N Organics Renewable

energy

Commercial Subsistence None

Cropping

Bio

div

ersi

ty

Yes Import N No N

No Import N Organics Renewable

energy

Commercial Subsistence None

Cropping

Bio

div

ersi

ty

Yes Import N No N

No Import N Organics Renewable

energy

Commercial Subsistence None

Cropping

B

iod

iver

sity

Yes Import N No N

No Import N Organics Renewable

energy

Commercial Subsistence None

Cropping

No N

Recycle &

Import N

Figure 29 a) decision matrix and b) decision trees for siting N sources. Commercial crop farmers need to continue

to import N to prevent losing cropland to N production. Biodiverse areas with no cropland are best kept for

conservation. Subsistence cropping in biodiverse areas can be assisted with N supplies to reduce encroachment

into natural areas.

Organics are suitable for subsistence or very low yield cropping. Azolla is a useful source of N in wetland rice

and seaweed is accessible in coastal zones. Renewable energy generation is suited to low biodiversity sites which

do not compete with cropping. If solar access is good and albedo low then solar is preferable, or if there is no sun

but good wind resources then wind power can be used. Otherwise none of these options are suitable.

65

biodiversity and cropping without additional policy intervention (Philibert 2005). Solar thermal also

has very much lower embodied energy and fewer material constraints for manufacture, with the

silver used in the mirrors as the major material constraint (Pihl et al. 2012). Solar thermal plants are

currently also slightly more land efficient. Because of their flexibility of location and scale, there

are currently about 30 times the installed capacity in PV compared to concentrated solar.

Sites suitable for solar power are chosen on the basis of not displacing cropping, having low levels

of biodiversity, and having sufficiently low albedo so that the increased radiative forcing does not

significantly undo the benefits of the reduced greenhouse gas emissions (Nemet 2009).

Figure 31 shows the 5.7 million km2 best suited to solar power, mostly in the southern hemisphere,

western North America and coastal Far East, and the location of solar power stations. This area

represents over 100 times the area needed to power global N production and more than four times

the area needed for the total world energy supply (Scheidel & Sorman 2012). To supply N

requirements of USA would require 30,000 MW (Leighty 2008), which is about 18 times the

installed solar capacity. Transmission losses due to distance from markets would be compensated

for by having a 30% efficiency gain compared to efficiency losses of fossil fuel combustion

(Jacobson & Delucchi 2011).

Currently only three solar power stations are in the regions most suited to solar power (Arizona,

New South Wales and South Australia), although many could be in more suitable places if they

were moved slightly. Several of the locations with the best solar resource and least impacts on

biodiversity are remote from major energy markets or large energy grids, as is the case in central

southern Africa, in Chile and Argentina and in Western Australia (Li 2013). Other regions have

their power stations better aligned with suitability, such as in southern Spain and the south-western

USA.

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Figure 31 Sites most suitable for solar power, and the location of existing solar power stations. Currently power stations

are not necessarily in the most suitable locations from the point of view of solar resource availability, conflict with

biodiversity and cropping and reducing albedo.

5.3.2 Wind

Figure 32 shows the 8.3 million km2 best suited to wind power globally, mostly at very low and

very high latitudes, and in Bolivia, Central Asia and Japan. These regions are unsuited to solar

power, they have very good wind resources, low biodiversity and would not be competing with

cropping. Most other global wind mapping only takes into account the wind resource available and

not land-use considerations (eg Grassi et al. 2015).

Figure 32 Sites most suitable for wind power. These are mostly at very high and very low latitudes.

5.3.3 Organic sources of N

The regions selected for organics (figure 33) are likely to be subsistence systems which are not part

of the cash economy and lack the income to buy fertiliser. Green manures were selected for

cropland where the yield gap is so high that their use would still increase their overall land use

efficiency (table 4) and where there is little competition with biodiversity. Marine algae are most

suited in low-yield systems within easy transport distance of the ocean (Antoine De Ramon & Iese

67

2014; Florentinus et al. 2008). Azolla is a useful source of N in wetland rice production and

aquaculture (Shridhar 2012), but only wetland rice is included here because terrestrial aquaculture

areas are too small for global mapping.

The regions most suitable for organic N sources, comprising 2.2 million km2 for green manure in

the areas with the lowest yields, 0.85 million km2 of coastal subsistence farming suited to marine

algae use, and azolla in 6.0 million km2 of wetland rice production. Yields would be able to be at

least maintained with N supplied in this way, although some of these regions would additionally

benefit from importing N (figure 34).

The N-efficiency of green manures assumes that the land is used solely for manure production.

There are management practices, such a zero-till seed drilling (Fischer et al. 2012), which produce

some N without consuming additional land, but these practices have not been included here.

Figure 33 Locations suitable for organic nitrogen sources. Relatively few places are highly suitable for green manures

because they use land inefficiently to produce N. Seaweed is most suitable on subsistence farmland adjacent to the coast

and azolla is selected for wetland rice production.

5.3.4 Cropland and high biodiversity regions

Figure 34 shows regions where competition with biodiversity or cropping makes N production

undesirable. For cropland in high biodiversity regions (29.8 million km2 globally), N would best be

brought in from other regions to reduce cropland expansion into biodiverse areas, and it is

preferable to retain natural ecosystems than to convert the land to N production. Assistance would

be needed to supply subsistence areas with N, at suitable levels to reduce encroachment, since their

income is insufficient to purchase N for themselves. Recycling agricultural residues makes sense in

all agricultural systems, and recycling household wastes would be beneficial in subsistence systems,

Seaweed

Azolla

Green manure

68

where feasible. If there is no cropland, high biodiversity areas should have no N production or

importation (Do nothing) to retain their conservation values.

Both cropland and biodiversity regions are based on existing locations which might change under

future climates.

Figure 34 Regions where, if there is existing cropland it is preferable to import N rather than use land for N production

in competition with crops or biodiversity (pink), or where high biodiversity and lack of cropland means no N use should

be used (blue).

5.3.5 Regions with no suitable options

Some areas, including northern Canada, North Africa, large parts of central Asia and inland eastern

Australia are unsuitable for any of these sources due to a combination of factors including lack of

solar or wind resource or high albedo (figure 35). None of the options in this study are suitable in

these areas, however, alternatives which do not adversely interact with albedo (eg geothermal) may

be suitable in some places. The use of recently developed white PV panels, produced to increase

albedo, may result in a net increase radiative forcing in desert regions and a reduction of the urban

heat island effect, although at an efficiency cost (Heinstein et al. 2015).

5.3.6 Prioritisation of N sources

Figure 35 shows the preferred N source at each location across the globe. Mostly options do not

overlap because the decision tree prioritises the best option for a given location. The main exception

to this is recycling which is combined with and importing N which are combined in figure 35.

Recycling wastes that are produced on-site uses no additional land area and improves soil condition

so is desirable wherever it is feasible. For household waste this may only be the case for small-

holders, because of transport costs. Although figure 35 presents organics and mineral N as

alternatives, it may be optimal to combine organics with mineral N (compare with figure 34).

Importing N from production sites that are highly cost- and land-efficient may benefit many areas

suitable for organics by increasing the productivity of organic systems. The use of organic

Do nothing

Import N

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fertilizers could reduce overall N-use and the resulting pollution of the biosphere and increase soil

health, soil water-holding capacity and drought tolerance in conventional commercial systems (Ali

et al. 2011).

There are risks with supplying N to subsistence farmers in biodiverse regions. There is the risk of

becoming dependent on a finite resource, which would result in food insecurity if the supply

discontinued. This is particularly the case if supplying N were to increase the carrying capacity in

the short-term to levels which could not be supported without it. Also the increased efficiency of

agriculture using mineral N can tend to make production more profitable, increasing areas under

production. Complementary planning measures are needed to reduce this risk (Phalan et al. 2016).

N pollution of the most sensitive regions is also a risk, unless the N is managed carefully.

With most area in the prioritised map (figure 34) selected for non-production of N (ie, either ‘Do

nothing’, ‘Import N or ‘No suitable solutions’), there is relatively little area highly suitable for any

of these options. However, there are sufficient highly suitable areas to meet all N needs using the

best option available, and even sufficient area selected for solar energy to meet total energy needs.

Prioritisation based on cost effectiveness is often suggested to optimally allocate resources (eg

Wilson et al. 2006). The prioritisation used in this chapter did not include costs for a number of

reasons. First, perhaps half of the world’s people and about a third of the agricultural land is under

management systems outside the economic system, so a cost-effectiveness prioritisation is

unhelpful in these systems. In order to include these systems, the prioritisation needed to target

factors accessible to those making the decisions. Second, the overall aim of the research was to

minimise pressure on biodiversity and food insecurity. Finally, price was the most volatile factor in

these systems, rapidly changing with markets and management practices, and so results based on

price are not very reliable.

Figure 35 Sources of N for cropping prioritised for biodiversity and cropland conservation. Solar is the most land-

efficient option, but is highly suitable in relatively few regions due to completion with biodiversity or cropping or

reducing the albedo of the site, contributing to global warming. Organics are very land inefficient for N production so

are only suited for use on land with low productivity and low biodiversity.

N sources Seaweed

Azolla

Green manure

Solar powered Haber-Bosch

Wind powered Haber-Bosch

Recycle and import N

Do nothing

No suitable solutions

70

5.3.7 Regions of interest

Three regions can draw on the full range of N sources without high negative impacts (figure 36).

The Caucasian region between the Black Sea and The Caspian Sea has much cropland which is of

such low productivity that yields could be improved with green manures, and high wind speeds

suited to wind power south of the Greater Caucasus Mountains between the Black Sea and the

Caspian Sea. Much of the coastal areas could be suitable for algae use. The Nile delta could

usefully use Azolla in rice production with Cyprus and eastern Caspian coastal areas suitable for

solar. Azerbaijan alone has the potential of about 800-1500 MW of economically feasible wind

power, the main barriers being regulatory (Safarov 2015). The first wind farm in Georgia, rated at

20.7 MW, began operations in 2016 (Caspian Energy Newspaper 2016).

In the Far East, Japan has good wind resources and, together with South Korea and China south of

Shanghai, has opportunities to use azolla in rice production, which is often practiced in China

(Biswas et al. 2005). North Korea has very good solar resources, some of which has already been

exploited with international assistance (Yi et al. 2011). Its coastal regions suit algae use, which they

harvest (Chennubhotla et al. 2013). North Korea has 2.8% of the world’s aquaculture but chronic

food and energy insecurity. The region produces over 10 million tonnes a year of marine algae,

mostly for food, with its main use as fertiliser in India.

Although much of Uruguay has no suitable N sources, its bordering regions are rich in resources.

There is abundant solar north western Argentina. Its border region with Brazil to the north would

benefit from azolla in wetland production and green manures and in the coastal area seaweed could

be used, with the southern coastal zone also suiting wind.

By contrast, the European region has relatively poor access to renewable N sources. Algae may be

viable along the coast of the Black sea, parts of the Iberian Peninsula, coastal Poland, parts of the

Baltic states and North Africa, which is also suitable for green manure because of its low

productivity. Small areas of the Mediterranean in Corsica and Sardinia, southern Italy, Greece and

Turkey and in Portugal and Spain have solar resources, which in Spain are largely exploited.

Coastal northern Russia and Norway may suit wind but many otherwise suitable areas are excluded

because of conflicts with wildlife or cropping. Plans such as Desertec which aim to provide Europe

with power using solar panels based in the Sahara is problematic due to the warming effect of

decreased albedo (Backhaus et al. 2015; Nemet 2009). The benefits from reduced GHGs by using

solar power are about 30 times the heating caused by solar panels when well placed, but the heating

can increase more than three-fold by placing solar collectors in the Sahara Desert.

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5.3.8 Significance and limitations

Renewable nitrogen fertiliser has not been spatially prioritised before. The 2008 US Farm Bill

allocation US$1 million per year in 2008-9 for a study of the feasibility of producing N from

renewable energy (Capehart & Stubbs 2007). Leighty & Holbrook (2008) conducted a comparison

of H2 and NH3 as potential storage for wind power, noting that NH3 can also be used for fertiliser.

Leighty (2010) also investigated the possibility of transmission of both fuels via pipeline and

concluded both the fuel and pipeline technology would accelerate conversion to renewables. It has

also been found that the efficiency of NH3 production could be increased by using humidified

carbon monoxide as a feedstock instead of hydrogen (Jiang & Aulich 2008). There is also a

Swedish study which compared a variety of technologies for producing renewable N and found that

wind powered N costs about 2.4 times the current price. The cheapest renewable technology,

Figure 36 Three regions with a wide range of options for sourcing

N, a) Caucasia and surrounding region, b) Japan, China, Koreas

and c) Uruguay region. In contrast, Europe (d) has a paucity of

options. Europe has little area highly suitable for solar or wind

because of competing land use and biodiversity and lack of solar

resource. The Sahara desert is not selected for solar power because

the decrease in albedo would contribute to global warming.

Uruguay

Argentina

Brazil Paraguay

Japan

S. Korea

Russia

China

Turkey

Iraq

Egypt

Iran

Russia Ukraine

France

Algeria Libya

Sweden

d)

a)

b)

c)

N sources Seaweed

Azolla

Green manure

Solar powered Haber-Bosch

Wind powered Haber-Bosch

Recycle and bring in nutrients

Do nothing

No suitable solutions

72

thermochemical gasification of biomass is not yet commercially available. They also found that

renewable N reduced the GHG emissions incurred by perhaps a factor of ten (Tallaksen et al. 2015).

This study has been conducted at a global scale and the maps are not at sufficiently high resolution

to be used locally, especially the biodiversity index. Rather, the presented method could be applied

locally, with the incorporation of additional, locally important criteria and local datasets, especially

biodiversity and habitat distributions, local land-use and planning zones.

5.4 Conclusion

This chapter has spatially prioritised methods for producing nitrogen for crop production with the

goal of minimising impact on biodiversity and reducing competition with cropping, taking into

account solar and wind resource constraints. Although solar power is the most land-efficient way to

power N production, there are relatively few areas which are very suitable for solar power stations,

and some of these are far from energy markets and grids. Alternative ways of producing N are also

suitable in relatively small areas with many regions continuing to benefit from bringing in N from

those more suitable to its production, as they do currently.

Some regions, particularly those with low-yielding, subsistence farms, could benefit from using

organic fertilisers. Biodiversity would benefit if low yield farms were supplied with N, to reduce

encroachment onto natural ecosystems, although care is needed to prevent unwanted side-effects.

This chapter used a threshold approach to determine suitability of areas for each source of N. It

would be beneficial to develop a suitability scale for each so that maps of relative suitability could

be produced. It would also be useful to consider industrialised sources of N such as waste from

intensive animal industries and municipal waste streams, and mechanisms of treating waste so that

the N content can be reused.

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Chapter 6: Conclusion

6.1 Introduction

This study investigated the effect of limits to the supply of petrochemical resources on the global

footprint of agriculture and the implications for biodiversity from a land-change science and

conservation science perspective. This dynamic is a problem for biodiversity because agriculture

has become highly dependent on petrochemicals, particularly on nitrogen fertiliser which accounts

for a large part of the productivity gains which have been made. This problem is becoming more

acute as petrochemicals are a finite resource but demand is growing (Kelly 2009). Constraints to the

oil supply create an oil price rise. Supply constraints have occurred on several occasions, most

recently during the Global Financial Crisis (GFC) in 2008. This reduces the use of fertiliser, the

price of which increased five-fold during the GFC (Benes et al. 2015; Hamilton 2009; Murray &

King 2012). With such price rises, it may be more affordable for farmers to extensify and reduce

their fertiliser use through the process of land-fertiliser substitution, and this process threatens

biodiversity (Brunelle et al. 2015).

This study aimed to find evidence for the impact of constraints to the oil supply on biodiversity, to

gauge the scale of the potential problem and examine interventions to minimise the impacts. It

evaluated empirical evidence for a change in agriculture’s footprint with oil supply constraints

during the GFC, and investigated the drivers behind the changes. It then put boundaries around the

potential impacts by exploring the minimum and maximum footprint for sources of nitrogen

fertiliser, and the possible impact on biodiversity, using current alternative solutions. A global

spatial prioritisation for nitrogen sources was then suggested, taking into account resource

availability and impacts. The questions addressed were: 1) How did deforestation and biodiversity

impact change with constraints to the oil supply of the global financial crisis, and what was driving

those changes? 2) What are the best- and worst-case biodiversity implications for constraints on N

production, and how should N sources be prioritised spatially?

In this chapter, I summarise the research findings in the context of other research, the theoretical

contribution which has been made, and the thesis limitations. It will then identify policy implication

and possible directions for future research.

6.2 Major findings

The results from Chapters 2-5 will be synthesised here around the research questions of the

relationship of the oil supply to biodiversity and how best to address this.

Question 1) How did deforestation and the impact on biodiversity change with constraints to the oil

supply of the global financial crisis, and what was driving those changes?

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I addressed this question in chapter 2 by globally mapping the change in deforestation rate before

and after the GFC and identifying where these changes may have impacted biodiversity. I reviewed

the drivers of land-cover change in the statistically significant areas of change and compared these

areas with the pattern of large-scale land acquisitions. The deforestation rate during the GFC)

increased by 29% compared with the six previous years. The GFC was a complex interaction of

factors including large-scale financial mismanagement of investment funds and changes in

agricultural incentives, but the levelling off of the global oil supply was implicated and there were

widespread food riots, with perhaps an additional 100 million people becoming short of food

(Bruinsma 2011; Murray & King 2012; Sipe & Dodson 2013; Turner 2012). The meta-analysis of

quantitative evidence of the drivers in the statistically significant hotspot areas of change showed

commercial agriculture was the dominant driver. These hotspot areas were particularly sensitive to

the five-fold increase in the price of nitrogen, leading to the conclusion that land-fertiliser

substitution was likely to be an important driver of deforestation at that time. There was also

evidence of policy success in resisting these changes in some places (Aabø & Kring 2012; Arima et

al. 2014; Herford et al. 2011; McGrath 2007; WWF Living Amazon Initiative 2014). Interestingly,

investigation of the concurrent rise in large-scale land acquisition showed no association with the

increases in deforestation, despite large areas having been acquired for agricultural development.

There may be a lag effect between the purchase of land and deforestation through agricultural

development and the promised returns to displaced local people (Deininger & Byerlee 2012).

Question 2) What are the best- and worst-case biodiversity implications for constraints on N

production, and how should N sources be prioritised spatially?

One of the main threats to biodiversity of the lack of nitrogen fertiliser lies in agricultural expansion

into forest land (Czucz et al. 2010). To assess how this threat would be affected by constraints to the

N supply, in chapter 3 I modelled a worst case scenario, with no nitrogen fertiliser, and in chapter 4,

a best case based on the most land-efficient replacement source of N. Then in Chapter 5 I prioritised

the spatial implications for global biodiversity of different N sources. If business-as-usual

agricultural production continued in the absence of mineral nitrogen application, production would

be pushed onto existing forest land and highly marginal land resulting in widespread food insecurity

and probably catastrophic biodiversity loss. Since nearly all land with any agricultural potential

would be used, any unused land would almost certainly lack the combination of attributes (such as

rainfall, soil nutrients and moderate temperatures) needed to support abundant life. Alternative

sources of nitrogen compete for space with other land uses. The most land-efficient means of

producing nitrogen with current technology would be to use solar energy to power the existing

ammonia production infrastructure. This would use 0.05% of the land area, and cause 0.001% of the

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impact on biodiversity compared with allowing agriculture to become more extensive through

reduced nitrogen application. Solar powered N production is also 200 – 800 times more land

efficient than the most efficient organic sources of nitrogen. However, the existing solar energy

plants are not located in the regions with the most insolation and nor are they ideally situated to

minimise their biodiversity impact.

When these factors are taken into account, the relatively small land area that is highly suitable for

solar power is sufficient to meet global nitrogen production, and even total energy needs. It would

be better for other regions to import their N from these more suitable areas, or to produce their own

N locally from organic sources if they have very large yield gaps. Subsistence producers do not

produce the farm income needed to support the purchase of fertiliser, so they need to access

fertilisers which can be acquired at no cost, including those grown on-site, such as green manure or

azolla in rice production, or organic sources which can be harvested nearby, for example marine

algae (Vanlauwe 2002).

6.3 Contributions to conservation science

The main areas in which this thesis contributes to conservation science: 1) the linking of

biodiversity to the oil supply through agriculture’s footprint, 2) the tendency for agricultural

extensification to target biodiversity; and 3) the need and method for spatially prioritising the N

supply and renewable energy to minimise the impact on biodiveristy. Specific details of the

contribution made in each chapter are given in Table 5.

6.3.1 The oil-fertiliser-biodiversity connection

This study conceptually links the global oil supply to biodiversity through the process of land-

fertiliser substitution. This concept is well established in economics (Ricardo 1817) but these

sources generally lack any mention of biodiversity. There is one paper which refers to the potential

impact of bioenergy development on biodiversity and an economic modelling study mentions the

potential impact of cropping expanding onto marginal land (Brunelle et al. 2015; Smeets et al.

2015). Although the oil supply and land-fertiliser substitution are well linked in economic studies,

they are rarely linked in conservation science. A recent book, ‘Peak Oil, Economic Growth, and

Wildlife Conservation’, only mentions fertiliser in the context of pollution and seems to favour

organic production (Gates et al. 2014). While the land sharing concept identifies the relationship

between the intensity of agricultural production, the land area occupied by agriculture and that

available for biodiversity, so far this has not been linked to the global oil supply. Without

understanding of the connection between the oil supply and cropland expansion, future oil

constraints could result in widespread deforestation, leaving conservation science lacking the

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strategies to intervene. The substantially higher land-use efficiency of industrial nitrogen synthesis

compared to biological nitrogen fixation make industrial nitrogen the better land sharing option.

Renewable feedstocks and energy sources are needed for sustainability of the food supply, but I

found that it was important to also select these for land-use efficiency for both food supply and

biodiversity.

6.3.2 Agricultural expansion targets biodiverse land

An additional implication is that agriculture tends to expand onto the areas of highest biodiversity.

This may be because agriculture requires the same resources as productive terrestrial ecosystems:

water, warmth, nutrients and suitable terrain. These relationships have been investigated with regard

to richness in particular taxonomic groups and are generally consistent, finding climatic factors,

terrain and vegetation explained most of the variation (Currie 2003; Currie & Paquin 1987), but

studies have not previously looked at a measure of overall biodiversity. Richness is limited as a

measure of biodiversity as it does not take into account abundance or biomass. These studies aimed

to explain existing patterns of species distribution rather than explain impacts of agricultural

expansion.

Table 5 Findings from this thesis which make a contribution to conservation science

Chapter 2

a) Provided empirical evidence for the link between constraints to the oil supply and impacts on

biodiversity.

b) Quantified the scale of the changes with the change in fertiliser price during the GFC.

c) Showed the global spatial co-incidence of the change in deforestation and biodiversity.

d) Provided a breakdown of the underlying drivers of change in the regions where the increase in the

rate of deforestation during the GFC was significant, showing that expansion of commercial

agriculture dominated.

e) Showed that the countries where deforestation increased the most were also the most sensitive to

the price of fertiliser.

f) Demonstrated that large-scale land acquisitions were not implicated in the increase in

deforestation, and that development of these concessions appears to have largely taken place on

existing agricultural land.

g) Showed that policies aimed at restricting deforestation and at fire management were able to resist

these changes.

Chapter 3

a) Demonstrated that the cropland expansion required to meet global food needs without N fertiliser

is not be feasible as it would exceed the available productive land.

b) Without a land-efficient replacement for mineral N, widespread food insecurity would result.

c) Also, without a land-efficient replacement for mineral N, very little biodiversity would remain as

productive land was used for agriculture.

d) When the sensitivity to the price of fertiliser is taken into account, the distribution of cropland

expansion tends to concentrate in regions which are less developed, increasing the impact on

biodiversity.

e) There appears to be a negative relationship between land shortage, food importation and

sensitivity to the price of nitrogen fertiliser.

f) At a national level, extensification, intensification and importation may be selected between on the

basis of cost comparison, extending the land-fertiliser substitution concept to include telecoupling.

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Chapter 4

a) Found that there was at least two orders of magnitude difference in the footprint of organic

sources of N compared to industrial N powered using renewable energy.

b) The most land-efficient option is to power existing industrial N production using solar power,

which is ten times more land-efficient than wind power.

c) Using solar energy to power N production is 2000 times more land efficient than the land

extensification that would result without mineral N fertiliser.

d) This extensification would have 81,000 times the impact on biodiversity because extensification

targets the land conditions common to biodiverse regions, whereas solar power is best placed in

dry regions.

Chapter 5

a) Suggested a prioritisation method for sourcing N which balanced biodiversity impact with food

security and resource constraints.

b) Provided an indicative global map of preferred N source, and calculations of areas which are most

suitable for each source, particularly using solar power.

c) Found areas highly suitable for solar are limited by constraints of conflict with biodiversity and

cropland and decreasing albedo, and that current solar power sites are not well selected.

d) There is sufficient area very suitable for solar to meet nearly twice global energy demand.

e) Relatively few cropping area are highly suited to green manures, and these have very large yield

gaps.

f) Europe lacks good access to renewable N resources and suggested plans to source solar power

from the Sahara are problematic as this would reduce the albedo and contribute to global

warming.

6.4 Policy implications

This research has resulted in policy suggestions in four areas.

6.4.1 Nitrogen supply to agriculture is a conservation issue

The major policy implications of this research are that nitrogen use in agriculture should be of

interest to conservation management because of its impact on the spatial footprint of agriculture,

and that nitrogen use is influenced by the oil supply and the oil price. Nitrogen fertiliser use is out

of the financial reach of much of the world’s agricultural producers without financial assistance.

Assistance should be considered, particularly in areas of high biodiversity where increasing the area

under agricultural production has a higher impact. These decisions are not simple, and may have

rebound effects, for example by making agriculture more profitable in a region, or increasing the

carrying capacity. Additional measures may be needed to reduce the risk of these rebound effects

(Phalan et al. 2016). There are also ethical considerations concerning increasing the dependency of

subsistence producers on outside support, and the implications for future food security and

biodiversity if that support were to become unavailable in the future.

Concern for the pollution effects of nitrogen from agriculture and the effects on soil health have

lead some landholders and systems which advocate sustainable agriculture, such as organic

agriculture or permaculture, to promote the use of organic fertilisers and oppose the use of mineral

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N. Widespread adoption of these practices, without measures to maintain land-use efficiency would

be unable to maintain the productivity to feed the global population (Fischer et al. 2012). For

improved overall sustainability, it would be better to balance on-farm considerations with the effect

on land-use efficiency. Perhaps a sustainable agriculture system could balance these goals rather

than follow particular rules or belief systems, and could use a combination of organic and mineral

N, which has been shown to be most efficient overall (Wu & Sardo 2010).

6.4.2 The risk to conservation of pollution abatement measures

Possible interactions with other policy processes include greenhouse gas (GHG) abatement and

nitrogen pollution abatement. Measures such as putting a price on carbon or emissions trading, or

similar measures which might be adopted to reduce N pollution could see downward pressure on N

fertiliser use, particularly among poorer farmers. This could in turn increase agriculture’s footprint.

There needs to be an understanding of these risks among policy makers, and such policies need to

address land use. This could include equalising the costs between land expansion and GHG/N

emissions. In the current policy for GHG abatement such as biofuel mandates, the biodiversity

impacts are better understood, but this has not yet resulted in the inclusion of biodiversity

considerations (Phelps et al. 2012). Advocating less intensive agriculture to reduce pollution would

increase land use and using organic sources of N could result in increased N pollution (Triberti et al

2008).

6.4.3 Incorporation of global scale factors in local decisions

The selection of sources of nitrogen for agriculture has implications of global significance which

are not considered in production decisions, and there are currently no mechanisms for taking into

account factors such as land-use efficiency, competition for cropland or with biodiversity or

alteration of albedo. There is the possibility of including these considerations in existing GHG

emission policies, but these target GHGs rather than biodiversity. REDD+ schemes have shown that

biodiversity targets tend to lag behind carbon emission reductions in these projects (Gardner et al.

2012; Phelps et al. 2012). Other large-scale revegetation programs such as the Great Green Wall in

China and India targeting soil conservation and water quality create landscapes with little value for

biodiversity (Burnett 2016). Similarly, biodiversity could be included in the International Nitrogen

Initiative (Nanjing Declaration), which aims to set limits on countries’ nitrogen footprints, to limit

pollution of the biosphere (Erisman 2004; Galloway et al. 2008; Giles 2005). As with GHG

abatement, biodiversity conservation would be an externality and difficult to address this way. In

the end, a global biodiversity conservation protocol, something akin to the Paris Agreement but

with ecological targets may be needed for biodiversity to be protected. The price of land is an

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important factor, and land is available without cost in many places. Without a price, alternatives

which incur a cost, such as intensification, seem relatively unattractive.

6.4.4 Land grabbing as a conservation opportunity

This study has shown that there are vast areas of forest which have been acquired for agricultural

development when agricultural commodity prices were high, and where deforestation had not taken

place by 2012. If these concessions have been acquired but not implemented, they may be loss-

making for the companies which acquired them, who may be willing to sell them (or even set them

aside) for conservation. Managed well, this could be a win-win for local people who could be

retained as conservation managers. Care is needed for such an approach to create benefits for local

people, and not to become a form of ‘green grabbing’(Corson & MacDonald 2012; Fairhead et al.

2012; Tom Blomley 2013).

6.5 Limitations of this study

While the study provides strong evidence regarding the land-fertiliser substitution, further

confirmation is needed. The land requirements of agriculture without petrochemical fertiliser did

not take into account the spatial distribution of land suitability as the data were unavailable at the

time of the work. They also did not take into account incompatible land uses such as mining and

urban areas. As such, the land requirements outlined in Chapter 3 are likely to be underestimated.

The spatial dataset of FAO’s Status of the world’s soil resources launched in December 2015 would

enable this analysis, but came out too late for the current study (FAO 2015).

There are processes not addressed in this research which are also having a major effect on

agricultural productivity, and the footprint of agriculture. These include dietary change, particularly

increased beef consumption, population growth, climate change and other effects of pollution and

land degradation. These factors could interact with the processes discussed in this study over a

comparable scale and timeframe, but these interactions were not systematically considered. This

study has isolated one factor in a complex socio-economic and biophysical system, to gauge its

scale, and possible feedbacks and interactions are not accounted for.

The suggested policy measures have not been tested; particularly the merits of intensifying

production in biodiverse regions, and considerable caution would be needed in case of unintended

consequences. Similarly, land sparing has been promoted in this study, but measures are required to

ensure it is of real benefit to biodiversity.

6.6 Recommendations for future research

Several research directions are suggested around understanding of the oil-land-fertiliser system and

for ascertaining the effectiveness of conservation intervention strategies.

80

6.6.1 Land-fertiliser substitution

The proposed link between the oil supply, land-fertiliser substitution and biodiversity requires

further investigation to confirm causation and to characterise the spatial dynamics and the elasticity

of land-fertiliser substitution. These relationships could be further investigated by looking for

additional empirical evidence. This might be compiled from local or national case studies and, in

the future from further oil constraints. Many previous oil constraint episodes have been linked with

economic recessions, but global deforestation datasets of sufficient resolution were lacking prior to

2000.

6.6.2 Systems dynamics of the oil-agriculture-biodiversity system

Another useful approach to investigating the complex interactions of social, biophysical and

economic systems involved in energy-land relationships would be to use systems dynamics

modelling, an approach which was considered in the current study. This was the approach used in

the Limits to Growth modelling, although this model did not include biodiversity or fully consider

price mechanisms, and included energy only as a part of global resources (Bardi 2009; Meadows et

al. 1972). An updated version of the World 3 model which could replicate the depressive economic

effects of high oil prices with oil constraints could indicate policy levers, and would engender

renewed confidence in the original modelling.

6.6.3 Land sparing as a conservation strategy

The biodiversity impacts identified in this study were based on the presumption of land sparing: that

if agriculture requires less land to produce the same quantity of food then the land otherwise

occupied can be ‘spared’ for greater biodiversity. In practice this may not be the case without

further measures. Investigation of interventions to secure spared land for biodiversity is needed to

ensure that this strategy is effective.

6.6.4 Agricultural Intensification as a conservation strategy

Similarly, a suggested policy implication of increasing agricultural intensity in biodiverse areas to

reduce extensification requires practical investigation to find out how this approach could be used

without the rebound effects of increased agricultural development or population growth. This might

involve trials with ongoing monitoring, to test the effectiveness of the complementary conservation

measures. An alternative might be observational studies from areas where donors have made

fertilisers available or they have been subsidised.

6.6.5 Land-grabbing as a future conservation threat

Although the large-scale land acquisitions which have taken place were not associated with the

increased deforestation of the 2006-12 period, they remain at risk from development. Future

81

monitoring, and the development of strategies to mitigate this risk are needed. They also present a

conservation opportunity, if acquired for reserves. This strategy requires sensitivity to the needs of

traditional users of the land who could be retained as conservation managers.

6.7 Conclusion

The global oil supply has not generally been considered in land use and conservation planning but,

through its effects on the intensity of agricultural production, there is the potential for very large

scale land use change. Some of the solutions proposed at the property-scale for land management

without petrochemical fertilisers reduce productivity and would have considerable land use

implications if scaled up to replace modern industrial agriculture. The scale of the problem,

potentially requiring 2.4 times the land area to maintain food supplies without petrochemical

fertiliser, cannot be entirely met with low biodiversity value, unused land, and the land required

would come at a massive biodiversity cost. The global oil supply has experienced constraints on

several occasions, as it did during the global financial crisis, and these are likely to increase as

global demand approaches global peak supply. If these supply constraints result in a surge in

agricultural land use then conservation science needs a concerted approach to dealing with this

extensification. Such an approach will need to draw on the strengths of disparate groups such as

industrial, organic and subsistence farmers and conservationists, renewable energy and industrial

ammonia producers, and those involved in global agreement negotiations such climate change and

pollution. The future for both biodiversity and food security is at stake. Currently available

solutions such as wind power and rooftop PV can contribute part of the solution (ie about 6% each).

Using remote energy to produce N fertiliser provides a solution to the transmission costs of remote

energy generation as N fertiliser is relatively cheap and easy to transport, as is presently practiced.

Far better solutions are available than is currently the norm, for example taking land use efficiency

and biodiversity into account in selecting and siting nitrogen and energy production. These

solutions are unlikely to be arrived at with current piecemeal decision-making.

82

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97

Appendix 1 Drivers of land cover change in deforestation acceleration hotspots

Table 6 Drivers of deforestation or wetland conversion for the biodiversity loss hotspots identified

in three recent reviews and classified by the underlying categories: non-subsistence, subsistence,

climate, social/technical and landscape. The unique drivers identified for unique regions were

counted to gauge the relative the predominance of the underlying influences.

Non-subsistence (N=66)

Industrial/commercial activities Lianyungang, China16

Agro-forestry Brazil9, Sumatra14,42, Kalimantan9, Sarawak14, Malaysia10,

Argentina13, Cambodia13, DR Congo13, Laos13, Zambia13, global4

Deforestation moratorium Brazilian Amazon19

Demands Indonesia44, Brazil39, Argentina2, Mozambique13, global13

Exchange & interest rates, communications

infrastructure, commercial zoning, future forest

product prices, cost/relative cost of: capital, inputs,

land clearing

Indonesia44

Economic growth, migration, increased income Southern China6,49

Balance of trade Zambia13

Returns/ha, relative returns, prices/relative prices Costa Rica32, 33, Honduras28, Indonesia44

Distance to major markets Brazilian Amazon30

Extensification/intensification Vietnam26

Expansion of agricultural and aquaculture Vietnam22,40, China: Jiangsu Province46, Ecuador37, Tangxunhu,

China45, Argentina20, Zambia13

Expanding ranchland Panama38

Biofuels Mozambique13, Zambia13

Mining, tourism Cambodia13, Zambia13

Poverty Cambodia13, Mozambique13, Zambia13

Urban development Pearl River Estuary48, Lianyungang16 & Tangxunhu45 wetland,

China

Timber value, Soil depth Guatemala25

Urban land value China17

Roads, infrastructure improvement, water Guatemala36, Honduras27; China, Lianyungang16, Laos4, Zambia13

Reclamation for arable land China, Lianyungang41; China, Guilin, Pearl River Estuary, China48;

Vietnam22, Cambodia13

Subsidies and colonisation programs Argentina7

Depopulation Panama38

Subsistence (N= 16)

Population/ population density Ecuador21, Brazilian Amazon18, Thailand15, Mozambique1

Population growth Southern Yucatán34

Wood extraction (small scale) Vietnam22, Zambia13, Mozambique13, Tanzania23

Depopulation Panama38

Reclamation for pastures/ arable Tanzania12, DR Congo13, Laos13, Mozambique13, Liberia13,

Zambia13

Climate (N= 5)

Precipitation Indonesia44, Southern Yucatan34, China Tangxunhu45, Argentina20

Extreme climate events Indonesia8

Social/technical (N= 22)

Deforestation moratorium/ban Brazilian Amazon19, Western Honduras27

Communications infrastructure Indonesia44

New soy varieties, bulldozers Argentina20

Law enforcement, illegal logging Southwest Sumatra5, Cambodia13, Laos4, Mozambique13

Unclear property rights, institutional weaknesses,

war funding, conflict

DR Congo13, Laos4, Mozambique13, Liberia13, Zambia13

Lack of energy alternatives Zambia13

Conservation policies Costa Rica35

Protected forest areas and policies discouraging

shifting cultivation

Vietnam26, Cambodia13

Landscape (N= 15)

Accessibility Malaysia29, Madagascar43, Guatemala25,36, Ecuador21, Honduras27

Elevation Madagascar43, Myanmar24,11, Veracruz, Mexico3

98

Terrain/slope Indonesia44, Costa Rica31,33, Veracruz, Mexico3, Malaysia29,

Madagascar43, Myanmar11 1Cropper et al. 1999, 2DeFries et al. 2010, 3Ellis et al. 2010, 4FAO 2006, 5Gaveau et al. 2009, 6Gong et al. 2013, 7Grau

et al. 2008, 8Hansen et al. 2009, 9Hansen et al. 2010, 10Hansen et al. 2013, 11Htun et al. 2013, 12Kashaigili et al. 2006, 13Kissinger et al. 2012, 14Koh et al. 2011, 15Laurance et al. 2002, 16Li et al. 2010, 17Li et al. 2012, 18Lopez et al. 2010, 19Margono et al. 2014, 20Matthews et al. 2010, 21Mena et al. 2006, 22Minh Thu and Populus 2007, 23Mitinje et al. 2007, 24Mon et al. 2012, 25Monzon-Alvarado et al. 2012, 26Muller and Zeller 2002, 27Munroe et al. 2002, 28Munroeaic et al.

2002, 29Olaniyi et al. 2012, 30Pfaff 1999, 31Pfaff 2009, 32Pfaff and Sanchez-Azofeifa 2004, 33Pfaff et al. 2007, 34Rueda

2010, 35Sanchez‐Azofeifa et al. 2007, 36Schmitt-Harsh 2013, 37Shervette et al. 2007, 38Sloan 2008, 39Soares-Filho et al.

2010, 40Son and Tu 2008, 41Song et al. 2010, 42Uryu et al. 2008, 43Vagen 2006, 44Wheeler et al. 2013, 45Xu et al. 2009, 46Xu et al. 2011, 47Zak et al. 2008, 48Zhao et al. 2010, 49Zhao et al. 2011

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101

Appendix 2 Input data layers for selection N production site selection

Wind resource available Solar DNI, solar resource for concentrated solar power stations

Coastal zone, for marine algae evaluation Albedo, for solar site suitability selection

102

Cassava yield gap Maize yield gap

Millet yield gap Potato yield gap

Rice yield gap Sorgham yield gap

Wheat yield gap Cropland for selecting area not in competition with crops

103

Paddy rice for selecting azolla Biodiversity index by ecoregion for avoiding conflict with biodiversity