Implications of Oil Depletion for Biodiversity Rowan Eisner B Sc., Grad Cert Cog Sci, MSWAP

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, .

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

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.

Biofuels

Habitat loss Oil Food supply crop yields

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

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 2

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.

Figure 2. The process by which food

re production displaces land for terrestrial native ecosystems. The total land area, once Land for food occupied by terrestrial ecosystems, has been gradually replaced by agriculture. This conversion process is predicted to increase

through agricultural extensification. Land for natu for Land

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,

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 Figure 3 Energy return on energy invested for US oil would provide sufficient fuel for about 9 years of discoveries. The proportion of energy required to extract oil from reserves increased rapidly until global energy usage with current technology. production peaked in 1970 (Guilford et al. 2011).

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.

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?]

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 6

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.

Biofuels Extreme Trade weather embargos Investment funds -ve -ve -ve Commodity -ve Food stock Food demand price Food production -ve Income Oil US$ depreciation price

-ve (relatively inelastic) 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) 7

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

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.

Land rent response

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

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

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

Infrastructure Agricultura Wood Other extension l expansion extraction factors

Demographics Economic Technological Policy Cultural factors factors factors factors factors

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

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.

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.

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.

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.

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

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 17

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

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.

Changes in oil supply and price

Alternative energy Fertiliser-land Land and food development substitution investment

Changes in demand for land

Rate of deforestation and biodiversity loss

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.

Crude oil 76.0 Elastic production 74.0

72.0

70.0

68.0 Inelastic production a 66.0 2000 2001 2002Energy2003 price2004 2005 2006 2007 2008 2009 2010 2011 2012 250 FAO food price index Fertiliser price 200

150

100

50 b 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

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).

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 20

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.

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.

2013 ⋮ 2000 No loss Water or no data

Biodiversity index High

Low b 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.

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: 23

 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.

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 a Change in deforestation 2000-2012 (ha)

Reduced impact

Increased impact Biodiversity impact from change in deforestation rate b

Hotspot of increase in impact on biodiversity Coldspot of decrease in impact on biodiversity 99% confidence

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).

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

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.

Europe 9542 Africa 17480 Sth America 9149 Central America 1601 Asia 30946 Nth America -4616 Russia -8815 Oceania -7341

-15000 -10000 -5000 0 5000 10000 15000 20000 25000 30000 35000

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.

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

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.

0 - 5% 5 - 50% 30 - 50% 50 - 100% 112% > 100% 182% 353% 498% 1381% 2135%

101%

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.

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 28

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 0.05 agricultural landholders may be affected by both price rises for agricultural 0.00 High: High: Upper Lower Low Hotspot inputs, particularly fertiliser, and by land OECD nonOECD middle middle countries -0.05 acquisitions of existing agricultural land, and hence drive deforestation for new -0.10 agricultural land. Other processes may account for the observed LUC, and -0.15 marginal elasticity analysis would be Elasticity necessary to determine causality. Locally -0.20 prices can vary considerably but local -0.25 price data was not available globally for this study. We would have liked to -0.30 compare the hotspots’ agricultural yield changes during this period, but, while -0.35 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

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.

0 10 20 30 40 50 60 70

Non-subsistence 66 Subsistence 16 Climate 5 Social/technical 22 Landscape 15

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

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.

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,

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.

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

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 Area of N supply constraints Habitat Cropland N price

N-use 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.

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 36

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.

7 France UK Egypt 6 Switzerland New Zealand 5 USA

4 Saudi Arabia 3 Myanmar Spain

Meancereal yield (t/ha) 2 El Salvador Australia Ethiopia Honduras 1 Algeria 0 0 20 40 60 80 100 120 140 160 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.

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. 37

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.

(a) Steps 1km % cropland 1) Expand cropland one Expand (b) pixel out into non-cropland 0% areas at lowest neighbouring cropland % Low % 2) Calculate area gained and increase the area by the Reached Medium % same amount by increasing target? the % cropland of each pixel High % 3) If the target has not been reached, repeat Intensify (c)

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.

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

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

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

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.

Percentage cropland 0 – 12% 12 – 38% 38 - 64% 64 – 72% > 72%

c 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.

Increased impact

N Price elasticity > 0%  0 – 10%  10 - 20%  20 - 30% > 30% 

c 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.

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).

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 Parts of Brazil and West Africa, East Africa and southern Africa, biodiversity impact parts of eastern and Southeast Asia and Myanmar, and Cuba, Hispanola and Jamaica in the Caribbean Lower biodiversity but high biodiversity Peru, Bolivia Argentina and parts of Brazil, Chad, Sudan, Ethiopia, impact parts of Tanzania and the Atlantic coast of Madagascar, Vanuatu Reduced biodiversity impact of N price Most of Central Asia, India, Vietnam, Thailand, China, Uganda, in lower income countries Nigeria, Uruguay, Cuba Biodiversity impact of cropland The Caribbean, the Mexican central valley, southern Pacific coast expansion, not hotspots during the GFC of Mexico, Colombia and the Brazilian Cerrado Biodiversity loss during the GFC, but Myanmar (mainly due to timber and resource extraction) not associated with cropland expansion

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

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.

a) b) Yes Enough Wait food? No

Cheapest viable

Extensify Intensify Import

Figure 24 (a) Land supply, food imports and N price. Countries may be less sensitive to N price if they are Yes short of land (so have less choice) or if they import their Afford food, so are not so dependent on N-use. But if they are Implement not short of land they may be less N price sensitive, it? preferring to expand. (b) Countries would likely choose No the most cost effective viable solution, as long as they Crisis can afford it. 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

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.

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.

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 48

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.

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

1 1 N was /2000 of the area required to maintain the food supply without mineral N, and resulted in /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).

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).

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). Haber-Bosch Natural gas feedstock Greenhouse emissions Non-renewable Minimal land footprint N-use

Area of N land Area of N pollution habitat footprint cropland of the green manure renewable biosphere N

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.

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 53

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

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

Photovoltaics Solar thermal

Wind power

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 55

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.

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).

Solar powered N footprint Higher biodiversity impact

Lower biodiversity impact

b c c b inset c

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.

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.

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.

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.

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

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 Data Threshold Reference inclusion Biodiversity To assess impact Ecoregional 0.1064 (Kier et al. 2009) biodiversity indices Commercial Space constraint for Cropland-yield gap >20% (Monfreda et al. 2008) cropland N production Green manure Farm income to Yield gap 0.513 (Monfreda et al. 2008) purchase fertilisers. Yield gap > area required to grow N Sun Most land efficient DNI for concentrated 4.93 (NASA 2011) solar NASA SWERE (Deign 2012) Wind Second most land NASA SSE 5.5ms-1 (NASA 2005) efficient (Blankenhorn & Resch 2014) Albedo Solar power can Albedo (1 month) lowest reflectance (NASA Earth contribute to global values Observations 2016) warming at high (lowest 20%) (Nemet 2009) albedo sites albedo 35 Wetland rice Azolla valuable N Pres/abs (Salmon et al. 2015) source, no land cost Aquaculture Data not found N.A. Seaweed No land cost Coastal zone y/n (Natural Earth 2016)

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. 63

Yes Import N No N

No Import N Organics Renewable Yes ImportYes NImport N energyNo N No N

Biodiversity No ImportCommercialNo NImport N OrganicsSubsistenceOrganics RenewableNone Renewable Cropping energy energy a)

Biodiversity Biodiversity Commercial Commercial Subsistence Subsistence None None Cropping Cropping Recycle &

No N Yes YesImport Import N N Import N No N No N

No NoImport ImportN N OrganicsOrganicsOrganics RenewableRenewableRenewable b) energy energyenergy

Biodiversity

Biodiversity Biodiversity CommercialCommercial Subsistence Subsistence None None CroppingCropping Recycle no yes Sun? Wind? Wind

Large yield gap? yes low Albedo? Green manure Rice/ high yes aquaculture? Solar No Azolla suitable solutions Coastal? yes Indicates a solution Seaweed to be mapped

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.

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

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.

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 66

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.

Seaweed Azolla Green manure

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,

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.

Import N Do nothing

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 68

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.

N sources Seaweed Azolla Green manure Solar powered Haber-Bosch Wind powered Haber-Bosch Recycle and import N Do nothing No suitable solutions

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. 69

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.

Ukraine Russia

Sweden

Turkey

Iran Iraq a) Egypt

France Russia

China

Japan S. Korea Algeria Libya b) d)

N sources Wind powered Haber-Bosch Seaweed Paraguay Recycle and bring in nutrients Azolla Brazil Do nothing Green manure No suitable solutions Solar powered Haber-Bosch 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 Uruguay options. Europe has little area highly suitable for solar or wind because of competing land use and biodiversity and lack of solar Argentina c) resource. The Sahara desert is not selected for solar power because the decrease in albedo would contribute to global warming.

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,

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.

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?

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

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

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. 76

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

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

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. 79

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 80

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.

References Aabø, E & Kring, T 2012, 'The political economy of large-scale agricultural land acquisitions: Implications for food security and livelihoods/employment creation in rural mozambique', United Nations Development Programme Working Paper, vol. 4, Abedin, J 1985, 'Input Demand and Output Supply Elasticities for Rice in Bangladesh—A Study Based on Thakurgaon Farmers', The Bangladesh Development Studies, vol. 13, no. 3/4, pp. 111-25 Alexandratos, N & Bruinsma, J 2012, World agriculture towards 2030/2050: the 2012 revision, ESA Working paper Rome, FAO. Ali, K, Munsif, F, Zubair, M, Hussain, Z, Shahid, M, Din, IU & Khan, N 2011, 'Management of organic and inorganic nitrogen for different maize varieties', Sarhad J. Agric, vol. 27, no. 4, pp. 525-9 Ali, S, Hamid, N, Rasul, G, Mehnaz, S & Malik, KA 1998, 'Contribution of non-leguminous biofertilizers to rice biomass, nitrogen fixation and fertilizer-N use efficiency under flooded soil conditions', in Nitrogen Fixation with Non-Legumes, Springer, pp. 61-73. Ambalam, K 2014, 'Food Sovereignty in the Era of Land Grabbing: An African Perspective', Journal of Sustainable Development, vol. 7, no. 2, p. p121, DOI 10.5539/jsd.v7n2p121. Anderson, D, Molton, P & Metting, B 1981, 'Assessment of blue-green algae in substantially reducing nitrogen fertilizer requirements for biomass fuel crops', in Proceedings of the July 1981, Subcontractors’ Review Meeting, Aquatic Species Program, pp. 69-73. Anseeuw, W 2012 'Transnational Land Deals for Agriculture in the Global South', Anseeuw, W, Boche, M, Breu, T, Giger, M, Lay, J, Messerli, P & Nolte, K 2012, 'Transnational land deals for agriculture in the global South', Antoine De Ramon, NY & Iese, V 2014, 'Marine Plants as a Sustainable Source of Agri-Fertilizers for Small Island Developing States (SIDS)', Impacts of Climate Change on Food Security in Small Island Developing States, p. 280 Arima, EY, Barreto, P, Araújo, E & Soares-Filho, B 2014, 'Public policies can reduce tropical deforestation: Lessons and challenges from Brazil', Land Use Policy, vol. 41, pp. 465-73, DOI 10.1016/j.landusepol.2014.06.026. Arizpe, N, Giampietro, M & Ramos-Martin, J 2011, 'Food security and fossil energy dependence: an international comparison of the use of fossil energy in agriculture (1991-2003)', Critical Reviews in Plant Sciences, vol. 30, no. 1-2, pp. 45-63 Arshad, FM & Hameed, AAA 2009, 'The long run relationship between petroleum and cereals prices', Global Economy & Finance Journal, vol. 2, no. 2, pp. 91-100 Bachev, H 2015, 'March 2011 earthquake, tsunami and Fukushima nuclear accident impacts on Japanese agri-food sector', Tsunami and Fukushima Nuclear Accident Impacts on Japanese Agri-Food Sector (January 21, 2015), Backhaus, K, Gausling, P & Hildebrand, L 2015, 'Comparing the incomparable: Lessons to be learned from models evaluating the feasibility of Desertec', Energy, vol. 82, pp. 905-13 Baffes, J 2007, 'Oil spills on other commodities', Resources Policy, vol. 32, no. 3, pp. 126-34, DOI 10.1016/j.resourpol.2007.08.004. Baldos, Uris Lantz C., and Thomas W. Hertel. "Debunking the ‘new normal’: Why world food prices are expected to resume their long run downward trend." Global Food Security 8 (2016): 27-38.

Baffes, J & Dennis, A 2013, 'Long-Term Drivers of Food Prices', DOI 10.1596/1813-9450-6455 Bardi, U 2009, 'Peak oil: The four stages of a new idea', Energy, vol. 34, no. 3, pp. 323-6 —— 2011, The limits to growth revisited, Springer Bardi, U, El Asmar, T & Lavacchi, A 2013, 'Turning electricity into food: the role of renewable energy in the future of agriculture', Journal of Cleaner Production, vol. 53, pp. 224-31 Barnosky, AD, Matzke, N, Tomiya, S, Wogan, GO, Swartz, B, Quental, TB, Marshall, C, McGuire, JL, Lindsey, EL & Maguire, KC 2011, 'Has the Earth/'s sixth mass extinction already arrived?', Nature, vol. 471, no. 7336, pp. 51-7 Bartelings, H, Kavallari, A, van Meijl, H & von Lampe, M 2014, 'Estimating the impact of fertilizer support policies: A CGE approach', Benes, J, Chauvet, M, Kamenik, O, Kumhof, M, Laxton, D, Mursula, S & Selody, J 2015, 'The future of oil: Geology versus technology', International Journal of Forecasting, vol. 31, no. 1, pp. 207-21 Bhattacharjee, RB, Singh, A & Mukhopadhyay, S 2008, 'Use of nitrogen-fixing bacteria as biofertiliser for non-legumes: prospects and challenges', Applied Microbiology and Biotechnology, vol. 80, no. 2, pp. 199-209 Biswas, M, Parveen, S, Shimozawa, H & Nakagoshi, N 2005, 'Effects of Azolla species on weed emergence in a rice paddy ecosystem', Weed Biology and Management, vol. 5, no. 4, pp. 176- 83 Blankenhorn, V & Resch, B 2014, 'Determination of Suitable Areas for the Generation of Wind Energy in Germany: Potential Areas of the Present and Future', ISPRS International Journal of Geo-Information, vol. 3, no. 3, pp. 942-67 Borlaug, N 2007, 'Feeding a hungry world', Science, vol. 318, no. 5849, pp. 359-, DOI 10.1126/science.1151062. Börner, J, Wunder, S, Wertz-Kanounnikoff, S, Hyman, G & Nascimento, N 2014, 'Forest law enforcement in the Brazilian Amazon: Costs and income effects', Global Environmental Change, vol. 29, pp. 294-305, DOI 10.1016/j.gloenvcha.2014.04.021. Borras Jr, SM & Franco, JC 2012, 'Global Land Grabbing and Trajectories of Agrarian Change: A Preliminary Analysis', Journal of Agrarian Change, vol. 12, no. 1, pp. 34-59, DOI 10.1111/j.1471-0366.2011.00339.x. Bot, AJ, Nachtergaele, F & Young, A 2000, 'Land resource potential and constraints at regional and country levels', World Soil Resources Reports, no. 90, Brands, E 2014, 'Prospects and challenges for sustainable sanitation in developed nations: a critical review', Environmental Reviews, vol. 22, no. 4, pp. 346-63 Bruinsma, J 2011, 'This chapter addresses some of these issues by unfolding the resource use implications of the crop production projections underlying the latest FAO', Looking ahead in worLd food and agricuLture: Perspectives to 2050, p. 233 Brunelle, T 2012 'The impact of global drivers on agriculture and land-use', AgroParisTech. Brunelle, T, Dumas, P & Souty, F 2014, 'The impact of globalization on food and agriculture: The case of the diet convergence', The Journal of Environment & Development, vol. 23, no. 1, pp. 41-65 Brunelle, T, Dumas, P, Souty, F, Dorin, B & Nadaud, F 2015, 'Evaluating the impact of rising fertilizer prices on crop yields', Agricultural Economics, vol. 46, no. 5, pp. 653-66, DOI 10.1111/agec.12161 83

Burnett, MT 2016, Natural Resource Conflicts [2 volumes]: From Blood Diamonds to Rainforest Destruction, ABC-CLIO Butler, CD 2009, 'Food Security in the Asia-Pacific: Malthus, Limits and Environmental Challenges', Asia Pacific Journal of Clinical Nutrition, vol. 18, no. 4, pp. 577-84 Camin, F, Perini, M, Bontempo, L, Fabroni, S, Faedi, W, Magnani, S, Baruzzi, G, Bonoli, M, Tabilio, M & Musmeci, S 2011, 'Potential isotopic and chemical markers for characterising organic fruits', Food Chemistry, vol. 125, no. 3, pp. 1072-82 Capehart, T & Stubbs, M 2007, 'Renewable Energy Policy in the 2008 Farm Bill', in. Carey, DE, Yang, Y, McNamara, PJ & Mayer, BK 2016, 'Recovery of agricultural nutrients from biorefineries', Bioresource technology, Carlini, M, Villarini, M, Esposto, S & Bernardi, M 2010, 'Performance analysis of greenhouses with integrated photovoltaic modules', in International Conference on Computational Science and Its Applications, pp. 206-14. Caspian Energy Newspaper 2016, First wind farm launched in Caucasus region viewed 11/11/2016, . Catterall, CP, Freeman, AN, Kanowski, J & Freebody, K 2012, 'Can active restoration of tropical rainforest rescue biodiversity? A case with bird community indicators', Biological Conservation, vol. 146, no. 1, pp. 53-61 Cavagnaro, TR 2015, 'Chapter Five-Biologically Regulated Nutrient Supply Systems: Compost and Arbuscular Mycorrhizas—A Review', Advances in Agronomy, vol. 129, pp. 293-321 Chase, JM & Leibold, MA 2002, 'Spatial scale dictates the productivity–biodiversity relationship', Nature, vol. 416, no. 6879, pp. 427-30 Chennubhotla, V, Rao, MU & Rao, K 2013, 'Exploitation of marine algae in Indo-Pacific region', Seaweed Research and Utilization, vol. 35, no. 1 & 2, pp. 1-7 Chianu, J & Tsujii, H 2004, 'Determinants of farmers' decision to adopt or not adopt inorganic fertilizer in the savannas of northern Nigeria', Nutrient cycling in agroecosystems, vol. 70, no. 3, pp. 293-301 Childs, NW & Kiawu, J 2009, Factors behind the rise in global rice prices in 2008, US Department of Agriculture, Economic Research Service Conceicao, P & Mendoza, RU 2009, 'Anatomy of the Global Food Crisis', Third World Quarterly, vol. 30, no. 6, pp. 1159-82, DOI 10.1080/01436590903037473. Corson, C & MacDonald, KI 2012, 'Enclosing the global commons: the convention on biological diversity and green grabbing', Journal of Peasant Studies, vol. 39, no. 2, pp. 263-83, DOI 10.1080/03066150.2012.664138. Coyle, W 2007, 'The future of biofuels', Economic Research Service, Washington, DC, Crosson, P 2013, The Cropland crisis: myth or reality?, Routledge Crucefix, D 1998, 'Organic agriculture and sustainable rural livelihoods in developing countries', Report by Natural Resources and Ethical Trade Programme, June, Currie, DJ 2003, 'Does climate determine broad‐scale patterns of species richness? A test of the causal link by natural experiment', Global Ecology and Biogeography, vol. 12, no. 6, pp. 461- 73 Currie, DJ & Paquin, V 1987, 'Large-scale biogeographical patterns of species richness of trees', Nature, vol. 329, no. 6137, pp. 326-7

Czech, B 2004, 'Urbanization as a threat to biodiversity: Trophic theory, economic geography, and implications for conservation land acquisition', Policies for managing urban growth and landscape change: a key to conservation in the 21st Century, vol. 265, pp. 8-13 —— 2014, 'The Conflict Between Economic Growth and Wildlife Conservation', in Peak Oil, Economic Growth, and Wildlife Conservation, Springer, pp. 99-117, DOI 10.1007/978-1- 4939-1954-3_5 Czucz, B, Gathman, JP & McPherson, GR 2010, 'The Impending Peak and Decline of Petroleum Production: an Underestimated Challenge for Conservation of Ecological Integrity', Conservation Biology, vol. 24, no. 4, pp. 948-56, DOI 10.1111/j.1523-1739.2010.01503.x. Czúcz, B, Gathman, JP & McPherson, GR 2010, 'The impending peak and decline of petroleum production: an underestimated challenge for conservation of ecological integrity', Conservation biology : the journal of the Society for Conservation Biology, vol. 24, no. 4, pp. 948-56, DOI 10.1111/j.1523-1739.2010.01503.x. de Castro, C, Mediavilla, M, Miguel, LJ & Frechoso, F 2011, 'Global wind power potential: Physical and technological limits', Energy Policy, vol. 39, no. 10, pp. 6677-82 de Ponti, T, Rijk, B & van Ittersum, MK 2012, 'The crop yield gap between organic and conventional agriculture', Agricultural Systems, vol. 108, pp. 1-9 Deign, J 2012, DNI: Measuring bang for your buck, viewed 19/10/2016, . Deininger, K 2011, 'Challenges posed by the new wave of farmland investment', The Journal of Peasant Studies, vol. 38, no. 2, pp. 217-47 Deininger, K & Byerlee, D 2012, 'The Rise of Large Farms in Land Abundant Countries: Do They Have a Future?', World Development, vol. 40, no. 4, pp. 701-14, DOI 10.1016/j.worlddev.2011.04.030. Deininger, KW, Byerlee, D & World, B 2011, Rising global interest in farmland: can it yield sustainable and equitable benefits?, World Bank, Washington, D.C, DOI 10.1596/978-0- 8213-8591-3 Demissie, F 2014, 'The new scramble over Africa's farmland: an introduction', African Identities, vol. 12, no. 1, pp. 1-7, DOI 10.1080/14725843.2014.922750 Diamond, JM 2005, Collapse : how societies choose to fail or succeed, Penguin Group, Camberwell, Vic. : Dobermann, A 2006, 'Invited paper: Nitrogen Use Efficiency in Cereal Systems', Europe, vol. 21, no. 1.2, p. 22.6 Dobermann, A & Cassman, KG 2005, 'Cereal area and nitrogen use efficiency are drivers of future nitrogen fertilizer consumption', Science in China Series C: Life Sciences, vol. 48, no. 2, pp. 745-58 Dodson, J & Sipe, N 2008, 'Shocking the suburbs: urban location, homeownership and oil vulnerability in the Australian city', Housing Studies, vol. 23, no. 3, pp. 377-401, DOI 10.1080/02673030802015619. Dommergues, Y & Ganry, F 2012, 'Biological nitrogen fixation and soil fertility', in Management of Nitrogen and Phosphorus Fertilizers in Sub-Saharan Africa: Proceedings of a symposium, held in Lome, Togo, March 25–28, 1985, vol. 24, p. 95. Dorward, A & Chirwa, E 2011, 'The Malawi agricultural input subsidy programme: 2005/06 to 2008/09', International journal of agricultural sustainability, vol. 9, no. 1, pp. 232-47

Dorward, A & Poulton, C 2008, 'The Global Fertiliser Crisis and Africa', Dosta, J, Galí, A, El-Hadj, TB, Macé, S & Mata-Alvarez, J 2007, 'Operation and model description of a sequencing batch reactor treating reject water for biological nitrogen removal via nitrite', Bioresource technology, vol. 98, no. 11, pp. 2065-75 Druilhe, Z & Barreiro-Hurlé, J 2012, Fertilizer subsidies in sub-Saharan Africa, ESA Working paper. Duflo, E, Kremer, M & Robinson, J 2009, Nudging farmers to use fertilizer: Theory and experimental evidence from Kenya, National Bureau of Economic Research. Dunn, R, Lovegrove, K & Burgess, G 2012, 'A review of ammonia-based thermochemical energy storage for concentrating solar power', Proceedings of the IEEE, vol. 100, no. 2, pp. 391-400 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, . 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. Energypedia 2015, Libya Energy Situation, viewed 28/09/2016, . Erisman, JW 2004, 'The Nanjing declaration on management of reactive nitrogen', Bioscience, vol. 54, no. 4, pp. 286-7 Fairhead, J, Leach, M & Scoones, I 2012, 'Green Grabbing: a new appropriation of nature?', Journal of Peasant Studies, vol. 39, no. 2, pp. 237-61, DOI 10.1080/03066150.2012.671770. Fairlie, S 2007, 'Can Britain Feed Itself', The Land, vol. Winter, FAO 2012, FAOSTAT, viewed 9/9/2014, . —— 2014, FAO statistical databases, viewed 21/05/2016, . FAO, I 2015, 'Status of the World’s Soil Resources (SWSR)–Main Report', Natural Resources and Environment Department, Food and Agriculture Organization (FAO) of the United Nations and Intergovernmental Technical Panel on Soils (ITPS), Rome, Italy, Fattouh, B & El-Katiri, L 2012, 'Energy subsidies in the Arab world', Fernandes, SD, Trautmann, NM, Streets, DG, Roden, CA & Bond, TC 2007, 'Global biofuel use, 1850–2000', Global Biogeochemical Cycles, vol. 21, no. 2, Ferretti-Gallon, K & Busch, J 2014, 'What Drives Deforestation and What Stops It? A Meta- Analysis of Spatially Explicit Econometric Studies', DOI 10.2139/ssrn.2458040 Fertilizeworks 2011, Granular urea basket price vs megu, viewed 9/9/2014, . Fischer, R, Byerlee, D & Edmeades, G 2012, Crop yields and global food security, Canberra: Australian Center for International Agricultural Research. Florentinus, A, Hamelinck, C, de Lint, S & van Iersel, S 2008, 'Worldwide potential of aquatic biomass', Utrecht, Ecofys, Foley, JA, Ramankutty, N, Brauman, KA, Cassidy, ES, Gerber, JS, Johnston, M, Mueller, ND, O/'Connell, C, Ray, DK, West, PC, Balzer, C, Bennett, EM, Carpenter, SR, Hill, J, Monfreda,

C, Polasky, S, Rockstrom, J, Sheehan, J, Siebert, S, Tilman, D & Zaks, DPM 2011, 'Solutions for a cultivated planet', Nature, vol. 478, no. 7369, pp. 337-42, DOI 10.1038/nature10452. Friis, Cecilie and Reenberg, Anette 2010, ‘Land grab in Africa: Emerging land system drivers in a teleconnected world.’, GLP Report No. 1, GLP-IPO, Copenhagen Fritz, S, See, L, McCallum, I, You, L, Bun, A, Moltchanova, E, Duerauer, M, Albrecht, F, Schill, C & Perger, C 2015, 'Mapping global cropland and field size', Global Change Biology, vol. 21, no. 5, pp. 1980-92 Galloway, JN, Townsend, AR, Erisman, JW, Bekunda, M, Cai, Z, Freney, JR, Martinelli, LA, Seitzinger, SP & Sutton, MA 2008, 'Transformation of the nitrogen cycle: recent trends, questions, and potential solutions', Science, vol. 320, no. 5878, pp. 889-92 Gardner, TA, Burgess, ND, Aguilar-Amuchastegui, N, Barlow, J, Berenguer, E, Clements, T, Danielsen, F, Ferreira, J, Foden, W & Kapos, V 2012, 'A framework for integrating biodiversity concerns into national REDD+ programmes', Biological Conservation, vol. 154, pp. 61-71 Gaston, KJ 2000, 'Global patterns in biodiversity', Nature, vol. 405, no. 6783, pp. 220-7 Gates, JE, Trauger, DL & Czech, B 2014, Peak oil, Economic growth, and wildlife conservation, Springer Gaveau, DL, Linkie, M, Levang, P & Leader-Williams, N 2009, 'Three decades of deforestation in southwest Sumatra: effects of coffee prices, law enforcement and rural poverty', Biological Conservation, vol. 142, no. 3, pp. 597-605 Geist, HJ & Lambin, EF 2002, 'Proximate Causes and Underlying Driving Forces of Tropical Deforestation', Bioscience, vol. 52, no. 2, pp. 143-50, DOI 10.1641/0006- 3568(2002)052[0143:pcaudf]2.0.co;2. Gerber, A 2014, 'Food security as an outcome of food systems', in 32nd international conference of the system dynamics society, pp. 20-4. Giampietro, M & Mayumi, K 2009, The biofuel delusion: The fallacy of large scale agro-biofuels production, Earthscan Giles, J 2005, 'Nitrogen study fertilizes fears of pollution', Nature, vol. 433, no. 7028, pp. 791- Gilland, B 2014, 'Is a Haber-Bosch World Sustainable? Population, Nutrition, Cereals, Nitrogen and Environment', The Journal of Social, Political, and Economic Studies, vol. 39, no. 2, p. 166 Glassey, C, Roach, C, Lee, J & Clark, D 2013, 'The impact of farming without nitrogen fertiliser for ten years on pasture yield and composition, milksolids production and profitability; a research farmlet comparison', in Proc. NZ Grassl. Assoc, vol. 75, pp. 71-8. Glazebrook, T & Kola-Olusanya, A 2013, 'Africa, Food, and Agriculture', DOI 10.1007/978-94- 007-0929-4_486 Godfray, HCJ, Beddington, JR, Crute, IR, Haddad, L, Lawrence, D, Muir, JF, Pretty, J, Robinson, S, Thomas, SM & Toulmin, C 2010, 'Food security: the challenge of feeding 9 billion people', Science, vol. 327, no. 5967, pp. 812-8 Google US 2016, Google Earth, viewed 21 July 2016, < https://www.google.com/earth/>. GRAIN 2012, GRAIN releases data set with over 400 global land grabs viewed 31/12/2013 2013, . Grassi, S, Veronesi, F, Schenkel, R, Peier, C, Neukom, J, Volkwein, S, Raubal, M & Hurni, L 2015, 'Mapping of the global wind energy potential using open source GIS data', 2nd International 87

Conference on Energy and Environment: bringing together Engineering and Economics, Guimarães, Portugal, 18-19 June, 2015, https://www.researchgate.net/profile/Stefano_Grassi/publication/274394014_Mapping_of_the _global_wind_energy_potential_using_open_source_GIS_data/links/5530dc5c0cf27acb0de88 cf2.pdf Groombridge, B & Jenkins, MD 2002, World atlas of biodiversity, University of California Press Berkeley, CA, DOI 10.1017/s0030605303240436 Guilford, MC, Hall, CA, O’Connor, P & Cleveland, CJ 2011, 'A new long term assessment of energy return on investment (EROI) for US oil and gas discovery and production', Sustainability, vol. 3, no. 10, pp. 1866-87 Guixia, L 2015, China’s development aid to Fiji: motive and method The Research Centre of the Pacific Island Countries, Liaocheng University, Shandong Province, China. , . Haberl, H, Fischer‐Kowalski, M, Krausmann, F, Martinez‐Alier, J & Winiwarter, V 2011, 'A socio‐ metabolic transition towards sustainability? Challenges for another Great Transformation', Sustainable Development, vol. 19, no. 1, pp. 1-14, DOI 10.1002/sd.410. Haider, M, Rammerstorfer, F, Böhm, H, Diendorfer, C & Toth, F 2015, FLOATING PLATFORM, US Patent 20,150,298,774. Haile, MG, Kalkuhl, M & Braun, Jv 2013, 'Inter-and intra-annual global crop acreage response to prices and price risk', in 2013 Annual Meeting, August 4-6, 2013, Washington, DC. Haile M G, Kalkuhl M and Von Braun J 2016 Worldwide acreage and yield response to international price change and volatility: a dynamic panel data analysis for wheat, rice, corn, and soybeans, Am. J. Agric. Econ. 98 172–90 Hall, CAS & Day, JW 2009, 'Revisiting the Limits to Growth After Peak Oil', American Scientist, vol. 97, no. 3, pp. 230-7 Hallam, D 2011, 'International investment in developing country agriculture—issues and challenges', Food Security, vol. 3, no. 1, pp. 91-8, DOI 10.1007/s12571-010-0104-1. Halweil, B 2006, 'Can Organic Farming Feed Us All? Probably-but that may not be the right question', World Watch, vol. 19, no. 3, p. 18 Hamilton, JD 2009, 'Causes and Consequences of the Oil Shock of 2007–08', Brookings Papers on Economic Activity, vol. 2009, no. 1, pp. 215-61, DOI 10.1353/eca.0.0047. Hansen, MC, Potapov, PV, Moore, R, Hancher, M, Turubanova, S, Tyukavina, A, Thau, D, Stehman, S, Goetz, S & Loveland, T 2013a, High-resolution global maps of 21st-century forest cover change, 6160, 0036-8075, . Hansen, MC, Potapov, PV, Moore, R, Hancher, M, Turubanova, SA, Tyukavina, A, Thau, D, Stehman, SV, Goetz, SJ, Loveland, TR, Kommareddy, A, Egorov, A, Chini, L, Justice, CO & Townshend, JRG 2013b, 'High-Resolution Global Maps of 21st-Century Forest Cover Change', Science, vol. 342, no. 6160, pp. 850-3, DOI 10.1126/science.1244693. Hansen, MC, Stehman, SV & Potapov, PV 2010, 'Quantification of global gross forest cover loss', Proceedings of the National Academy of Sciences, vol. 107, no. 19, pp. 8650-5, DOI 10.1073/pnas.0912668107.

Headey, D 2011, 'Rethinking the global food crisis: The role of trade shocks', Food Policy, vol. 36, no. 2, pp. 136-46, DOI 10.1016/j.foodpol.2010.10.003. Headey, D & Fan, S 2008, 'Anatomy of a crisis: the causes and consequences of surging food prices', Agricultural Economics, vol. 39, no. s1, pp. 375-, DOI 10.1111/j.1574- 0862.2008.00345.x. Heakal, R 2015, Economics Basics: Elasticity, Retrieved January, viewed 14/07/2016, . Hecht, SB & Saatchi, SS 2007, 'Globalization and forest resurgence: changes in forest cover in El Salvador', Bioscience, vol. 57, no. 8, pp. 663-72, DOI 10.1641/B570806. Heffer, P & Prud’homme, M 2015, 'Fertilizer Outlook 2015-2019 ', in 83rd IFA Annual Conference held in Istanbul, Turkey. Heinberg, R & Fridley, D 2010, 'The end of cheap coal', Nature, vol. 468, no. 7322, pp. 367-9 Heinstein, P, Perret-Aebi, L-E, Escarre Palou, J, Cattaneo, G, LI, H-Y, Mussolino, V, Sansonnens, L & Ballif, C 2015, 'Energy harvesting and passive cooling: A new BIPV perspective opened by white solar modules', in Proceedings of International Conference CISBAT 2015 Future Buildings and Districts Sustainability from Nano to Urban Scale, pp. 675-80. Henry, N & Rae, J 2012, Biodiversity in Australia, viewed 12/02/2015 2015, . Herford, I, Armstrong, R & Daniel, G 2011, 'Fire Management Plan for the Conservation of Biodiversity and Cultural Heritage Values in the Great Western Woodlands', Unpublished Report, Department of Environment and Conservation, Perth, Hertel, T, Steinbuks, J & Baldos, U 2013, 'Competition for land in the global bioeconomy', Agricultural Economics, vol. 44, no. s1, pp. 129-38, DOI 10.1111/agec.12057. Hertel, Thomas W., Uris Lantz C. Baldos, and Dominique van der Mensbrugghe. "Predicting Long- Term Food Demand, Cropland Use, and Prices." Annual Review of Resource Economics 8 (2016): 417-441 Hobbs, RJ 2012, Old fields: dynamics and restoration of abandoned farmland, Island Press Höhne, N, Braun, N, Fekete, H, Brandsma, R, Larkin, J, den Elzen, M, Roelfsema, M, Hof, A & Böttcher, H 2012, 'Greenhouse gas emission reduction proposals and national climate policies of major economies', ECOFYS policy brief, November. h ttp://www. ecofys. com/files/files/ecofys_pbl_iiasa_2012_analysis_of_domestic_climate_change_policies_new. pdf, Holt-Giménez, E 2009, 'The Agrofuels Transition: Restructuring Places and Spaces in the Global Food System', Bulletin of Science, Technology & Society, vol. 29, no. 3, pp. 180-8, DOI 10.1177/0270467609333730. Hubbert, MK 1956, 'Nuclear energy and the fossil fuel', Drilling and production practice, International Energy Agency 2007, World Energy Outlook, France, DOI 10.1787/weo-2007-en Jacobson, MZ & Delucchi, MA 2011, 'Providing all global energy with wind, water, and solar power, Part I: Technologies, energy resources, quantities and areas of infrastructure, and materials', Energy Policy, vol. 39, no. 3, pp. 1154-69 Jiang, J & Aulich, T 2008, JV Task-121 Electrochemical Synthesis of Nitrogen Fertilizers, University Of North Dakota. Jones, M 2013, Scientists search for ways for plants to thrive without nitrogen fertiliser, Imperial College London, viewed 17/07/2016, 89

. Jones, NF, Pejchar, L & Kiesecker, JM 2015a, 'The energy footprint: how oil, natural gas, and wind energy affect land for biodiversity and the flow of ecosystem services', Bioscience, p. biu224 Jones, P, Comfort, D & Hillier, D 2015b, 'Spotlight on solar farms', Journal of Public Affairs, vol. 15, no. 1, pp. 14-21 Kelly, C 2009, 'Lower external input farming methods as a more sustainable-solution for small- scale farmers'. Kerschner, C, Prell, C, Feng, K & Hubacek, K 2013, 'Economic vulnerability to Peak Oil', Global Environmental Change, vol. 23, no. 6, pp. 1424-33 Kier, G, Kreft, H, Lee, TM, Jetz, W, Ibisch, PL, Nowicki, C, Mutke, J & Barthlott, W 2009, 'A global assessment of endemism and species richness across island and mainland regions', Proceedings of the National Academy of Sciences, vol. 106, no. 23, pp. 9322-7, DOI 10.1073/pnas.0810306106. Kissinger, G, M. Herold, V. De Sy 2012, Drivers of Deforestation and Forest Degradation: A Synthesis Report, for REDD+ Policymakers. Lexeme Consulting, , Vancouver Canada. Kruger, P 2006, Alternative energy resources: the quest for sustainable energy, Wiley New Jersey Kurbatova, T & Khlyap, H 2015, 'GHG emissions and economic measures for low carbon growth in Ukraine', Carbon Management, vol. 6, no. 1-2, pp. 7-17 Lagi, M, Bertrand, K & Bar-Yam, Y 2011, 'The food crises and political instability in North Africa and the Middle East', Available at SSRN 1910031, Lambin, EF, Folke, C, George, PS, Homewood, K, Imbernon, J, Leemans, R, Li, X, Moran, EF, Mortimore, M, Ramakrishnan, PS, Richards, JF, Turner, BL, Skånes, H, Steffen, W, Stone, GD, Svedin, U, Veldkamp, TA, Vogel, C, Xu, J, Geist, HJ, Agbola, SB, Angelsen, A, Bruce, JW, Coomes, OT, Dirzo, R & Fischer, G 2001, 'The causes of land-use and land-cover change: moving beyond the myths', Global Environmental Change, vol. 11, no. 4, pp. 261-9, DOI 10.1016/s0959-3780(01)00007-3. Lambin, EF & Meyfroidt, P 2011, 'Global land use change, economic globalization, and the looming land scarcity', Proceedings of the National Academy of Sciences, vol. 108, no. 9, pp. 3465-72, DOI 10.1073/pnas.1100480108. LaRue, TA 2013, 'Chemical and biological nitrogen fixation', Future Sources of Organic Raw Materials: CHEMRAWN I: CHEMRAWN Chemical Research Applied to Words Needs, p. 389 Leighty, B 2008, 'Two Farm Bill Research Initiatives Promise New Markets, Transmission, and Firming Storage for Diverse, Large-Scale Renewables as Hydrogen and Ammonia', in The NHA Annual Hydrogen Conference 2008. Leighty, B & Holbrook, J 2008, 'Transmission and firming of GW-Scale wind energy via hydrogen and ammonia', Wind Engineering, vol. 32, no. 1, pp. 45-66 Leighty, WC 2010, 'Transmission and annual-scale firming storage alternatives to electricity: gaseous hydrogen and anhydrous ammonia via underground pipeline', in Proceedings of the International Colloquium on Environmentally Preferred Advanced Power Generation, Costa Mesa, California, USA. Li, D 2013, 'Using GIS and Remote Sensing Techniques for Solar Panel Installation Site Selection', PhD thesis, https://uwspace.uwaterloo.ca/bitstream/handle/10012/7960/Li_Dongrong.pdf?sequence=1

Li, F-W & Pryer, KM 2014, 'Crowdfunding the Azolla fern genome project: a grassroots approach', GigaScience, vol. 3, no. 1, p. 1 Margono, BA, Potapov, PV, Turubanova, S, Stolle, F & Hansen, MC 2014, 'Primary forest cover loss in Indonesia over 2000-2012', Nature Climate Change, vol. 4, no. 8, pp. 730-5, DOI 10.1038/Nclimate2277. Matassa, S, Batstone, DJ, Hülsen, T, Schnoor, J & Verstraete, W 2015, 'Can direct conversion of used nitrogen to new feed and protein help feed the world?', Environmental science & technology, vol. 49, no. 9, pp. 5247-54 Matthews, R & De Pinto, A 2012, 'Should REDD+ fund ‘sustainable intensification’as a means of reducing tropical deforestation?', Carbon Management, vol. 3, no. 2, pp. 117-20 Matthews, R, Swallow, B, van Noordwijk, M, Milne, E, Minang, P, Bakam, I, Brewer, M, Muhammed, S, Poggio, L & Glenk, K 2010, 'Development and application of methodologies for reduced emissions from deforestation and forest degradation (REDD+)—Phase I', Final Report for Project CEOSA, vol. 803, May, PH, Millikan, B & Gabara, M 2010, 'The context of REDD+ in Brazil', Drivers, agents and institutions. Second Edition (Occasional Paper). Bogor: Center for International Forestry Research, DOI 10.17528/cifor/003287 McGrath, C 2007, 'End of broadscale clearing in Queensland', EPLJ, vol. 24, pp. 5-13 McMichael, P 2009, 'A food regime analysis of the ‘world food crisis’', Agriculture and human values, vol. 26, no. 4, pp. 281-95, DOI 10.1007/s10460-009-9218-5. Meadows, D, Meadows, D & Randers, J 2004, 'Limits to growth: The 30-year update', book, DOI 10.1007/s11573-007-0035-2 Meadows, DH, Randers, J & Behrens III, WW 1972, The Limits to Growth: A Report to The Club of Rome (1972), Universe Books, New York, DOI 10.1349/ddlp.1 Mediavilla, M, de Castro, C, Capellan, I, Miguel, LJ, Arto, I & Frechoso, F 2013, 'The transition towards renewable energies: Physical limits and temporal conditions', Energy Policy, vol. 52, pp. 297-311, DOI 10.1016/j.enpol.2012.09.033. Minear, BA 2015, 'The Effects of Changing Fertilizer Production Costs on US Agricultural Markets: A Partial Equilibrium Analysis', University of Missouri--Columbia. Miranowski, J 2014, 'Technology Forcing and Associated Costs and Benefits of Cellulosic Ethanol', Choices, vol. 29, no. 1, Mishkin, FS 2010, Over the cliff: from the subprime to the global financial crisis, National Bureau of Economic Research. Monfreda, C, Ramankutty, N & Foley, JA 2008, 'Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000', Global Biogeochemical Cycles, vol. 22, no. 1, Montgomery, J 2014, Renewables in North Africa: A Nation-By-Nation Report Card, viewed 29/09/2016, . Morton, DC, DeFries, RS, Shimabukuro, YE, Anderson, LO, Arai, E, del Bon Espirito-Santo, F, Freitas, R & Morisette, J 2006, 'Cropland expansion changes deforestation dynamics in the southern Brazilian Amazon', Proceedings of the National Academy of Sciences, vol. 103, no. 39, pp. 14637-41

Mueller, ND, West, PC, Gerber, JS, MacDonald, GK, Polasky, S & Foley, JA 2014, 'A tradeoff frontier for global nitrogen use and cereal production', Environmental Research Letters, vol. 9, no. 5, p. 054002 Mueller, SA, Anderson, JE & Wallington, TJ 2011, 'Impact of biofuel production and other supply and demand factors on food price increases in 2008', Biomass and Bioenergy, vol. 35, no. 5, pp. 1623-32, DOI http://dx.doi.org/10.1016/j.biombioe.2011.01.030. Mulder, A 2003, 'The quest for sustainable nitrogen removal technologies', Water Science and Technology, vol. 48, no. 1, pp. 67-75 Müller, K & Arlt, W 2013, 'Status and development in hydrogen transport and storage for energy applications', Energy Technology, vol. 1, no. 9, pp. 501-11 Murphy, DJ 2014, 'The implications of the declining energy return on investment of oil production', Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 372, no. 2006, p. 20130126, DOI 10.1098/rsta.2013.0126. Murphy, DJ & Hall, CA 2010, 'Year in review—EROI or energy return on (energy) invested', Annals of the New York Academy of Sciences, vol. 1185, no. 1, pp. 102-18 Murray, J & King, D 2012, 'Oil's tipping point has passed', Nature, vol. 481, no. 7382, pp. 433-5, DOI 10.1038/481433a NASA 2005, NASA SSE monthly average wind data at one-degree resolution of the world viewed 19/10/2016, . —— 2011, NASA solar direct normal viewed 19/10/2016, . NASA Earth Observations 2016, Albedo (1 month), viewed 19/10/2016, . Natural Earth 2016, Coastline, viewed 19/10/2016, . Neff, RA, Parker, CL, Kirschenmann, FL, Tinch, J & Lawrence, RS 2011, 'Peak oil, food systems, and public health', American journal of public health, vol. 101, no. 9, pp. 1587-97, DOI 10.2105/ajph.test.2011.300123. Nemet, GF 2009, 'Net radiative forcing from widespread deployment of photovoltaics', Environmental science & technology, vol. 43, no. 6, pp. 2173-8 Newell, JP & Simeone, J 2014, 'Russia’s forests in a global economy: how consumption drives environmental change', Eurasian Geography and Economics, vol. 55, no. 1, pp. 37-70, DOI 10.1080/15387216.2014.926254. Ng, F 2008, Who are the net food importing countries?, vol. 4457, World Bank Publications Niedertscheider, M, Kastner, T, Fetzel, T, Haberl, H, Kroisleitner, C, Plutzar, C & Erb, K-H 2016, 'Mapping and analysing cropland use intensity from a NPP perspective', Environmental Research Letters, vol. 11, no. 1, p. 014008 NIIR Board 2004, The complete technology book on bio-fertilizer and organic farming, National Institute of Industrial Research Orden, D, Cheng, F, Nguyen, H, Grote, U, Thomas, M, Mullen, K & Sun, D 2007, Agricultural producer support estimates for developing countries: Measurement issues and evidence from India, Indonesia, China, and Vietnam, vol. 152, Intl Food Policy Res Inst

Pearce, F 2012, 'Land-grabbers are stealing the earth', New Scientist, vol. 214, no. 2870, pp. 28-9, DOI 10.1016/s0262-4079(12)61619-4 Phalan, B, Green, RE, Dicks, LV, Dotta, G, Feniuk, C, Lamb, A, Strassburg, BB, Williams, DR, Zu Ermgassen, EK & Balmford, A 2016, 'How can higher-yield farming help to spare nature?', Science, vol. 351, no. 6272, pp. 450-1 Phalan, B, Onial, M, Balmford, A & Green, RE 2011, 'Reconciling food production and biodiversity conservation: land sharing and land sparing compared', Science, vol. 333, no. 6047, pp. 1289-91 Phelps, J, Friess, D & Webb, E 2012, 'Win–win REDD+ approaches belie carbon–biodiversity trade-offs', Biological Conservation, vol. 154, pp. 53-60 Philibert, C 2005, 'The present and future use of solar thermal energy as a primary source of energy', International Energy Agency, Paris, France, Piesse, J & Thirtle, C 2009, 'Three bubbles and a panic: An explanatory review of recent food commodity price events', Food Policy, vol. 34, no. 2, pp. 119-29, DOI 10.1016/j.foodpol.2009.01.001. Pihl, E, Kushnir, D, Sandén, B & Johnsson, F 2012, 'Material constraints for concentrating solar thermal power', Energy, vol. 44, no. 1, pp. 944-54 Pimentel, D & Pimentel, MH 2007, Food, energy, and society, CRC press, DOI 10.1201/9781420046687 Pretty, J, Sutherland, WJ, Ashby, J, Auburn, J, Baulcombe, D, Bell, M, Bentley, J, Bickersteth, S, Brown, K & Burke, J 2010, 'The top 100 questions of importance to the future of global agriculture', International journal of agricultural sustainability, vol. 8, no. 4, pp. 219-36, DOI 10.3763/ijas.2010.0534. Ragnarsdóttir, K, Koca, D & Sverdrup, H 2012, Assessing long term sustainability of global supply of natural resources and materials, INTECH Open Access Publisher Ragnarsdottir, KV 2008, 'The role of geochemists in the era of "Peak Everything"', Geochimica Et Cosmochimica Acta, vol. 72, no. 12, pp. A772-A Ramankutty, N, Foley, JA & Olejniczak, NJ 2002, 'People on the land: Changes in global population and croplands during the 20th century', AMBIO: A Journal of the Human Environment, vol. 31, no. 3, pp. 251-7 Ramankutty, N, Graumlich, L, Achard, F, Alves, D, Chhabra, A, DeFries, RS, Foley, JA, Geist, H, Houghton, RA & Goldewijk, KK 2006, 'Global land-cover change: Recent progress, remaining challenges', in Land-use and land-cover change, Springer, pp. 9-39. Rane, AA & Deorukhkar, A 2007, Economics of agriculture, Atlantic Publishers & Dist Reay, D 2015, 'Agricultural Nitrogen and Climate Change Mitigation', in Nitrogen and Climate Change, Springer, pp. 145-57. Renner, JN, Greenlee, LF, Ayres, KE & Herring, AM 2015, 'Electrochemical Synthesis of Ammonia: A Low Pressure, Low Temperature Approach', The Electrochemical Society Interface, vol. 24, no. 2, pp. 51-7 Ricardo, D 1817, 'On rent', The Economics of Structural Change, vol. 1 Ringler, Claudia, et al. "Global linkages among energy, food and water: an economic assessment." Journal of Environmental Studies and Sciences 6.1 (2016): 161-171. Rockström, J, Steffen, W, Noone, K, Persson, A, Chapin, FS, Lambin, EF, Lenton, TM, Scheffer, M, Folke, C, Schellnhuber, HJ, Nykvist, B, de Wit, CA, Hughes, T, van der Leeuw, S, Rodhe, 93

H, Sorlin, S, Snyder, PK, Costanza, R, Svedin, U, Falkenmark, M, Karlberg, L, Corell, RW, Fabry, VJ, Hansen, J, Walker, B, Liverman, D, Richardson, K, Crutzen, P & Foley, JA 2009, 'A safe operating space for humanity', Nature, vol. 461, no. 7263, pp. 472-5 Rosenthal, E 2010, 'Solar industry learns lessons in Spanish sun', The New York Times, March, vol. 8, Rosset, P 2013, 'Re-thinking agrarian reform, land and territory in La Via Campesina', Journal of Peasant Studies, vol. 40, no. 4, pp. 721-75, DOI 10.1080/03066150.2013.826654. Safarov, V 2015, 'Renewable Energy Perspectives of oil exporter Azerbaijan', Renewable Energy, Salmon, JM, Friedl, MA, Frolking, S, Wisser, D & Douglas, EM 2015, 'Global rain-fed, irrigated, and paddy croplands: A new high resolution map derived from remote sensing, crop inventories and climate data', International Journal of Applied Earth Observation and Geoinformation, vol. 38, pp. 321-34 Scheidel, A & Sorman, AH 2012, 'Energy transitions and the global land rush: Ultimate drivers and persistent consequences', Global Environmental Change-Human and Policy Dimensions, vol. 22, no. 3, pp. 588-95, DOI 10.1016/j.gloenvcha.2011.12.005. Scholz, RW, Ulrich, AE, Eilitta, M & Roy, A 2013, 'Sustainable use of phosphorus: A finite resource', Science of the Total Environment, vol. 461, pp. 799-803, DOI 10.1016/j.scitotenv.2013.05.043. Scudder, T 2005, The future of large dams: Dealing with social, environmental, institutional and political costs, Earthscan Shridhar, BS 2012, 'Review: nitrogen fixing microorganisms', Int J Microbiol Res, vol. 3, no. 1, pp. 46-52 Sipe, N & Dodson, J 2013, 'Oil Vulnerability in the American City', in Transport Beyond Oil, Springer, pp. 31-50. Smeets, E, Tabeau, A & VAN MEIJL, H 2015, 'An assessment of the global land use change and food security effects of the use of agricultural residues for bioenergy production', in Paper Prepared for Presentation at the International Conference Food in the Bio-based Economy; Sustainable Provision and Access, pp. 27-9. Smil, V 2004, Enriching the earth: Fritz Haber, Carl Bosch, and the transformation of world food production, MIT press —— 2006, 'Energy at the Crossroads', Global Perspectives and Uncertainties, —— 2008, Energy in nature and society: general energetics of complex systems, MIT Press —— 2011, 'Harvesting the Biosphere: The Human Impact', Population and Development Review, vol. 37, no. 4, pp. 613-+, DOI 10.1111/j.1728-4457.2011.00450.x. Sovacool, BK 2009, 'The intermittency of wind, solar, and renewable electricity generators: Technical barrier or rhetorical excuse?', Utilities Policy, vol. 17, no. 3, pp. 288-96 Speelman, EN, van Kempen, MM, Barke, J, Brinkhuis, H, Reichart, G-J, Smolders, AJ, Roelofs, JG, Sangiorgi, F, de Leeuw, JW & Lotter, AF 2009, 'The Eocene Arctic Azolla bloom: environmental conditions, productivity and carbon drawdown', Geobiology, vol. 7, no. 2, pp. 155-70 Szondy, D 2016, Giant wave-riding platform design puts solar power out to sea., viewed 20 July 2016, .

Tallaksen, J, Bauer, F, Hulteberg, C, Reese, M & Ahlgren, S 2015, 'Nitrogen fertilizers manufactured using wind power: greenhouse gas and energy balance of community-scale ammonia production', Journal of Cleaner Production, vol. 107, pp. 626-35 The Future Agricultures Consortium 2008, The Global Fertiliser Crisis and Africa The Government Office for Science 2011, Foresight. The Future of Food and Farming, vol. Final Project Report, London The Land Matrix Global Observatory 2013, Land Matrix: get the detail, viewed 8/9/2013 2013, . Thomas, R, Graven, DH, Hoskins, SB & Prentice, IC 2016, 'What is meant by ‘balancing sources and sinks of greenhouse gases’ to limit global temperature rise?', Tom Blomley, FF, Fred Nelson, Dilys Roe 2013, 'Conservation and Land Grabbing: Part of the Problem or Part of the Solution?', in London zoo. Triberti, L., Nastri, A., Giordani, G., Comellini, F., Baldoni, G. and Toderi, G., 2008. Can mineral and organic fertilization help sequestrate carbon dioxide in cropland?. European Journal of Agronomy, 29(1), pp.13-20. Tscharntke, T, Clough, Y, Wanger, TC, Jackson, L, Motzke, I, Perfecto, I, Vandermeer, J & Whitbread, A 2012, 'Global food security, biodiversity conservation and the future of agricultural intensification', Biological Conservation, vol. 151, no. 1, pp. 53-9, DOI 10.1016/j.biocon.2012.01.068. Turner, BL, Lambin, EF & Reenberg, A 2007, 'The emergence of land change science for global environmental change and sustainability', Proceedings of the National Academy of Sciences, vol. 104, no. 52, pp. 20666-71 Turner, GM 2008, 'A comparison of The Limits to Growth with 30 years of reality', Global Environmental Change, vol. 18, no. 3, pp. 397-411, DOI 10.1016/j.gloenvcha.2008.05.001. Turner, GM 2012, 'On the Cusp of Global Collapse? Updated Comparison of The Limits to Growth with Historical Data', GAIA-ECOLOGICAL PERSPECTIVES FOR SCIENCE AND SOCIETY, vol. 21, no. 2, pp. 116-24 Turney, D & Fthenakis, V 2011, 'Environmental impacts from the installation and operation of large-scale solar power plants', Renewable and Sustainable Energy Reviews, vol. 15, no. 6, pp. 3261-70 Tverberg, GE 2012, 'Oil supply limits and the continuing financial crisis', Energy, vol. 37, no. 1, pp. 27-34, DOI 10.1016/j.energy.2011.05.049. U.S. Geological Survey 2013, Land Change Science Program, viewed 4/03/2014, . UNFCCC, I 2007, 'Investment and financial flows to address climate change', Bonn: UNFCCC, van Asselen, S, Verburg, PH, Vermaat, JE & Janse, JH 2013, 'Drivers of Wetland Conversion: a Global Meta-Analysis', PloS one, vol. 8, no. 11, p. e81292, DOI 10.1371/journal.pone.0081292. Vanlauwe, B 2002, Integrated plant nutrient management in sub-Saharan Africa: from concept to practice, CABI Wagner, GM 1997, 'Azolla: a review of its biology and utilization', The Botanical Review, vol. 63, no. 1, pp. 1-26 Wanzala, N 2010, Implementation of the Abuja Declaration on Fertilizer for an African Green Revolution, Seventh Progress Report January-December. 95

Wheeler, D, Hammer, D, Kraft, R, Dasgupta, S & Blankespoor, B 2013, 'Economic dynamics and forest clearing: A spatial econometric analysis for Indonesia', Ecological Economics, vol. 85, pp. 85-96, DOI 10.1016/j.ecolecon.2012.11.005. Wiginton, L, Nguyen, H & Pearce, JM 2010, 'Quantifying rooftop solar photovoltaic potential for regional renewable energy policy', Computers, Environment and Urban Systems, vol. 34, no. 4, pp. 345-57 Wikipedia 2016a, List of photovoltaic power stations, viewed 21 July 2016, . —— 2016b, List of solar thermal power stations, < https://en.wikipedia.org/wiki/List_of_solar_thermal_power_stations>. Wilson, KA, McBride, MF, Bode, M & Possingham, HP 2006, 'Prioritizing global conservation efforts', Nature, vol. 440, no. 7082, pp. 337-40 Woltjer, GB 2013, Forestry in MAGNET: a new approach for land use and forestry modelling, Wettelijke Onderzoekstaken Natuur & Milieu. Wood, A, Stedman-Edwards, P & Johanna, M 2000, The root causes of biodiversity loss, Earthscan, DOI 10.1016/s1066-7938(00)00095-6 Wu, J & Sardo, V 2010, 'Sustainable Versus Organic Agriculture', in E Lichtfouse (ed.), Sociology, Organic Farming, Climate Change and Soil Science, Springer Netherlands, Dordrecht, pp. 41-76, DOI 10.1007/978-90-481-3333-8_3, . WWF Living Amazon Initiative 2014, Deforestation Fronts in the Amazon Region: Current Situation and Future Trends a preliminary summary, . Yi, S-K, Sin, H-Y & Heo, E 2011, 'Selecting sustainable renewable energy source for energy assistance to North Korea', Renewable and Sustainable Energy Reviews, vol. 15, no. 1, pp. 554-63 Zilberman, D, Hochman, G, Rajagopal, D, Sexton, S & Timilsina, G 2012, 'The impact of biofuels on commodity food prices: Assessment of findings', American Journal of Agricultural Economics, p. aas037, DOI 10.1093/ajae/aas037 Zoomers, A 2010, 'Globalisation and the foreignisation of space: seven processes driving the current global land grab', Journal of Peasant Studies, vol. 37, no. 2, pp. 429-47, DOI 10.1080/03066151003595325.

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 Indonesia44 infrastructure, commercial zoning, future forest product prices, cost/relative cost of: capital, inputs, land clearing 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, DR Congo13, Laos4, Mozambique13, Liberia13, Zambia13 war funding, conflict Lack of energy alternatives Zambia13 Conservation policies Costa Rica35 Protected forest areas and policies discouraging Vietnam26, Cambodia13 shifting cultivation Landscape (N= 15) Accessibility Malaysia29, Madagascar43, Guatemala25,36, Ecuador21, Honduras27 Elevation Madagascar43, Myanmar24,11, Veracruz, Mexico3

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

Bibliography Cropper, M. et al. (1999). Roads, Population Pressures, and Deforestation in Thailand, 1976-1989. Land Economics, 75(1): 58-73. Defries, R.S., et al. (2010). Deforestation Driven By Urban Population Growth and Agricultural Trade in the Twenty-First Century. Nature Geoscience, 3(3), 178-181. Ellis, E. A., et al. (2010). Land Use/Land Cover Change Dynamics and Drivers in a allow-Grade Marginal Coffee Growing Region of Veracruz, Mexico. Agroforestry Systems, 80(1): 61-84. Food and Agriculture Organization of the United Nations (FAO), 2010. Global Forest Resources Assessment 2010. FAO Forestry Paper 163. Food and Agriculture Organization, Rome, Italy. Gaveau, D.L.A. et al. (2009). Three Decades of Deforestation in Southwest Sumatra: Effects of Coffee Prices, Law Enforcement and Rural Poverty. Biological Conservation, 142(3): 597-605. Gong, Chongfeng. 2013. Determining socioeconomic drivers of urban forest fragmentation with historical remote sensing images, Landscape and urban planning, 117, 57 - 65-65. Grau, HR & Aide, M 2008, 'Globalization and land-use transitions in Latin America', Ecology and Society, vol. 13, no. 2, p. 16 Hansen, MC, Stehman, SV, Potapov, PV, Arunarwati, B, Stolle, F & Pittman, K 2009, 'Quantifying changes in the rates of forest clearing in Indonesia from 1990 to 2005 using remotely sensed data sets', Environmental Research Letters, vol. 4, no. 3, p. 034001 Hansen, MC, Stehman, SV & Potapov, PV 2010, 'Quantification of global gross forest cover loss', Proceedings of the National Academy of Sciences, vol. 107, no. 19, pp. 8650-5, DOI 10.1073/pnas.0912668107. Hansen, MC, Potapov, PV, Moore, R, Hancher, M, Turubanova, SA, Tyukavina, A, Thau, D, Stehman, SV, Goetz, SJ, Loveland, TR, Kommareddy, A, Egorov, A, Chini, L, Justice, CO & Townshend, JRG 2013, 'High-Resolution Global Maps of 21st-Century Forest Cover Change', Science, vol. 342, no. 6160, pp. 850-3, DOI 10.1126/science.1244693. Htun, N. Z. et al. (2013). Changes in Determinants of Deforestation and Forest Degradation in Popa Mountain Park, Central Myanmar. Environmental Management, 51: 423 Kashaigili, JJ, Mbilinyi, BP, Mccartney, M & Mwanuzi, FL 2006, 'Dynamics of Usangu plains wetlands: Use of remote sensing and GIS as management decision tools', Physics and Chemistry of the Earth, Parts A/B/C, vol. 31, no. 15, pp. 967-75, Kissinger, G, M. Herold, V. De Sy 2012, Drivers of Deforestation and Forest Degradation: A Synthesis Report, for REDD+ Policymakers. Lexeme Consulting, , Vancouver Canada. Koh, LP, Miettinen, J, Liew, SC & Ghazoul, J 2011, 'Remotely sensed evidence of tropical peatland conversion to oil palm', Proceedings of the National Academy of Sciences, vol. 108, no. 12, pp. 5127-32 Laurance, WF, Albernaz, AK, Schroth, G, Fearnside, PM, Bergen, S, Venticinque, EM & Da Costa, C 2002, 'Predictors of deforestation in the Brazilian Amazon', Journal of biogeography, vol. 29, no. 5‐6, pp. 737-48

Li, Y, Zhu, X, Sun, X, Wang, F, 2010. Landscape effects of environmental impact on bay-area wetlands under rapid urban expansion and development policy: A case study of Lianyungang, China. Landscape and Urban Planning,94, 218-227. Li, M., et al. (2012). Identifying Drivers of Land Use Change in China: A Spatial Multinomial Logit Model Analysis. Land Economics, 89(4): 632-654. Lopez, S et al. (2010). Tropical Deforestation in Ecuadorian Chocó: Logging Practices and Socio- Spatial Relationship. The Geographical Bulletin, 51(1): 3-22. Margono, BA, Potapov, PV, Turubanova, S, Stolle, F & Hansen, MC 2014, 'Primary forest cover loss in Indonesia over 2000-2012', Nature Climate Change, vol. 4, no. 8, pp. 730-5, DOI 10.1038/Nclimate2277. Matthews, R, Swallow, B, van Noordwijk, M, Milne, E, Minang, P, Bakam, I, Brewer, M, Muhammed, S, Poggio, L & Glenk, K 2010, 'Development and application of methodologies for reduced emissions from deforestation and forest degradation (REDD+)—Phase I', Final Report for Project CEOSA, vol. 803, Mena, C. F. et al. (2006). Socioeconomic Drivers of Deforestation in the Northern Ecuador Amazon. Environmental Management, 37(6): 802-815. Minh Thu, P, Populus, J, 2007. Status and changes of mangrove forest in Mekong Delta: case study in Tra Vinh, Vietnam. Estuarine, Coastal and Shelf Science, 71, 98-109. Mitinje, E, Kessy, J & Mombo, F 2007, 'Socio-economic factors influencing deforestation on the Uluguru Mountains, Morogoro, Tanzania', Discovery and Innovation, vol. 19, no. 1/2 (Special Edition), pp. 139-48 Mon, M.S., et al. (2012). Factors Affecting Deforestation and Forest Degradation in Selectively Logged Production Forest: A Case Study in Myanmar. Forest Ecology and Management, 267: 190-198. Monzon-Alvarado, C. et al. (2012). Land-Use Decision-Making After Large-Scale Forest Fires: Analyzing Fires as a Driver of Deforestation in Laguna del Tigre National Park, Guatemala. Applied Geography, 35(1-2): 43-52. Muller, D. and Zeller, M. (2002) Land use dynamics in the central highlands of Vietnam: a spatial model combining village survey data with satellite imagery interpretation. Agricultural Economics, 27(3): 333-354. Munroe, D. K. et al. (2002). The Dynamics of Land-Cover Change in Western Honduras: Exploring Spatial and Temporal Complexity. Agricultural Economics, 27(3): 355-369. Munroeaic, DK, Southworth, J & Tucker, CM 2002, 'The dynamics of land‐cover change in western Honduras: exploring spatial and temporal complexity', Agricultural Economics, vol. 27, no. 3, pp. 355-69 Olaniyi, A. O., et al. (2012). Assessment of Drivers of Coastal Land Use Change in Malaysia. Ocean & Coastal Management, 67: 113-123. Pfaff, A. S. P. (1999). What drives Deforestation in the Brazilian Amazon? Evidence from Satellite and Socioeconomic Data. Journal of Environmental Economics and Management. 37(1): 26-43

Pfaff, A, Robalino, J, Sanchez-Azofeifa, GA, Andam, KS & Ferraro, PJ 2009, 'Park location affects forest protection: Land characteristics cause differences in park impacts across Costa Rica', The BE Journal of Economic Analysis & Policy, vol. 9, no. 2, Pfaff, A. et al. (2007). Road Investments, Spatial Spillovers, and Deforestation in the Brazilian Amazon. Journal of Regional Science, 47(1): 109-123. Pfaff, A.S.P., and Sanchez-Azofeifa, G.A. (2004). Deforestation Pressure and Biological Reserve Planning: A Conceptual Approach and an Illustrative Application for Costa Rica. Resource and Energy Economics, 26: 237-254. Rueda, X 2010, 'Understanding deforestation in the southern Yucatán: insights from a sub-regional, multi-temporal analysis', Regional Environmental Change, vol. 10, no. 3, pp. 175-89 Sanchez‐Azofeifa, G. A., et al. (2007). Costa Rica's payment for environmental services program: intention, implementation, and impact. Conservation Biology, 21.5: 1165-1173. 99

Schmitt-Harsh, M 2013, 'Landscape change in Guatemala: driving forces of forest and coffee agroforest expansion and contraction from 1990 to 2010', Applied Geography, vol. 40, pp. 40-50 Shervette VR, Aguirre WE, Blacio E, Cevallos R, Gonzalez M, Pozo F, Gelwick F (2007) Fish communities of a disturbed mangrove wetland and an adjacent tidal river in Palmar, Ecuador. Estuarine, Coastal and Shelf Science, 72, 115-128. Sloan, S. (2008). Reforestation Amidst Deforestation: Simultaneity and Succession. Global Environmental Change, 18(3): 425-441. Soares-Filho, et al. (2010). Role of Brazilian Amazon Protected Areas in Climate Change Mitigation. Proceedings of the National Academy of Sciences, 107(24): 10821-10826. Son, N.T., and Tu, N.A. (2008). Determinants of Land-Use Change: A Case Study from the Lower Mekong Delta of Southern Vietnam. Electronic Green Journal, 27: 19. Uryu Y, Mott C, Foead N, et al (2008) Deforestation, Forest Degradation, Biodiversity Loss and CO2 Emissions in Riau, Sumatra, Indonesia: One Indonesian Province's Forest and Peat Soil Carbon Loss Over a Quarter Century and its Plans for the Future. WWF Indonesia. Vagen, T. G. (2006) Remote Sensing of Complex Land Use Change Trajectories - A Case Study from the Highlands of Madagascar. Agriculture, Ecosystems and Environment, 115(1-4): 219- 228. Wheeler, D, Hammer, D, Kraft, R, Dasgupta, S & Blankespoor, B 2013, 'Economic dynamics and forest clearing: A spatial econometric analysis for Indonesia', Ecological Economics, vol. 85, pp. 85-96, DOI 10.1016/j.ecolecon.2012.11.005. Xu, K, Kong, C, Wu, C, Liu, G, Deng, H & Zhang, Y 2009, 'Dynamic changes in Tangxunhu wetland over a period of rapid development (1953–2005) in Wuhan, China', Wetlands, vol. 29, no. 4, pp. 1255-61 Xu C, Sheng S, Zhou W, Cui L, Liu M (2011) Characterizing wetland change at landscape scale in Jiangsu Province, China. Environmental Monitoring and Assessment, 179, 279-292. Zak, MR, Cabido, M, Cáceres, D & Díaz, S 2008, 'What drives accelerated land cover change in central Argentina? Synergistic consequences of climatic, socioeconomic, and technological factors', Environmental Management, vol. 42, no. 2, pp. 181-9 Zhao H, Cui B, Zhang H, Fan X, Zhang Z, Lei X (2010) A landscape approach for wetland change detection (1979-2009) in the Pearl River Estuary. Procedia Environmental Sciences, 2, 1265- 1278. Zhao, H. et al. (2011). Do Trees Grow with the Economy? A Spatial Analysis of the Determinants of Forest Cover Change in Sichuan, China. Environmental and Resource Economics, 50: 61-82.

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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

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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

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Paddy rice for selecting azolla Biodiversity index by ecoregion for avoiding conflict with biodiversity

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