master of science thesis the future of...

Master of Science Thesis

The Future of Food

Scenarios and the Effects on Resource Use in Agriculture

Ingrid Ym Ruth Odegard, BSc.

Institute of Environmental Sciences, Leiden University

June 2011

M.Sc. Thesis

I.Y.R. Odegard

2

The Future of Food

Food Scenarios and the Effects on Resource Use in Agriculture

Master of Science Thesis

For obtaining the degree of Master of Science in Industrial Ecology from Leiden University and Delft

University of Technology

Ingrid Ym Ruth Odegard, BSc.

June 2011

Institute of Environmental Sciences - Leiden University

Dr. E. van der Voet

Drs. R. Huele

Center for Industrial Ecology – Yale University

Prof. T.E. Graedel

M.Sc. Thesis

I.Y.R. Odegard

3

© I.Y.R. Odegard, B.Sc.

All rights reserved.

M.Sc. Thesis

I.Y.R. Odegard

4

Preface

This thesis presents the results of my graduation research, carried out as part of the Industrial Ecology

M.Sc. program at Delft University of Technology and Leiden University. It was performed under guidance

of my supervisors dr. Ester van der Voet and drs. Ruben Huele at the Institute of Environmental Sciences

in Leiden and was partially conducted at the Center for Industrial Ecology at Yale University in the USA.

Even though sustainability has been the focal point of my studies since 2003, when I started studying

Sustainable Molecular Science and Technology at the TU Delft and Leiden University, the sustainability of

our food seemed an undervalued topic in the sustainability discourse. The past years attention has been

increasing, and today the ‘sustainability of food’ is a common topic on conferences, in newspapers and in

the supermarket. For me, it has proven to be a deserving topic; people have always been interested in

my findings.

I would like to thank my first supervisor, dr. Ester van der Voet, for her support, patience and confidence.

Your advice and comments were always helpful and you steered me in the right direction to make me

produce the work I wanted to deliver. I would also like to thank my second supervisor, drs. Ruben Huele,

for his helpful ideas and pushing me to see the broader picture. Finally, I would like to thank prof.

Graedel for welcoming me at the Center for Industrial Ecology and for taking the time to meet with me

and discuss my work. It had always been a dream of mine to live and study in the USA for a while, and I

would like to thank my supervisors dr. Van der Voet, and prof. Graedel very much for giving me that

opportunity.

Without my parents I would not have been where I am now. Thank you for your love and support and for

always believing that I would be able to achieve what I wanted to. I would also like to thank everyone

who read part of my thesis and gave me feedback: papa Dave, Andrew, Renske, Sebastiaan, Julia and

Martijn. Your feedback was very useful and I enjoyed sharing my work with you. Furthermore, I would

like to thank everyone who has shown an interest in my study and were curious as to my final results; it

was always motivating to talk about my study and find that you really wanted to know whether you

should reduce your meat consumption or become vegetarians.

Finally I would like to urge everyone who reads this to reconsider their consumption pattern, and thank

you if you do. I hope I can make a contribution to making our food system more sustainable, and that we

are all healthy and well-fed in 2050.

Ingrid Ym Ruth Odegard

Delft, June 2011

M.Sc. Thesis

I.Y.R. Odegard

5

Summary

The aim of this study was to design four global food scenarios for the year 2050 and to evaluate these

quantitatively with respect to their use of the natural resources land, water and fertilizers in agriculture.

Resource use and food are popular topics in the current sustainability debate, because of population

growth and the change seen in diet composition, related to increased welfare levels, with increased

demand for animal products in developing countries. To evaluate resource use for a complete diet in

different food scenarios, this study integrates three sub-studies:

1) Four scenarios were designed, which include different trends related to population, economic

development, policy, technological development and diet and are specified to the year 2050.

2) A methodology was developed – Virtual Resource Content – with which the use of resources in

agriculture can be calculated.

3) A model was created, with which the scenarios are quantified with respect to their resource use.

The scenarios are all evaluated on a global scale, including two that are also evaluated on a regional scale.

These regions are the OECD90 region (countries in the OECD in 1990), the REF region (the countries

under reform such as the former Soviet Union), the ASIA region (Asia) and the ALM region (Africa, Latin

America and the Middle East). The Virtual Resource Content comprises of factors for Virtual Land

Content (ha kg-1), Virtual Water Content (m3 kg-1) and Virtual Fertilizer Content (for N, P2O5 and K2O, kg

kg-1), on a regional and global scale.

In the Affluent World (A1 – global) high, globally dispersed economic and technological

development are coupled to low population growth leading to high apparent consumption of

animal products.

In the Full World (A2 – regional) high population growth is coupled to low regional economic and

technological development, leading to lower increases in meat consumption.

In the Vegetarian World (B1 – global) consumption of animal products is limited to milk and eggs.

Low population growth is coupled to medium-high global economic growth and high

technological development, and environmental issues are considered important.

In the Low-Input World (B2 – regional) awareness concerning environmental issues results in low

fertilizer inputs and is coupled to medium population growth and medium economic growth.

The results show that it is impossible to continue current trends related to meat consumption at a global

level. The Affluent World shows that it may be attainable to contain land use within feasible limits, but

that such a development comes at a cost: high fertilizer use. Due to intensive management of pasture

areas, fertilizer use rises to a level where, in the year 2050, only 2 years of K2O use and 55 years of P2O5

use remain, based on estimates of the global resource base. Moreover, a situation such as in the Affluent

World were fertilizers run out between 2050 and 2100 would never progress to such a stage because

fertilizer (and thus meat) prices would have risen too much to justify such use. As shown for the Full

World, less intensive management of pastures and such can cut fertilizer use; in the Full World 66 years

of K2O use and 121 years of P2O5 use remain, but pasture area almost doubles. The Vegetarian World

shows picture of potential natural resource use that is manageable. Water and land use stay within

acceptable limits, and fertilizer reserves are large enough to continue those practices for another 100 to

M.Sc. Thesis

I.Y.R. Odegard

6

254 years, for respectively K2O and P2O5. It is, however, unlikely that people are willing to give up meat

consumption. While the Low-Input World incorporates lower meat consumption, the low inputs of

fertilizer pushes land use beyond all possible limits, in all regions. Total production increases with 40%,

which is related to an increased water use of 63% (compared to 2005). On a global level this is within the

moderate water stress limit, however, the ASIA region exceeds the moderate water stress level by 47%.

Figure 1 shows the results for the four scenarios, compared to the situation in the year 2005.

Figure 1: Results - resource use in 2050 in the four scenarios

The results show that trade-off issues are important and need to be addressed when discussing the

future of food. An assessment of resource use is only valuable when a complete picture is given.

Therefore, the present study provides valuable input for assessing problem areas, but also for identifying

opportunities in our agricultural system.

Keywords: Food, Agriculture, Virtual Resource Content, Natural Resource Use, Water, Land, Fertilizers, Scenarios, 2050

M.Sc. Thesis

I.Y.R. Odegard

7

Contents

Preface ........................................................................................................................................................... 4

Summary ....................................................................................................................................................... 5

List of Figures ................................................................................................................................................. 9

List of Tables ................................................................................................................................................ 11

List of Abbreviations .................................................................................................................................... 13

1. Introduction ......................................................................................................................................... 14

1.1 Research Goal and Research Question ....................................................................................... 16

1.2 Scientific Relevance and Contribution of Present Research ....................................................... 18

1.3 Guide to the Reader .................................................................................................................... 19

2. Methodology ....................................................................................................................................... 20

2.1 Scope Definition .......................................................................................................................... 20

2.2 Scenario Methodology ................................................................................................................ 24

2.3 Virtual Resource Content ............................................................................................................ 28

2.4 The VRC Model ............................................................................................................................ 29

3. Current Situation ................................................................................................................................. 31

3.1 Food Consumption ...................................................................................................................... 32

3.2 Food Production .......................................................................................................................... 38

3.3 Appropriation of Natural Resources............................................................................................ 41

3.4 Productivity ................................................................................................................................. 57

3.5 Supply and Demand .................................................................................................................... 58

3.6 Losses and Wastes ....................................................................................................................... 59

3.7 Driving Forces .............................................................................................................................. 62

4. Driving Forces and Trends ................................................................................................................... 63

4.1 Population ................................................................................................................................... 64

4.2 Economic Development .............................................................................................................. 68

4.3 Policy ........................................................................................................................................... 71

4.4 Technological Change .................................................................................................................. 73

4.5 Diet Change ................................................................................................................................. 76

5. The Future of Food Storylines ............................................................................................................. 82

5.1 Linkages between Driving Forces ................................................................................................ 82

5.2 A1 – The Affluent World .............................................................................................................. 85

5.3 A2 – The Full World ..................................................................................................................... 86

5.4 B1 – The Vegetarian World ......................................................................................................... 87

5.5 B2 – The Low Input World ........................................................................................................... 88

M.Sc. Thesis

I.Y.R. Odegard

8

6. Linkages ............................................................................................................................................... 89

6.1 Demand ....................................................................................................................................... 89

6.2 Supply .......................................................................................................................................... 92

6.3 VRC Factors .................................................................................................................................. 99

6.4 Modeling Protocol ..................................................................................................................... 102

7. Results .............................................................................................................................................. 103

7.1 Production and Consumption ................................................................................................... 103

7.2 Land Use .................................................................................................................................... 107

7.3 Water Use .................................................................................................................................. 111

7.4 Fertilizer Use .............................................................................................................................. 113

8. Conclusions ....................................................................................................................................... 117

8.1 Scenario Conclusions ................................................................................................................. 117

8.2 Resource Use in Agriculture ...................................................................................................... 120

9. Discussion .......................................................................................................................................... 124

10. Recommendations......................................................................................................................... 130

Bibliography ............................................................................................................................................... 133

Appendix 1 World Regions ............................................................................................................... 138

Appendix 2 Commodity Group Assumptions ................................................................................... 140

Appendix 3 Waste ............................................................................................................................ 142

Appendix 4 Feed ............................................................................................................................... 143

Appendix 5 Seed ............................................................................................................................... 144

Appendix 6 Other Utilitization ......................................................................................................... 145

Appendix 7 Yield Projections ............................................................................................................ 146

Appendix 8 Feed-mixes and Feeding Efficiency ............................................................................... 151

Appendix 9 Production of Oil and Sugar Crops ................................................................................ 156

Appendix 10 Land Potential ................................................................................................................ 157

Appendix 11 Virtual Water Content ................................................................................................... 158

Appendix 12 Water Potential ............................................................................................................. 160

Appendix 13 Meat consumption ........................................................................................................ 161

Appendix 14 Virtual Fertilizer Content ............................................................................................... 163

Appendix 15 Yield Projection Calculations OECD90 Region ............................................................... 166

Appendix 16 FAO Productivity Projections ......................................................................................... 168

Appendix 17 Results – Data ................................................................................................................ 169

M.Sc. Thesis

I.Y.R. Odegard

9

List of Figures

Figure 1: Results - resource use in 2050 in the four scenarios ...................................................................... 6

Figure 2: Food production, natural resource use and associated environmental issues............................ 15

Figure 3: IPCC SRES scenario orientation .................................................................................................... 17

Figure 4: Schematic representation of the four IPCC SRES Scenarios. ........................................................ 27

Figure 5: Relationships between driving forces, supply, demand, VRC and resource use. ........................ 30

Figure 6: Apparent consumption (kcal/cap/day) in countries with different levels of development. ....... 32

Figure 7: Global average apparent food consumption (kg/cap/year) ......................................................... 33

Figure 8: Regional apparent consumption (kg/cap/year). .......................................................................... 35

Figure 9: Meat consumption (kg/capita/year) for all 185 countries for the year 2004 .............................. 36

Figure 10: Regional land use per commodity group (in ha) in 2005 ........................................................... 42

Figure 11: Multiple cropping zones, rain-fed conditions ............................................................................ 43

Figure 12: Share of total water use of the global total by region ............................................................... 46

Figure 13: Fraction of global water use in agriculture per commodity group ............................................ 47

Figure 14: Share of irrigated land in arable land and permanent crops ..................................................... 48

Figure 15: Consumption of nitrogenous fertilizers, 1950-1990 .................................................................. 49

Figure 16: Regional average nitrogenous fertilizer consumption (kg/ha/year) in 2005 ............................. 51

Figure 17: Fertilizer use estimates (ton/year). ............................................................................................ 52

Figure 18: Recommended and estimated regional fertilizer use. ............................................................... 53

Figure 19: Commodity balance calculation method flowscheme. .............................................................. 59

Figure 20: World population projections until 2050 ................................................................................... 65

Figure 21: OECD90 region population projection until 2050 ...................................................................... 66

Figure 22: REF region population projections until 2050 ............................................................................ 67

Figure 23: ASIA region population projections until 2050 .......................................................................... 67

Figure 24: ALM region population projections until 2050 .......................................................................... 68

Figure 25: World PPP Projections until 2050 .............................................................................................. 70

Figure 26: Regional GDP projections (PPP). ................................................................................................ 71

Figure 27: Cereal yield projections .............................................................................................................. 76

Figure 28: Population and global food production indices, 1966-1998 ...................................................... 77

Figure 29: Meat consumption trends in different scenarios ....................................................................... 80

Figure 30: Scenario Characteristics ............................................................................................................. 84

Figure 31: Population between 19050-2050 (in billions) in A1 ................................................................... 85

Figure 32: Population between 1950-2050 (in billions) in A2 ..................................................................... 86

Figure 33: Population between 1950-2050 (in billions) in B1 ..................................................................... 87

Figure 34: Population between 1950-2050 (in billions) in B2 ..................................................................... 88

Figure 35: Linkages between driving forces, supply, demand, VRC and resource use. .............................. 89

Figure 36: Example of Virtual Land Content.............................................................................................. 100

Figure 37: Example of Virtual Water Content. .......................................................................................... 101

Figure 38: Example of Virtual Fertilizer Content ....................................................................................... 101

Figure 39: Apparent consumption and intake in the four scenarios in 2050 ............................................ 103

Figure 40: Production in 2005 and 2050 ................................................................................................... 104

M.Sc. Thesis

I.Y.R. Odegard

10

Figure 41: Total production, production of food and feed, apparent consumption and intake in 2005 .. 105

Figure 42: Comparison of total production, production for food and feed, apparent consumption and

intake (kg/cap/year) for the four scenarios .............................................................................................. 106

Figure 43: Arable land and permanent crops, harvested area in 2005 and in the four scenarios ............ 108

Figure 44: Total land use in A2 and B2 ...................................................................................................... 108

Figure 45: Total land use in 2005 and for the four scenarios in 2050 ....................................................... 109

Figure 46: Total land use per capita in 2005 and for the four scenarios in 2050 ...................................... 110

Figure 47: Total water use in 2005 and 2050 ............................................................................................ 111

Figure 48: Regional water use in A2 and B2 .............................................................................................. 112

Figure 49: Nitrogen fertilizer use in 2005 and 2050 .................................................................................. 113

Figure 50: Phosphorous fertilizer use in 2005 and 2050 ........................................................................... 114

Figure 51: Potassium fertilizer use in 2005 and 2050 ............................................................................... 115

Figure 52: Scenario characteristics ............................................................................................................ 117

Figure 53: Scenario results - land use, water use and fertilizer use in 2050 for the four scenarios. ........ 121

Figure 54: Processing scheme oil crops ..................................................................................................... 156

Figure 55: Processing scheme sugar crops ................................................................................................ 156

Figure 56: Engel curve for meat consumption .......................................................................................... 161

M.Sc. Thesis

I.Y.R. Odegard

11

List of Tables

Table 1: Research characteristics ................................................................................................................ 18

Table 2: Data: source and specifications ..................................................................................................... 21

Table 3: Table of boundary conditions ........................................................................................................ 22

Table 4: Table of assumptions ..................................................................................................................... 23

Table 5: Scenario Methodology................................................................................................................... 25

Table 6: Regional population size and level of economic development (in PPP) in the year 2005 ............ 31

Table 7: Regional food supply (apparent consumption in kg/cap/year) in 2005 ........................................ 33

Table 8: Regional and global production of vegetable commodity groups ................................................ 38

Table 9: Regional and global production of animal commodity groups. .................................................... 39

Table 10: Import, export and net import for the four regions in the year 2005 ......................................... 40

Table 11: Area under cultivation for the years 1995, 2000 and 2005 ......................................................... 41

Table 12: Gross extents of land with rain-fed cultivation potential............................................................ 44

Table 13: Water use in agriculture and fraction of total water use in 2005 ............................................... 45

Table 14: Crop evapotranspiration for the four regions ............................................................................. 46

Table 15: Irrigated area ............................................................................................................................... 48

Table 16: Fertilizer production and consumption ....................................................................................... 50

Table 17: Efficiencies of animal food production, land requirements and water requirements ................ 56

Table 18: Yields for the seven commodity groups in the four regions for the year 2005 .......................... 57

Table 19: Supply and demand ..................................................................................................................... 58

Table 20: Uses other than food, % of total global production in the year 2005 ......................................... 60

Table 21: Household wastes as percentage and fraction of (edible) food supply. ..................................... 61

Table 22: Population projections ................................................................................................................ 65

Table 23: Final populations 2050 (in billions) .............................................................................................. 66

Table 24: Economic Development Projections ........................................................................................... 69

Table 25: GDP Projections (in PPP) per capita for the year 2050 ................................................................ 70

Table 26: Policy Characteristics ................................................................................................................... 72

Table 27: Technological development trends ............................................................................................. 75

Table 28: Changes in the commodity composition of food by major country groups ................................ 78

Table 29: Diet Trends. ................................................................................................................................. 79

Table 30: Scenario driving forces' linkages and characteristics. ................................................................. 82

Table 31: Economic Growth Rates and Income per Capita in the A1 World .............................................. 85

Table 32: Economic Growth Rates and Income per Capita in the A2 World .............................................. 86

Table 33: Economic Growth Rates and Income per Capita in the B1 World ............................................... 87

Table 34: Economic Growth Rates and Income per Capita in the B2 World ............................................... 88

Table 35: Demand-side assumptions and rationale. ................................................................................... 90

Table 36: Supply-side assumptions and rationale ....................................................................................... 93

Table 37: VRC factors and their specifications. ........................................................................................... 99

Table 38: Increase in total production compared to increases in fertilizer use. ....................................... 114

M.Sc. Thesis

I.Y.R. Odegard

12

Table 39: Fertilizer use and reserve base. ................................................................................................. 116

Table 40: Definition of the regions. ........................................................................................................... 138

Table 41: Commodity group assumptions................................................................................................. 140

Table 42: Items included in the commodity groups. ................................................................................. 141

Table 43: Waste (during transport, storage and processing) in 2007. ...................................................... 142

Table 44: Feed in 2007 .............................................................................................................................. 143

Table 45: Seed in 2007 .............................................................................................................................. 144

Table 46: Other utilization in 2007. ........................................................................................................... 145

Table 47: Cereal yield projection ............................................................................................................... 146

Table 48: Top three yielding countries per region for fruits and vegetables ............................................ 148

Table 49: Maximum attainable yields (MAYs) and yields in 2005 and in 2050 ......................................... 148

Table 50: Feedcrop yields in 2005 and 2050 ............................................................................................. 149

Table 51: Choices for correspondence of Wirsenius' regions to IPCC regions. ......................................... 151

Table 52: Feed-mixes for the five animal products per region ................................................................. 152

Table 53: Availability of edible-type crops by-products in terms ............................................................. 155

Table 54: Division of the ‘global agro-ecological zones-study’ regions. ................................................... 157

Table 55: Global and regional gross and net extents of cultivable land ................................................... 157

Table 56: Virtual Water Content per commodity group (m3 per ton) for the four regions ...................... 159

Table 57: Global and regional renewable water resources and water stress thresholds ......................... 160

Table 58: Countries for which data was used from AQUASTAT. ............................................................... 160

Table 59: Countries for which data on renewable water resources is lacking.......................................... 160

Table 60: Parameters of the Engel curve .................................................................................................. 161

Table 61: Number of countries in income regimes - related to meat consumption - in 2050 ................. 162

Table 62: Global and regional meat consumption .................................................................................... 162

Table 63: Basis of estimates for fertilizer requirements. .......................................................................... 164

Table 64: Virtual Fertilizer Content ........................................................................................................... 165

Table 65: Future change in productivity equation .................................................................................... 166

Table 66: Parameters that represent the effect of technology on potential yield and yield gap ............. 167

Table 67: Estimated relative changes in crop productivity due to technology development .................. 167

Table 68: FAO productivity projections ..................................................................................................... 168

Table 69: Total production, losses, feed, apparent consumption and household and retail waste. ........ 169

Table 70: Total land use per scenario and total cropland use in the A2 and B2 scenarios ....................... 171

Table 71: Total water use in the scenarios and water use in the A2 and B2 scenarios. ........................... 172

Table 72: Total fertilizer use (N, P2O5 and K2O) in the four scenarios. ...................................................... 173

M.Sc. Thesis

I.Y.R. Odegard

13

List of Abbreviations

ALM IPCC SRES region - Africa, Latin America and Middle East region, see Appendix 1

ASIA IPCC SRES region - Asia excluding Japan, see Appendix 1

AQUASTAT Database – FAO’s global information system on water and agriculture, developed by the Land and

Water Division of the FAO.

EF Ecological Footprint

FAO Food and Agriculture Organization of the United Nations

FAOSTAT Database – provides time-series and cross sectional data relating to food and agriculture for some

200 countries

FBS Food Balance Sheet

FERTISTAT Database – Fertilizer Use Statistics, Plant Production and Protection Division of the FAO

IFPRI International Food Policy Research Institute

IPCC Intergovernmental Panel on Climate Change

SRES Special Report on Emissions Scenarios

OECD Organization for Economic Co-operation and Development

OECD90 IPCC SRES region – the countries in the OECD in 1990, see Appendix 1

REF IPCC SRES region – the countries under reform, see Appendix 1

UN United Nations

UNDP United Nations Development Programme

VRC Virtual Resource Content

WF Water Footprint

WWF World Wildlife Fund

M.Sc. Thesis

I.Y.R. Odegard

14

1. Introduction

Food fulfills many different purposes. We need it every day to sustain ourselves, we dine together and

socialize, and it may be a comfort at times. Furthermore, it helps feed the animal we eat, and we use it

to produce non-food products such as biofuel. To be able to eat strawberries in the winter, and fresh

pineapple in the Netherlands, we transport food all over the world. Our relationship to food is one of

deprivation on the one hand, and of waste on the other. According to various researchers, we produce

enough to sustain the current global population [Rosegrant, 2001a, Penning de Vries, 1995], but

approximately 1 billion people are deprived of basic nutrition [UN, 2010] while many people in the

Western world, despite high caloric intakes, suffer from malnutrition.

Three natural resources are of key importance in our agricultural system: land, water and fertilizers. The

impact of agriculture on our land use, water use and fertilizers use is significant; agriculture is the major

consumer of these resources. According to the Food and Agriculture Organization of the United Nations,

the FAO, “Over the next 30 years, many of the environmental problems associated with agriculture will

remain serious” [FAO, 2002, p. 7]. It is important to realize that there is no one fixed course in which the

food-system will develop. Because agriculture is a human activity, we can influence the course of such

development. This study will explore the factors related to agriculture and natural resource use, and will

quantify the effects of four different scenarios related to the food system.

Impacts of Natural Resource Use in Agriculture

The impacts related to the use of natural resources in agriculture are large. As stated by Wirsenius: ‘The

food and agriculture system is among the largest anthropogenic activities in terms of appropriation of

land and biological primary production, as well as alteration of the grand chemical cycles of carbon,

water, and nitrogen’ [Wirsenius, 2003]. Globally, agriculture – croplands and pastures – accounts for 40%

of total land use [Lotze-Campen, et al. 2006; Foley et al., 2005] and 85% of the freshwater we use is used

in agriculture [WWF, 2006; Foley et al., 2005]. In different parts of the world the area under cultivation is

either decreasing (Europe) or expanding (South America, Asia). Fossil fuels are used extensively in the

food production chain, for transport purposes but also for the production of agricultural chemicals.

‘Industrial fixation of N fertilizer increased from <10 Tg/year in 1950 to 80 Tg/year in 1990’ [Vitousek et

al., 1997, p. 497].

Figure 2 shows the linkages between these appropriations and the environmental problems we face

today. These linkages are well documented and described [Vitousek, 1997; Tilman, 2005; Foley, 2005]

and will not be part of this research. To stress the relevance and importance of research on this topic it is,

however, important to gain some basic insight into the effects of the use of these three natural

resources.

M.Sc. Thesis

I.Y.R. Odegard

15

Food

Production

Water Fossils Land

Water ScarcityClimate

Change

Eutrophication

Degraded

Water Quality

Loss of

biodiversitySoil Erosion

Salinization

Abiotic

Depletion

Society

Habitat

Destruction

Deforestation

Figure 2: Food production, natural resource use and associated environmental issues.

Increasing our food supply can either be done by technological development (e.g. irrigation, fertilization

or seed improvements) or by land expansion through conversion. The former can cause environmental

problems due to various types of pollution and potentially depletion of water resources. Technological

development could, however, also play a role in making agriculture more sustainable, for instance with

technologies that improve fertilizer efficiency. Land expansion is also coupled to major environmental

issues, for example habitat destruction and climate change impacts through deforestation.

Food production accounts for about 30% of the greenhouse gas emissions of the Dutch consumer

[Nijdam, 2003]. A large share of this contribution is due to meat consumption. Using crops to feed cattle

is relatively inefficient, and livestock is also responsible for a significant part of methane emissions.

Furthermore, significant amounts of synthetic fertilizer and livestock-wastes enter surface water and

groundwater, causing eutrophication [Tilman, 2001]. Fertilizers and other agrochemicals are part of the

“green revolution technologies”; during the past 40 years fertilizer use has increased by approximately

700 %. This increase in fertilizer use has degraded water quality in many regions [Foley, 2005].

While irrigation may increase crop yields, it can cause soil salinization and eutrophication in downstream

aquatic ecosystems, furthermore, it can have a significant impact on streams and rivers because of

unsustainable water use and damming [Tilman, 2001]. Worldwide, the loss to salinization of soils is 1.5

million hectares per year [Foley, 2005]. According to the WWF currently around 50 countries are

experiencing continual severe water stress, while many more have this problem in certain periods during

the year [WWF, 2006]. Irrigated cropland area has increased by 70% over the past 40 years [Foley, 2005].

Around the year 2000 cropland and pastures occupied 27% and 18%, respectively, of the total land area

[Ewert, 2006]. World grain harvests have doubled over the past 40 years, which is only partially

attributable to a 12% increase in cropland area [Foley, 2005]. The IPCC – the Intergovernmental Panel on

Climate Change - identifies four major sources of land-use emissions related to agriculture: (net)

deforestation leading to CO2 emissions, rice cultivation and enteric fermentation of cattle leading to CH4

emissions, and emissions of N2O from fertilizer application [IPCC, 2000]. These four sources of GHG-

emissions account for nearly all land-use emissions of CO2, 53% of land-use emissions of CH4 and 80% of

land-use emissions of N2O [IPCC, 2000]. Land use conversion is a major cause of habitat destruction, of

M.Sc. Thesis

I.Y.R. Odegard

16

which species extinction is an irreversible impact [Tilman, 2001]. Furthermore, it contributes

substantially to emissions of greenhouse gasses: about 20% of anthropogenic greenhouse gas emission

and a substantial part of the emission of methane (74%) and nitrous oxides [Vitousek, 1997; IPCC, 2000].

This is partially due to loss of carbon stock when converting from forest to crop (loss of 42%) or pasture

to crop (loss of 59%) [Ewert, 2006]. Land use conversion also contributes to climate change by an

increased albedo and decreased surface roughness, the regional net-effect of which is an increase in

temperature and a decrease in precipitation [Vitousek, 1997].

1.1 Research Goal and Research Question

When one looks at the environmental impact, agriculture and related activities score high. As pointed

out above, use of the natural resources can be linked to the most prominent environmental problems of

today. It is of key importance to study the linkages between our food-system – with its large

environmental contribution – and forces that may change the current situation. Potential changes in our

agricultural system and/or the forces that drive agriculture will have significant consequences.

Technological progress can increase productivity, but will this be enough to counteract the projected

increase in the global population and the increase in economic development? The question of whether

we will be able to feed an increasing world population is the subject of many studies. The importance of

adequately feeding all people on earth is obvious; without sufficient nourishment people are not able to

rise to their full potential. As also acknowledged by the UN, eradicating hunger is one of the conditions

for sustainable development [UN, 2010]. Many scientists state that we will have enough food to feed an

ever-growing population [Rosegrant, 2001a, Penning de Vries, 1995]. A question that is not as well

researched is the effect this will have on our use of natural resources on the long term. The focus of this

master thesis is the construction of global food system scenarios and the quantification of the

consequences of four of these radically different states on natural resource use.

The goal of this research is to evaluate resource use for four different scenarios for the year 2050. This

study will only concern the resource effects of certain food scenarios. The societal effects, e.g.

malnutrition, food crises and economic consequences of changes in the food system are beyond the

scope of this study. However, in order to design scenarios, choices about societal issues (population

growth, economic and social development) and governance issues (policy) will have to be made. The

driving forces which will be used to design the scenarios are: population growth, economic and social

development, policy, technological development and diet change. Trends related to these driving forces

will be quantified for each of the scenarios. The Virtual Resource Content is a new concept by which the

differences in natural resource use between the scenarios can be assessed.

The main research question is: What are the regional and global consequences with respect to the use

of natural resources – concerning land, water and fertilizers – for four food scenarios evaluated for the

year 2050?

In order to answer this question different trends in driving forces’ related to final food production and

consumption will be evaluated. Scenario building methodology is an important tool in this research, as is

M.Sc. Thesis

I.Y.R. Odegard

17

the concept of Virtual Resource Content. Scenario-studies help provide a realistic and broad view of

what effects of alternative futures would be. This will be elaborated on in the paragraphs below.

1.1.1 Scenario Methodology

The IPCC Special Report on Emission Scenarios (SRES) will be used as a basis in this study. The scenarios

in this IPCC report were developed to explore the possible range of future greenhouse gas emissions,

‘representing the range of driving forces and emissions in the scenario literature’ [IPCC, 2000]. These

driving forces include demographic, economic, technological, energy, governance, land-use and

agricultural aspects. There are four scenarios (A1, A2, B1 and B2) which are either globally (A1, B1) or

regionally (A2, B2) oriented and either focused on economy (A1, A2) or environment (B1, B2).

A2

B1 B2

A1

RegionalGlobal

Environmental

Economic

Figure 3: IPCC SRES scenario orientation

In the IPCC SRES several scenarios are defined as sub-scenarios of the A1 scenario. In these four A1

scenarios, the direction of technological change differs, resulting in different compositions of energy

supply. In this study, the A1 scenario was based on the IPCC A1F1 scenario, in which energy sources are

predominantly fossil based. For simplicity, the scenario will be called A1 throughout the report. Because

the IPCC SRES scenario storylines are so elaborated they provide a good basis for the scenarios that will

be developed in this study.

Four of the driving forces used by the IPCC will be used here: population growth, economic and social

development, policy and technological change. A fifth was added: diet change. In line with IPCC

reasoning that people’s energy choices (possibly steered in a certain direction, thus linked to e.g. policy)

determine in part the climate change potential, social trends were incorporated in this study. Diet

change is an important social driver of change in the food system. Changes towards different diets were

incorporated to present a diverse set of food scenarios.

1.1.2 Virtual Resource Content

The linkages between the agricultural food system and the use of natural resources will be modeled

using the Virtual Resource Content (VRC) concept. This is a new concept, created as part of this study,

based on the Virtual Water Content concept by Hoekstra et al [Hoekstra, 2008; Hoekstra, 2009]. The

Virtual Water Content represents all water (in m3) used during the whole life cycle to produce that

commodity. It is thus a ‘virtual content’, because the foodstuff does not literally contain it, but rather

M.Sc. Thesis

I.Y.R. Odegard

18

requires it during its life cycle. It can be used to calculate the water footprint of for example a country by

linking virtual water data of foodstuffs to international trade data. The new concept of the VRC is

comparable to the virtual water concept. The VRC is a measure of the use of resources – land, water and

fertilizers – in agriculture needed to produce a quantity of foodstuff in a certain commodity group. VRC

factors will be calculated; these are assessments of land requirements (ha kg-1), water requirements (m3

kg-1) and fossil fuel requirements (kg kg-1) in agriculture for all main commodity groups. If resource use

during all phases of the life cycle were included, so-called footprints could be calculated, for instance the

footprint of a foodstuff or of a person’s diet. Here, it will be used to assess global resource use in

agriculture in the future. A food system model will be developed in which the VRC factors are

incorporated. Consumption per capita of food commodity groups (kg year-1) serves as input. The model,

in combination with the VRC factors, can be used for various purposes. Here, food scenarios will be

quantified, as elaborated on above. These scenarios will be evaluated with the model with respect to

their resource use in agriculture.

1.2 Scientific Relevance and Contribution of Present Research

Substantial research has been done concerning food scenarios and the linkages between food

production and the use of natural resources [Ewert, 2005; Rosegrant, 2001a; Wirsenius, 2000; Wirsenius,

2010; Alexandratos, 2006; De Fraiture, 2010]. Every study, however, has its limitations, and ‘missing

links’. Of the studies named above, one is geographically bounded – Ewert focuses solely on Europe.

Others have a limited temporal scale – the IFPRI research by Rosegrant has a scope of up to 2020 and

Wirsenius [Wirsenius, 2000] assesses the current situation. Of some studies the link to the use of natural

resources is weak, and is not quantified – Ewert solely includes potential land use, Rosegrant does not

give an holistic presentation, Penning de Vries is only interested in whether feeding the global

population is feasible, Alexandratos only mentions impacts on natural resources briefly and De Fraiture

does not include fertilizer use and solely focuses on cereal production. Last but not least, most of the

studies mentioned do not present a set of scenarios, which would allow for the comparison of different

futures – only Ewert and Penning de Vries present a set of possible food system futures. De Fraiture

provides scenarios, and while these are very useful, their focus is on water use. Wirsenius’ published an

article regarding food scenarios in August of 2010, which shows how relevant the topic is. Aspects of

these studies will be integrated in this study.

Table 1: Research characteristics

Scope Aim

Temporal scope Assessment for the year 2050

Geographical Scale Global assessment, with a subset of four regions

Link to Use of Natural Resources Assessment of Virtual Resource Content – includes land

use, water use and fertilizer use, leading to total global

and regional natural resource uses.

Scenario Study Assessment of four different scenarios

M.Sc. Thesis

I.Y.R. Odegard

19

Table 1 presents the research characteristics as proposed for this present study. It will contain four

different scenarios and the geographical scale will be global, with a subset of four regions. Furthermore,

it will contain and assessment of the use of the natural resources land, water and fertilizer, and will

discuss the four situations (for the four scenarios) in the year 2050.

At the Industrial Ecology seminar, in Delft, March 2010, Allenby stated that the goal of IE is to ‘develop

those options that will help us adapt in the future’. From an Industrial Ecology perspective this study is

relevant because in order to know the focus of such options for the future, we must have an idea about

the problems we may face in the future. A scenario study – integrating aspects related to technology,

policy, society and environmental consequences – helps us understand the problems we may face and

the options we have to reduce our environmental impact in the future.

1.3 Guide to the Reader

The previous sections have elaborated on the relevance of this study and its general research goal and

methodology. In Chapter 2 an elaboration of the different methodologies used will be given, along with

the assumptions and boundary conditions. Chapter 3 will present the current situation concerning our

food system. Global and regional consumption and production statistics of different commodity groups

will be given, along with the current appropriation of natural resources by agriculture. Conclusions are

drawn at the end of Chapter 3 as to the main driving forces related to changes in the food system and

the use of natural resources. In Chapter 4 these driving forces will be elaborated on, and they will be

linked together leading to internally consistent scenarios in Chapter 5. The scenario storylines are given

in this chapter. In Chapter 6 the linkages between the driving forces will be clarifies and all the

assumptions will be quantified. In Chapter 7 the results will be presented; they will show the natural

resource use by agriculture of the global and regional population in the four different scenarios and the

change in resource use compared to the current situation. In Chapter 8 the research question will be

answered and the conclusions will be presented. Chapter 9 will subsequently discuss relevant aspects of

the methodology and the model and the assumptions that have been made. Finally, in Chapter 10

recommendations for further research and use of the model will be given.

M.Sc. Thesis

I.Y.R. Odegard

20

2. Methodology

This chapter will discuss all methodological issues important to this research. Section 2.1 will elaborate

on the goal and the scope of this study. The boundary conditions and assumptions will also be discussed

in this section. Section 2.2 will discuss scenario methodology and how it was used in this study. Sections

2.3 and 2.4 will subsequently discuss the Virtual Resource Content (VRC) concept and the VRC model and

the way in which the VRC concept will be applied here.

2.1 Scope Definition

The goal of this research is to explore different global food scenarios, which relate food production to

the use of the natural resources land, water and fossil fuels. Several driving forces will be identified and

will be used to quantify natural resource use for 4 different scenarios in the year 2050. The spatial scope

of this research is global, with a subset of 4 regions, and includes 184 countries. These regions were

defined by the IPCC; the lists of countries in each region are given in Appendix 1. Small changes were

made because the FAO does not include certain countries in its analysis, and because countries changed

names. The OECD90 region features the 26 countries in the OECD in 1990: consisting of Western Europe,

North America and Australia, New Zealand and Japan. The REF region consists of the 27 countries

undergoing economic reform: Central and East-European countries, the former Soviet Union and newly

independent states in the Middle-East. The ASIA region features the 31 Asian and Oceania countries

except for Japan, Australia and New Zealand (OECD). The fourth region, the ALM region, consists of all

African countries, Latin America and the Caribbean, a total of 98 countries. These regions were chosen

because they represent ‘four "macro-regions" common to all different regional aggregations across the

six models’ [IPCC, Section 1.7.2]. They roughly correspond to the regions defined in the United Nations

Framework Convention on Climate Change. There is another reason this division makes sense when

designing scenarios; the OECD90 region and the REF respectively represent the developed or

industrialized world, while the ASIA and the ALM region correspond to the developing world.

2.2.1 Data

Data were obtained from various sources, the most important one being the various database of the FAO:

FAOSTAT, AQUASTAT and FERTISTAT. When this study was started, FAO-data availability was an issue, as

data was not put online yet. During the course of this study, data was put online by the FAO – a process

which is still in progress. Table 2 shows the data which was used to define the current situation, as well

as the driving forces population and economic development. Table 2 summarizes the data that were

used, their source and a description of their content. In Chapter 6 all the data which was used to

determine the other factors important for quantification of the scenarios are elaborated on.

The FAO does not report for some countries. This could be due to lack of data or because the value of

the data-point is zero. This is, however, not specified. Sometimes, when downloading large size queries,

the FAO leaves out the ones which values are zero. This can be annoying, as it takes more time to

process when certain countries are missing from the list. For such missing data concerning production, it

M.Sc. Thesis

I.Y.R. Odegard

21

is assumed that no production takes place. As such an assumption would be nonsensical regarding

consumption it is assumed that these countries have a consumption level which is equal to the average

consumption level of their region.

Table 2: Data: source and specifications

Topic Source Specifications

Current Situation

Supply Utility Accounts FAOSTAT Content: Production, Import, Export, Stock Variation, Domestic

supply, Feed, Seed, Waste (storage, transport, processing),

Processing, Other utilities, Food.

Commodity groups: Cereals, Fruits, Oilcrops, Pulses, Roots and tubers,

Sugar & Sweeteners, Sugarcrops, Vegetable oils, Vegetables, Eggs,

Milk, Bovine meat, Pork, Poultry, Mutton and goat meat.

Spatial scope: Data per country for 174 countries

(OECD90: 26, REF: 27, ASIA: 27, ALM: 94)

Temporal scope: 1961-2007

Production data FAOSTAT Content: Production, Harvested area, Yield

Commodity groups : Cereals, Fruit, Oilcrops, Pulses, Roots and tubers,

Vegetables


(OECD90: 26, REF: 26, ASIA: 33, ALM: 105)


Food Supply FAOSTAT Content: Food supply in: tonnes, kg/capita/yr, kcal/capita/day, g

protein/capita/day

Commodity groups: Cereals Excluding Beer, Fruits Excluding Wine,

Oilcrops, Pulses, Starchy roots, Sugar and Sweeteners, Sugarcrops,

Vegetables Oils, Vegetables



Land FAOSTAT Content: total land area, total arable land, total permanent crop land

and total pastures


Temporal scope: 1961-2008 (when data was processed only available

for 1995, 2000 and 2007)

AQUASTAT Content: Irrigation cropping patterns


Temporal scope: 2000

FAOSTAT Content: irrigated land


Temporal scope: 1961-2008 (when data was processed for: 1994-1996, 1999-2001, 2005, 2006, 2007)

Water WFN

Content: total renewable water resources (m3 per year)


Temporal scope: Available as average of 1997-2001

M.Sc. Thesis

I.Y.R. Odegard

22

AQUASTAT Content: total renewable water resources (m3 per year)


Temporal scope: data does not change, value years between 160-

2010

Fertilizers FERTISTAT Content: Fertilizer use (N, P2O5, K2O) per hectare per crop, area under

cultivation

Crops: Cereal: 7, Roots and tubers:4, Oilcrops: 7, Sugarcrops: 2,Fruit: 4

Commodity groups: vegetables, pulses


Temporal scope: between 1996 and 2004

Not all data is reported for each crop, for each country

FAOSTAT Content: Total fertilizer (sum of N, P2O5, K2O) production and

consumption, import and export.



Driving Forces

Population UN Content: Medium Population Projection

Spatial Scope: Data per country (181), sub-region (24) or region (4),

regions do not have global coverage, sub-regions (regions do not

correspond to IPCC regions)

Temporal scope: (5 year interval)

IIASA Content: Low and High Population Projection

Spatial scope: Data per region for 13 regions (corresponds to IPCC)

Temporal scope: 2008-2100 (1 year interval)

Economic development PBL Content: GDP in PPP (US$-1995)

Spatial scope: Data per region or country, for 17 regions

Temporal scope: 1970-2050 (5 year interval)

2.2.2 Assumptions and Boundary Conditions

There are several important boundary conditions and assumptions that need elaboration. Table 3 below

shows a thematic list of the boundary conditions and an explanation. Table 4 does the same for the

assumptions.

Table 3: Table of boundary conditions

Topic Boundary Conditions

Life Cycle Phases Resource use is evaluated for agriculture and animal husbandry. Post-harvest wastes are taken into account, i.e. waste during transport, storage and processing, and household and retail waste.

Commodity groups Vegetable commodity groups included are: cereals, roots and tubers, oil crops, sugar crops, pulses, vegetables and fruit. These categories account for 96% of the global vegetal caloric supply in the year 2005 [based on FAOSTAT, 2011]. The remaining calories come from alcoholic beverages, stimulants, nuts, spices and miscellaneous

M.Sc. Thesis

I.Y.R. Odegard

23

products. 70% of the aggregate of those categories is due to alcoholic beverages. Animal commodity groups included are: bovine meat, pork, poultry meat, milk and eggs. These categories account for 79% of the global animal caloric supply in the year 2005 [based on FAOSTAT, 2011]. The remaining 21% constitutes of by-products such as butter and offals, and are thus implicitly included.

Fisheries Fisheries and fish consumption will not be taken into account. Fish only contributes 2% to human food supply [Duarte, 2009]. Furthermore, land use and water use are not relevant in the ‘fish extraction phase’.

Spices, Stimulants and Miscellaneous Crops

Research will be limited to the main agricultural commodity groups, excluding spices, stimulants and other miscellaneous crops.

Economic principles Food prices and their consequences – e.g. influence of reduced demand on price and subsequent increased demand in other regions – and other economic issues will not be taken into account.

Table 4: Table of assumptions

Topic Assumption

Availability of Natural Resources

If present in a region, the natural resources land, water and fossils are assumed to be freely available and equitably distributed over the region.

Land Categories ‘arable land’, ‘pastures’ and ‘forests’ will be assumed to be of equal quality on a country level, and will therefore be assumed to be interchangeable in case land conversion is called for. Other land uses are assumed constant; expansion of urban land area and thus the removal of prime agricultural land will not be taken into account. ‘There is little evidence that the process of converting land to urban uses poses a serious threat to future global food production.’ [Rosegrant, 2001

a, p.78]

Land Land quality is assumed to be constant on a regional level.

Water Water availability is assumed constant/continuous on a regional level.

Fertilizers Availability and distribution of fertilizers is assumed equal in the regions for the regional scenarios or globally in the global scenarios.

Population Absolute population figures on a regional scale and on a global scale are considered. No distinction is made between e.g. rural and urban populations.

Economic and Social Development

Economic and social development is assumed to be well described by purchasing power parity.

Policy Policy is assumed to be strictly enforced.

Environmental Degradation

Is not considered beyond the state of the world now, e.g. effects of climate change are not taken into account.

Distribution Food is assumed to be distributed equally and equitable over the regions in the regional scenarios and globally in the global scenarios. A threshold for undernutrition is taken into account to ensure low prevalence of undernutrition.

Other Purposes of Food Production

Purposes of food production other than food or feed, i.e. seed, waste, processing and other utilities are assumed constant relative to total production. Thus biofuel policy is not taken into account. Currently, <2% of the total agricultural area is put toward biofuel production [De Fraiture, 2010]. It is assumed that future biofuel policy does not compete with food production.

Crop Production Crop variety is assumed to remain constant over regions.

M.Sc. Thesis

I.Y.R. Odegard

24

2.2 Scenario Methodology

A scenario describes a future state of the world, given a collection of e.g. socio-economic, demographic

and governance trends. According to Ringland a definition was given by Porter in 1985 as: “An internally

consistent view of what the future might turn out to be – not a forecast, but one possible future

outcome” [Ringland, 1998]. Scenario building will be used in this study to explore possible and distinctly

different futures with regard to our world food system and the impacts these futures would have on land

use, water use and fossil fuel use.

For years, scenarios have been used by companies and policy makers for various purposes. Shell is

known for its use of scenario building, which has helped the company predict and anticipate certain

events; e.g. the fall of Communism and the effect this had on natural gas prices [Ringland, 1998]. The

world food system is very complex, which makes the use of scenarios interesting. With scenarios one

does not predict what will happen, for instance a global shift towards a ‘Western’ diet, but what the

likely effect will be (here: on land use, water use and fossil fuel use) would such a change occur. Of

course, such a shift can occur to varying degrees, which can be studied using scenarios. In this study the

influence of various trends and uncertainties will be considered.

Scenario variants fit into one of the following three types: (1) probable, also referred to as predictive, (2)

possible, or explorative, and (3) preferable, or normative [Börjeson, 2006]. These types of scenarios

differ on several aspects, i.e. focus, quantitative or qualitative results, the time-frame, the system

structure (one or several scenarios) and whether the central focus is on internal or external factors. For

this study, explorative scenarios – yielding both qualitative and quantitative results, with a long time-

frame, a focus on internal and external factors and a structure consisting of several scenarios – will be

the focal point.

Emphasis is placed by various authors on the difference between internal and external factors [Börjeson,

2006, Shearer, 2005, Ringland, 1998]. This is important when scenarios are made or used in industries or

organizations, as it clarifies which factors can be influenced by the organization, and which are beyond

the organization’s control. As stated by Shearer: ‘It is useful to distinguish between events and actions’

[Shearer, 2005, p. 67]. As stated above, what-if scenarios mainly focus on external factors. There will be

no organization which has active control in changing trends or uncertainties central in this study, so all

changes will be viewed as events – or external factors, even though some events require positive action

on the part of a certain stakeholder, e.g. policy. Such factors are also called ‘drivers of change’

*Schoemaker, 1995+ or ‘driving forces’ *De Jong, 1992+. Driving forces (as they will be called from now on)

will be identified, which affect the use of natural resources in the food system. Some of the driving forces

incorporated here were also identified in the IPCC SRES, while others were incorporated because the

literature study showed their importance. For driving forces, e.g. population, significantly different

trends or projections can be identified. Such trends need to be linked to yield internally consistent

scenarios. The diversity of the trends related to the driving forces will ensure the scenarios cover a wide

spectrum of alternative futures.

M.Sc. Thesis

I.Y.R. Odegard

25

Linking the driving forces will result in a qualitative part; the “storylines”. These storylines describe in a

qualitative way the state of the world would such a combination of driving forces’ trends occur. When

such storylines are translated into corresponding quantitative evaluations, they are called scenarios.

According to the IPCC; “Scenarios can be viewed as a linking tool that integrates qualitative narratives or

stories about the future and quantitative formulations based on formal modeling. As such they enhance

our understanding of how systems work, behave and evolve” [IPCC, 2000]. The IPCC SRES will provide the

basis for the design of food scenarios in this study. Although a lot of scenario studies design a business-

as-usual (BAU) scenario, and compare the other scenarios to that case, in the IPCC Special Report on

Emission Scenarios (SRES) it is explicitly stated that there is no BAU scenario and that one scenario is not

necessarily more probable than the other. Section 2.2.1 elaborates on the IPCC SRES scenarios. After the

development of four storylines, these four different ‘states of the world in 2050’ will be quantified as to

the effect on the use of the natural resources land, water and fossil fuels, yielding four scenarios. The

new concept ‘Virtual Resource Content’ will be used for quantification, which is elaborated on in Section

2.4 and Section 6.3.

Table 5: Scenario Methodology [based on Schoemaker, 1995; Ringland, 1998; De Jong, 1992]

Step What Where 1. Goal and Scope Definition

Set temporal scope and boundary conditions [Schoemaker, 1995] Identify the issues [Ringland, 1998] Identify the stakeholders. De Jong and Zalm [De Jong, 1992] identify different world regions and their comparative strengths.

Chapter 2: Methodology Goal and scope definition and explanation of boundary conditions and assumptions Chapter 3: Current Situation Assessment of the demand, supply and the current appropriation of land, water and fossil fuels in the four regions.

2. Identify Basic Trends and Uncertainties

*Schoemaker, 1995+: ‘What political, economic, societal, technological, legal and industry trends are sure to affect the issues you identified in step one?’ According to Ringland [Ringland, 1995, p.195], one needs to give a:

Definition of the trend or uncertainty

Explanation of its importance

Review of immediate history and current situation

Speculation of alternative outcomes Identify relationships among uncertainties; identify the importance of the linkages among all pairs of uncertainties using a correlation matrix [Schoemaker, 1995].

Chapter 3: Current Situation Identification of driving forces Chapter 4: Driving forces Explanation of driving forces and definition of four alternative trends for each driving force. Chapter 5: Storylines Identification of linkages between driving forces and trends with the use of a correlation matrix.

3. Construct Scenarios Construct scenario themes; the trends identified in step 2 can be used in various ways [Schoemaker, 1995]; Check for consistency and plausibility. Three tests for internal consistency [Schoemaker, 1995]: 1) Are the trends compatible with the chosen time-frame? 2) Do the scenarios combine outcomes of uncertainties

Chapter 4: Storylines Evaluation of linkages between driving forces and trends.

M.Sc. Thesis

I.Y.R. Odegard

26

that go together? 3) Are the major stakeholders placed in positions they do not like and can change? Develop ‘learning scenarios’ and identify further research needs [Schoemaker, 1995] Develop quantitative models [Schoemaker, 1995] and quantify the consequences of different scenarios Criteria for a good scenario are:

1) Relevance 2) Internal consistency 3) Archetypal 4) ‘Describes a state in which the system might

exist for some time’ [Schoemaker, 1995].

Chapter 5: Storylines Development of scenario storylines Chapter 6: Linkages Development of Virtual Resource Content model and calculation of resource use. Chapter 7: Results and Chapter 8: Conclusions Evaluation of scenario outcomes and their relevance

Consistency is important in ‘what if?’ scenario studies. It is more important to present futures that are

internally consistent than futures that are necessarily the most likely to happen. Because a wide range of

possible futures is presented the truth will be somewhere in the middle. When scenarios are used by

governments or companies, the desirability of the outcomes helps determine which driving forces to

focus on, or which trends to stimulate. Of course, the difference between causation and correlation

needs to be kept in mind; while economic development increases meat consumption, imposing

measures to increase meat consumption will most likely not increase economic development. Here, the

diversity of the scenarios ensures a wide range of outcomes.

It is important to note that there are no probabilities connected to the scenarios, and that the likeliness

of their occurrence is uncertain. Furthermore, no verdict is given as to whether a scenario outcome or

the development of a certain trend is ‘good’ or ‘bad’.

2.2.1 IPCC Emission Scenarios

The scenarios described in the Special Report on Emission Scenarios (SRES) by the Intergovernmental

Panel on Climate Change (IPCC) will provide the backbone for the scenarios which will be designed in this

study. The scenarios in the SRES replace an earlier set of emission scenarios developed by the IPCC.

‘These scenarios cover a wide range of the main driving forces of future emissions, from demographic to

technological and economic developments. The scenarios encompass different future developments that

might influence greenhouse gas (GHG) sources and sinks, such as alternative structures of energy systems

and land-use changes’ [IPCC, 2000, Section 1.1]. Because of the unpredictability and the presence of

uncertainties, scenarios play an essential role in the assessment of climate change by the IPCC. The

effectiveness of mitigation strategies, adaptation strategies and climate change policy can be assessed

with the use of these scenarios. It also provided input for the Third Assessment Report (TAR) by the IPCC;

the TAR is part of a series of reports by the IPCC concerning climate change [IPCC, 2000, Section 1.1].

M.Sc. Thesis

I.Y.R. Odegard

27

The IPCC set several objectives with regard to their scenarios that are relevant to this research. The

scenarios had to:

Cover as much as possible of the range of major underlying "driving forces" of emissions scenarios identified in the open literature;

Have sufficient spatial resolution to allow regional assessments of climate change in the global context;

Cover a wide spectrum of alternative futures to reflect relevant uncertainties and knowledge gaps;

Be transparent with input assumptions, modeling approaches and results open to external review;

Be reproducible - input data and methodology are documented adequately enough to allow other researchers to reproduce the scenarios;

Be internally consistent - the various input assumptions and data of the scenarios are internally consistent to the extent possible.

These objectives will also be a guide to the current research. The first three points relate to the research content and will help to structure this study. The broad range of driving force trends and the wide spectrum of alternative futures, along with an appropriate spatial resolution, will ensure a diverse set of scenario outcomes. The last three topics relate to the research approach and the report and form the basis to any scientific study, but their importance will be kept in mind.

The four IPCC scenarios are usually presented in a four-quadrant figure, and are named A1, A2, B1 and

B2. On the horizontal axis the scenarios are divided into either globally oriented – the ‘1’ scenarios – or

regionally oriented – the ‘2’ scenarios. On the vertical axis the scenarios are economically focused – the

‘A’ scenarios, or environmentally focused – the ‘B’ scenarios. This is depicted in Figure 4.

A2

B1 B2

A1

RegionalGlobal

Environmental

Economic

Driving Forces

Figure 4: Schematic representation of the four IPCC SRES Scenarios: A1, A2, B1, B2, showing whether the focus is on global or regional factors, and on economic or environmental factors.

M.Sc. Thesis

I.Y.R. Odegard

28

Basic driving forces for future change like population growth, economic and social development, policy

and technological change were identified by the IPCC as major influences on future change and will be

used to design scenarios in this study. A fifth driving force was added: diet change. Similar to the

importance of energy use and changes in energy use in the IPCC scenarios, diet and diet change is of

importance to the impact of our food system.

Research into the trends related to these driving forces, e.g. historic and current population growth, will

lead to a presentation of four different ‘worlds’. These worlds will have different populations which are

spread differently over the regions, they will have different policy objectives and they will have different

paradigms concerning environmental issues. The presentation of these worlds will be specific to the year

2050. The way in which certain characteristics (e.g. a vegetarian diet) come about will not be specified.

Rather, it will be assumed that those characteristics are the case, and the Virtual Resource Content and

subsequently global and regional natural resource use will be modeled from there.

2.2.2 Food Scenarios

As explained above, different driving forces will be incorporated in the food scenarios in this study.

Whether a scenario has a global or a regional focus will determine whether regions trade the food

products made in their region (the global scenarios) or whether regions will have to be self-sufficient

(regional). The focus on economy or environment determines whether there will be restraints on the use

of natural resources (environmental) or not (economy). A distinguishing factor will be the diet that will

be prevalent in the different worlds; in the ‘A’ scenarios the population will adopt a ‘Western Diet’, in

which meat consumption increases with increasing GDP (in PPP). In the ‘B1’ scenario the global

population will adopt a vegetarian lifestyle, while in the ‘B2’ scenario all food will be grown and raised

organically. A central question for each scenario can be framed, which clearly shows the difference

between the scenarios and the core theme;

A1 – What will the effects be given a worldwide shift to Western agricultural management practices and

a Western Diet?

A2 – What will the effects be when regions have to be self-sufficient in a world where population growth

was high and economic development low between now and the year 2050?

B1 – What will the effects be of a global shift to a vegetarian diet?

B2 – What will the effects be when regions have to be self-sufficient given low-input agricultural

practices?

2.3 Virtual Resource Content

The linkages between the agricultural food system and the use of natural resources will be modeled

using the concept of the Virtual Resource Content (VRC). This is a new concept based on the Virtual

Water Content concept by Hoekstra et al [Hoekstra, 2008; Hoekstra, 2009]. The Virtual Water Content of

a commodity represents all water (in m3) used during the whole life cycle to produce that commodity.

With locally specified Virtual Water Contents for a full range of different commodity groups, for all

M.Sc. Thesis

I.Y.R. Odegard

29

phases of the life cycle, it is possible to calculate the water footprint of for example a Dutch citizen, a

chocolate chip cookie or an organization.

The new concept of the Virtual Resource Content is comparable to the Virtual Water Concept. The VRC is

a measure of the use of resources in agriculture – land, water and fertilizers – needed to produce a

commodity. In this study this will be limited to the agricultural phase of the life cycle. Like the Virtual

Water Concept, the VRC will be specified to regional characteristics. This means resources and

production characteristics (e.g. water use, fertilizer use and yield per hectare) are assessed on a regional

level.

VRC factors will be calculated; these are assessments of land requirements (ha kg-1), water requirements

(m3 kg-1) and fossil fuel requirements (kg kg-1) in agriculture for all main commodity groups on a regional

level. A food system model will be developed in which the VRC factors are incorporated. Consumption of

food commodity groups per capita (kg cap-1 year-1) serves as input. The model can be used for various

purposes. Here it will be used to evaluate scenarios. Four food scenarios will be developed, as elaborated

on above. The food system model will be used to compare the four scenarios quantitatively with regard

to their resource use. The VRC factors named above will be coupled to projections that will be made

concerning consumption of commodity groups. This will allow comparison of use of resources between

scenarios, and regions.

2.4 The VRC Model

Figure 5 shows the relationships between the driving forces, supply, demand, Virtual Resource Content

and resource use as used in the VRC model. Five driving forces are identified (in Chapter 3, elaborated on

in Chapter 4): population growth economic development, technological change, policy and diet. Diet can

be seen as a secondary driving force, as it is influenced by economic development and policy. Waste – in

households and retail – is determined by the level of economic development and the policy paradigm.

Wastes are included in the per capita definition of diet, which when multiplied by the regional or global

population yields the regional and global demands. The demand is multiplied by a standardized factor

representing the losses that occur between production and retail; i.e. seed, processing, other utilities,

and storage, transport and processing wastes. Feed required to produce the animal foodstuff demand is

included and linked to the amount of animal products generated; it thus changes with changing demand

for animal products.

Policy does not only influence the demand-side, but also the supply side. Technological change can be

steered in a certain direction by the policy paradigm, e.g. demanding higher irrigation efficiencies.

Technological change also determines how much can be produced, through improvements in

management practices resulting in higher yield projections. However, the supply may not actually be

attainable because policy measures limit the use of natural resources. In the model, production will be

modeled such that it keeps up with the supply. Virtual Resource Content Factors are coupled to

production. The results will show whether it is possible to match supply to demand given the limits to

the use of natural resources. This will yield information about the use of natural resources in the future

and whether supply can match demand.

M.Sc. Thesis

I.Y.R. Odegard

30

VRC-factors

Population

DietEconomic

DevelopmentTechnological

Change

Policy

Policy

Losses

Waste

Demand ProductionSupply

Resource Use

Figure 5: Relationships between driving forces, supply, demand, Virtual Resource Content and resource use.

The model does not assess the potential impact of the use of natural resources, but provides a way to

assess natural resource use given different trends connected to the driving forces named above. The

results will be compared to estimates of land available for agriculture, water stress thresholds and

reserves of natural resources for fertilizer production.

M.Sc. Thesis

I.Y.R. Odegard

31

3. Current Situation

In this section the current situation with respect to agriculture and the use of natural resources will be

presented. Section 3.1 will elaborate on food consumption. Food consumption and food availability has

grown steadily over the past decades, which will be shown in this section. Data on differences in

consumption patterns in different regions of the world will be given, which will provide valuable input for

the definition of potential diets in 2050. Table 6 below shows the population size and the level of

economic development for the four regions for the year 2005.

Table 6: Regional population size and level of economic development (in PPP) in the year 2005 [UN, 2009; Van Vliet, 2010].

Region Population

(in billions)

Economic Development

(PPP in 1000 US$-1995)

OECD90 0.9581 29.68

REF 0.4039 5.25

ASIA 3.4704 3.86

ALM 1.6637 5.01

Section 3.2 will present information about agricultural production. Data on a global scale about the

quantity of commodity groups that are produced and where these are produced will be given. This will

give valuable information about the global and regional availability of supply. Import and export data will

also be provided to yield insight into regional self-sufficiency. This is followed by Section 3.3 in which the

current appropriation of natural resources by agriculture on a global and regional scale will be presented.

Land use, water use and fertilizer use will be elaborated on, as well as organic agriculture and animal

husbandry. Appropriation will be linked to the production data given in Section 3.4, to establish

productivity in the different regions, linked to land use, water use and use of agrochemicals. Comparison

of supply and demand, elaborated on in Section 3.5, will show the need to investigate efficiency and

waste in the food production-consumption system. Losses and wastes will thus be discussed in Section

3.6.

In Section 3.7 a summary of the current situation will be given regarding linkages between production,

consumption and the use of natural resources. Factors of importance will be identified – the driving

forces – which will provide the basis for the design of the scenarios and the quantification of the

potential future appropriations of natural resources by agriculture.

M.Sc. Thesis

I.Y.R. Odegard

32

3.1 Food Consumption

Food availability in kcal per person per day has been increasing steadily over the past decades in most

parts of the world, even though the global population has risen from 3.3 billion in 1965 to 6.5 billion in

2000.

Figure 6: Apparent consumption (kcal/cap/day) in countries with different levels of development, components appear in the same order as in the legend [data from Bruinsma, 2003].

Figure 6 shows the amount of kcal available per person per day, including a projection for the coming 20

years, for countries in three different stages of development. The transition countries have experienced

a decline following the collapse of the Soviet Union, but, as can be seen in the figure, they have

experienced growth since the turn of the century. Both industrial countries and developing countries

have seen a steady increase over the given period. But even though the increase in food availability in

the developing countries is about twice as high as in the industrial countries, the difference is still around

600 kcal per person per day in favor of the industrial countries.

As can be seen in Figure 7, food production has more than kept up with population growth; the global

average food consumption per person has increased for most commodity groups, only roots and tubers

and pulses show a slight decline compared to 1965, although consumption has been increasing since

1985.

M.Sc. Thesis

I.Y.R. Odegard

33

Figure 7: Global average apparent food consumption (kg/cap/year), components (in 2007) appear in the same order as in the legend [FAOSTAT, 2011].

3.1.1 Regional Distribution

The table below shows the food supply in kilogram per capita per year per region for those commodity

groups that are taken into account in this study: Cereals, Fruits, Oil crops, Pulses, Roots, Sugar and

Sweeteners (sugar crop derivative), Vegetable oils (oil crop derivative), Vegetables, Meat, Milk and Eggs.

Food supply is called apparent consumption because it is the available supply on a per capita basis,

which still includes household and retail waste.

Table 7: Regional food supply (apparent consumption in kg/cap/year) in 2005 [based on FAOSTAT, 2011].

Commodity group Food supply (apparent consumption)

Kg/cap/year in 2005, per region – includes trade

OECD90 REF ASIA ALM WORLD

Cereals 122.2 156.7 153.7 126.7 142.3

Fruits 108.4 56.8 49.6 70.7 64.1

Oil crops

Vegetable oils

5.8

22.6

1.5

12.0

8.2

7.8

5.7

9.2

6.8

5.3

Pulses 4.1 2.0 5.1 9.0 5.7

Roots and tubers 62.0 108.5 47.6 90.8 64.6

Sugar crops

Sugar and Sweeteners

0.0

48.3

0.0

38.4

5.6

13.4

2.8

23.7

3.7

22.7

Vegetables 126.1 120.0 137.2 16.2 83.1

Meat 87.9 50.9 26.4 30.4 35.7

Milk 215.3 176.0 41.7 63.4 71.1

Eggs 13.3 11.5 8.2 4.8 7.6

M.Sc. Thesis

I.Y.R. Odegard

34

These values include all foodstuffs derived from these basic commodity groups, e.g. cheese is included in

‘milk’. Table 7 clearly shows that there are significant differences as to commodity supply between

regions. The OECD90 has the largest per capita supply of milk, meats, eggs, vegetable oils and sugar and

sweeteners, while people in the other regions eat more cereals and roots and tubers (except for the ASIA

region). As can be seen, apparent consumption of the oil crop and sugar crop derivatives, vegetable oils

and sugar and sweeteners respectively, is much higher than consumption of the primary commodities.

While differences between regions are large, differences between countries in the same region can also

be quite substantial. For example, in the OECD90 region Luxembourg and the United States of America

lead in meat consumption with respectively 138 and 123 kg per capita per year. Turkey and Japan are the

lowest meat consumers in the OECD90, consuming respectively 21 and 44 kg per capita per year. The

Netherlands have a somewhat below average per capita consumption of meat: 71 kg per year.

Consumption of milk, however, is quite high in The Netherlands: 343 kg per capita per year. On a global

scale, only the average Swede and Fin consume more milk: respectively 370 kg and 340 kg per capita per

year in 2005.

3.1.2 Commodity Distribution

The progress which has been made in feeding the global population was accompanied by a significant

change in the type of the commodity groups that were consumed. A shift toward higher meat

consumption is particularly important, as this has large consequences; because animal need to be fed,

and are inefficient in their conversion, input of natural resources is much higher per generated kcal.

Figure 8 below presents the information given in Table 7 above. It is clear that vegetable food sources

prevail in the developing world (ASIA and ALM regions), and that food from animal sources, i.e. meat,

milk and eggs, accounts for a much larger part of the diet in the developed world (OECD90 and REF

regions). This means that a potential change in living standard, which causes such diet transitions, is

important for the assessment of the environmental impact of food consumption. This difference in

commodity distribution is linked to economic development and will be further discussed in Section 4.5.

M.Sc. Thesis

I.Y.R. Odegard

35

Figure 8: Regional apparent consumption (kg/cap/year) [based on FAOSTAT, 2011].

As was also pointed out above, however, interregional diversity remains, as can be seen in Figure 9

below. The average global meat consumption is 38.5 kg/capita per year. From this figure it can be

deduced that the average meat consumption differs significantly between the regions, and that meat

consumption is substantially higher in the developed world – OECD90 region and REF region, the first

and second ‘triangles’ – than in the developing world – ASIA region and ALM region, the third and fourth

‘triangles’. The average consumption in the ASIA region and the ALM region is well below the global

average, while people in the REF region and the OECD90 region consume much more meat. The OECD90

region has the highest consumption per capita and this region also consumes a large share of the total;

34.2% of all meat is consumed in this region, which in population size is only 15% of the global total. The

ASIA region is the largest region in population size – 53% of the global total – but only consumes 36.7%

of all meat. This is clearly reflected in the low per capita consumption. The ALM region consumes 20.8%

of all meat, with a population that is 26% of the global total, while the REF region consumes 8.3% with a

population that is 6% of the global total.

M.Sc. Thesis

I.Y.R. Odegard

36

Figure 9: Meat consumption (kg/capita/year) for all 185 countries for the year 2004 [based on data from FAO, 2010].

M.Sc. Thesis

I.Y.R. Odegard

37

BOX 3.1 How much do we need?

Even though the world has made substantial progress in feeding the global population, hunger is still

a widespread problem in certain parts of the world. The question is what a sufficient diet would be.

As pointed out in Chapter 2, the supply – often referred to as ‘consumption’ by the FAO – may either

underestimate (for developing countries) or overestimate (for developed countries) actual

consumption. Desired levels of caloric intake are quite easy to establish per person; the resting

metabolic rate (allows for functioning at rest) is in the range of 1300-1700 kcal per adult per day,

depending on their physiological condition. When adding light activity, this range shifts to 1700-1965

kcal per person per day in the developing countries, based on population structures in the year 2000

[Alexandratos, 2006].

The FAO states that the threshold for eliminating under-nutrition is a national average of >2900

kcal/capita/day. Given a ‘coefficient of variation’ (CV), which measures national inequality, of 0.2,

only 1% of the population is undernourished (at a national average of 2800 kcal/cap/day this 2.5%).

Countries CV’s are in the range of 0.2-0.36, thus the 2900 kcal threshold is a conservative measure. Of

course, depending on one’s physiological condition – age, sex, bodyweight and height – the desired

caloric intake differs. The threshold of 2900 kcal/person/day may seem high to people with a Western

– and thus on average a sedentary – lifestyle, but people in developing countries, with less service-

oriented economies, have more active lifestyles. What also needs to be kept in mind is that, as stated

above, distribution is not uniform. If the poorest segments of the population are to be provided with

adequate nutrition – i.e. the incidence of undernourishment be kept sufficiently low – the average

supply needs to be quite a bit higher than the average need [Alexandratos, 2006].

Of course, whether people’s food intake is nutritionally adequate depends on the food-product, and

cannot be based on caloric value alone. Corn can be eaten ‘on the cob’ or can be used to produce

high-fructose corn syrup, or aspartame – main ingredients in soft-drinks. While it was long thought

that with regard to food there is an inelasticity of demand – one person’s demand cannot vary too

much because – the production of soft-drinks has proved differently. While not giving any

sustenance, large quantities can be consumed. Whether these drinks are ‘light’ or not, consumption

of primary inputs can increase significantly [Pollan, 2006]. Such nutritionally inadequate over-

nourishment is a factor in the high incidence of obesity we experience in the industrialized world

today.

Last but not least, food losses and wastes are included in the food supply (kcal/capita/day) data, and

the food supply threshold could thus be different if the projections on losses would be changed. It is,

however, unclear to what extent losses are incorporated in the 2900 kcal threshold. Significant

amounts of food are wasted in the industrialized countries, depending on the commodity, ranging

from 2% to 56% [Schneider, 2007]. In less developed countries, food losses are smaller [Smil, 2001].

Indeed, the FAO is working on including such losses [Alexandratos, 2006].

M.Sc. Thesis

I.Y.R. Odegard

38

3.2 Food Production

In this section food production per region will be presented, as well as some intraregional variations.

Import and export data are aggregated into ‘net import’ figures, showing the quantitative self-sufficiency

per commodity of the regions. In Section 3.5 the production data will be compared to actual demand

estimates to assess whether current food production is sufficient to nourish the present global

population. Table 8 and Table 9 present the production of the vegetable commodity groups cereals,

fruits, oil crops, pulses, roots and tubers, sugar crops, and vegetables (Table 8), along with the animal

products bovine meat, pork, poultry meat, sheep and goat meat, milk and eggs (Table 9) in the four

regions. Production used for feed, seed and ‘other utilities’, and production that is lost, is included in

these production data. Specific amounts used for these purposes are given in Appendices 3-6 and

discussed in Section 3.6.

Table 8: Regional and global production of vegetable commodity groups (in million tons) [based on FAOSTAT, 2011].

Commodity group Production of vegetable commodity groups (million tons) in 2005


Cereals 712 250 974 313 2,250

Fruits 105 21 205 195 526

Oil crops 33 9 69 32 143

Pulses 15.0 4 24 17 60

Roots and tubers 80 92 285 164 721

Sugar crops 227 69 527 750 1,570

Vegetables 136 53 553 123 865

Note: figures may not add up due to round-off errors.

Table 8 shows that there are wide interregional differences. The ‘bold’ values show that the ASIA region

has the highest absolute production for all vegetable commodity groups, except for sugar crops, which

are mainly produced in the ALM region. The difference in land devoted to agriculture is reflected in these

numbers, as the ASIA region and ALM region have a much larger (agricultural) land base than the

OECD90 region and the REF region (see Section 3.3.1). As with consumption, food production also differs

quite substantially within regions. For example, the USA alone produces 51% of the cereals produced in

the OECD90 region, while this country accounts for 39% of the land under cultivation in this region. For

all commodity groups except pulses, the USA is the top producer in the OECD90 region; Canada and

Australia produce respectively 4.6 million tons of pulses and 2.6 million tons of pulses, while the USA

produces 2.2 million tons. In the REF region, the Russian Federation is the largest producer in all

categories, not surprisingly, as it is by far the largest country in the world. Production of sugar crops in

the ALM region is dominated by Brazil, producing 56% of the sugar crops on 53% of the land under

cultivation for sugar crops in the ALM region. About 75% of all oil crops produced in the ASIA region is

produced by China, Indonesia and Malaysia, producing respectively 24%, 27% and 25% of the total.

Production per hectare, however, varies quite substantially. China accounts for 31% of the total acreage

under cultivation in the ASIA region, while Indonesia and Malaysia only account for respectively 8.3% and

4%. This variety in productivity is reflected by the situation in India. While India produces only 15% of the

M.Sc. Thesis

I.Y.R. Odegard

39

oil crops, it accounts for 41% of the area under cultivation for such crops. Productivity will be further

elaborated on in Section 3.4.

Table 9 below shows the production of animal derived food products. The OECD90 region leads in

production of bovine meat, with the USA as top producer; 47% of all bovine meat produced in the

OECD90 region is produced in the USA. The OECD90 region also leads in the production of poultry and

milk. The ASIA region leads in sheep and goat meat production, egg production and pork production, the

last of which is dominated by China, producing 86% of the total. Moreover, China leads in all categories

of animal derived food products in the ASIA region. In the REF region, the Russian Federation – being by

far the largest country in the region – leads in all categories, except for pork, in which Poland takes a

slight lead. In the ALM region, top producers of animal food products are Brazil, Mexico and Iran.

Table 9: Regional and global production of animal commodity groups (in million tons) [based on FAOSTAT, 2011].

Commodity group Production of animal commodity groups (million tons) in 2005


Meats 89.6 17.1 91.5 55.0 253.2

Bovine meat 23.8 4.9 11.3 21.7 61.7

Poultry 31.7 4.5 20.9 22.8 79.9

Pork 31.4 7.1 53.9 6.8 99.2

Mutton and goat meat 2.7 0.7 5.4 3.7 12.5

Milk 263.0 99.0 169.0 115.2 646.2

Eggs 14.4 5.04 32.1 9.5 61.0

Import and Export

Import and export data is given by FAOSTAT per commodity group and per country. Data on the origin

and destination of commodity imports and exports are, however, not provided. Interesting in the

present context is how big the gap is between production and supply within a region. Table 10 below

shows the import and export data for the commodity groups listed above, for the four regions, along

with the net import. Net import was calculated by subtracting aggregated export from aggregated

import, thus ignoring intra-regional trade. This means the net import indicates whether the regions as

defined here are self-sufficient in their present demand.

As with the information presented in earlier sections, import and export data vary wildly between

countries in the same region. For example, in the ALM region, Brazil and Argentina together account for

83% of the total meat export. In this context, however, the net import is most interesting, and shows

regional self-sufficiency. As can be seen in Table 10 the OECD90 region is the largest exporter; it exports

cereals, oil crops, sugar crops, sugar and sweeteners, pulses, meats, milk and eggs. Total export amount

to 79.9 million tons by the OECD90 and 18.6 million tonnes by the REF region. The ASIA region and the

ALM region are net importers; they imported respectively 40.9 and 12.9 million tons in 2005. Funnily

enough, import does not equal export. For the commodity groups fruits, roots and tubers and sugar

crops, aggregated export is lower than aggregated import.

M.Sc. Thesis

I.Y.R. Odegard

40

Table 10: Import, export and net import for the four regions in the year 2005 (in million tons). Net import shows the relative self-sufficiency for the regions; if positive the commodity is imported on a net basis for that region, if negative the commodity is exported from the region [based on data from FAOSTAT, 2011]. Figures for net import may not add up due to round-off errors.

Commodity Groups Quantities (million tons)

OECD90 REF ASIA ALM

Cereals Import 105.1 13.3 54.9 126

Export 201.8 44.8 35.2 40.4

Net import -96.6 -31.5 19.7 85.8

Fruits

Import

Export

Net import

75.6

41.0

34.5

13.6

5.5

8.05

7.3

12.6

-5.3

8.8

44.6

-35.9

Oil crops Import 37.5 1.4 37.3 12.5

Export 42.6 4.0 4.2 38.5

Net import -5.1 -2.7 33.1 -26.0

Vegetable Oils Import 23.0 3.3 19.0 11.5

Export 14.6 2.3 33.0 12.5

Net import 8.5 1.1 -14.0 -1.0

Sugar Crops Import 0.2 0.6 0.0 0.0

Export 0.5 0.2 0.0 0.1

Net import -0.2 0.3 -0.0 -0.0

Sugar and sweeteners Import 19.2 8.9 11.2 1.5

Export 20.4 4.1 6.7 31.0

Net import -1.2 4.8 4.5 -15.8

Pulses Import 3.6 0.2 3.4 2.3

Export 6.3 0.4 2.2 0.9

Net import -2.6 0.2 1.3 1.3

Roots and tubers Import 18.8 2.2 18.7 2.9

Export 18.1 1.1 18.3 1.9

Net Import 0.7 1.0 0.5 1.0

Vegetables Import 34.4 6.1 4.7 6.3

Export 28.8 3.5 12.0 10.1

Net Import 5.6 2.6 -7.35 -3.8

Meat Import 16.7 5.2 2.9 4.8

Export 20.6 13.6 22.4 8.0

Net Import -3.9 3.8 0.7 -3.2

Milk Import 48.4 5.8 10.7 16.7

Export 68.1 11.7 2.6 6.2

Net Import -19.6 -5.9 8.1 10.5

Eggs Import 1.0 0.1 0.1 0.2

Export 1.1 0.1 0.3 0.1

Net Import -0.1 -0.0 -0.2 0.1

M.Sc. Thesis

I.Y.R. Odegard

41

3.3 Appropriation of Natural Resources

The green revolution has played a major role in feeding the growing population these past centuries.

Irrigation, fertilization and cultivation of new cultivars have steadily increased yields over the years.

Growth in cereals production since 1970 is mainly due to higher yields, most land expansion occurred

earlier [Molden, 2007, p.58]. While feeding the expanding global population would have been impossible

without the green revolution, yields have not increased uniformly all around the world. Furthermore, it

has also put a significant pressure on our natural resources. In the following sections the appropriation

of the natural resources land, water and fossil fuels by agriculture will be examined. Data on land use,

use of water resources, and use of fertilizers will be linked to productivity in Section 3.4 to provide a

basis for scenario construction.

3.3.1 Land

Table 11 shows the appropriation of land for food production in the four regions for the years 1995,

2000 and 2007. The categories ‘arable land’, ‘permanent crops’ and ‘pastures’ together account for the

total extent of the area under cultivation for food production. The portion of the total land area used for

food production in 2007 was respectively 35.6% in the OECD90 region, 27.6% in the REF region, 50.8% in

the ASIA region and 38.9% in the ALM region.

Table 11: Area under cultivation – according to the FAO – for arable land, permanent crops and pastures (in 1000 ha) for the years 1995, 2000 and 2005 [based on data from FAO, 2010].

Region Land Area (1000 ha)

Total

Land

Area

Arable Landa Permanent Cropsb Pasturesc

1995 2000 2007 1995 2000 2007 1995 2000 2007

OECD90 3,092,036 373,791 373,544 359,722 23,049 23,479 24,090 760,654 747,442 719,190

REF 2,260,500 260,540 245,420 238,222 7,477 6,941 6,428 372,570 378,995 378,655

ASIA 2,074,915 392,876 406,135 412,660 51,018 53,778 61,295 583,698 595,612 581,580

ALM 5,499,124 370,137 372,632 400,290 46,288 48,020 50,586 1,679,940 1,699,442 1,690,467

WORLD 12,926,575 1,397,344 1,397,731 1,410,894 127,832 132,218 142,399 3,396,862 3,421,491 3,369,892

Notes: a ‘Arable land is the land under temporary agricultural crops (multiple-cropped areas are counted only once), temporary

meadows for mowing or pasture, land under market and kitchen gardens and land temporarily fallow (less than five years). The

abandoned land resulting from shifting cultivation is not included in this category. Data for “Arable land” are not meant to

indicate the amount of land that is potentially cultivable’ [FAO4, 2010].

b ‘Permanent crops are sown or planted once, and then occupy the land for some years and need not be replanted after each

annual harvest, such as cocoa, coffee and rubber. This category includes flowering shrubs, fruit trees, nut trees and vines, but

excludes trees grown for wood or timber’ [FAO4, 2010].

c ‘Permanent meadows and pastures is the land used permanently (five years or more) to grow herbaceous forage crops, either

cultivated or growing wild (wild prairie or grazing land)’ [FAO4, 2010]. This category is called ‘pastures’ in the FAO’s Statistical

Yearbook 2009, and ‘permanent pastures and meadows’ in the FAO’s glossary. It is assumed to be the same.

M.Sc. Thesis

I.Y.R. Odegard

42

As can be seen in Table 11, the category arable land has been decreasing in the OECD90 region and the

REF region, while it has been increasing in the ASIA region and ALM region. What stands out in the

category permanent crops is that the area under cultivation is much smaller for the REF region than for

the other regions. Furthermore, while the area has been increasing for the time period shown for the

other regions, permanent cropland area has decreased in the REF region. This is – at least in part – due

to the fall of the Soviet Union and the subsequent shrinkage of the economies involved. For the pasture

land category, it is striking that the area in the ALM region is much larger than in the other regions. This

can be explained by the much larger total land area, and the fact that a larger portion of the feed-mix is

provided by pastures in the developing world. This will be further elaborated on in Section 3.3.5. As can

be seen in Figure 10, cereal production accounts for the main share of land use for agriculture, from 49%

of the agricultural land in the ALM region, to 74% in the REF region. With currently available data, it

seems that in the REF region and in the OECD90 region only respectively 55% and 59% of the land is

accounted for by the major crops. The sum of the amount of land for arable land and permanent crops

given by the FAO is depicted by the black lines. It seems as though all land is accounted for in the ASIA

region, however, double cropping is much more common in this region, as can be seen in Figure 11

below. Certain crops are not included in the present analysis, such as coffee, cocoa and tea, and thus the

gap in land use data in the ALM region can be explained. There are several explanations for differences

between land use data and data on cultivated area. Double cropping is not specified by the FAO, and

thus land could be counted twice. This would, however, only increase the gaps. Part of the agricultural

land base is left – temporarily – fallow, for example for the Netherlands this was 7,600 hectares in 2008

[FAO, 2010]. Furthermore, temporary meadows and pastures are included in the ‘arable land’ category.

These may be lands that could be used to grow foodcrops, but are used to grow animal feed – called

cropland pasture by Wirsenius [Wirsenius, 2000]. It is unclear whether this explains the gap fully, land

area for temporary meadows and pastures is not given by the FAO. The FAO did not answer multiple

requests for explanation.

Figure 10: Regional land use per commodity group (in ha) in 2005, compared to the arable land and temporary crops land base in 2007, according to FAOSTAT [based on FAOSTAT, 2011], items in the legend appear bottom-up in the chart.

M.Sc. Thesis

I.Y.R. Odegard

43

As can be seen in Figure 11 below, single cropping prevails throughout the OECD90 region and the REF

region, while double cropping or even triple cropping is quite common in the ASIA region and the ALM

region. It is clear that in areas around the equator double and triple cropping is common, which is made

possible by little seasonal change in temperature. The number of ‘growing period days’ can be two or

three times as high in these regions. According to Cassman, about 25% of global rice production is

produced in Asia in double or triple cropping rice systems. Furthermore, the main cereal production

system in Northern India, Pakistan, Nepal and Southern China is a double crop system alternating both

rice and wheat once a year [Cassman, 1999]. AQUASTAT, ‘FAO’s Information System on Water and

Agriculture’, gives data on cropping intensity for a selection of countries, totaling 91 countries. Of these

countries, 17 are located in the ASIA region, 73 in the ALM region and 1 in the OECD90 region (Turkey). It

is assumed multiple cropping only takes place on irrigated areas. Cropping intensity is defined as the

aggregated area of all irrigated crops divided by the area equipped for irrigation. It is given as a

percentage and shows whether multiple cropping takes place (cropping intensity > 100%), or whether

the area equipped for irrigation is being underused (cropping intensity < 100%), or e.g. was unnecessarily

equipped for irrigation. The data as reported by AQUASTAT cover respectively 92.7% and 94.9% of the

irrigated area in the ASIA region and the ALM region in the year 2000 (see also Table 15), and is

therefore assumed to give a fair representation. According to the AQUASTAT data, the cropping intensity

in the ALM region is slightly lower than 100%: 94.9%. In the ASIA region it is well above 100%: 138.3%.

Figure 11: Multiple cropping zones, rain-fed conditions [Fischer, 2002]

Land available for further expansion of agriculture seems to be limited. According to the FAO, land

suitable for rainfed agriculture, with yields above a minimum acceptable level, amount to 2.8 billion

hectares in the developing world [Bruinsma, 2003, p.14]. Around 34% of this land is currently under

cultivation. Most of the remaining land is concentrated in a few countries in South America and Sub-

Saharan Africa, and is ‘very unevenly distributed’, and can therefore not be considered a ‘land reserve’

M.Sc. Thesis

I.Y.R. Odegard

44

[Bruinsma, 2003, p.15]. Land availability is even more limited in parts of the REF region (Near East and

North Africa) and the ASIA region (South Asia). Not only do many countries in South Asia and the Near

East/North Africa have no spare land left, the land that is left is not suitable for agriculture due to

environmental constraints. Furthermore, ‘a good part of the land with agricultural potential is under

forest or in protected areas, in use for human settlements, or suffers from lack of infrastructure and the

incidence of disease. Therefore, it should not be considered as being a reserve, readily available for

agricultural expansion’ [Bruinsma, 2003, p.15].

Assuming the FAO data on agricultural land are correct, see Table 11, there is a substantial portion of

agricultural land unaccounted for in the OECD90 region and the REF region, respectively 153 million

hectares and 104 million hectares. The data in Table 11 show that agricultural land area has been

decreasing in these regions. This could be due to various processes, e.g. urbanization or reverting land to

nature. It could also be that land is left fallow, because overproduction, decreasing populations and

increasing yields reduce the need for agricultural land area to remain the same.

Table 12: Gross extents of land with rain-fed cultivation potential, in 1000 ha and % of total land area [Fischer, 2002]

Region Gross extents with rain-fed cultivation potential

VS + S + MS landa

(1000 ha) (arable land and permanent crops as % potential)

OECD90 707,000 54.3%

REF 380,800 64.3%

ASIA 590,400 80.3%

ALM 1,973,500 22.9%

Note: a VS, S and MS denote ‘very suitable’, ‘suitable’ and ‘moderately suitable’, see also Appendix 10.

Table 12 above shows the gross extent of land with rain-fed cultivation potential, according to the IIASA

and the FAO [Fisher, 2002]. It is an aggregation of the land that is very suitable (VS), suitable (S) and

moderately suitable (MS), with a ‘maximizing technology mix’ and measures the gross maximum

available land under rainfed conditions in the four regions. Net available area is between 10% and 30%

lower (see Appendix 10).

3.3.2 Water

Agriculture accounts for 85% of the global water withdrawals [Foley, 2005]. Rainfed agriculture is still

predominant on a global scale. 78% of the water consumed by crops comes from green water – rainfall

stored in soil moisture. These crops are grown on 71% of the world’s agricultural land, and provide 62%

of the gross value of global food production. Irrigation water, provided by surface water and

groundwater sources (blue water), is used on the remaining 28% of agricultural land. [De Fraiture, 2010].

Irrigation efficiency is assumed to be 60%, which means that additional water – 1050 km3 – is withdrawn

from surface and groundwater sources provide the 1570 km3 needed for crop evapotranspiration [De

Fraiture, 2010, p. 503]. The data shown in Table 13 and Table 14 are based on data from the Water

Footprint Network [Hoekstra, 2008]. Not all countries are included in these data, but the missing

countries account for only 3% of the land dedicated to arable land and permanent crops, and 3% of the

global population, in the year 2005.

M.Sc. Thesis

I.Y.R. Odegard

45

Table 13: Water use in agriculture and fraction of total water use in 2005 [based on data from Hoekstra, 2008].

Region Water

(109 m3 year-1)

Agricultural water use

(% of regional totals)

Renewable water Total water use

Water use in

agriculture

% of regional

renewable water

% of regional

total water use

OECD90 9,536 1,730 1,099 11,5% 63,5%

REF 5,728 569 481 8,4% 84,4%

ASIA 14,773 3,082 2,828 19,1% 91,8%

ALM 22,926 1,802 1,781 7,8% 98,9%

A higher fraction of water use is consumed in industry and in domestic settings in developed countries

[Molden, 2007]. As developing countries advance, it can be expected that the water demand in sectors

other than agriculture will grow. The regional averages do not provide insight into actual water scarcity.

According to the FAO, countries that use more than 40% of their renewable water resources in

agriculture are in a critical situation (critical water stress). Countries using more than 20% of their

renewable water in agriculture pass the ‘threshold which could be used to indicate impending water

scarcity’ (moderate water stress) [Bruinsma, 2003, p.15]. As can be seen in Table 13 the ASIA region as a

whole is already close to that threshold. Out of the 20 countries in the ASIA region for which the Water

Footprint Network provided data, 4 countries experience moderate water stress and 5 experience critical

water stress. For the OECD90 region (23 countries) the same numbers apply; 9 countries in total are in a

water stress situation. The REF region (20 countries) has 4 countries with moderate water stress and 3

with critical water stress. The ALM region (78 countries) has the highest percentage of countries with

water stress: 11 countries have moderate water stress and 36 have critical water stress. Of the last group,

15 countries (7 in the Middle East and 5 in North Africa and 2 elsewhere) use more than 100% of their

renewable sources in agriculture [based on data from Hoekstra, 2008]. Table 14 shows the annual

regional consumption (the crop evapotranspiration), which is an aggregation of national consumption

rates for that region. Crop evapotranspiration is ‘the combination of two separate processes whereby

water is lost on the one hand from the soil surface by evaporation and on the other hand from the crop

by transpiration’ [FAO, 1998, Ch. 1, p. 1]. According to Smil, evapotranspiration rates can be used to

determine water needs of crops [Smil, 2001, p. 40]. The Water Footprint Network uses the term

‘evapotranspiration’ for the portion of the water footprint due to water use in agriculture. It appears

that the estimation by Hoekstra may be somewhat conservative. According to the International Water

Management Institute (IWMI), around 7,130 cubic kilometers are consumed annually by crops grown for

the production of food [De Fraiture, 2007, p.91]. As stated above, 3% of agricultural land is not included

in the Water Footprint Network (WFN) data. However, the estimate by the WFN is a significant 13%

lower. The difference is probably due to the fact that in the WFN data, irrigation efficiency is not taken

into account, but the water requirements are ‘defined as the total water needed for evapotranspiration,

from planting to harvest for a given crop in a specific climate region, when adequate soil water is

maintained by rainfall and/or irrigation so that it does not limit plant growth and crop yield’ [Chapagain,

2008]. As no country-specific data are provided by the IWMI, the WFN data will be used.

M.Sc. Thesis

I.Y.R. Odegard

46

Table 14: Crop evapotranspiration (109 m

3 per year) for the four regions [based on data from Hoekstra, 2008].

Region Crop Evapotranspiration (109 m3 per year)

(average over the years 1997-2001)

Regional consumption

(% of total regional use)

For export

(% of total regional use)

Total

(% of total global use)

OECD90 m3

720 379 1,099

% 66% 33% 18%

REF m3 420 59 481

% 87% 23% 8%

ASIA m3 2,655 174 2,828

% 94% 6% 46%

ALM m3 1,432 315 1,781

% 80% 20% 28%

Total

m3

%

5,237

85%

927

15%

6,189

100%

When we take a closer look at the share for each region in the total global use, it is clear that the ASIA

region is the main user of water for agriculture. This is also true per hectare. As can be seen in Figure 12,

the fraction per region of water use (in m3 ha-1) is quite similar to the fractions for the regions of the

global total. The main reason why the water use per hectare is much higher in the ASIA region than in

the other regions, is that rice production is quite water-intensive, and that the rice production in ASIA is

high. According to Hoekstra, on average on a global scale, 2291 m3 of water is needed to produce a ton

of rice. Coupled to high production outputs, this yields the high water use in the ASIA region.

As can be seen in Figure 12 below, different crops have different water needs. This is also true for the

same crop in different regions. The diversity in water use per crop (left pie-chart), and per kg of a certain

crop (right pie-chart) is shown in Figure 14. As can be seen, different commodity groups account for

quite different appropriations. Cereals take up more than half of the global water used in agriculture.

Water use by region

Share of total water use and water use in m3 per hectare

Figure 12: Share of total water use of the global total by region (OECD90 18%, REF 8%, ASIA 46% and ALM 28%), and water use (m

3 ha

-1) by region (OECD90 2767 m

3 ha

-1, REF 1899 m

3 ha

-1, ASIA 6150 m

3 ha

-1, ALM 4152 m

3 ha

-1) [based on Hoekstra, 2008].

OECD90

REF

ASIA

ALM

M.Sc. Thesis

I.Y.R. Odegard

47

When we look at how much water is needed to produce a kg of a certain commodity, it shows a

completely different picture (see Figure 14, right pie-chart). Stimulants (i.e. coffee, tea, cocoa and spices)

and nuts are large consumers on a per generated weight basis. So is the category ‘other’, which consists

of tobacco (15% of water use in that category) and natural rubber (85% of water use in that category).

Global water use for commodity groups

Fraction of total global use and use per weight unit

Figure 13: Fraction of global water use in agriculture per commodity group (pie-chart on the left) and fractions based on use

per weight unit (pie-chart on the right) [based on Hoekstra, 2008].

As stated above, around 22% of the water use in agriculture is provided by irrigation. Table 15 below

shows the irrigated area for the different regions, and that area as a percentage of the regional total

area dedicated to arable land and permanent crops. As we have seen that this area may be

overestimated by the FAO, actual irrigated area may account for a larger share than shown in Table 15.

As can be seen, the fraction of the total land base that is irrigated seems quite stable, but the absolute

numbers show that the irrigated area is expanding a little in all regions except the REF region. This is in

line with the shrinkage of the agricultural sector in the former Soviet Union. As the data show, the

fraction of the land that is irrigated in the ALM region is close to stable, while the irrigated area is

increasing. This is in line with expert opinion stating land expansion is ongoing in this region.

The majority of irrigated land area expansion happened between 1950 and 2000. This area increased

from 50 Mha worldwide in 1950 to over 250 Mha in the year 2000 [Smil, 2001]. According to Cassman

expansion of irrigated area has slowed down recently; water supply and environmental issues are the

main limiting factors for future expansion [Cassman, 1999, p.5952].

Cereals

Roots and tubers

Sugar crops

Pulses

Oil crops

Vegetables

Fruits

Stimulants

Nuts

Fodder

Fibre crops

Other

M.Sc. Thesis

I.Y.R. Odegard

48

Table 15: Irrigated area (1000 ha) and percentage of irrigated area of total area for arable land and permanent crops in the years 1995, 2000 and 2007 for the four regions [based on FAO, 2010].

Region Irrigated Area (1000 ha)

irrigated area as % of total agricultural area

1995 2000 2007

OECD90 Area 45,244 47,628 49,372

% 11.40 12.00 12.86

REF Area 26,776 25,720 25,136

% 9.99 10.19 10.27

ASIA Area 146,710 157,170 163,107

% 33.05 34.17 34.41

ALM Area 45,016 47,036 49,111

% 10.81 11.18 10.89

The prevalence of irrigation within the regions is substantial, as can be seen in Figure 14. Irrigation

intensity is correlated to geographical location (agro-ecological zone) and to economic development.

Water requirements are not globally uniform, there are several factors that determine the water

requirement for a certain crop. De Fraiture mentiones climate, mode of cultivation – rainfed or irrigated,

high input or low input agriculture -, crop variety, length of growing season and crop yield as factors that

determine crop water requirements [De Fraiture, 2010, p. 503]. In developing countries with

unpredictable rainfall and uncertainty concerning the output price of crops, investments in agricultural

technology like irrigation may carry too great a risk for (small) farmers [Molden, 2007].

Figure 14: Share of irrigated land in arable land and permanent crops [FAO, 2009]

Competition over rainwater resources is usually not an issue; precipitation – the water used in rain-fed

agriculture –– can be thought of as a characteristic of the land. For irrigation, however, water needs to

be extracted from groundwater or surface water resources and thus competes with other sectors

[WWAP, 2009]. According to the International Water Management Institute, ‘Irrigation water was

M.Sc. Thesis

I.Y.R. Odegard

49

essential to achieve the gains from high-yielding fertilizer responsive crops’ [Molden, 2007, p. 59]. This is

collaborated by the FAO, stating that ‘Irrigated crops require higher levels of fertilization for optimal

productivity, and there is often synergy between the irrigation and the fertilizers’ [FAO, 2006, p.20].

Combining the use of fertilizers to irrigation can raise yields significantly. An experiment with wheat

production in Morocco showed that yields were increased by 18% and 35% (for different types of wheat)

by adding irrigation, and by 68% and 71% by combining irrigation and fertilization [FAO, 2006].

In the developing countries, irrigation will play an important role in agricultural development. The FAO

projects a 40 million hectare expansion, totaling 242 million ha equipped for irrigation in the developing

countries in the year 2030. This figure represent 60% of the total potential in these countries. Cropping

intensity will be raised, because of this trend [Bruinsma, 2003]. These FAO projections include raising

irrigation efficiency, from 38% to 42%. Such efficiency improvements are especially necessary in

countries where water withdrawals account for high portions of the renewable sources (see section on

water stress above).

3.3.3 Fertilizer

Use of synthetic fertilizers has increased steadily over the past half century [Pimentel, 1990; Vitousek,

1997]. The production of synthetic nitrogen fertilizers on an industrial scale started in 1913 with the

commercialization of what became known as the Haber-Bosch process. Figure 15 shows the increased

consumption of nitrogenous fertilizers on a global scale, for the USA and for China. Some of the obvious

reasons of this increased use are the expansion of agricultural land and the increase in yields due to the

use of higher yielding varieties, which also have higher harvest indexes. Another, not so obvious, reason

is pointed out by Pimentel. Agricultural businesses have become more and more specialized (in the USA

partially due to tax incentives), a process which separated the livestock industry from feed grain

production. This discouraged the recycling of animal waste and the use of crop rotation schemes to

introduce nitrogen through growth of leguminous crops, leading to the increased need for synthetic

fertilizers [Pimentel, 1990].

Figure 15: Consumption of nitrogenous fertilizers, 1950-1990 [Smil, 2002].

M.Sc. Thesis

I.Y.R. Odegard

50

Nitrogen fertilizer provides one of the three macronutrients, while P2O5 and K2O fertilizer provides

phosphorous and potassium. Phosphorous containing fertilizers are produced from phosphate rock,

which is a non-renewable resource. Current global production (in 2005) is estimated at 1.2% of the

current reserve, and at 0.4% of the potential reserve base [based on USGS, 2009 and IFA, 2011].

Potassium shows similar figures; production in 2005 was at 0.7% of the current reserve base, and at 0.3%

of the potential reserve base [based on USGS, 2009 and IFA, 2011].

Three factors determine the fertilizer requirements by a crop on a specific site: (1) the nutrient demand

of the crop, (2) the nutrients made available by other sources (for example nitrogen by leguminous crops

grown off-season), and (3) the efficiency of fertilization, which determines the levels of nutrients

available for the plant [Appel, 1994]. While optimum fertilization rates for a specific crop may vary

considerably between sites, one may reasonably presume that regional averages should be estimable.

Policy has, however, had a significant impact on fertilizer consumption, unrelated to actual requirements;

‘In countries where a centrally planned system, with its heavy support to agriculture and the allocation of

fertilizers according to plans, was replaced around 1990 by a market-oriented system, fertilizer

consumption fell abruptly’ [FAO, 2006, p.43]. Currently, government support is available for the use of

synthetic fertilizers in certain developing countries, causing their use to increase [FAO, 2006, p. 43].

Whether this raises fertilizer use to appropriate levels is uncertain. Increased application of fertilizer is

not necessarily better. The ‘yield response curve’ usually shows a decreasing additional yield with further

increasing fertilizer input, beyond a certain level of fertilizer input [FAO, 1984; Howarth, 2002].

Furthermore, the FAO states that excessive application of fertilizer can suppress yields [FAO, 1984].

The link between fertilizer use and policy is corroborated by Smil, who states that heavy subsidies in the

former Communist countries and in the European Union led to excessive use of nitrogen fertilizers [Smil,

2001, p.108]. Table 16 shows the annual production and consumption of fertilizers (the sum of N, P2O5

and K2O fertilizers) in 2007 as well as the net import for the four regions. When positive, ‘net import’ is

the amount of fertilizer imported, while a negative ‘net import’ means that amount will be exported.

Table 16: Fertilizer production and consumption (sum of N, P2O5 and K2O) in 2007, and estimated average use in kg per hectare per year [based on FAO, 2010].

Region Production of Fertilizers

(N, P2O5, K2O) in tons

Consumption of Fertilizer


Net Import


OECD90 48,338,121 55,152,447 6,814,326

REF 29,271,173 10,084,498 -19,186,675

ASIA 75,147,624 85,864,873 10,717,249

ALM 22,804,196 28,370,527 5,566,331 a Use in kg per ha was estimated by dividing consumption by the area under cultivation on arable land and permanent crops as

given by the FAO. As stated above, this area may be overrated, which would result in underestimation. On the other hand,

multiple cropping is not taken into account, which results in overestimation.

M.Sc. Thesis

I.Y.R. Odegard

51

According to the FAO, around 10 million ton is applied to pastures [FAO, 2006, p.60], a little less than 6%

of the total fertilizer production. Figure 16 gives insight into regional differences, and differences

between crops. Figure 16 shows the N-fertilizer consumption per hectare per year for the various crops,

per region [based on FERTISTAT, 2011]. Average regional consumption per crop for K2O and P2O5

fertilizers are given in Appendix 14.

Figure 16: Regional average nitrogenous fertilizer consumption (kg/ha/year) in 2005, [based on FERTISTAT, 2011].

In Figure 17 a comparison is made between the actual use of N, P2O5 and K2O fertilizer on a global scale

(FAOSTAT data, only available as aggregated figure), the use recommended by the FAO (see Appendix 14)

and an estimate of global fertilizer consumption based on an estimate of regional consumptions based

on data from the FAO [FERTISTAT, 2011], as was also used for Figure 16 above. It shows that the

estimated use is a lower than the actual use in 2005. This may be due to the fact that not all foodstuff

production is included in the present analysis (e.g. treenuts are excluded), furthermore, data is only

reported for 155 countries. The figure also shows that the estimated recommended use is significantly

higher than the actual use. When comparing recommended and actual use on a regional scale it shows

that the recommended use of potassium fertilizer (K2O) is much higher in all regions than the actual use.

Recommended use of phosphorous fertilizer (P2O5) is higher than the actual use in all regions except the

ASIA region. Figure 18 shows the recommended and estimated fertilizer use in the OECD90 region.

Figures for the other three regions are given in Appendix 14.

M.Sc. Thesis

I.Y.R. Odegard

52

Figure 17: Fertilizer use estimates (ton/year): actual, recommended and estimated fertilizer use based on regional data [based on FAO, 2010; FAO, 1984; FERTISTAT, 2011].

The ‘recommended use rate’ given in Figure 17 may be higher for several reasons. First, fertilizer

efficiency is likely to have improved between the 1980s and now. Furthermore, how and whether

fertilizer efficiency is included in the FAOs recommendation is unclear. Use of the word ‘requirements’

implies a baseline value, but due to the much higher than average values, it is more likely that

‘application rate’ is meant. This leaves room for further optimization of application rate through better

management practices. Another explanation could be that currently incorrect NPK ratios are applied.

Smil states that fertilizer applications in China specifically have been deficient in P and K. He gives a

worldwide average NPK ratio of 100:18:22 (probably based on data from the mid-nineties, according to

the data in Figure 17 the NPK ratio was 100:44:30 in the year 2005). For China this was 100:14:8,

whereas in the United States it was 100:16:35 [Smil, 2001, p.311]. According to the data from Figure 17,

the ratio should be 100:38:62.

M.Sc. Thesis

I.Y.R. Odegard

53

Figure 18: Recommended and estimated regional fertilizer use (kg fertilizer per ton generated product), [based on FAO, 2984; FERTISTAT, 2011].

The FAO predicts that fertilizer use will increase in the developing countries, although at a slower rate

than in the past, and not in all countries. According to the FAO, East Asia would still have the highest

fertilizer consumption rate in 2030, of 266 kg per hectare, while Sub-Saharan Africa would still have the

lowest, of under 10 kg per hectare [Bruinsma, 2003, p.17]. There are several reasons the FAO predicts a

slowdown in the increase in fertilizer use. The current levels of fertilizer applied are deemed quite high,

agricultural production growth is predicted to decelerate and the FAO predicts an increase in fertilizer

use efficiency [Bruinsma, 2003].

Fertilizer uptake rates vary considerably, Appel reports uptake rates on different sites of between 33%

and 96% [Appel, 1994]. There are many ways in which such fertilizer losses can be reduced. Direct

measures, pointed out by Smil, include ‘soil testing, choice of appropriate fertilizer compounds,

maintenance of proper nutrient ratios, and attention to the timing and placement of fertilizers’ [Smil,

2000, p.114]. Furthermore, the need for fertilization can be reduced, and the efficiency improved, by the

planting of leguminous crops, or by optimizing conditions for other diazotrophs (bacteria that fix

nitrogen) and by ‘good agronomic practices embracing crop rotations, conservation tillage and weed

control’ [Smil, 2000, p.114]. The FAO states that fertilizer input to wheat can be reduced by 30-40 kg

N/ha in the case of rotation with a leguminous crop. For potatoes this value is even higher: 40-50 kg

N/ha [FAO, 1984]. According to Smil, significant gains can be made during the next two generations

(assumed to be 50 years from the year 2000). Fertilizer efficiency could be raised by at least 25-30%. For

‘modernizing countries’ this would mean average uptakes rates of around 50-55%, for ‘affluent nations’

such uptake rates would be around 65-70% [Smil, 2000]. Crop yield response to fertilizer and irrigation

go hand in hand, as investing in either technology improves overall yields when the other is implemented

as well.

M.Sc. Thesis

I.Y.R. Odegard

54

3.3.4 Organic Agriculture

The term organic agriculture is described by Badgley as ‘farming practices that may be called

agroecological, sustainable, or ecological; utilize natural (non-synthetic) nutrient-cycling processes;

exclude or rarely use synthetic pesticides; and sustain or regenerate soil quality’ [Badgely, 2006] and will

be used as such here. There are advantages to raising crops organically, such as better water retention,

lower fossil energy inputs, higher soil organic matter and higher soil nitrogen [Pimentel, 2005]. According

to the FAO, the agricultural area managed organically in 2008 was a little over 14 million hectares. This

represents 0.3% of the current agricultural land base. Of the organic agricultural area, 66% is located in

South-America, 12% in North America, respectively 7% and 9% in Southern Europe and Northern Europe.

Western Europe, Eastern Europe and Western Asia account for the remaining 6%, of which two-thirds is

located in Western Asia [FAOSTAT, 2010]. Critics claim that organic agriculture would not be able to feed

the current population. Lower yields would mean expansion of agricultural area, thereby offsetting the

environmental advantages [Badgely, 2007; Smil, 2001]. According to Smil, ‘The only way to support 10

billion people by traditional cropping dependent solely on recycling of organic matter and rotations with

legumes would be to double, or even to triple the extent of currently cultivated land’ [Smil, 2001, pp. 46-

47].

Others, however, claim that with suitable management practices, such as rotation cropping and growing

leguminous crops off-season, reasonable yields can be achieved. Badgley et al, in a literature research

consisting of a global dataset of 293 examples, compared organic (or low input) agricultural yields to

conventional yields. Their analysis shows that organic yields vary from 0.816 to 1.005 times conventional

yields in the developed world, and between 1.573 to 3.995 times conventional yields in the developing

world [Badgley, 2007]. Mäder et al, state that in their 21-year study done in Central Europe, comparing

organic crop yields to conventional crop yields, mean crop yields were found to be 20% lower in the

organic system. Depending on the crop, yields in the organic system ranged from 58% to 90% of those in

the conventional system. In the organic system, inputs of fertilizer were reduced 34% to 53% and inputs

of pesticides were reduced by 97%. They state that currently, organic yields in Europe are typically 60%

to 70% of current conventional yields [Mäder, 2002]. Pimentel et al also compare organic and

conventional yields, from data gathered from a 21-year study in the US comparing two different organic

systems to a conventional system. Corn yields were significantly lower in the first five years of the study

(20% and 30%), but after this transition period, yields were similar (differing only 2% and 3%).

Furthermore, they found that corn yields in dry years were significantly higher in the organic systems

(28% and 34% higher). During a drought, soybean yields also responded favorably in the organic systems,

with yields being 35% to 50% higher than in the conventional system. It was found that improved water

retention in the organic systems accounted for these higher yields [Pimentel, 2005]. Poudel et al also

report organic crop yields (for tomato and corn), which are comparable to conventional yields [Poudel,

2002].

The summary above, of some studies comparing organic yields to conventional yields, shows that

researchers can achieve similar-to-conventional yields with organic or low-input agriculture. The

question is whether these results can be obtained on a large scale. As stated by Smil ‘even a complete

recycling of all organic wastes from the current harvested land and from all confined domestic animals

M.Sc. Thesis

I.Y.R. Odegard

55

would not be able to supply all macronutrients removed from soils by modern high-yield cropping’ [Smil,

2001, p. 46]. This does, however, exclude the use of growing cover crops and leguminous crops that

increase nitrogen availability by biofixation. Badgley et al. estimate that growing cover crops off-season

would make enough nitrogen available to replace synthetic fertilizer [Badgley, 2007]. The amount of

nitrogen available after growing leguminous crops depends on the harvesting index – the ratio of edible

or usable biomass to total biomass. Harvesting indexes have gone up substantially with the green

revolution, and differ widely for different crops; between 30% for beans and 80% for soybeans [Smil,

2001]. Soybeans may not even be able to fix all the nitrogen they need, and in the US around 20% of the

planted area for soybeans receive additional fertilizer. Grown as green manure (crops grown to fixate

nitrogen that are plowed back completely into the soil) soybeans, like clover and alfalfa, provide good

enrichment. One of the advantages of green manures is that nitrogen recovery is much higher than it is

for synthetic fertilizers. Recovery for green manures is generally around 70%, but can be up to 90%.

Uptake rates can be as low as 18% for synthetic fertilizer, and are in general a little over half of the

applied nitrogen [Smil, 2001].

Growing cover crops off-season is a good way to provide natural fertilization, but economic viability may

hamper implementation. The same argument applies to the growing of green manures. According to

Smil, in 1975 green manure cultivation was at its peak, at 9.9 Mha, which dropped to only 4 Mha in 1989

[Smil, 2001, p. 120]. Smil states that the reason this practice lost its desirability is the pressure to

produce more food on limited land resources. Another issue related to economic viability is the use of

animal manure and specifically the transportation costs involved. As stated above, animal manure does

not provide enough nutrients to supply the global demand for micronutrients. This does not mean that

animal manure cannot be used to provide fertilization on a local scale. The costs of transportation,

however, are in many cases too high to be financially more attractive than the use of synthetic fertilizers.

Furthermore, there would be a need to supplement with green manures, off-season cover crops and/or

low inputs of synthetic fertilizer. In addition, in the developed world, agriculture and animal husbandry

have developed as separate industries (see also Section 3.3.3), which makes it difficult to close cycles. In

regions where agriculture is less developed and in general already more integrated with animal

husbandry, possibilities for joint development incorporating the closing of cycles (thus the use of animal

manures) may be promising.

3.3.5 Animal Husbandry

We have seen that the production of animal products has increased significantly over the past decades.

As will be explained in Chapter 4, this increased demand for animal products is correlated to the level of

economic development. An assessment of the linkages between the use of natural resources and animal

production has to start with an analysis of the current feeding methods. While pasturing, at first glance,

may seem intensive from a land use perspective, feeding animals feed cereals and other food crops is

much more land intensive because of the production of these crops. According to Smil, actual space

needed to keep animals is much lower than the land area needed to grow feed crops; ‘the optimum

allotment of space for growing and finishing pigs is about one m2/head; the two animals that occupy

sequentially that area during one year will consume 600 kg of feed, which assuming that the pigs are

raised on a mixture of concentrate feed, will need on the order of 1,000 m2 of arable land to grow’ [Smil,

M.Sc. Thesis

I.Y.R. Odegard

56

2001]. The land needed for the production of feed accounts for most of the land use during the whole

life cycle of the animal food production industry. Pastures – also providing feed – are included. Table 17

shows the feeding efficiencies for different animals. These differences stem from differences in size and

thus metabolic rate, and differences in gestation and lactation periods. As can be seen, cattle are least

efficient in metabolizing feed to meat, while poultry is most efficient.

Table 17: Efficiencies of animal food production, land requirements and water requirements [Smil, 2001].

Efficiency factor Commodity

Beef Pork Poultry Eggs Milk

Feed (kg/kg EWa) 20.0 7.3 4.5 2.8 1.1

Land requirementsb (m

2/Mcal) 6-10 2-2.5 2.5-3 1.5-2 1-1.5

Water requirementsc (g/kcal) 25-35 5 6 1.5 10-15

Food energy (kcal/kg d

) 1,200 3,100 1,800 1,600 650 a

EW = edible weight. b Requirements are based on a common share of 20% of the total coming from by-products, with a minimum 15% share of

ruminant roughage. Average feed crop yield was assumed to be 6 t/ha. c Requirements are based on animal food production in temperate climates.

d Slaughter-weight – weight as reported by FAO

There are differences between animal production in the developing world and in the developed world.

The feed/meat ratio – the feed input divided by the meat output - is much lower in developing countries

for several reasons: (1) Animals are more often pastured, (2) animals receive household waste as food

input, and (3) harvest by-products are more often fed to animals in developing countries [SOW-VU, 2005;

Wirsenius, 2000]. The FAO estimates that 666 million tonnes of cereals, 35% of the total global cereal

consumption, are currently used as feed [Alexandratos, 2006, p.51]. The data in Appendix 4 show that

this figure was 31.7% of global cereal production in 2007. Regional data show that in the OECD90, the

REF and the ALM region cereal feed production is higher than average, with respectively 48.5%, 42.3%

and 36.8% of regional cereal production being used as feed, while in the ASIA region it is almost half of

the global average: 15.9%.

A quick estimate showing the difference in feeding methods can be given by dividing the tonnage of

animal products over the available acreage of pastures. While this does not yield useable information on

the yield of animal products per hectare because animals are also fed food crops, it does show the

significant difference between the regions, which reflects the increased use of such food crops as feed in

the OECD90 region. In this region the ‘yield’ of animal products is around 504 kg/hectare. The ASIA

region comes close to that figure, with 494 kg/hectare. Production of pork is high in the ASIA region, and

pigs need less feed to gain weight (half of what is needed to produce beef) and the edible weight is also

higher (15% higher) for pork than for beef *Smil, 2001, p.157+. In the REF region the ‘yield’ comes to 322

kg/hectare, and the ALM region has the lowest ‘yield’ of 106 kg/hectare. Feeding efficiencies are higher

in the industrial world [Wirsenius, 2000; Wirsenius, 2010] and the numbers reflect that pasturing animals

is more common in the regions with lower ‘yields’ of animal products per hectare. Furthermore,

slaughter weight is lower in the developing world [FAO, 2000]. Appendix 8 elaborates on feed-mixes and

feeding efficiencies.

M.Sc. Thesis

I.Y.R. Odegard

57

3.4 Productivity

Table 18 shows the global and regional yields (in tons per hectare) for the seven commodity groups, in

ton of generated product per harvested hectare, for the year 2005. Yield increases, as documented by

the FAO between the early sixties and the late nineties, varied between an 11% increase (sunflower) and

a 106% increase (wheat). The three main cereal crops maize, rice and wheat showed increases of

respectively 99.6%, 84.4% and 106% [FAO, 2000]. Several factors played a role in the ‘Green Revolution’

in the mid-twentieth century. As summarized by Cassman, there were three ‘production factors’ that

increased yields so substantially that food production kept up with population growth. Technological

development played a large role in the development and implementation of these factors. The three

factors are (1) the introduction of new varieties of cereal crops which have a higher harvesting index and

‘better’ plant characteristics such as increased stalk strength, (2) the introduction of synthetic nitrogen

fertilizer, and (3) increased use of irrigation. These ‘production factors’ – driven by technological

development – were accompanied by government policies and economic and social development

[Cassman, 1999]. Analysis of FAOSTAT data has shown that yields have been increasing for all commodity

groups, but that the yield gap – the difference between the maximum attainable yield and the current

yield – is much smaller in the industrialized world than in the developing world. Thus even though yields

are lower in the developing for most commodity groups, room for improvement is larger.

Table 18: Yields (tons per hectare harvested) for the seven commodity groups in the four regions for the year 2005 [based on FAOSTAT, 2011].

Commodity Group Yield in 2005 (tons per hectare harvested)


Cereals 4.92 2.43 3.61 1.98 3.33

Fruits 13.06 5.10 9.81 10.06 10.04

Oil Crops 1.56 1.34 2.02 1.12 1.58

Pulses 1.86 1.73 0.75 0.62 0.86

Roots and Tubers 36.00 13.55 17.04 9.74 13.64

Sugar Crops 64.90 31.88 60.21 69.08 62.23

Vegetables 27.84 16.15 16.81 13.64 17.28

Note: Yields are given in tons per hectare harvested.

In the future it can be expected that a combination of policies, economic and social development and

technological development will define agricultural practices. As explained, the potential yield depends on

the agro-ecological zone, and therefore cannot be changed. Agricultural management practices, socio-

economic conditions and policy also have an influence, and, according to the FAO adoption of better

varieties and fertilization will be able to increase yields where there is the agroecological potential for it

to happen [Bruinsma, 2003, p.15]. More information about the maximum attainable yields and potential

future yields can be found in Appendix 7.

M.Sc. Thesis

I.Y.R. Odegard

58

3.5 Supply and Demand

To assess whether current food production is sufficient to nourish the present global population, and

whether the regional populations can be self-sufficient, supply data will be compared to demand data.

The ‘current supply’, given in Table 19, is based on the FAO estimate of food available in the four regions

in 2005 [FAOSTAT, 2011]. However, this includes import and export. To assess the amounts that regions

would be able to feed their inhabitants, allowance needs to be made for imports and export. Food may

be exported for economic reasons while inhabitants of that country are insufficiently nourished.

Therefore, the food fraction of the net import (as given in Section 3.2) was subtracted. Because it is

unknown for which purpose foodstuff is traded, the food fraction of net import was assumed equal to

the food fraction of total production; i.e. the fraction of total production which is destined to be food.

The demand data are based on the FAO’s threshold of sufficient nourishment of between an average of

2800 and 2900 kcal per person per day [FAO, 2006]. A category ‘other’, which includes nuts and alcoholic

beverages provides on average on a global around 77 kcal per capita per day. The example demand

proposed in Table 19 provides a reference for comparison to the regional supplies.

Table 19: Supply and demand [Based on data from Bruinsma, 2003; FAOSTAT, 2011]

Commodity group Example

Demanda

Current Supplyb (kcal/cap/day)

(2005)

OECD90 REF ASIA ALM World

Cereals, food 1503 1482 1693 1328 905 1251

Roots and tubers 156 139 242 107 204 146

Sugar and sweeteners

229 471 270 118 303 217

Vegetable oils

263 422 251 240 228 253

Oil crops 49 16 57 57 53

Pulses 56 55 22 45 79 54

Meats 196 499 225 142 339 248

Milk 119 359 291 60 11 104

Eggs 52 45 32 45 39

Vegetablesc 80 76 92 12 69

Fruita

99 51 64 111 80

Other 277

Total kcal/capita/day) 2800 3709 3183 2291 2297 2519

Note: values may not add up due to round-off errors. a Demand is based on the global commodity composition in 1997-1999, which adds up to 2804 kcal/cap/day [Bruinsma, 2003, p.

53]. Other food includes fruits, vegetables, eggs, oil crops, nuts and alcoholic beverages, these are not specified by Bruinsma. b Supply data is based on ‘food’ minus ‘food fraction of net import’, thus neglecting imports and exports and is given for the year

2005 [FAOSTAT, 2011].

As explained in Box 3.1, the 2800-kcal threshold takes inequitable distribution into account. Many

sources state that there is enough food to feed the global population, but the data given in Table 19 do

not corroborate this. When allowing for unfair distribution – setting a required average of 2800 kcal per

capita per day – the global supply is not enough to meet global demand in kcal. As can be seen, the

global average comes to 2519 kcal per capita per day.

M.Sc. Thesis

I.Y.R. Odegard

59

When taking a closer look at the regional supply averages, it becomes clear that the differences are vast.

In the OECD90 region, supply exceeds demand in all categories except for ‘roots and tubers’, and totals

at 3709 kcal per capita per day. The REF region comes in second with an, also high, value of 3183 kcal per

capita per day. The ASIA region and the ALM region both do not produce adequate amounts of food the

ASIA region has an average of 2291 kcal per capita per day, while the ALM region had 2297 kcal per

capita per day. Because import and export are not included, actual availability is in reality a little more

favorable for the developing regions. The ASIA region ‘imports’ 39 kcal/cap/day and the ALM region 118

kcal/cap/day. This still does not raise supply enough to meet actual demand. Furthermore, the fact

remains, however, that on a global scale the threshold for sufficient nourishment is not met.

3.6 Losses and Wastes

Losses and wastes occur during all stages of the food life cycle. National food supplies are calculated by

the FAO by subtracting the export quantity and adding the import quantity and the stock changes to the

production quantity. This yields the domestic supply quantity. From this other uses, i.e. feed, seed, other

utilities, food that is processed and cannot be converted to primary equivalents, and waste (i.e. wastes

that occur during transport, processing and storage) are subtracted to yield the food supply. This is

shown in Figure Pre-harvest and harvest losses are not included, but are not included in production data

either, and neither are household and retail losses.

Production Quantity

Import Quantity

Export Quantity

Stock Variation

Domestic Supply

Quantity Feed Seed Processed Food

Other Utilities+ + - = - - - - =Waste-

Figure 19: Commodity balance calculation method flowscheme [based on FAOSTAT, 2011].

Here, the term losses is used in the broader sense, including all losses, both unpreventable and

preventable, between harvest and retail. Food used for the following purposes (categories as defined by

the FAO), are thus considered losses: feed, seed, waste (during transport, storage and processing), other

utilities and processing. The term waste is only used for those losses that are preventable, i.e. household

and retail waste. Household and retail waster includes both edible losses, which can be considered waste,

and in-edible losses, such as peels, which are called refuse and are unpreventable. A note that should be

made is that the category ‘food’ still includes refuse, i.e. for unprocessed fresh vegetables and fruits, and

meat (bone-in weight) and thus the edible supply is lower still. The ‘food’ fraction of production in Table

20 therefore underestimates the amount of edible food for certain commodity groups.

Loss of foodstuff towards processed food is most significant for oil crops and sugar crops, and in these

cases the production of the secondary foodstuffs (i.e. vegetable oils and sugar and sweeteners) are

included in the present study. Processing of other commodity groups ranges between 0.1% (vegetables)

and 10% (fruits) and are considered losses because of lack of information. Wine is, according to the FAO,

not included in the ‘fruit-data’, although it is the only commodity mentioned under ‘crops processed’

with a fruit-origin. Fruit that is processed is processed into juice, and the pulp is removed and used as

animal feed. Some sort of allocation would be necessary to account for the use of fruit for this purpose,

M.Sc. Thesis

I.Y.R. Odegard

60

which the FAO does not do. ‘Processed’ (as well as feed, seed, waste and other utilities) is subtracted

from the domestic supply, and thus is not included in what the FAO estimates is available as ‘food’. An

estimation of the fraction of production that is not available as either food or feed can be made by

subtracting the global data for seed, waste, processed food and other utilities from the global production

data and comparing these result to what the FAO estimates as ‘food’. Such a method can only be applied

on a global scale, thereby avoiding confusion because of trade.

Table 20: Uses other than food, % of total global production in the year 2005 [based on FAOSTAT, 2010].

Commodity Purposes of Food Production

(% of total global production in 2005)

Food Feed Seed Wastea Processing Other Utilities

Cereals 46 36.7 3.2 4 6.6 3.5 Fruits 80.8 0.9 0 8.9 9.1 0.3 Oil Crops 9.7 5.8 2.3 2.7 77 2.5 Vegetable oils 56.5 0.7 0 1 4.4 37.4 Pulses 62.3 24.4 6.1 4.2 1.9 1.1 Roots and tubers 58.1 22.3 4.8 8.2 1.1 5.5 Sugar crops 1.7 1.4 1.3 0.9 93.9 0.8 Sugar and sweeteners 84.6 0.3 0 0.1 4.8 10.2 Vegetables 86.6 4.5 0 8.4 0.5 0 Eggs 88.5 0.1 6 4.5 0 0.9 Meat 99.1 0 0 0.4 0.1 0.4 Milk 83.4 11.6 0 2.3 0.2 2.5 a Waste during storage, transport and processing.

Appendix 3 shows the transport, processing and storage wastes as recorded by the FAO for the different

commodity groups in the four regions, for the year 2007. Notable are the high wastes in the ALM region;

this region has higher than average wastes in all but one category (eggs) and the highest wastes in 6 out

of 10 categories. This is explained by the fact that wastes are often estimated as a fixed percentage of

the available supply, dependent on the region and that distribution wastes are higher is countries with

more humid and hotter climates. It is important to realized that these wastes are quantified based on

climate rather than economic development. While this latter factor may play a role, it is uncertain

whether it would cut wastes due to higher efficiency, or raise wastes due to higher quality standards and

higher quantities being processed.

Edible Food Waste

It is difficult (and outside the scope of this thesis) to assess whether the waste during storage, transport

and processing as reported by the FAO are preventable. Therefore it is assumed here that they are

unpreventable. Losses that occur in the household stage, however, can be significant and are for a large

part preventable. When considering losses on the household level, one can distinguish between refuse –

unpreventable losses – and wastes – preventable losses. Losses of processed products can be considered

a 100% waste, while unprocessed products are still partially in-edible. The portion of the primary

commodity groups that is processed varies significantly with wealth level, and thus varies between

regions. According to the FAO, most of the calories we consume come from processed food. On a global

level 75-80% of the calories consumed come from processed products, thus only 20-25% are consumed

M.Sc. Thesis

I.Y.R. Odegard

61

in their primary product form. The difference between developing and developed countries in their

processing rates are large. In the developed countries 55% of the production is processed, while this

figure is 22% in the developing world.

Economic development is a major factor in the creation of food waste, especially in households.

According to Smil, for the years 1992-1994 the difference between the FAO’s FBS and consumption

measured by intake surveys in the USA was between 40 and 45 percent. This figure was a little lower for

Japan in the mid-1990s: 30 percent. Compared to countries in transition and developing countries this

gap is big; around 2000 it was under 15 percent in China, under 10 percent in India and seemed to be

non-existent in African countries. According to Smil around 10-15% of food losses may be unpreventable,

but the losses in developed countries are inexcusable [Smil, 2001, p. 210]. From this quote it becomes

clear that Smil includes both refuse and wastes in his estimation of household losses. The figures are,

however, corroborated by the data in Table 19. Average available supplies in the OECD90 and in the REF

region are well above the 2900-kcal threshold, respectively 28% and 10%. At the same time, supplies in

the ASIA region and the ALM region are well below the threshold; both 21%. Table 21 shows an estimate

of the waste that occurs at the retail level and at the ‘foodservice and consumer’ level and is defined as

losses from the edible food supply. Refuse losses are thus already subtracted. The data applies to the US,

for the year 1995 [Kantor, 1997]. Similar data were used by Cuéllar in 2010, based on data from 1995

[Cuéllar, 2010]. In that year GDP in the USA, in PPP, was a little over 27,000 US$. In comparison, PPP in

Japan in that year was around 22,800, but in China and India it was only respectively 3,300 US$ and

1,460 US$.

Table 21: Household wastes as percentage and fraction of (edible) food supply [Kantor, 1997].

‘Losses from edible food supply’

Data for the USA in 1995 (Kantor, 1997)

(% of edible food supply)

Group as defined by Kantor Retail food loss Foodservice and consumer food loss

(%) (%)

Cereals ‘Grain products’ 2 30

Fruits ‘Fruit’ 2 23

Vegetables ‘Vegetables’ 2 24

Pulses ‘Dry beans, peas and lentils’ 1 15

Vegetable oils ‘Fats and oils’ 1 32

Roots and tubers - - -

Sugar and sweeteners ‘Caloric sweeteners’ 1 30

Milk ‘Dairy products’ 2 30

Eggs ‘Eggs’ 2 29

Meats ‘Meat, poultry, and fish’ 1 15

No specific data on the inedible fraction of food supply is available. The data in Table 21 can be used to

estimate the difference between apparent consumption (supply, which still includes household and retail

waste and refuse) and intake. It needs to be kept in mind, however, that this will yield an overestimation

of availability for some commodity groups, specifically fruits, vegetables and meat.

M.Sc. Thesis

I.Y.R. Odegard

62

3.7 Driving Forces

Factors that are important to determine the future potential appropriation of land, water and fertilizers,

by agriculture can now be identified. The previous sections have shown that there are substantial

differences in the kind and the quantity of the commodity groups that are consumed and produced in

the four regions. It was shown that there is a correlation between economic development and the type

of food consumed; increased welfare increases the consumption of animal protein in people’s diet. The

amount of food wasted also increases with increasing welfare. It can also be assumed that such waste

depends on policy and that losses can be reduced by policy measures. Furthermore, and obviously, there

is a link between population size and the total amount of food consumed. Section 3.5 showed that

supply is sufficient in the OECD90 and the REF region to fulfill demand, while in the ASIA and ALM

regions supply does not yet equal demand.

In Section 3.4 the differences in productivity in the four regions were shown. Yields (productivity per ha)

vary substantially and were shown to be related to the level of technological development. The use of

irrigation and fertilizer are part of the agro-technology development in a country, and their use is

correlated to higher yields. Furthermore, policy in a country was shown to have a significant influence on

the way natural resources are used. The driving forces identified here – population, economic

development, policy, technological development and diet – will be discussed in Chapter 4.

M.Sc. Thesis

I.Y.R. Odegard

63

4. Driving Forces and Trends

Chapter 2 describes the scenario methodology used in this research and elaborates on the importance of

driving forces. These driving forces – those that are important when studying potential food futures –

will be elaborated on in this chapter. As was pointed out in Chapter 2, the IPCC (Intergovernmental Panel

on Climate Change) design regarding differences and similarities in possible futures is taken as a

guideline for building food scenarios in this study.

Five driving forces were identified in Chapter 3, and were selected to assess the potential impact on land

use, water use and energy use in the food system. These include some basic driving forces defined by the

IPCC, but do not include the driving forces used by the IPCC SRES (Special Report on Emission Scenarios)

that relate to energy and resource availability. Furthermore, the IPCC driving force ‘land-use changes’ is

not considered, as this is what will be modeled in this study. As the IPCC does not consider feedback

driving forces – e.g. land quality degradation as a result of climate change – such forces will not be

considered here. The driving force ‘diet change’ was added, as is it an important driver for changes in the

environmental impact of the food system.

The driving forces considered in this study are:

Population

Economic and Social Development

Policy

Technological Change

Diet Change

The importance of these five forces will be clarified in the following sections. A qualitative description of

the matter will be given, as well as possible trends. Trends are quantitative descriptions of the topic

resulting from different assumptions as to what the future will look like. For example, population growth

will be very different in a low-fertility world than in a high-fertility world. These basic assumptions will

link the different trends of the driving forces in significantly different ways. This process will lead to four

different ‘internally consistent views of what the future might turn out to be’ [Ringland, 1998]. The IPCC

SRES scenarios will be used as a guideline and basis for these four different ‘worlds’ throughout this

section. The driving forces’ trends and their importance in the context of agriculture and our regional

and global food system are explored in detail in the following sections. As the IPCC has already

determined which trends fit which scenarios for certain driving forces, for example for population, the

SRES scenario name (e.g. A1) will be given for those trends in the following sections. For driving forces

which are more open to interpretation, e.g. diet change, trends are projected consistent with other

driving forces that are already linked to a certain scenario. The linkages between driving forces and

trends in driving forces will be described in Chapter 5, where all trends will be coupled to a specific

scenario and the scenario storylines will be given. This will create the basis the quantification of trends

given in Chapter 6.

M.Sc. Thesis

I.Y.R. Odegard

64

4.1 Population

Population is one of the key driving forces used by the IPCC. Quantification of the emission scenarios

depends significantly on the population change in the studied period. This means the variety in

outcomes of the scenarios is very much correlated to the large variety in population sizes for the

different scenarios. The same is true for food scenarios; a large population will need much more food

than a small population.

Not only the magnitude of the global population is important to evaluate the extent of our

environmental impact, the locations where the population is increasing or decreasing will also influence

land use, water use and fossil fuel use. Population growth will have a different impact on the food

system in different regions. When regional self-sufficiency is an issue, it is important to know the

population size in the region, and the amount of food which will be demanded. Zero population growth

does not necessarily translate into zero growth in demand for food. In regions with inadequate food

consumption levels, demand will continue to rise until the demand is met. Increasing consumption levels

and population growth is linked to economic and social development, which will be elaborated on in

Section 4.5.

In this section population projections for the world and the 4 regions are presented. These were

compiled using 2008 data from the UN (United Nations) and the IIASA (International Institute for Applied

Systems Analysis). In Section 5.1 the relationship between the population growth trends and the four

alternative scenario paradigms will be elaborated on.

Population Trends

To fully explore the complete spectrum of potential futures, the four IPCC scenarios use three different

population projections; the A1 and the B1 scenarios use the same projection. The projections and the

characteristics of these three projections that will be used for the definition of the scenarios are shown

in Table 22. Because the IPCC Emissions Scenarios are nine years old, adjustments have been made to

these earlier predictions.

As is shown in Table 22 the population projections were updated from the ones used by the IPCC. The

sources remain the same; IIASA data was used for the low and high projections, UN data was used for

the medium projection. The IIASA low and high projections represent the outer limits to an 80 percent

uncertainty range. This means there is a 10% chance the population will be higher and a 10% chance it

will be lower in 2050, than the projections that are given.

M.Sc. Thesis

I.Y.R. Odegard

65

Table 22: Population projections [IPCC, 2000; Lutz, 2008; UN, 2009]

Growth Characteristics Projection in SRES Projection Update Scenario

Low Lowest population trajectory. Low

fertility, low mortality and central

migration rates.

IIASA “low” (1996) IIASA “low” (2008) A1 and B1

Medium Medium population trajectory.

Approaching stabilization of

population in Asia between 2050-

2100 and in the rest of the

developing world towards 2100.

UN Long Range

medium projection

(1998)

UN Long Range

medium projection

(2009)

B2

High Highest population trajectory.

Declining fertility in most regions

and stabilization above

replacement levels.

IIASA “high” (1996) IIASA “high” (2008) A2

The following figures show the low, medium and high population projections for the world and the four

regions. A list of the countries in each region is provided in Appendix 1. Comparing initial and final

population sizes, the global population shows an increase in population size for all three projections over

the given time period, which is shown in Figure 20. The IIASA-low projection is the only projection which

shows a decline before 2050; the population size in 2045 is slightly higher than in 2050. The final

populations for respectively the IIASA-low, the UN-medium and the IIASA-high projections are 7.78

billion, 9.15 billion and 9.9 billion.

Figure 20: World population projections until 2050 [based on UN, 2009; Lutz, 2008], lines appear in same order as in the legend.

Figures 2 to 5 show population projections until 2050 for the four regions. The final populations for the

world and the four separate regions for the three different projections are given in Table 23. As is shown,

the final global population will most likely be somewhere between 7.78 billion and 9.9 billion, with the

M.Sc. Thesis

I.Y.R. Odegard

66

UN medium projection being 9.15 billion. In all projections the ASIA region is close to half of the total

global population.

Table 23: Final populations 2050 (in billions) [based on Lutz, 2008; UN, 2009]

Region IIASA-low UN-Medium IIASA-high

OECD90 0.88 1.06 1.15

REF 0.31 0.37 0.41

ALM 2.39 3.09 3.57

ASIA 3.78 4.60 4.91

World 7.78 9.15 9.9

Figure 21 below shows the three population projections for the OECD90 region. In the IIASA low

projection, the population starts to decline starting in the year 2025, while growth stagnates in the UN

medium projection. In the IIASA high projection the population continues to increase, mostly due to

growth in North America; 76% of the population growth between 2010 and 2050 happens there.

Figure 21: OECD90 region population projection until 2050 [based on UN, 2009; Lutz, 2008], lines appear in same order as in the legend.

In Figure 22 below, the population projections are given for the countries in the REF region. From 2025

on all three projections show a decline, with the decline starting in respectively 1995 and 2010 for the

UN medium and IIASA low projections. The dip shown around 1995 could be due to the fact that for data

until 2005 the UN medium projection was used.

M.Sc. Thesis

I.Y.R. Odegard

67

Figure 22: REF region population projections until 2050 [based on UN, 2009; Lutz, 2008], lines appear in same order as in the legend.

As stated above, around half of the global population in 2050 can be found in the ASIA region. According

to the IIASA low projection population starts to decline in 2035 in this region, while the population

continues to increase over the given period in the other two projections, even though growth rates

decline, as can be seen in Figure 23.

Figure 23: ASIA region population projections until 2050 [based on UN, 2009; Lutz, 2008], lines appear in same order as in the legend.

Figure 24 below shows the population projections for the ALM region: Africa, Latin America and the

Middle East. As can be seen, this region has the highest growth rates, and none of the projections show

any significant slowing down of these growth rates. Between 2005 and 2050 the population of the ALM

region increases by 40% (for the IIASA low projection) to 112% (for the IIASA high projection).

M.Sc. Thesis

I.Y.R. Odegard

68

Figure 24: ALM region population projections until 2050 [based on UN, 2009; Lutz, 2008], lines appear in same order as in the legend.

The IIASA projections, unlike the UN projection, do not project on a per country basis, but have defined

13 regions for which population projections are given: Japan/Oceania, Western Europe, North America

(together forming the OECD90 region), Central Asia, Eastern Europe, European Soviet Union (together

forming the REF region), South Asia, China Region, Pacific Asia (together forming the ASIA region), and

North Africa, Sub-Saharan Africa and the Middle East (together forming the ALM region). Projections per

country were estimated by calculating the fractions of the populations in the year 2005 of the 13 regions

named above, and applying these same fractions to the population projections for those regions for the

year 2050.

4.2 Economic Development

Economic development is an important driving force when studying any environmental impact on a

global scale and it is therefore one of the key driving forces in the IPCC SRES. Such development is linked

to increases in resource use and in consumption and is therefore essential when studying the future

impact of our food system. The environmental effects of economic development depend on the level of

development of the country. With increasing GDP (Gross Domestic Product), the poor will increase their

spending on food first. Furthermore, as will be further elaborated in Section 4.5, with increasing wealth,

diets tend to change toward increased meat consumption which is directly linked to an increase in

foodcrop production for feed and pasture requirements.

The IPCC uses GDP as a measure of economic development in the SRES scenarios. The IPCC has received

some criticism for using market exchange rate (MER) as opposed to purchasing power parity (PPP),

because the latter more accurately reflects the increase in purchasing power. In this study PPP will be

used because it gives a more accurate view of the ability people have of spending money on certain

items. As is described in numerous articles, people change their diet both quantitatively and qualitatively

M.Sc. Thesis

I.Y.R. Odegard

69

with increasing wealth; ‘As their purchasing power grows, countries in relatively early stages of

modernization will see appreciable increases in average per capita intakes of both food energy and

protein’ [Smil, 2001, p. 207]. In the following section PPP projections for the four regions up to 2050 for

four different scenarios of economic development will be presented.

Economic Development Trends

In the SRES scenarios 4 different levels of economic development are explored. These are shown in Table

24 together with the data and the source which was used for respectively the SRES scenarios and this

study. The data used by the IPCC in the SRES scenarios was developed specifically for the SRES by the

IPCC, because long-term economic development projections were not available. For this study, PPP data

was obtained from the PBL (Planbureau voor de Leefomgeving or Netherlands Environmental

Assessment Agency), which defined PPP for regions compatible with this study. The PBL based the PPP

data on the World Development Indicators (WDI) from the World Bank.

Table 24: Economic Development Projections [van Vliet, 2010; IPCC, 2000; PBL, 2009]

Growth Characteristics Projection in SRES Projection Update

Low Business-as-usual economic growth in

industrialized countries, slow growth in other

countries. Large gaps in economic prosperity

and between regions.

IPCC SRES “A2” (2001) PBL “A2” (2010)

Low-Medium Medium economic growth and slow

convergence between countries.

IPCC SRES “B2” (2001) PBL “B2” (2010)

Medium-High High economic growth with convergence

between industrialized and less-industrialized

countries.

IPCC SRES “B1” (2001) PBL “B1” (2010)

High Highest economic growth. Development

converges and income gaps between regions

decrease.

IPCC SRES “A1” (2001) PBL “A1” (2010)

As can be seen in Table 24, the PBL names their PPP projections for each of the four SRES scenarios: A1,

A2, B1 and B2. As such, these data will be used for quantification of each scenario in Chapter 6. The

figures below show the PPP projections for the incomes per capita for the world and the four separate

regions, given in US$-1995 [Van Vliet, 2010]. The data is summarized in Table 25 below.

M.Sc. Thesis

I.Y.R. Odegard

70

Table 25: GDP Projections (in PPP) per capita for the year 2050 (1000 US$-1995) [van Vliet, 2010].

Region GDP in PPP (1000 US$-1995)

Low Low-Medium Medium-High High

2000 2050 2050 2050 2050

OECD90 26.5 41.5 50.4 54.5 64.9

REF 4.3 11.5 19.1 22.3 32.2

ALM 4.4 10.6 11.7 19.1 21.8

ASIA 3.1 7.1 15.4 16.6 23.9

World 6.8 11.9 17.7 22.2 28.4

As can be seen in Table 25 and Figure 25 the GDP projections show a marked increase over the coming

40 years. The A1 projection shows the highest growth, following by the B1, B2 and A2 projections,

respectively. Differences between regions are, even in the medium-high and high projections, however,

still significant. This is shown in Figure 26.

Figure 25: World PPP Projections until 2050 [based on Van Vliet, 2010], lines appear in same order as in the legend.

As can be seen in Figure 26, the difference between the projections for the OECD90 region and the other

three regions is still substantial in 2050. Even so, the income gap between the OECD90 region and the

other regions declines. In the year 2000, PPP in the OECD90 region was respectively 6.2, 8.5 and 6.0 as

high as in, respectively, the REF region, the ASIA region and the ALM region. Depending on the projection,

these numbers have declined to respectively 2.0-3.0, 3.0-5.8 and 2.7-3.9. Thus, while the income gap is

still significant, progress has been made towards closing this gap.

M.Sc. Thesis

I.Y.R. Odegard

71

Figure 26: Regional GDP projections (PPP) [based on Van Vliet, 2010], lines appear in same order as in the legend.

The PPP projections given above will serve as a measure for economic and social development. Such

development is linked to the food system in various ways. For example, countries with a higher GDP

generally have higher levels of technological development, making it possible to produce agricultural

products using less labor. In this study, the PPP projections will provide the basis for the determination

of changes in diet and of technological change.

4.3 Policy

‘Government policies are among the dynamics that influence population growth, economic and social

development, technological change, resource exploitation, and pollution management’ [IPCC, 2000,

p.155]. Policy should be seen as a broad concept; here it will help explore how the perceived importance

of various issues will impact the use of natural resources, and thereby it will provide the backbone to the

scenarios. The ‘scenario world’ is a simplified version – a model – of the real world, and so it is assumed

that policy will be strictly enforced. This is in line with IPCC reasoning, stating that policies cannot be

quantified, and that the scenario storylines ‘give a broad characterization of the areas of policy emphasis

thought to be associated with particular economic, technological and environmental outcomes, as

reflected in alternative scenario assumptions in the models’ [IPCC, 2000, Section 3.7.3].

M.Sc. Thesis

I.Y.R. Odegard

72

Policy Trends

The IPCC mentions two kinds of policies that are important in this context: agriculture policies and

environmental policies [IPCC, 2000]. Agriculture policies are focused on government support, which

steers agriculture into a certain direction through the use of subsidies, tariffs or price controls.

Environmental policy influences consumer choices, e.g. by taxing meat, and poses restraints on the use

of natural resources, e.g. prohibiting the expansion of agricultural land in certain areas.

Table 26 below gives a description of the policy characteristics related to the four different aspects that

are the main characteristics of the IPCC scenarios; a focus on either economic or environmental aspects

combined with a focus on either globalization or regionalization. There are four specific sub-topics whose

interpretation is determined or influenced by policy: trade, use of resources, diets and losses. Trade is be

restricted in the regional scenarios; regions will have to be self-sufficient. In the global scenarios there

are no trade barriers. Expansion of the use of natural resources is restricted in the ‘environment’

scenarios, whereas the ‘economy’ place no restrictions on the use of resources. Furthermore, efficiency

of the use of resources – water and fertilizer - is a specific policy objective in the ‘environment’ scenarios.

Issues related to diet will be discussed elaborately in Section 4.5. Policy influences diets by either

maintaining the status quo (a Western diet in the A scenarios) or directing people and industry towards

more sustainable choices (a vegetarian or organic diet in the B scenarios). It seems as though the

‘Western diet trend’ is the way we are headed, however, in a scenario study it is important to explore a

range of options. Table 26: Policy Characteristics [based on IPCC, 2000; PBL, 2009; own interpretation]

Policy Characteristics Consequences Scenario

Paradigm

Globalization

A policy focus on globalization will

promote free interaction between

industrialized countries and less-

industrialized countries.

Policy will allow free trade of food

between regions; no trade barriers are in

place and food will be distributed

equitably.

1

Regionalization A policy focus on regionalization will

limit interaction between regions by

e.g. trade barriers.

Trade between regions will be difficult and

regions will try to be self-sufficient. Food

will have to be produced and distributed

within regions.

2

Economy A focus on economy will mainly not

be a focus on environment.

Governments will not try to influence

people’s food choices or subsidize

sustainable practices significantly.

Combined with globalization, a focus

on economic development will

stimulate technological

development.

Policy will not pose restraints on the use

of natural resources to produce food.

People’s diet will become more and more

‘Western’.

There is no (governmental) pressure to

reduce household and retail wastes.

A

M.Sc. Thesis

I.Y.R. Odegard

73

Environment Measures are taken to ensure

environmental sustainability and

resource efficiency.

People are directed toward

sustainable food choices and policy

will favor technological innovation

focused on sustainable practices.

Policy will control resource use; land

expansion for agriculture will be

restricted, especially in high-biodiversity

areas like rainforests.

Irrigation and fertilization will be used

sustainably.

People’s diet will shift toward either

vegetarian or organic.

There is a focus on resource efficiency and

reduction household and retail waste.

B

4.4 Technological Change

In the IPCC scenarios the driving force ‘technological change’ focuses on changes in energy supply. As

this is not relevant in this context, the basic dynamics – e.g. rate of change and direction – will be taken

as guidelines to describe technological change in agriculture. The PBL (The “Netherlands Environmental

Assessment Agency”) gives an indication of the direction of change related to the food system. These

sources, together with a personal interpretation, provide the basis for what ‘technological change’ will

look like in different scenarios.

Technological change or development in agriculture is seen here as all measures that are related to crop

management. The main parameter influenced by technological change is yield. In Chapter 3 it was shown

that yields have increased substantially during the green revolution, and that they are still increasing in

all regions for all commodity groups. Three aspects of technological development important to yield

projections with clear linkages to natural resource use were identified in Chapter 3: irrigation, use of

synthetic fertilizer and different agricultural management practice. This section will elaborate on the

linkages between these three factors and yield increase projections and will lay the foundation for

quantification of yields and yield increases for 2050, for the four scenarios in the four different regions.

There is more to technological development than irrigation and fertilization. Research in biotechnology

and in plant and pest ecology should be integrated with plant and animal production to optimize soil,

water and nutrient use efficiencies [Bruinsma, 2003]. Information and communication technologies

could also play a major role here; such technologies can be used to e.g. optimize irrigation and

fertilization [Smil, 2001]. Another matter of importance is the geographical range in research. As many

people continue to consume locally produced food and depend on rainfed agriculture, it is important for

research to have a broad focus because demand continues to grow in many regions. Technological

development will only occur given certain investments, policy implementation and knowledge transfer.

According to the FAO, ‘The need for further increases in production in the future while conserving the

resource base of agriculture and minimizing adverse effects on the wider environment, calls for ever

greater contributions from agricultural research’ [Bruinsma, 2003, p.17]. This scenario-study will not go

into those issues, but will consider levels of technology development that are consistent with the given

M.Sc. Thesis

I.Y.R. Odegard

74

scenarios. These issues will not be elaborated on, but what is taken into account in yield projections is

whether it is plausible that such development takes place.

Ewert et al. give two factors which result in an increase in yields that are driven by technological

development; increasing the potential yield (or the ‘maximum attainable yield’) and closing of the ‘yield

gap’ *Ewert, 2005+. The yield gap is the difference between the potential yield and the actual yield. The

potential yield depends on the agro-ecological zone (which cannot be changed for a certain location and

is related to e.g. solar radiation and temperature) and is defined as the yield in that environment where

nutrients and water are not limiting factors, and pests and diseases are controlled [Cassman, 2003].

External factors, however, limit the potential yield to the “attainable yield”, for which the available water

resources are taken into account. The actual yield is limited further by other factors related to

technological development, policy and the socioeconomic environment (e.g. external inputs like fertilizer

or pest control). In practice it turns out that yields stagnate at the 80% of the potential yield threshold

because of a lack of economic viability [Cassman, 2003].

Irrigation and Fertilization

As was elaborated on in Chapter 3, both the use of irrigation and the use of fertilization increase crop

yields. Their simultaneous use even increases the yield more than the sum of the individual benefits. The

question here is what projections related to fertilization and irrigation, and their respective efficiencies,

consistent with the technology development trends above, would look like. In Table 27 below the rate of

change and associated characteristics of technological development in the four scenarios are described.

As was shown in Chapter 3, between 10% (OECD90 region) and 34% (ASIA region) of the arable land and

permanent crops was irrigated in the year 2007. Whether irrigation is profitable depends on annual

rainfall, the agro-ecological zone and the crop. In general, yields are higher on irrigated lands than they

are with rainfed agriculture. Current wheat yields (global average) are 42% higher on irrigated lands, and

rice yields show an even more pronounced difference; yields are 112.5% higher on irrigated lands [De

Fraiture, 2007]. The current yield gap is roughly the same for irrigated agriculture as for rainfed

agriculture because not only the current yields are higher for irrigated agriculture, the maximum

potential is also higher *De Fraiture, 2007+. Irrigation competes with other sectors for the use of “blue

water” (extracted from groundwater and surface water sources), especially in areas where water scarcity

is an issue. Irrigation efficiency (or water requirement ratio) is defined as the ratio between the irrigation

water requirements and the amount of water withdrawn for irrigation [WWAP, 2009]. Implementation

of innovative irrigation measures can improve this ratio, thereby decreasing the “blue water” water that

is extracted.

As was also shown in Chapter 3, fertilizer use differs per region and per crop. Higher inputs are

correlated to higher yields, although this relation does not continue indefinitely; yields approach a

certain value after which additional application does not increase the yield any further. According to the

International Institute for Applied Systems Analysis (IIASA), ‘On average, long-term achievable yields are

10%, 20%, and 55% lower than maximum attainable yields, at high, intermediate, and low levels of inputs,

respectively’ [Fisher, 2002]. Such values are corroborated by Ewert et al. Their projections for the year

M.Sc. Thesis

I.Y.R. Odegard

75

2050 in the EU are that in the A scenarios (high level of inputs) the yield is roughly 30% lower than the

maximum projected yield in 2080, while the yields in the B1 scenario (intermediate level of inputs) are

projected to be 38% lower. In the B2 scenario (low level of inputs) the yields are projected to be 52%

lower [Ewert, 2005]. Fertilizer application rates seem to be higher than absolutely necessary. Like water

use for irrigation, fertilizer use can be decreased without loss of with minor loss in productivity with

implementation of better application methods and timing, and different – less volatile – sources.

According to Smil, application rates can be dropped significantly with minor loss in productivity [Smil,

2001].

Productivity Trends

In Table 27 the rate of technological change and the characteristics related to this change are

summarized. These characteristics were based on ‘scenarios of European agricultural land use’

developed by Ewert et al [Ewert, 2005], the IPCC SRES, interpretation of the IPCC scenarios by the PBL,

the personal interpretation of the information given in these, and other, sources.

Table 27: Technological development trends [IPCC, 2000; Ewert, 2005; Ewert, 2006; PBL, 2009; personal interpretation]

Rate of

Change

Characteristics Consequences Scenario

Slow Technological change is slow and

more fragmented than in other

scenarios. There is no global

dispersion of technological

innovation and the focus is on local

solutions.

Potential yield does not increase much and the yield

gap is not closed. Irrigation is implemented in all

suitable areas. Fertilization and irrigation efficiencies

do not change.

A2

Slow-

Medium

Technological change is less rapid

and focused on a diverse set of

solutions, on a local scale, with an

emphasis on the quality of life.

Potential yield is increased a little, the yield gap is

not closed. Use of synthetic fertilizer is minimized,

which reduces yields. Irrigated area may increase

regionally to feed the regional population and

irrigation and fertilizer efficiencies are raised.

B2

Medium Technological change is directed by

societal concerns. There is a focus

on the global introduction of clean

and resource-efficient

technologies.

Potential yield is increased, and yield gap is partially

closed. Irrigation efficiency is raised, and fertilizer

efficiency is optimized.

B1

Rapid Technological change is focused on

a rapid and global introduction of

new and innovative technologies.

Increasing the food supply is done by focusing on

potential yield increases and efforts are made

toward closing the yield gap.

Irrigation is implemented in all suitable areas.

Fertilization and irrigation efficiencies are not

considered important from and environmental

perspective, but improve due to implementation of

new and innovative technologies.

A1

M.Sc. Thesis

I.Y.R. Odegard

76

Yield Projections

The most important conclusion which needs to be drawn from the assessment in Table 27 are

projections related to yields. Various studies have been made related to cereal yields, as cereal crops are

the main staple in the global diet. Furthermore, as meat consumption is projected to increase, cereal

crops will further gain in importance because of their contribution as feedstuff. Figure 27 shows the

historic global average cereal yield between 1960 and 2005, extrapolated with 6 different yield

projections. The yield up to 2005 includes both rainfed and irrigated yield, which is why it is possible for

one of the projections (for rainfed agriculture) for the year 2050 to dip below the yield in 2005.

Figure 27: Cereal yield projections, ‘I’ indicates irrigated yield, while ‘R’ indicates rainfed yield [based on De Fraiture, 2010], lines appear in the same order as in the legend.

4.5 Diet Change

It was shown in Chapter 3 that different foodstuffs have different land, water and fertilizer requirements.

Economic growth will ensure an adequate food supply for a growing number of people, but it will also

change the composition of people’s diets. Such changes in consumption patterns will act as a driving

force of potential changes in our food system over the coming years. For example, Vinnari points out

that ‘meat consumption has been identified as problematic from at least three perspectives: an

environmental perspective, an animal perspective and a human perspective’ [Vinnari, 2009, p.269]. As

was described in Section 4.3, policy can encourage or discourage certain trends. This makes diet change

a different type of driving force in this study than for example population growth; it is a derivative of

economic development and policy. Trends that relate to changes in diet composition are examined in

this section, and will be linked directly to trends in economic development and policy.

M.Sc. Thesis

I.Y.R. Odegard

77

Traditional diets evolved over hundreds of years, yielding nutritionally adequate diets of complementary

and regionally available foodstuffs [Pollan, 2008; Gerbens-Leenes, 2005]. Fueled by the rapid

globalization, modern transportation and conservation methods of the past century, a much wider

variety of foodstuffs and beverages has become available to many people in large parts of the world.

Furthermore, welfare has been rising steadily, which not only influences the quantity of food people eat,

but also changes the composition of people’s diet qualitatively. According to Lotze-Campen, economic

factors, mainly food prices and income, are the main determinants of people’s diet [Lotze-Campen, 2006,

p.112]. Gerbens-Leenes adds several important factors: ‘personal preference, habit, convenience, social

relations, ethnic heritage, religion, tradition, culture and nutritional requirements’ [Gerbens-Leenes,

2005]. The relationship between different food choices and the environmental impact related to those

choices has steadily gained attention. Specifically the increase in meat consumption, the associated

increase in cereal production for feed and the relation of meat consumption to welfare levels have been

studied [Vinnari, 2009; Gerbens-Leenes, 2004; Keyzer, 2005; Grigg, 1995; Smil, 2001].

Figure 28: Population and global food production indices, 1966-1998 [Rosegrant, 2001b]

Diet Trends

As mentioned above, per capita income is the main determining factor in food consumption and dietary

transition. Several researchers have described the diet transitions and the changes in commodity

distribution [Smil, 2001; Keyzer, 2005; Alexandratos, 2006], which are partially due to economic

development. Not only meat intake increases with increasing wealth; the complete composition of

people’s diet changes. This is shown in Table 28 below, which presents the diet projections by the FAO

for the industrial countries and the developing countries for the year 2050.

M.Sc. Thesis

I.Y.R. Odegard

78

Table 28: Changes in the commodity composition of food by major country groups [Alexandratos, 2006, p.25].

Kg/cap/year 1969/71 1979/81 1989/91 1999/01 2030 2050

Developing Countries

Cereals, food 146.3 161.7 173.7 165.7 166 163

Cereals, all uses 191.8 219.1 238.6 238.0 268 279

Roots and tubers 78.8 69.6 60.1 67.0 75 77

Sugar and sweeteners 14.7 17.5 19.2 20.7 25 26

Pulses, dry 9.2 7.8 7.3 6.7 7 7

Vegetable oils, oilseeds and

products

4.9 6.5 8.6 10.4 14 16

Meat (carcass weight) 10.7 13.7 18.2 26.7 38 44

Milk and dairy (excl. butter) 28.6 34 38.1 45.2 67 78

Other food (kcal/cap/day) 123 140 171 242 285 300

Total food (kcal/cap/day) 2111 2308 2520 2654 2960 3070

Industrial Countries

Cereals, food 132.3 139.4 154.4 162.4 159 156

Cereals, all uses 531.1 542.0 543.7 591.8 641 665

Roots and tubers 74.2 67.1 69.4 66.7 61 57

Sugar and sweeteners 40.5 36.7 32.6 33.1 32 32

Pulses, dry 3.4 2.8 3.2 3.6 4 4

Vegetable oils, oilseeds and

products

13.2 15.7 18.5 21.5 24 24

Meat (carcass weight) 69.7 78.5 84.3 90.2 99 103

Milk and dairy (excl. butter) 189.1 201.0 211.2 214.0 223 227

Other food (kcal/cap/day) 486 500 521 525 565 580

Total food (kcal/cap/day) 3046 3133 3292 3446 3520 3540

Note: Cereals food consumption includes the grain equivalent of beer consumption and of corn sweeteners.

Both industrial countries and developing countries have seen remarkable increases in the consumption

of several commodity groups, but there are various interesting trends. Meat consumption in the

developing countries (the ASIA region and the ALM region) in the year 2000 was about two and a half

times as high as in 1970, while, in the same period, meat consumption rose with 30% in the industrial

countries (the OECD90 region). Even so, a person in the developing world only consumed approximately

30% of the amount of meat consumed per capita in the industrial world. Not only does the amount of

meat consumed increase with increasing purchasing power, the type of meat – beef, pork or poultry -

also changes. It is, however, beyond the scope of this research to incorporate this. Trends for milk

consumption are similar; consumption increased by 60% in the developing countries and by 13 % in the

industrial countries, while milk consumption is still 4.7 times as high in the industrial countries. Cereal

consumption provides other interesting information. While consumption of cereals as a food source was

2% higher in the developing world than in the industrial world in the year 2000, cereal consumption

including feed was 2.5 times as high in the industrial world. This is, of course, due to the much higher

intake of meat products in the industrial world.

M.Sc. Thesis

I.Y.R. Odegard

79

As can be seen in Table 28, consumption of sugar has been decreasing in the industrialized world, and

increasing in the developing world. The gap is, however, still significant; consumption is about 30% lower

per capita in the developing world. Consumption of vegetable oils has been increasing steadily in both

regions, but the consumption in the industrialized world is twice as high as in the developing world.

Pulses constitute a larger part of the diet of poorer people, as do roots and tubers. The consumption of

the latter is decreasing in the industrialized world, while the FAO projects their consumption will be

increasing in the time period until 2050. The consumption of pulses seems to be converging;

consumption in the industrialized world is increasing again (since 1980) and is lower than the

consumption in the developing world, where consumption seems to be decreasing. According to the FAO,

fruit consumption in Africa has risen 6% between 1970 and 2000; from 51.4 kg/cap/year to 54.6

kg/cap/year. During that same period, fruit consumption in Western Europe rose 8.5%, but was close to

double the consumption in Africa; 99.1 kg/cap/year in 1970 and 107.4 kg/cap/year in 2000. Vegetables

consumption shows a similar trend, even though consumption rose faster. Consumption rose 24%

between 1970 and 2000 in Africa, while it rose 12% in Western Europe. However, total consumption was

almost twice as high in Western Europe in 2000: 98.7 kg/cap/year as opposed to only 54.15 kg/cap/year

in Africa.

Most authors agree that there are no indications of a transition away from a per capita increase in meat

consumption. Scenario studies, however, become more interesting when a diverse set of futures is

presented. In the IPCC SRES there are two scenarios in which sustainability is incorporated, while two

take a business-as-usual approach to energy use. Here, likewise, two trends are based on the ‘Western

Diet’ diet transition – a trend based on the increase in meat consumption related to economic growth,

without taking environmental issues into account (scenarios A1 and A2). Two alternatives are proposed:

the ‘Vegetarian Diet’ (a diet based on vegetable products, dairy products and eggs, based on business-as-

usual caloric intake) and the ‘Low-input Diet’ (agriculture with low-inputs of fertilizers, and reduced meat

consumption). Meat consumption projections made by the PBL and a method developed by the Centre

for World Food Studies to calculate meat consumption based on PPP are taken as a guideline for the A

scenarios, while food consumption in the B scenarios is interpreted more freely. Table 29 elaborates on

the diet trends.

Table 29: Diet Trends [PBL, 2010; own interpretation].

SRES Projection

[PBL, 2010]

SRES

Scenario

Current Interpretation Trend

‘Fast increase in per

capita consumption of

livestock products as a

result of GDP increase’

A1 Business-as-usual increase in meat consumption, based

on PPP, calculation with ‘Keyzer equation’ *Keyzer,

2005]. Average apparent consumption needs to exceed

the FAO threshold of 2900 kcal per capita per day.

Western Diet

‘Slow increase in per

capita consumption of



A2 Business-as-usual increase in meat consumption, based

on PPP, calculated with ‘Keyzer equation’ *Keyzer,

2005]. Consumption of vegetable sources is based on

historic trends. Average apparent consumption needs to

exceed the FAO threshold of 2900 kcal per capita per

day.

Western Diet

M.Sc. Thesis

I.Y.R. Odegard

80

‘Per capita

consumption of

livestock products is

10% lower than in A1

scenario in 2050 and

20% lower than in A1

in 2100’

B1 Business-as-usual increase in caloric intake, based on

PPP. Diet based on vegetable sources, dairy and eggs.

Consumption of sugar and sweeteners is reduced and of

vegetables and fruits increased due to health concerns.

Average apparent consumption conforms to a lowered

FAO threshold of 2800 kcal per capita per day.

Vegetarian

Diet

‘Moderate increase in

per capita

consumption of



B2 Meat consumption conforms to half of that determined

by the ‘Keyzer equation’. Consumption of sugar and

sweeteners is reduced and of vegetables and fruits

increased due to health concerns. Average apparent

consumption conforms to a lowered FAO threshold of

2800 kcal per capita per day. Agriculture uses low-inputs

of fertilizers.

Low-input Diet

As presented in Table 29, meat consumption in the A scenarios follows a business-as-usual trend based

on purchasing power. A mathematical relationship between PPP and meat demand was established by

Keyzer et al, based on data for 125 countries over a 26 year period [Keyzer, 2005]. Figure 29 shows meat

consumption projections.

Figure 29: Meat consumption trends in different scenarios [based on Keyzer, 2005; Van Vliet, 2010; Alexandratos 2006].

For the A scenarios, these were based on the equation derived by Keyzer, combined to data on PPP. The

FAO projections for different subregions in the ASIA region and the ALM region were averaged relative to

population size, to fit the ASIA region and the ALM region. The projection for the B2 scenario is, like the

A scenarios, based on the Keyzer equation, but adjusted to fit the scenario paradigm; projected meat

consumption is cut in half.

M.Sc. Thesis

I.Y.R. Odegard

81

While in both A scenarios the ASIA region and the ALM region consume the least meat, consumption in

these regions goes up from respectively 27 kg and 32 kg per capita per year in the year 2005, to

respectively 53 kg and 57 kg per capita per year in the year 2050 for the A2 scenario and respectively 91

kg and 85 kg per capita per year in the year 2050 for the A1 scenario. The global average meat

consumption increases significantly in both scenarios; it increases 60% in the A2 scenario and 141% in

the A1 scenario. Increase in consumption is much higher in the developing regions than in the developed

regions. The increase in the developing regions is between 80% (ALM, A2) and 239 (ASIA A1), while the

increase in the developed regions is between 20% (OECD90, A2) and 94% (REF A1).

M.Sc. Thesis

I.Y.R. Odegard

82

5. The Future of Food Storylines

In Section 5.1 the 5 driving forces identified in Chapter 3 and discussed in Chapter 4 are grouped to

create four consistent scenarios. These scenarios are subsequently described in Section 5.2 to 5.5 in,

mostly qualitative, scenario storylines.

5.1 Linkages between Driving Forces

To understand the importance of the driving forces in scenarios, it is necessary to explore the linkages

between these forces. Certain driving forces may have a positive feedback on others, while some may be

completely unrelated. For example, a low population growth is positively related to high economic

development. The linkages reflect IPCC reasoning in compatibility of trends and consistency between

them. Some pre-defined linkages, e.g. the relation between population growth to economic and social

development, will not be changed here. Table 30 shows how the main driving forces and their sub-

themes are fitted into consistent scenarios. The plusses and minuses indicate the development of the

trend related to the topic named. A plus or minus either indicates whether something is true (+) or not (-

), e.g. for ‘globalization’, or whether the trend is projected to increase at a higher (+) or lower (-) than

average rate, e.g. for population growth. The source and rationale for these choices is given. Where no

source is mentioned, the trend projection is a personal interpretation.

Table 30: Scenario driving forces' linkages and characteristics.

Driving

Forces

Themes A1 A2 B1 B2 Source and rationale

Population

growth

- + - +/- [IPCC, 2000; UN, 2009; Lutz, 2008]

Low population growth is correlated to (high) economic

development and (rapid) technological development.

Economic and

social

development

+ - + +/- [IPCC, 2000; UN, 2009; Lutz, 2008]

High economic development is correlated to rapid

spread of technological knowledge and thus to

globalization.

Policy Globalization

Regionalization

Economy

Environment

+

-

+

-

-

+

-

+

+

+

-

-

-

-

+

+

[IPCC, 2000]

The main scenario directions are linked in different

ways to create 4 significantly different scenarios.

Globalization allows for free trade and global dispersion

of knowledge. Self-sufficiency is considered important

in the ‘regional’ scenarios. The focus on ‘economy’ or

‘environment’ determines aspects related to

technological change.

Wastea + - - - [Kantor, 1997; Cuéllar, 2010; Bender, 1994; own

interpretation]

High levels of household and retail waste are related to

high levels of economic development, while low levels

of waste are related to environmental consciousness

M.Sc. Thesis

I.Y.R. Odegard

83

and to low levels of economic development.

Technological

change

Rate of change ++ - + +/- [IPCC, 2000]

Higher economic development levels are correlated to

higher rates of change.

Fertilizer use + + +/- - Policy stimulates (‘economy scenarios’) or discourages

(‘environment scenarios’) the use of fertilizer.

Increase

irrigated area

+ + - - Increase of the area under irrigation is related to levels

of economic development, and policy restrictions on

water use in water-scarce areas.

Fertilizer and

irrigation

efficiency

+ - + + High levels of efficiency are related to a focus on

environmental issues, and also to economic

development and dispersion of technological

innovations.

Productivity ++ - + +/- Productivity (yield) is correlated to economic growth,

use of fertilizer and irrigation, and technological

development. Globalization leads to dispersion of

technological innovations.

Diet change Rise in meat

consumption

+ + - - Meat consumption is correlated to levels of economic

development. A policy focus on the environment and a

greater environmental consciousness can lead to a

decrease in meat consumption.

Note: a plus or a minus (or a double plus or plus-minus) indicates the growth rate of the topic in the specific scenario, relative to

the other scenarios; thus while population growth is positive in the A1 scenario compared to now, its relative growth rate is low

compared to population growth in e.g. A2. The four IPCC paradigm indicators – globalization, regionalization, economy and

environment - are either ‘true’ or ‘false’ for a specific scenario, although this can be viewed a s a growth rate as well; a world is

never truly solely globalized or regionalized. a Household and retail waste

Figure 28 summarizes the information from Table 30 above. The labeling used by the IPCC, i.e. A1, A2, B1,

2, is used in this figure. Additionally, scenario names are added to increase recognizability. Table 30 and

Figure 28 provide the basis for the scenario storylines in the following sections.

M.Sc. Thesis

I.Y.R. Odegard

84

A2

The Full World

High Population Growth

Low Economic Growth

Slow Spread of Agro-Technology

Medium Fertilizer Efficiency

Low Irrigation Efficiency

Medium Productivity

Western Diet

B1

The Vegetarian World

Low Population Growth

Medium-High Economic Growth

Spread of Sustainable Agro-technologiy

High Irrigation Efficiency

High Fertilizer Efficiency

High Productivity

Vegetarian Diet

B2

The Low-Input World

Medium Population Growth

Low-Medium Economic Growth

Spread of Sustainable Agro-technology



Low Productivity

Organic Diet

A1

The Affluent World


High Economic Growth

Rapid Spread of Agro-Technology

Medium Irrigation Efficiency


High Productivity

Western Diet

RegionalizationRegional Food

Distribution

GlobalizationGlobal Food

Distribution

EnvironmentEfficient Use of Natural

Resources

EconomicUnrestrained Use of

Natural Resources

Figure 30: Scenario Characteristics

M.Sc. Thesis

I.Y.R. Odegard

85

5.2 A1 – The Affluent World

In the A1 world, there is a focus on globalization and

economic development. Regional and global

population growth is shown in Figure 28. The world

population will have grown to 7.78 billion in 2050.

The combination of a focus on globalization and

economic development results in a decrease in

inequity between regions and a convergence of

welfare on a global scale. For this to happen,

developing countries will undergo a remarkable

transition in economic development. Dispersion of

technological knowledge is both a driver and a

outcome of this development. The relatively low

population growth is coupled to very high GDP

growth.

Economic development will happen rapidly on a global scale. Table 31 shows the economic growth rates

(in % per year) and the income per capita in the world and its 4 regions. By 2050, GDP in the poorest

region – the ALM region – will have increased to the current level of OECD90 development.

Table 31: Economic Growth Rates and Income per Capita in the A1 World [IPCC, 2001; Van Vliet, 2010]

Region Economic Growth Rates (% per year)

Income per Capita in PPP (103 US$-1995 per capita)

1950-1990 1990-2050 2000 2050 World 4.0 3.6 6.8 28.4

OECD90 3.9 2.0 26.5 64.9

REF 4.8 4.1 4.3 32.2

ASIA 6.4 6.2 3.1 23.9

ALM 4.0 5.5 4.4 21.8

Policy will be focused on stimulating the economy, rather than protecting the environment, and will be

focused on achieving a global food market. Important for the future of the food system are policies that

create an increasing interaction between regions. Trade barriers between countries will be lifted,

resulting in the absence of restrictions on trade between the four regions. In all four regions, diets will

continue to converge towards a diet which is high in meat consumption, the ‘Western’ diet, as a result of

the high economic and social development. This will create incentives for technological development in

agriculture, resulting in higher feeding efficiencies and relatively high yields; yield gaps are closed by 80%

of the gap in the year 2005. The incentives to create a larger supply continue to be focused mostly on the

supply side, and household and retail waste are high. The rapid technological change and the

interactions between regions will push the use of irrigation in agriculture as well as the high use of

agricultural chemicals.

Figure 31: Population between 19050-2050 (in billions) in A1 [based on Lutz, 2008].

M.Sc. Thesis

I.Y.R. Odegard

86

5.3 A2 – The Full World

While the main focus in the A2 world is on regionalization and economy, economic growth rates are low

and as can be seen in Table 32 development is distributed very unequally.

Table 32: Economic Growth Rates and Income per Capita in the A2 World [IPCC, 2001; Van Vliet, 2010]


Income per Capita in PPP (103 US$-1995 per capita)

1950-1990 1990-2050 2000 2050 World 4.0 2.3 6.8 11.9

OECD90 3.9 1.6 26.5 41.5

REF 4.8 2.3 4.3 11.5

ASIA 6.4 3.9 3.1 7.1

ALM 4.0 3.8 4.4 10.6

Interactions between regions are limited, which

displays itself in high inequities between regions, and

only very slowly converging demographic trends.

Population growth is high, and is still increasing in 2050

in all regions except the REF region, as can be seen in

Figure 28. The ALM region in particular shows no sign

of a decreasing population growth. The world

population will have grown to 9.9 billion in 2050.

Policy will be formulated and implemented on a

regional scale. This will result in protection of local

markets and limits to global trade of food products.

The environment is not much of a policy issue, and no

limitations will be placed on acreage expansion and

irrigated area expansion in an effort to feed a world

population of close to 10 billion people. No measures

are taken to decrease animal product consumption or have consumers make healthier food choices,

which increases the use of natural resources. Because technological change is slow, and is focused on a

regional scale, specific technologies that would raise yields or lower the environmental impact of

agriculture are implemented on a limited scale. This results in only a partial closure of the yield gap and

no improvement in irrigation efficiency and fertilizer efficiency. Because economic development is low,

household and retail waste is not as high as in the A1 world.

Figure 32: Population between 1950-2050 (in billions) in A2 [based on Lutz, 2008].

M.Sc. Thesis

I.Y.R. Odegard

87

5.4 B1 – The Vegetarian World

The B1 world is a world in which globalization and

taking care of the environment are core values.

Similar to the A1 world, the B1 world experiences

the same low population growth, shown in Figure 30,

and a high growth in GDP, shown in Table 33. The

world population will have grown to 7.78 billion in

2050. The B1 world distinguishes itself from the A1

world by the explicit focus on the environment. This

will influence the impact agriculture has on the

environment through policy, the types of

technological change and the diet of the population.

Policy in the B1 world will have a strong focus on

conservation of the environment. For agriculture this

means that there will be no conversion of nature to

cropland, if possible, cropland will be given back to

nature.

Table 33: Economic Growth Rates and Income per Capita in the B1 World [IPCC, 2001; Van Vliet, 2010]


Income per Capita in PPP (103 1995US$ per capita)

1950-1990 1990-2050 2000 2050 World 4.0 3.1 6.8 22.2

OECD90 3.9 1.8 26.5 54.5

REF 4.8 3.1 4.3 22.3

ASIA 6.4 5.5 3.1 16.6

ALM 4.0 5.0 4.4 19.1

Technological change, linked to the fairly high economic growth, will increase yields and feeding

efficiencies. Yield gaps will be close to 80% of the year-2005 gap, and because of improvements in

fertilizer and irrigation efficiency, resource input is more effective in terms of generated output. Irrigated

area does not expand as much as in the A worlds. While the focus in the A1 worlds to increase supply is

on the supply-side, in the B worlds the demand side gets attention and while household and retail waste

increase due to economic development it is only half of that in the A1 world. Furthermore, healthy food

choices are important in the B1 world. While meat consumption is cut entirely, protein consumption is

considered in diet choices, leading to a higher intake of milk, eggs, oil crops and pulses. Moreover, sugar

and sweetener intake decreases, while intake of fruits and vegetables increases.

Because globalization is a driver in this world, there will be no trade restrictions, and food will be

distributed fairly over the world. Furthermore, because of greater equity, apparent consumption can be

lower than the under-nutrition threshold of 2900 kcal set by the FAO, by a 100 kcal.

Figure 33: Population between 1950-2050 (in billions) in B1 [based on Lutz, 2008].

M.Sc. Thesis

I.Y.R. Odegard

88

5.5 B2 – The Low Input World

In the B2 world the environment and regionalization play a large role. The B2 world will experience a

medium growth in GDP, but as can be seen in Table 34 the differences between the regions in income

per capita will still be quite substantial in 2050.

Table 34: Economic Growth Rates and Income per Capita in the B2 World [IPCC, 2001; Van Vliet, 2010]


Income per Capita in PPP (103 1995US$ per capita)

1950-1990 1990-2050 2000 2050 World 4.0 2.8 6.8 17.7

OECD90 3.9 1.4 26.5 50.4

REF 4.8 3.0 4.3 19.1

ASIA 6.4 5.5 3.1 15.4

ALM 4.0 4.1 4.4 11.7

As shown in Figure 31, the B2 world experiences a

medium population growth, hitting a maximum

population a little later than 2050. As can be seen in

the figure on the right, population in the OECD90 and

REF regions experience very little growth and negative

growth respectively, and the ASIA and ALM region will

still be growing steadily in 2050. The world population

will have grown to 9.15 billion in 2050.

This is a world where attention to sustainability is

focused on a local scale. For agriculture this will mean

a shift towards food grown with low inputs of

fertilizers. This is supported by local policy which has a

strong focus on local environment and environmental

solutions on a local scale. This does, however, lead to

low yields; half of those in the A2 world. While

fertilizer input is low, fertilizer efficiency does increase. Similar to the B1 world, policy has a tendency to

protect nature, but because solutions are implemented on a local scale there is no trend towards a

globally efficient food system. Technological change is less rapid than in the A1 and B1 scenarios.

Irrigated area and irrigation efficiency increase at the same rate as in the B1 world. Diets shift to being

their diet towards either a ‘western’ or a ‘vegetarian’ lifestyle –meat consumption is still half that

predicted by the economic growth rates–, but instead purchase their food locally, in an effort to be more

environmentally friendly. Similar to the B1 world, people make healthier choices concerning their diet

and increase their fruits and vegetables consumption. Because of the combination of moderate

economic growth and environmental consciousness, household and retail waste levels are half of those

in the A1 world.

Figure 34: Population between 1950-2050 (in billions) in B2 [based on UN, 2009]

M.Sc. Thesis

I.Y.R. Odegard

89

6. Linkages

The following section will discuss the quantification of the driving forces elaborated on in previous

chapters. Table 35 and Table 36 give detailed information about the assumptions made, respectively

those related to demand and supply. Figure 35 below shows the relationships between the driving forces

and the demand and supply of food. As can be seen some driving forces have an influence on demand

while others have an influence on supply. Where the quantification is not straightforward, the method of

quantification is explained below.

VRC-factors

Population

DietEconomic

DevelopmentTechnological

Change

Policy

Policy

Losses

Waste

Demand ProductionSupply

Resource Use

Figure 35: Linkages between driving forces, supply, demand, Virtual Resource Content and resource use.

6.1 Demand

As shown in Figure 35 above, economic development and policy influence diets, while population of

course influences the total consumption. Furthermore, economic development and policy also have an

impact on the amount of retail and household waste. The basis for the diets in the four scenarios are the

projections made by the FAO. These projection are made for the world, for developing countries, for

industrial countries and for transition countries, for the years 2030 and 2050. These projections have

been adapted to be consistent with the scenarios in this study. Changes have been made that are related

to the level of economic development and its influence on meat consumption. Meat consumption in the

A scenarios was calculated using a relationship between meat consumption and PPP, defined by Keyzer

et al [Keyzer, 2005]. In the B scenarios, people make diet choices related to greater health consciousness,

which for example increases fruits and vegetables consumption. Furthermore, in the B2 scenario meat

consumption as defined by the Keyzer equation is cut in half, while in the B1 scenario meat consumption

is cut altogether. Also, the level of apparent consumption is based on the undernutrition threshold,

which is reduced in the B scenarios because of higher equity and lower household waste levels. Meat

consumption, consumption of other commodity groups and levels of household and retail waste are

quantified in Table 35 for the year 2005 and for the four scenarios. In the A1, B1 and B2 scenarios the

total food quantity in kcal/cap/day is slightly lower than the level of the related FAO projection. In the A2

scenario this quantity is slightly higher in the OECD90 region and the REF region, while slightly lower in

the ASIA region and the REF region, which corresponds to the difference in definition of regions.

M.Sc. Thesis

I.Y.R. Odegard

90

Table 35: Demand-side assumptions and rationale.

Scenario Topic Quantification Source and rationale

All Meat type In 2050, total meat consumption is

divided between the different types as

such: of total consumption: 26% =

beef, 33% = poultry, 41% = pork.

[FAOSTAT, 2010]

Proportions in 2005, global level.

Household and

retail waste

Waste fraction for oil crops, sugar

crops and roots and tubers is set equal

to the waste fraction for pulses.

[Kantor, 1997]

No values are given for the commodity

groups mentioned; a low waste fraction is

chosen: pulses.

Eradication of

undernutrition

Caloric apparent consumption exceeds

the FAO undernutrition threshold of

an average of 2900 kcal per capita per

day in the A scenarios and of a

reduced level of 2800 kcal per capita

per day in the B scenarios.

[Alexandratos, 2006]

Greater equity and lower waste levels

allow for a lower undernutrition threshold

level.

A1, A2 Meat

consumption

Meat consumption is based on the

‘Keyzer equation’, inputs: PPP

projections and population

projections.

*Keyzer, 2005+; ‘Keyzer equation’, based

on PPP.

[Van Vliet, 2010]; PPP projections for A1,

A2, B2 scenarios.

[IIASA, 2008] high and low population

projections (IIASA divides the world into

13 regions; the proportion of populations

on a country level in 2050 is assumed

equal to the proportion in 2000).

[UN, 2009]

2005 Meat

consumption

Global average consumption of meat

in 2005 according to the FAO.

[FAOSTAT, 2010]

Other

commodity

groups

Global average consumption of

commodity groups in 2005 according

to the FAO.

[FAOSTAT, 2010]

Household and

retail waste

Global household and retail waste is

half of the 1995 USA levels defined by

Kantor.

[Kantor, 1997] Household and retail waste

is quite high in the industrialized regions,

but close to non-existent in the

developing world.

A1 Meat

consumption

Average meat consumption:

OECD90 = 130 kg/cap/year

REF = 99 kg/cap/year

ASIA = 91kg/cap/year

ALM = 85 kg/cap/year

World = 93 kg/cap/year

Based on [Keyzer, 2005]

Other

commodity

groups

Basis: diet in ‘industrial countries in

2050’, according to FAO.

[Alexandratos, 2006, p.25]

Because of high economic development,

diet transition occurs rapidly on a global

scale.

M.Sc. Thesis

I.Y.R. Odegard

91

Eggs, oil crops, sugar crops, vegetables

and fruit are set to the apparent

consumption level of the year 2005.

[FAOSTAT, 2010]

Household and

retail waste

Level of food waste in the USA in

1995.

[Kantor, 1997]

Household and retail food waste is

positively correlated to purchasing power

and negatively to environmental

consciousness; waste levels in A1 are the

highest.

A2 Meat

consumption

Average meat consumption:

OECD90 = 107 kg/cap/year


ASIA = 53 kg/cap/year

ALM =57 kg/cap/year

World = 62 kg/cap/year

Based on [Keyzer, 2005]

Other

commodity

groups

Basis: diets in 2030 according to FAO.

OECD90 = ‘industrial countries’

REF = ‘transition countries’

ASIA = ‘developing countries’

ALM = ‘developing countries’


Because of low economic development,

diets undergo a slower transition.

Eggs, oil crops, sugar crops, vegetables

and fruit are set to the apparent

consumption level of the year 2005.

[FAOSTAT, 2010]

Household and

retail waste

Household and retail waste is half of

the 1995 USA levels defined by

Kantor.

[Kantor, 1997]

Lower waste levels because of lower

economic development levels.

B1 Meat

consumption

Meat consumption is zero.

Other

commodity

groups

Basis for the diet is the global average

diet in 2050 according to the FAO.

Consumption of eggs, oil crops, pulses

and milk is increased to increase the

diet’s protein content (between

brackets former figure):

Oil crops = 10 kg/cap/year (7)

Pulses = 10 kg/cap/year (6)

Eggs = 10 kg/cap/year (8)

Milk = 130 kg/cap/year (100)

Sugar consumption is cut in halfb.


Low waste levels and greater equity allow

for lower apparent consumption rates.

Healthier choices are made in the B

scenarios; apparent meat consumption is

cut in half, and so is apparent sugar

consumption. Apparent consumption of

fruit and vegetables is high at 300

gr/cap/day.

Apparent consumption of fruit and

vegetables consumption is set to 148

kg/cap/year, based on a healthy intake

of 300 gr/cap/dayb, excluding 25%

M.Sc. Thesis

I.Y.R. Odegard

92

refuse and 10% household waste.

Household and

retail waste



Kantora.

[Kantor, 1997]

Lower waste levels because of higher

environmental consciousness.

B2 Meat

consumption

Diet of reduced meat consumption.

The average meat consumption is half

of that given by the ‘Keyzer equation’:

OECD90 = 57.5 kg/cap/year


ASIA = 41 kg/cap/year

ALM =30.5 kg/cap/year

World =37 kg/cap/year

Environmental consciousness influences

peoples diet choices to a certain degree,

but not as much as in B1.

Other

commodity

groups

Basis: diets in 2050 according to FAO.

OECD90 = ‘industrial countries’

REF = ‘transition countries’

ASIA = ‘developing countries’

ALM = ‘developing countries’

Fruit and vegetable consumption is set

to the levels in the B1 scenario.

Sugar consumption is cut in half.


Similar to FAO projections, population

growth rates and economic development

are intermediate, thus diets follow the

FAO projections for 2050. Some healthier

choices are made related to consumption

of fruits, vegetables and sugar and

sweeteners.

Household and

retail waste



Kantor.

[Kantor, 1997]

Lower waste levels because of higher

environmental consciousness.

6.2 Supply

As shown in Figure 35 above, policy and technological change influence supply. Furthermore, policy also

has an impact on the amount of ‘losses’ of foodstuffs – the fraction of the total production which is used

for other purposes. This section discusses such losses, irrigated area, irrigation efficiency, fertilizer

efficiency and productivity. These topics are quantified and summarized in Table 36.

A portion of the production of potential foodstuffs is used towards other purposes than food. A

significant quantity is used as animal feed. Furthermore, part of the production quantity is lost during

storage, processing and transportation, is used as seed, and is used to produce other utilities (e.g.

biofuel). Relative values (the fraction of total production) of seed, waste (storage, processing and

transport), processed food and other utilities are kept constant over all scenarios and all regions to

increase transparency. Foodstuff used as feed depends on total meat consumption, the type of meat

consumed and on the global or regional feeding efficiency, and thus varies between regions and

scenarios. Feeding efficiencies are based on data from Wirsenius [Wirsenius, 2000]. For the global

scenarios a relatively high feeding efficiency, equal to the feeding efficiency of the ‘North America and

Oceania’ region (as defined by Wirsenius), was chosen, because of the rapid global technology transfer in

these scenarios. For the regional scenarios regional feeding efficiencies were chosen, based on data

M.Sc. Thesis

I.Y.R. Odegard

93

availability and prospects for growth; regions with the higher feeding efficiency were chosen because of

potential improvements in the next decades. The feeding efficiencies can be found in Appendix 8.

For the definition of the linkage between productivity and irrigation, and definition of irrigated area, the

scenarios by the International Water Management Institute were taken as a guideline [De Fraiture, 2007;

De Fraiture, 2010]. For the countries for which irrigation cropping patterns are available (mostly

developing countries), 75% of the area under irrigation is cultivated for cereals. Another 8.5% is

cultivated for oil crops, while the other commodity groups each account for only a couple percent. De

Fraiture, however, states that 9% of the irrigated area is accounted for by cotton (which was included in

the estimate for oil crops above, which may thus be underestimated). For the four scenarios it was

assumed that 75% of the irrigated area is used for cereal production. Because of lack of data, irrigated

area and irrigation efficiency are not accounted for in the 2005 situation.

Separate yields for rainfed and irrigated cultivation are only given for cereals, as most irrigation takes

place on cereal crops. Some data about the maximum attainable yield for some crops, for both irrigated

agriculture and rainfed agriculture, is available. However, the data on current and historic yields are an

average of rainfed and irrigated yields, and data on irrigated area and rainfed area per crop, on a country

level, is not available. Potential yields in the year 2050 were estimated using the methodology based on

closure of the yield gap, also used by De Fraiture to estimate potential cereal yields [De Fraiture, 2010].

The maximum attainable yield for the different commodity groups in the different regions was estimated

based on data from the FAO and IIASA [Fisher, 2002]. In the scenarios with a high or rapid economic and

technological development (A1 and B1), 80% of the yield gap was closed. In the low development

scenario (A2) 20% of the yield gap was closed. In the B2 scenario regional yields were defined as being

half the regional yields in the A2 scenarios, which results in low-input agriculture as fertilizer input is

defined on a per generated output basis. Appendix 7 gives an elaborate explanation of the methodology

and the results related to yields.

Table 36: Supply-side assumptions and rationale

Scenario Topic Quantification Source and rationale

All Losses Use of foodstuff for seed, wastea,

processing and other utilities is set at a

fixed rate of production in all scenarios

in all regions. The fraction of the total

production destined for food and feed:

Absolute use of foodstuff for these

purposes will increase with population, it

is reasonable to assume a consistent

relative relation. While the FAO estimates

that waste during storage, processing and

transportation is higher in developing

countries due to warmer and more humid

climates, waste is also reported to

increase with increasing level of

development.

Fractions are based on global averages in

the year 2005 [FAOSTAT, 2010].

Cereals

Fruit

Pulses

Roots and tubers

Vegetables

Oil crops

Sugar crops

Eggs

Meat

Milk

0.827

0.817

0.867

0.804

0.911

0.595

0.828

0.886

0.991

0.834

M.Sc. Thesis

I.Y.R. Odegard

94

Processing of

oil crops

Vegetable oils are extracted from oil

crops. The extraction rate is fixed at the

same value for 2005 and for all regions

and all scenarios.

The extraction rate is based on the

production of vegetable oils and the

processed amount of oil crops, in the year

2005b.

[FAOSTAT, 2010]

Processing of

sugar crops

Sugar and sweeteners are extracted

from sugar crops. The extraction rate is

fixed at the same value for 2005 and for

all regions and all scenarios.

The extractions rate is based on the

production of sugar and sweeteners and

the processed amount of sugar cropsb.

[FAOSTAT, 2010]

Irrigation and

multiple

cropping

Of the area under irrigation, 75% is

cultivated for cereals.

[Aquastat, 2011], [De Fraiture, 2010]

Figure is based on data on countries for

which cropping patterns are available

(mostly developing countries)

[AQUASTAT, 2011].

Irrigated and

rain-fed yields.

Only for the commodity group ‘cereals’

a distinction is made between rain-fed

and irrigated yields.

Cereal yields are well documented, and

are divided into irrigated and rain-fed

yields. Such a division is usually not made

for the other commodity groups.

2005 Productivity Global average yields for 2005,

calculated by dividing total production

over total harvested area.

Yields for oil crops and sugar crops

were converted using extraction rates

given in Appendix 9.

[FAOSTAT, 2010]

The FAO reports specific oil crop yields

(e.g. soybeans) and sugar crop yields (e.g.

sugar cane) per country in unconverted

values, but the average in converted

values, which thus need to be

reconverted.

Irrigated area

and irrigation

efficiency

Irrigated area and irrigation efficiency

was not taken into account in the

modeling of the 2005 situation.

Water use Average water use (m3/ton): [based on Hoekstra, 2008]

Global average crop evapotranspiration,

elaborated on in Appendix 11. Cereals

Fruits

Oil crops

Pulses

Roots and tubers

Sugar crops

Vegetables

1,571

844

2,209

3,790

375

165

264

Fertilizer use

and efficiency

Fertilizer use for 2005 was modeled

using global average baseline

requirements c.

Fertilizer requirements based on

[FERTISTAT, 2011] and [FAO, 1984].

Estimated requirements are compared to

aggregations of regional average, and to

the total global use as estimated by the

FAO, see Appendix 14 and Section 3.3.3.

A1 Productivity Commodity group yields (ton/ha): Yields are based on bridging the yield gap

by 80% [De Fraiture, 2010], methodology

in Appendix 7.

M.Sc. Thesis

I.Y.R. Odegard

95

Fruit

Oil crops

Pulses

Sugar crops

Roots

Vegetables

19.01

4.48

3.13

69.18

37.37

27.06

Cereal yields based on “Optimistic

scenario (2050)”. Global average cereal

yield:

[De Fraiture, 2010]

Rainfed

Irrigated

3.88 ton/ha

5.74 ton/ha

Feeding efficiency based on the ‘North

America and Oceania’ region as defined

by Wirsenius.

[Wirsenius, 2000]

Rapid technology diffusion and economic

growth leads to adoption of feeding

efficiencies as they were defined by

Wirsenius for the ‘North America and

Oceania’ region.

Permanent pasture are managed

intensively; yields are equal to those of

the pasture yield in Western Europe

(3.2 ton DM /ha). Harvested-conserved

grass-legume is also assumed to be

harvested from high-intensively

managed land, giving the same yield as

mentioned for permanent pasture.

Based on [Wirsenius, 2000] and [FAO

1984].

Irrigated area

and irrigation

efficiency

With an emphasis on area expansion,

irrigated area will increase to 454

million hectare worldwide; the area as

projected by the AREA-scenario.

[De Fraiture, 2010]

Current irrigation efficiency of 60% is

maintained.

[De Fraiture, 2010]

Emphasis on area expansion rather than

efficiency.

Water use Equal to ‘water use 2005’ – global

average use in m3/ton of generated

product.

Global average crop evapotranspiration

[based on Hoekstra, 2008].

Fertilizer use

and efficiency

Fertilizer use is assumed linearly

correlated to yields. Requirements are

set 10% lower than baseline

requirements (kg NPK/kg foodstuff)c.



Higher fertilizer efficiency due to rapid

global technological development.

A2 Productivity Commodity group yields (ton/ha), range

indicates difference between regions

(regionally specified data can be found

in Appendix 7):

Yields are based on bridging the yield gap


in Appendix 7.

Fruit

Oil crops

6.18-16.07

1.86-2.83

M.Sc. Thesis

I.Y.R. Odegard

96

Pulses

Sugar crops

Roots

Vegetables

1.27-2.23

35.52-70.62

17.51-37.45

18.15-30.64

[De Fraiture, 2010]

Cereal yields based on “Pessimistic

scenario (2050)”. Regional average

cereal yieldse (respectively rainfed and

irrigated):

OECD90

REF

ASIA

ALM

5.2 ton/ha

7.2 ton/ha

2.5 ton/ha

4.2 ton/ha

2.6 ton/ha

4.9 ton/ha

2.25 ton/ha

4.3 ton/ha

Feeding efficiency based on the

following regions as defined by

Wirsenius:

[Wirsenius, 2000]

OECD90

REF

ASIA

ALM

‘North America

and Oceania’

‘East Europe’

‘East Asia’

‘Latin America and

Caribbean

Feeding efficiencies lag behind compared

to the global scenarios. Regional

efficiencies as defined by Wirsenius were

chosen based on data availability (i.e. no

pork production takes place in the North

Africa & West Asia region) and growth

prospects.

Permanent pasture yields are equal to

the global average pasture yield (1.6

ton DM/ha). Cropland pasture is

managed intensively and has a higher

yield (3.2 ton DM/ha), as does

harvested-conserved grass-legume.

Based on [Wirsenius, 2000] and [FAO,

1984] and [FERTISTAT 2011]

Irrigation area

and irrigation

efficiency

Current irrigation efficiency of 60% is

maintained.

[De Fraiture, 2010]

Emphasis on area expansion rather than

efficiency.

With an emphasis on area expansion,

irrigated area will increase to 453

million hectare worldwide; the area as

projected by the AREA-scenario.

Regional irrigated area:

[De Fraiture, 2010]

OECD90

REF

ASIA

ALM

50 million ha

37 million ha

304 million ha

62 million ha

Water use Water use (m3/ton), range indicates

difference between regions (regionally

Region specific crop evapotranspiration

[based on Hoekstra, 2008]. Elaborated on

M.Sc. Thesis

I.Y.R. Odegard

97

specified data can be found in Appendix

11):

in Appendix 11.

Cereals

Fruits

Oil crops

Pulses

Roots and tubers

Sugar crops

Vegetables

1,099-2,069

844

2,002-2,226

3,790

375

122-165

264

Fertilizer use

and efficiency


correlated to yields. Requirements are

equal to baseline requirements (kg

NPK/kg foodstuff)c.



B1 Productivity Commodity group yields (ton/ha): Yields are based on bridging the yield gap


in Appendix 7.

[De Fraiture, 2010]

Fruit

Oil crops

Pulses

Sugar crops

Roots

Vegetables

19.01

4.48

3.13

69.18

37.37

27.06

Cereal yields based on “Trade scenario

(2050)”. Global average cereal yield:

Rainfed

Irrigated

3.9 ton/ha

4.94 ton/ha

Feeding efficiency based on the ‘North

America and Oceania’ region as defined

by Wirsenius.

[Wirsenius, 2000]

Rapid technology diffusion and economic

growth leads to adoption of feeding

efficiencies as they were defined for the

North America and Oceania region.

Pasture yields are equal to the global

average pasture yield (1.6 ton DM/ha),

as are yields for harvested-conserved

grass-legume. There is no intensive

management of cropland pasture, for

which the yields are thus chosen to

correspond to those of permanent

pasture.

Based on [Wirsenius, 2000].

Irrigated area

and irrigation

efficiency

Irrigated will expand in line with the

YIELD-scenario; irrigated area will

increase to 363 million hectare

worldwide in 2050.

Irrigation efficiency increases to 65%.

[De Fraiture, 2010]

[De Fraiture, 2010]

Emphasis on optimal strategies and

efficient use of resources.

Water use Equal to ‘water use 2005’ – global Global average crop evapotranspiration

M.Sc. Thesis

I.Y.R. Odegard

98

average use in m3/ton of generated

product.


Fertilizer use

and efficiency


correlated to yields. Fertilizer use is

15% lower than the baselinec.

Fertilizer requirements are based on


Higher fertilizer efficiency is due to

increased environmental consciousness

resulting in better agricultural practices.

B2 Productivity Commodity group yields (ton/ha):

Fertilizer use rates are related to the

yields. Reducing fertilizer input means

reducing yields; regional B2 yields are half

of those in A2.

Cereals, rainfed

Cereals, irrigated

Fruit

Oil crops

Pulses

Sugar crops

Roots

Vegetables

1.125-2.6

2.1-3.6

3.09-8.035

0.93-1.06

0.635-1.115

17.76-35.31

8.755-18.725

9.045-15.32

Feeding efficiency based on the

following regions as defined by

Wirsenius:

[Wirsenius, 2000]

Feeding efficiencies lag behind compared

to the global scenarios. Regional

efficiencies as defined by Wirsenius were

chosen based on data availability (i.e. no

pork production takes place in the North

Africa & West Asia region) and growth

prospects.

OECD90

REF

ASIA

ALM

‘North America

and Oceania’

‘East Europe’

‘East Asia’

‘Latin America and

Caribbean

Pasture yields are equal to the global

average pasture yield (1.6 ton DM/ha),

as are yields for harvested-conserved

grass-legume. There is no intensive

management of cropland pasture, for

which the yields are thus chosen to

correspond to those of permanent

pasture.

Based on [Wirsenius, 2000].

Irrigated area

and irrigation

efficiency

Irrigated area will increase to 363

million hectare worldwide in 2050.

Regional irrigated area (million

hectares):

[De Fraiture, 2010]

[De Fraiture, 2010]

Emphasis on optimal strategies and

efficient use of resources.

OECD90

REF

ASIA

ALM

45

34

237

47

Irrigation efficiency increases to 65%.

M.Sc. Thesis

I.Y.R. Odegard

99

Water use Equal to regional average water use in

A2.

Regional average crop evapotranspiration


Fertilizer use

and efficiency

Yields are assumed linearly correlated

to fertilizer use. Use levels are set to

85% of the baseline requirements (kg

N/kg foodstuff), yields are half of those

in the year 2005c.

Fertilizer requirements are based on


Fertilizer efficiency increases with

technological development and

environmental consciousness. a Waste includes waste during storage, processing and transport [FAO, 2010].

b Appendix 9 shows flowschemes for oil crops and sugar crops .

c Appendix 14 discusses fertilizer requirements.

6.3 VRC Factors

The previous sections have shown all assumptions made related to supply and demand. It is, however,

important to distinguish between the scenario study and the Virtual Resource Content methodology. The

Virtual Resource Content factors and the basic structure of the model, which were developed for

evaluating the scenarios, can be used for purposes other than the quantification of the scenarios defined

in this study. The characteristics of the Virtual Resource Content factors are elaborated on in Table 37.

They are specified per region, and variations are made per scenario. For the Virtual Land Content factors

these variations are incorporated in the factors – these represent the yield improvements. For the

Virtual Water Content factors and the Virtual Fertilizer Content factors these are defined separately to

increase transparency in the model; they represent the irrigation efficiency and the fertilizer efficiency.

Table 37: VRC factors and their specifications.

VRC factor Unit Specified for Spatial and temporal

specifications and linkage to

scenario characteristics

Virtual Land Content m2/kg

(or yield in

ton/ha)

Commodity groups: irrigated

cereals, rainfed cereals, fruit,

oil crops, pulses, roots and

tubers, sugar crops,

vegetables.

Feed crops: whole maize,

pasture, cropland pasture,

harvested-conserved grass

legume.

Global

2005

2050-A1/B1: 80% closure of yield

gap

Regional

2050-A2: 20% closure of yield gap

2050-B2: Half of A2 yields –

between 51% and 102% of yields

in 2005.

Virtual Water Content m3/ton Commodity groups: irrigated

cereals, rainfed cereals, fruit,

oil crops, pulses, roots and

tubers, sugar crops,

vegetables.

Feed crops: whole maize.

Regionally specified

2005: basis

2050: basis plus incorporation of

irrigation efficiency (60%-65%) for

cereals.

M.Sc. Thesis

I.Y.R. Odegard

100

Virtual Fertilizer Content kg N/kg

kg K2O/kg

kg P2O5/kg

Commodity groups: cereals,

fruit, oil crops, pulses, roots

and tubers, sugar crops,

vegetables. Feed crops: whole

maize, fodder (other

feedstuffs).

Regionally specified

2005: basis

2050: basis plus incorporation of

fertilizer efficiency (reduction of

requirements of between 0% and

15%) for all commodity groups.

The following figures show examples of the three different Virtual Resource Contents. Figure 36 shows

the Virtual Land Contents (in m2/kg of generated product) for rainfed and irrigated cereals. These Virtual

Land Content-factors are specified to scenario characteristics; the partial closure of the yield gap differs

per scenario and is incorporated in the Virtual Land Content factors, per commodity group. Appendix 7

gives more information concerning Virtual Land Content. Yield projections (inverse of Virtual Land

Content factors) and how these were derived is elaborated on for all commodity groups and scenarios

there.

The Virtual Water Content factors are defined per commodity and per region, and are averaged to obtain

a global factor per commodity. The factors are given in m3/ton of generated product. Figure 37 shows

the different values for cereals as an example: one global factor and four regionalized factors. These

represent requirements: irrigation efficiency is not incorporated in these factors. Appendix 11 gives more

information on the Virtual Water Content factors. Values are given per region for all commodity groups.

Figure 36: Example of Virtual Land Content - cereals in the A1 scenario and the A2 scenario.

M.Sc. Thesis

I.Y.R. Odegard

101

Figure 37: Example of Virtual Water Content - cereals in the four regions.

Virtual Fertilizer Content factors are defined for the three macronutrients and given per commodity (e.g.

kg N/kg generated product). Figure 38 shows the global average Virtual Fertilizer Contents for all

commodity groups and for the three different macronutrients. Such values were calculated for all four

regions. Appendix 14 elaborates on the methodology behind the Virtual Fertilizer Content, and the way

in which such values were derived for all commodity groups and regions.

Figure 38: Example of Virtual Fertilizer Content - all commodity groups and all three fertilizer, global figures.

M.Sc. Thesis

I.Y.R. Odegard

102

6.4 Modeling Protocol

The way in which the assumptions, elaborated on in the previous sections, are put together to provide

the VRC model is explained below, by explaining the key concepts.

Regional and Global Scenarios

While A1 and B1 scenarios are global, the A2 and B2 scenarios are regional and thus all assumptions are

quantified on a regional level. This provides the opportunity to compare regions, evaluate self-sufficiency

and gain deeper insight into the importance of driving forces.

Sugar and Oil

Demand for sugar and sweeteners and vegetable oils is converted to demand for sugar crops and oil

crops and added to those commodity groups. It is assumed sugar crops and oil crops are primarily

produced for their sugar and oil content. The by-products may be used as feed, and thus do have

economic value. They are, however, not allocated to; production is solely allocated to food production.

Feed Requirements

Feed requirements are calculated for the 5 animal products separately, and subsequently aggregated per

feed type. Those feed types that are also food crops are added to the total demand for food, so that

fertilizer requirements and water requirements are taken into account for such feed production of

foodcrops. Demand for by-products (edible-type, conversion-type, and non-eaten food) is checked

against total by-product production; 50% of by-product production is considered suitable for feed. If

availability is too low, the missing portion is substituted by the feed-type most appropriate for the

specific animal. Calculation of by-product production is done over the total production, i.e. food, feed

and production for other purposes.

Irrigated Area and Irrigation Efficiency

For all scenarios and regions it is assumed that 75% of the irrigated area is under cultivation for cereals.

Total production, given the higher irrigated cereal yield, on these areas is subtracted from the total

demand, yielding the production on non-irrigated areas. Irrigation is only considered for cereals, thus

irrigation efficiency is only relevant for irrigated cereal production. Water requirements (in m3/ton

cereals) were divided by the respective irrigation efficiencies: 0.6 for the A scenarios and 0.65 for the B

scenarios.

Fraction of Production for Other Purposes

The fraction of food production for other purposes – i.e. seed, processing, other utilities and waste – was

assumed equal to that fraction in the year 2005, on a global scale. After aggregation of food demand and

feed demand, every commodity is multiplied by a set value, which is the same for the global scenarios,

and for each region in the regional scenarios.

M.Sc. Thesis

I.Y.R. Odegard

103

7. Results

In this chapter the results regarding the resource use in the four scenarios are presented. Section 7.1 will

elaborate on the scenario results concerning production and consumption in the year 2050. Sections 7.2,

7.3 and 7.4 respectively discuss land use, water use and fertilizer use. The results for the four scenarios

are compared to the situation in the year 2005. These results were modeled in the same way as the

resource uses for the scenarios, using global average data. These data were fitted so that the result

matched the data given by the FAO.

7.1 Production and Consumption

Figure 1 shows the apparent consumption and the intake for the year 2005 for the four scenarios in kcal

per person per day. As can be seen, apparent consumption exceeds the FAO ‘sufficient nutrition

threshold’ of 2900 kcal in the A scenarios and in the B2 scenario. Because of the assumption of higher

equity in the B1 scenario, the threshold was set to a lower 2800-kcal limit, which it exceeds.

Figure 39: Apparent consumption (AC) and intake in the four scenarios in 2050, components in the legend are shown bottom-up in the bar chart. Lines show level of apparent consumption (solid line) and intake (dashed line) in 2005.

It is clear that animal sources provide a greater part of the total caloric value in the A scenarios than in

the B scenarios. In A1 meat consumption accounts for 25% of apparent consumption (and 27%) of the

intake in caloric value. In A2, B1 and B2 this is respectively 17%, 8% and 14%. The total production of the

10 main commodity groups is shown in Figure 40. This includes feed, seed, processing, other utilities,

waste during storage, transportation and processing. In these charts feed is included only where it

concerns food crops that are grown for feed; cereals, pulses, roots and tubers and vegetables are all fed

M.Sc. Thesis

I.Y.R. Odegard

104

to animals. While the population is lowest in the A1 and B1 scenarios, the total production is highest in

the A1 scenario. Total production of animal products is a little over 60% higher in A1 than in A2. This is

reflected by the higher cereal production in the A1 scenario. The feed mix in the A2 and B2 scenarios

includes non-eaten food for pork production. Because availability is too low, it is substituted by

vegetables, which accounts for the comparatively high vegetables production in the A2 and B2 scenarios.

Sugar crops are not fed directly to animals, but as can be seen, production is much higher in the A

scenarios; people in the B scenarios make healthier food choices. This also explains the higher fruits and

vegetables production in the B scenarios.

Figure 40: Production in 2005 and 2050, components in the legend are shown bottom-up in the bar chart.

Figure 41 shows the total global production of foodcrops in kilograms per commodity per capita in 2005.

This includes other uses, i.e. seed, processing, other utilities, waste during storage, transportation and

processing. Furthermore, in the same bar diagram, the production of food and feed, the apparent

consumption and the intake (food minus household and retail waste), are shown. Production and

consumption of vegetable oils and sugars and sweeteners are included in respectively the categories ‘oil

crops’ and ‘sugar crops’. Of the total production, 15.5% is used for other purposes than food and feed.

Of the production of foodcrops, 30% is used as feed. Between total production (including animal

products) and intake, 44% is lost.

M.Sc. Thesis

I.Y.R. Odegard

105

Figure 41: Total production, production of food and feed, apparent consumption and intake in 2005 (kg/cap/year), components in the legend are shown bottom-up in the bar chart.

The figures below show the same, for the four scenarios. Production of food and feed is proportionate to

the total production. Therefore, relative losses between total production and the portion of the total

production destined for food and feed is similar in all scenarios: between 17.9% and 15.5%. Because

animal products are included in this calculation, and their losses are a little lower relatively, losses in the

B scenarios are at the higher end of that spectrum. As can be seen, the difference between apparent

consumption and intake (thus household and retail waste) is much larger in the A1 scenario than in the

other scenarios; 28% of caloric supply is wasted, while this is only 14-15% in the other scenarios.

M.Sc. Thesis

I.Y.R. Odegard

106

Figure 42: Comparison of total production, production for food and feed, apparent consumption and intake (kg/cap/year) for the four

scenarios, components in the legend are shown bottom-up in the bar chart.

Production and Consumption – Analysis

The figures presented in the previous section represent all assumptions made related to supply and

demand in 2050 for the four scenarios. A few issues deserve attention. First of all, total production

(aggregated weight) increases in all scenarios. In 2005, 8.3 billion tons of food-crops were produced. This

increases by respectively 80%, 73%, 6% and 40% in the A1, A2, B1 and B2 scenarios. Second, incidence of

undernourishment is low, because apparent consumption levels exceed the 2800 or 2900 kcal/cap/day

threshold, depending on the scenario (elaborated on in Chapter 3). The FAO estimates that in 2050,

there will still be 15 countries with average apparent consumption levels of under 2700 kcal, totaling 746

million people. With a low coefficient of variation (CV) of 0.2, this means it should be assumed that 2.5%

of these people are undernourished, totaling 18.65 million people. Compared to the estimated 1 billion

in 2009, this is a reduction of 98%. The FAO estimates all developing countries (102) to have an average

apparent consumption level of 3070 kcal/cap/day in 2050. This is higher than the modeled apparent

consumption levels for 2050 in the ASIA region (2909 kcal/cap/day in A2, 2895 kcal/cap/day in B2) and

the ALM region (2930 kcal/cap/day in A2, 2838 kcal/cap/day in B2). However, because a small group of

food products is not taken into account in this scenario study (e.g. nuts, for full list of boundary

conditions, see Chapter 2) and because food consumption may be underestimated for developing

countries, it can be assumed that apparent consumption is a little higher (around 100 kcal/cap/day).

Incidence of undernourishment can thus be expected to be similarly low as in the FAO projection.

What stands out when taking a closer look at apparent consumption and intake, is that global average

apparent consumption is highest in A1 – almost 500 kcal per capita per day higher – but average intake is

M.Sc. Thesis

I.Y.R. Odegard

107

a little lower – a little over 100 kcal/capita/day lower. Especially wastage animal of animal products

indirectly leads to higher production, because the associated feed can also be seen as waste. The wasted

animal products in A1 are associated with 602 million tons of cereals, 4.9 million tons of pulses, 2.8

million tons of roots and tubers, and 6.8 million tons of oil crops, respectively 11%, 6.3%, 0.5% and 6.5%

of the total production.

Finally, the most obvious difference between the scenarios are the much higher total production levels in

the A1 scenario. Feed is included in the production figures, and without a doubt the higher consumption

of animal products causes the high per capita production of foodcrops. In A1 37% of the crop production

that is left after losses are taken into account (‘food and feed’), is for feeding purposes. In A2 this is 29%,

which is comparable to the situation in 2005, when it was 30%. In the B scenarios these figures are

substantially lower; in B2 it is 19% and in B1 only 9%. As was pointed out above, vegetables production is

comparatively high in the A2 and B2 scenarios because there is insufficient non-eaten food to feed pigs.

Vegetables provide the substitute. Per capita apparent consumption of vegetables in A2 is comparable

that in A1, while in B2 it is comparable to that in B1.

7.2 Land Use

Figure 43 shows the total global land use (in harvested area) for the year 2005 and the four scenarios.

This is the land needed to produce food, feed and all other uses. The horizontal lines indicate the area

suitable for agriculture. As explained in Section 3.3 and Appendix 10, land can be suitable but not

available; between 70 and 90% of the land suitable may be available. Furthermore, there are different

classes of land; very suitable (VS), suitable (S), moderately suitable (MS) and marginally suitable (mS).

The lines in Figure 43 indicate, from bottom to top: 70% of all VS+S land, 70% of all VS+S+MS land, 90%

of all VS+S land and 90% of all VS+S+MS land. As can be seen land use in B2 exceeds all those categories.

The bars show the harvested area, while the lines shows actual land. This means that if the cropping

intensity is higher than 100%, the required land area is lower than the harvested area. For the B2

scenario, however, this is not an option. Low-input agriculture, as is practiced in B2, should be combined

with longer fallow periods; lowering the cropping intensity below 100%.

Land use in A2 comes very close to the lowest limit. Figure 44 below shows the total land use in the A2

and B2 world, specified for the four regions. The grey line indicates the harvested area in the year 2005.

Only for the ASIA region, this value exceeds the lower boundary of all VS+S+MS land. Cropping intensity,

is, however quite high in the ASIA region. Assuming an average cropping intensity of 138% on irrigated

lands (current estimated average [based on FAO/NRL, 2011]), the actual area under cultivation in 2050

could be 116 million hectares lower. This would still lead to an exceedance of available VS+S land. In the

B2 scenario, only VS+S land is considered appropriate for agriculture, however, as can be seen below, all

regions lack enough of this resource to fulfill the demand.

M.Sc. Thesis

I.Y.R. Odegard

108

Figure 43: Arable land and permanent crops, harvested area in 2005 and in the four scenarios. Solid lines show lower and upper limit of VS+S land (relevant for the B scenarios), dotted lines show lower and upper limits of VS+S+MS land (relevant for the A scenarios), components in the legend are shown bottom-up in the bar chart.

Figure 44: Total land use in A2 and B2. Grey line indicates current harvested area. For A2, dotted lines indicate the lower

and upper limit of total available area (VS+S+MS land), for B2 dotted lines indicate the lower limit of VS+S land area,

components in the legend are shown bottom-up in the bar chart.

The main factors which create the difference between the scenarios are meat consumption, feeding

efficiency and yield projections. For example, average apparent consumption in kcal per capita is around

15% higher in A1 than in A2. Even though yields are much higher in A1 than in A2, harvested cropland

area per capita is around 17% higher in A1 than in A2. If land used for pastures and non-foodcrop feed

M.Sc. Thesis

I.Y.R. Odegard

109

(harvested grass legumes and whole cereals) is added, it becomes clear that feeding efficiency is much

higher in A1. This is shown in Figure 45 below. While aggregated total production is higher in A1 than in

A2, total land use is higher in A2.

The black lines between ‘2005’and ‘A12050’ indicate what the FAO estimates as ‘arable land’ (lower line)

and ‘permanent meadows and pastures’ in 2005 *FAOSTAT, 2011+. As can be seen, this does not

correspond to the area given for 2005. As discussed in Chapter 3, there is a discrepancy between ‘arable

land’ and the aggregate of all harvested areas for all commodity groups. This is at least partially due to

the fact that cropping intensity is not taken into account. The discrepancy between the modeled area for

pasture and the other non-food feedcrops and what the FAO claims is land devoted to permanent

pasture could be due to the fact that not all permanent pastures are used as intensively as they could be.

Another way of looking at this is that the pasture area in the model is used to the max, while in the real

world they are not. Even so, Figure 45 shows that pasture area increase significantly in A2 and B2.

Figure 45: Total land use in 2005 and for the four scenarios in 2050. The black lines indicate 'arable land' (1.534 billion hectares) and 'permanent meadows and pastures' (3.382 billion hectares) in 2005 according to the FAO [FAOSTAT, 2011], components in the legend are shown bottom-up in the bar chart.

Land use for food and feed crops in A1, A2 and B2 is higher than in 2005. Total land use, including land

use for animal feed such as pasture, is, however, lower in A1 and B1. This also means land use per capita

in A1 and B1 is lower than in 2005, shown in Figure 46 below.

M.Sc. Thesis

I.Y.R. Odegard

110

Figure 46: Total land use per capita in 2005 and for the four scenarios in 2050, components in the legend are shown bottom-up in the bar chart.

This shows the effect of higher yields and higher feeding efficiencies. Even though the populations in A1

and B1 are higher than in 2005, the land use per capita is low, as is the total land use. As can be seen,

total land use in B1 is about half that of A1, which is due to the lower consumption animal products.

Land Use – Analysis

Land use for foodcrops (including foodcrop-feed) increases in all scenarios but the B1 scenario. Total land

use – including pastures and other non-food feeds – show that a combination of high yields, high feeding

efficiencies and intensive management of pastures decreases total land use in A1. Due to a much lower

consumption of animal products and higher feeding efficiencies and yields, total land use – including

pastures – in B1 is lower than cropland use in 2005 (land use excluding non-food feed). In 2005, the

harvested cropland area was 1.28 billion hectares, this increases by 31% in A1, to 1.68 billion hectares,

by 50% in A2 to 1.92 billion hectares and by 165% in B2 to 3.39 billion hectares. In B1 harvested cropland

area actually decreases by 33%, to 0.86 billion hectares. Land use for pastures and such decreases in the

A1 and B1 scenarios, by respectively 41% and 85%, from 2.3 billion hectares in 2005 to respectively 1.3

billion hectares and 0.3 billion hectares. In the A2 and B2 scenarios land use for pastures and such

increase by respectively 93% and 132%, to 4.4 billion hectares and 5.3 billion hectares.

When focusing on the four regions in the A2 and B2 scenarios, it shows that land use increases in all

regions for both scenarios. In A2, the ASIA region will not be able to be self-sufficient, and the REF region

comes very close reaching the lower limit of available suitable land. In B2, because of policy restraints,

agriculture is restricted to VS+S land. However, the demand exceeds the available land area in all regions.

M.Sc. Thesis

I.Y.R. Odegard

111

7.3 Water Use

Figure 47 shows the total global water use for the year 2005 and the four scenarios. Direct water use by

animals (i.e. drinking water) is insignificant compared to the water needed to grow crops, it adds up to

around 0.1% of total water use. Water needed to grow food-crop feed (including whole maize) and other

uses is included. The lines in Figure 47 indicate the global threshold for moderate water stress (lower line)

and critical stress (upper line), based on FAO criteria (see Chapter 3 and Appendix 11). The fact that

water use in the A1 and A2 scenarios already exceed the moderate water stress threshold, by

respectively 43% and 18%, and that the B2 scenarios comes quite close, indicates that many countries

will have water problems in all scenarios.

From an estimated value of 6,010 km3 in 2005, the water use increases to 15,091 km1 in A1, an increase

of 151%. In A2 it also increases substantially, to 12,497 km3, a rise of 108%. Water use in the B scenarios

are significantly lower, 8,682 km3 in B1, an increase of 44%, and 9,783 km3 in B2, an increase of 63%.

Figure 47: Total water use in 2005 and 2050. Lines indicate global thresholds for moderate water stress (lower line) and critical water stress (upper line), components in the legend are shown bottom-up in the bar chart.

Furthermore, comparing global water use to global average water stress thresholds may underestimate

regional issues. Figure 48 below shows the regional situations for the A2 and B2 scenario (note that the

values on the y axis differ). As can be seen, the ASIA region exceeds the moderate water stress threshold

in the A2 scenario, by 84%, and is only 8% short of the critical water stress threshold. The OECD90 region,

the REF region and the ALM region come quite close to the moderate water stress threshold; they are

respectively 6%, 0.5% and 5% short of this threshold.

M.Sc. Thesis

I.Y.R. Odegard

112

Figure 48: Regional water use in A2 and B2, components in the legend are shown bottom-up in the bar chart. Lines indicate the

moderate water stress and critical water stress levels (the latter is not shown for B2).

In B2, water use in the ASIA region also exceeds the moderate water stress level, by 47%, and it is 26%

short of the critical water stress limit. Water use in the other regions are below the moderate water

stress thresholds, respectively 30%, 38% and 25% for the OECD90 region, the REF region and the ALM

region.

Water Use – Analysis

The water use was modeled at 6,019 km3 per year in 2005, which is somewhat lower than the estimate

made by the Water Footprint Network: 6,189 km3 per year [Hoekstra, 2008]. The International Water

Management Institute estimates water use in agriculture to be even higher: 7,130 km3 per year [De

Fraiture, 2007]. Irrigation was not taken into account in modeling the 2005 situation, which means that

water use is slightly underestimated because irrigation efficiency is around 60%. As can be seen in Figure

47, in all scenarios the bulk of water use is required for cereals production. The slightly higher irrigation

efficiency, a raise from 60% to 65%, saves respectively 3% and 1.5% of total water use in B1 and B2.

Furthermore, because certain commodity groups, i.e. stimulants, nuts and spices (totaling only 5.5% of

global water use in agriculture in 2000 [Hoekstra, 2008]), are not taken into account, a lower 2005

estimate is reasonable. As Figure 47 shows, high feeding efficiencies come at a price: high water use in

agriculture. As it is, A1 and A2 both already exceed the moderate water stress threshold, by respectively

43% and 18%. Their water uses are respectively 15,091 km1 in A1 and 12,497 km3 in A2, which represent

increases of respectively 151% and 108%. Water uses in the B scenarios are significantly lower, 8,682

km3 in B1, an increase of 44%, and 9,783 km3 in B2, an increase of 63%.

M.Sc. Thesis

I.Y.R. Odegard

113

7.4 Fertilizer Use

As was shown in Chapter 3, actual fertilizer use as estimated by the FAO, requirements based on

recommendations by the FAO and an aggregation of average regional use differ. The fertilizer use was

modeled using the use levels based on estimated requirements, combined to higher fertilizer efficiencies

in the A1, B1 and B2 scenarios. Figure 49 shows nitrogenous fertilizer use levels for the year 2005 and for

the four scenarios in 2050. Two assessments of fertilizer use in 2005 are given. ‘2005-FAO’ is the total

amount of N-fertilizer used in 2005, as estimated by the FAO. This is given as an aggregated value, and is

not available per commodity group (it is shown in dark blue, but includes all commodity groups, not just

irrigated cereals). It includes fertilizer used on intensively managed pastures. ‘2005’ is the modeled use,

and as can be seen is a little higher: 13%. Fertilizer use for the four scenarios was modeled using the

estimated recommended use rates based on requirements.

Figure 49: Nitrogen fertilizer use in 2005 and 2050. '2005-FAO' indicates actual use as estimated by the FAO, while '2005' indicates the use rate modeled with requirement levels.

Figure 49, Figure 50 and Figure 51 respectively show N, P2O5 and K2O fertilizer use for the total

production, including feed and other uses such as seed and other utilities. Fertilizer use follows

production; it is defined as fertilizer input per generated output. The reason the increases in fertilizer use

are not equal to the increase in total production (in tons produced) is because the composition of the

total production changes. Table 38 shows the increase in the total production, and the increases in

fertilizer use compared to the situation in 2005. The comparison is made based on the recommended

use rates as shown in the figures under ‘2005’.

M.Sc. Thesis

I.Y.R. Odegard

114

Table 38: Increase in total production compared to increases in fertilizer use.

Scenario Increase in total production of crops

(increase in % since 2005)

Increase in total fertilizer use

(increase in %, compared to ‘2005’ –

estimated requirements)

N P K

A1 80 408.5 277.3 455.4

A2 73 127.0 103.3 46.6

B1 6 -2.5 4.8 4.7

B2 40 19.2 24.4 -18.8

While fertilizer use does follow production, increased fertilizer efficiency can lower fertilizer use while

maintaining yields. Fertilizer use on irrigated cereals in A1 and A2 seem equal, and indeed their use rates

are very close. However, production of irrigated cereals is 15% higher in A1 than in A2, and fertilizer

efficiency is 10% higher. B1 also provides an interesting example. While total production in B1 is 6%

higher than it was in 2005, N-fertilizer consumption was 2.5% lower, while use of K2O and P2O5 fertilizers

is respectively 4.7% and 4.8% higher.

Figure 50: Phosphorous fertilizer use in 2005 and 2050. ‘2005--FAO' indicates actual use as estimated by the FAO, while '2005' indicates the use rate modeled with requirement levels.

M.Sc. Thesis

I.Y.R. Odegard

115

Figure 51: Potassium fertilizer use in 2005 and 2050. ‘2005--FAO' indicates actual use as estimated by the FAO, while '2005' indicates the use rate modeled with requirement levels.

Fertilizer Use – Analysis

The most striking result concerning fertilizer use are the high use rates in A1. Use of fertilizers to produce

fodder (animal feed) is much higher in the A1 scenario than in the other scenarios. The reason for this is

that permanent pastures, as well as cropland pastures and harvested-conserved grass-legumes, are

intensively managed. These three categories are included in the ‘fodder’ category in the fertilizer use

figures. It illustrates a common topic in sustainability issues; a trade-off is made land use and water and

fertilizer use. Land use is relatively low in A1, but water use and fertilizer use exceed the limits of reason.

Fertilizer use for fodder is much lower in A2, even though total meat is also high, however, pastures are

not intensively managed in A2, which is reflected in the much higher land use for pastures in A2.

There are a number of other interesting aspects. Fertilizer use on irrigated area is similar in the A

scenarios; this is a coincidence. While irrigated area is the same in both regions, yields are higher in A1

and thus is the associated fertilizer use. Furthermore, there is reason to believe, as described in Section

3.3, that the current NPK ratio of application is too much in favor of N and P fertilizer, while K2O fertilizer

is underused. This explains the gap between what the FAO estimates as use (‘2005-FAO’) and what was

estimated as recommended use (‘2005’). Because K2O and P2O5 fertilizers are non-renewable resources,

thresholds of maximum use cannot be given, as they were for land and water. However, the use rates

can be checked against reserve bases to give an indication whether the use rates exceed reasonable

limits, which is shown in Table 39.

M.Sc. Thesis

I.Y.R. Odegard

116

Table 39: Fertilizer use and reserve base [based on IFA, 2011; USGA, 2009].

Average Use

(between 2005 and 2050 - modeled)

Use in 2050

Reserves left

(‘000 ton) (‘000 ton) (in years at 2050 use-level)

A1 K2O 238873 449580 2

P2O5 113622 177340 55

A2 K2O 73413 118660 66

P2O5 72733 95563 121

B1 K2O 56457 84748 100

P2O5 49572 49241 254

B2 K2O 46988 65811 135

P2O5 54238 58572 211

Note: Average use rates between 2050 and 2005 were based on the average between use in 2005 and modeled use in 2050. Use

in 2005 was taken as the average of production in the years 1999-2009. Assessment of the reserve base includes reserves that

are currently uneconomical to mine, but that may be in the future.

Table 39 may give an explanation for the lower than optimal application rates of K2O. The K2O reserves

will be depleted by the year 2051 at the levels applied in A1. In A2 too resources are depleting fast, there

will only be 67 years’ worth of K2O resources left. The B scenarios show more promising figures; in 2050

there will be over 100 years left for K2O and over 200 years for P2O5.

M.Sc. Thesis

I.Y.R. Odegard

117

8. Conclusions

The aim of this study was to design four food scenarios for the year 2050 and to evaluate these

quantitatively with respect to their use of the natural resources land, water and fertilizers. To achieve

this, a model was designed which would enable the evaluation of natural resource use with the Virtual

Resource Content concept, for the different scenarios and for the seven main vegetal food commodity

groups and 5 main animal food commodity groups. In the previous chapter the land use, the water use

and the fertilizer use have been presented and analyzed. In this chapter an answer will be given to the

main research question: What are the regional and global consequences with respect to the use of

natural resources – concerning land, water and fertilizers – for four food scenarios evaluated for the

year 2050?

8.1 Scenario Conclusions

Figure 52 shows the main characteristics of the four scenarios. For the A2 and B2 scenarios the results

were regionally and globally specified, while results were given on a global scale for the A1 and B1

scenarios. For the regional scenarios self-sufficiency for the four regions was evaluated, while for the

global scenarios a global average diet was modeled and evaluated. Each scenario has its own story to tell

with respect to its use of the natural resources land, water and fertilizers. These stories will be given in

the next sections.

A2

The Full World

High Population Growth

Low Economic Growth

Slow Spread of Agro-Technology

Medium Fertilizer Efficiency

Low Irrigation Efficiency

Medium Productivity

Western Diet

B1

The Vegetarian World


Medium-High Economic Growth

Spread of Sustainable Agro-technologiy



High Productivity

Vegetarian Diet

B2

The Low-Input World

Medium Population Growth

Low-Medium Economic Growth

Spread of Sustainable Agro-technology



Low Productivity

Organic Diet

A1

The Affluent World


High Economic Growth

Rapid Spread of Agro-Technology

Medium Irrigation Efficiency


High Productivity

Western Diet

RegionalizationRegional Food

Distribution

GlobalizationGlobal Food

Distribution

EnvironmentEfficient Use of Natural

Resources

EconomicUnrestrained Use of

Natural Resources

Figure 52: Scenario characteristics

M.Sc. Thesis

I.Y.R. Odegard

118

8.1.1 A1 – The Affluent World

The question central to the A1 scenario – The Affluent World – is: What will the effects be given a

worldwide shift to Western agricultural management practices and a Western Diet?

Production of foodcrops increases by 80% (weight basis) in the Affluent World. This is due to the high per

capita apparent consumption of meat: 93 kg per capita per year. This is higher than the current apparent

consumption in the Netherlands ~73.8 kg in 2005. It is, however, much lower than the level in the USA

123.6 kg. Another factor is the relatively high losses and wastes in A1; 28% of total caloric supply is

wasted in retail and households. The losses for other purposes than food and feed are proportionate to

the total production, and thus high in the Affluent World because production of feed is relatively high.

The FAO undernutrition threshold of 2900 kcal/cap/day for apparent consumption is passed. However,

because household and retail waste is high, intake is estimated at a lower level than for the Full World,

even though levels of apparent consumption in the Full World are lower.

Not only meat consumption follows a ‘western’ trend; feeding efficiency is high and so are yields. The

Affluent World clearly illustrates the trade-off that can be made between the different resource uses.

Land use for crop production increases 31%, even though corresponding production increases 80%,

while land use for pastures and such decreases 41%. This has to be compensated elsewhere, and water

use increases by 151%, and exceeds the global average threshold for moderate water stress by 43%.

Furthermore, fertilizer use – including pastures and such - increases between 277% and 455%. Especially

these last figures show the effects of intensive management of pastures, for example, of the total

increase in use of N fertilizer, 59% is used for intensive management of pasture lands. In light of the

reserve base, this is clearly an undesirable and uneconomical situation, as only 1 year (K2O) and 52 years

(P2O5) of resources remain in 2050.

The high fertilizer use and high water use in the Affluent World illustrate a common topic in

sustainability issues; a solution may seem sustainable from one angle – land use is low in the Affluent

World – but unachievable and impractical from another – fertilizer use and water use are extremely high.

8.1.2 A2 – The Full World

The main question in the A2 scenario – The Full World – is: What will the effects be when regions have

to be self-sufficient in a world where population growth was high and economic development low

between now and the year 2050?

Because of the low economic and technological development, yields increase at a much slower rate in

the Full World scenario; only 20% of the yield gap is closed. Meat demand increases along with PPP, and

thus increases significantly; 60% on a global level relative to 2005, increasing from 39 kg/cap/year in

2005 to 62 kg/cap/year in 2050. This is still much lower than the increase in meat consumption in the

Full World, which is 140%. With a population that is 27% higher in the Full World than in the Affluent

World, total production is only 4% lower in A2, corresponding to an increase of 73% relative to 2005.

M.Sc. Thesis

I.Y.R. Odegard

119

Because yields and feeding efficiencies only improve a little, land use is high. Land area under cultivation

for foodcrops (including those used for feed and other purposes) expands by 50%. Land for pastures and

such almost doubles; it increases by 193%. The fact that pastures are less intensively managed is

reflected in the lower fertilizer uses; they increase by between 47% and 127%. Still substantial increases,

but significantly lower than those in the Affluent World.

Because the Full World is regionally specified, regional results deserve attention. The land area

potentially suitable for agriculture in the Full World is defined as between 70% and 90% of the total

available area of very suitable, suitable and moderately suitable land. The OECD90 region and the ALM

region both use little over half of the lower limit, respectively 55% and 56%. The REF region comes quite

close to the lower limit, it is only 6% short. The ASIA region exceeds both limits, and thus will not be able

to supply its demand, even with higher cropping intensities. Water use increases by 108% in the Full

World, globally leading to an exceedance of the moderate water stress threshold by 18% of water a year.

Similar to land, use of water in the ASIA region is problematic. This region exceeds its moderate water

stress level by 84%, and is only 8% short of the critical water stress level. The other regions come quite

close to their respective moderate water stress levels; the OECD90, REF and ALM region are respectively

6%, 0.5% and 5% short of this level. Use of fertilizers is high, but can be sustained for another 67 years

for K2O and 115 years for P2O5 if levels are retained at their 2050 value.

8.1.3 B1 – The Vegetarian World

The question central to the B1 scenario – The Vegetarian World – is: What will the effects be of a global

shift to a vegetarian diet?

Because of higher equity in the B scenarios, the undernutrition threshold was lowered to 2800

kcal/cap/day, and even though total production only increases by 6% this threshold is exceeded.

Compared to 2005, the share of crop production for food and feed used as feed drops from 37% to 9%.

High yields and feeding efficiencies, combined with low consumption of animal products make the

Vegetarian World the scenario with the lowest use of natural resources. The harvested cropland area

decreases relative to 2005; it drops to 0.86 billion hectares, a reduction of 33%. Water use increases by

44%, to 8,682 km3.

Furthermore, production of fruits and oil crops increases, while production of vegetables and sugar crops

is lower than in 2005. The former two have higher water needs per generated ton; respectively an

average of 844 m3/ton and 2209 m3/ton, to 264m3/ton and 165 m3/ton for vegetables and sugar crops.

Even though water use increases by 44%, it is still 1911 km3 or 18% short of the moderate water

threshold. Fertilizer use does not change much relative to 2005; use of nitrogen fertilizer decreases 2.5%

relative to 2005, while use of phosphorous and potassium fertilizers increase respectively 4.8% and 4.7%.

The Vegetarian World shows a much better situation regarding the remaining reserve base of these

fertilizers; use can be sustained for another 103 years for K2O and 246 years for P2O5 if levels are retained

at their 2050 value.

M.Sc. Thesis

I.Y.R. Odegard

120

8.1.4 B2 – The Low-Input World

The B2 scenario – The Low-Input World – revolves around the following question: What will the effects

be when regions have to be self-sufficient given low-input agricultural practices?

The Low-Input World is characterized by medium population growth, medium economic development

and a meat consumption which is similar to the global level in 2005, but more equitable distribution

between the regions. The main characteristic, and the most significant and influential, are the lower

yields as a results of reduced inputs of fertilizers. Thus while total production of crops increases by 40%,

and only 19% of the production for food and feed is dedicated to feed, the global land use increases by

165%. Furthermore, regional land use exceed the defined limits for the Low-Input World; the 70% limit

of very suitable and suitable land. Because pasture lands are not intensively managed, this area increases

from an estimated 2.3 billion ha in 2005 to 5.3 billion hectares in 2050. Water use increases by 63%, to

9,783 km3, only 809.6 km3 or 7.6% short of the moderate water stress threshold. A closer look at regional

values shows that the ASIA region exceeds the threshold, by 47%. The other regions – the OECD90 region,

the REF region and the ALM region – are respectively 30%, 38% and 25% short of their moderate water

stress thresholds. As expected, fertilizer use increases at much lower rates than it does in the A scenarios.

Use nitrogen and phosphorous fertilizer increase with respectively 19.2% and 24.4%, while use of

potassium fertilizer decreases by 18.8%. The Low-Input World aims to reduce external inputs in

agriculture. It shows a much better situation regarding the remaining reserve base of these fertilizers

than the A scenarios; use can be sustained for another 140 years for K2O and 203 years for P2O5 if levels

are retained at their 2050 value.

8.2 Resource Use in Agriculture

The previous sections elaborated on conclusions per scenario. Figure 53 below shows the main results

regarding land use, water use and fertilizer use in the different scenarios in 2050. The figures can all be

found, with full explanation, in Chapter 7. Here they are presented to explain the main conclusions. As

can be seen in the figures, land use exceeds availability in the B2 scenario, the Low-Input World.

Furthermore, in both A scenarios, the Affluent World and the Full World, the moderate water stress

threshold is exceeded. Relatively low land use in the A1 scenarios, the Affluent World, is offset by very

high fertilizer use. The only scenario in which the limits to the natural resources are respected is the B1

scenario, the Vegetarian World.

M.Sc. Thesis

I.Y.R. Odegard

121

Figure 53: Scenario results - land use, water use and fertilizer use in 2050 for the four scenarios.

In any study aiming to give result on a global level, data availability and reliability is an issue. Therefore,

when evaluating the results of the scenarios it is important to keep in mind that they are ‘what if?’

scenarios, based on a list of assumptions. If the quantifications to these assumptions would change,

naturally, so would the results. It is important to remember that scenarios of this kind are not made to

assess what will happen, but what could happen, given such a set of consistent assumptions. The results

should also be evaluated in this light. Because economic variables, such as prices, were not taken into

account, the effects on resource use were modeled given the assumption that a given demand is fulfilled.

It is not reasonable, however, to assume that if this creates an unsustainable situation, such a situation

would not occur; such situations occur at present, e.g. countries in the Middle East using more than

M.Sc. Thesis

I.Y.R. Odegard

122

100% of their renewable water in agriculture. Assuming that situations that seem untenable will not

occur is thus too simple a proposition. The results show that trade-off issues are important and need to

be addressed when discussing the future of food; it is important to show the impacts of all three major

inputs when presenting scenarios. This is substantiated by the results shown in Figure 53, which clearly

show that for example low land use in the Affluent World/ A1 scenario is offset by high fertilizer us. The

opposite is true for the Low-Input World/ B2 scenario; land use exceeds the limits of availability, while

fertilizer use is relatively low. An assessment of resource use is only valuable when a complete picture is

given. Therefore, the present study provides valuable input for assessing problem areas, but also for

identifying opportunities in our agricultural system.

Resource use differs widely between the scenarios. While land use in the Affluent World is low, water

use and fertilizer use exceed the limits, which makes it an unsustainable and even improbable situation.

This is also true for the Full World, which shows that all regions are close to, or by far exceed (i.e. the

ASIA region) the moderate water stress level. As total production in the Affluent World is similar to that

of the Full World, it is certain that similar regional figures for water use apply to the Affluent World. The

Low-Input World shows the opposite case; while fertilizer inputs may be low, land use exceeds all limits.

Only in the Vegetarian World all the resource use is within the limits these resources pose.

From a demand-side perspective it can be concluded that current trends in meat consumption cannot

continue indefinitely due to lack of natural resources to support such a system. Moreover, a situation

such as in the Affluent World were fertilizers run out between 2050 and 2100 would never progress to

such a stage because fertilizer prices would have risen too much to justify such use. Or, put differently,

such use rates would raise meat prices to a level where they would only be affordable to the very rich,

and the global average consumption would not reach the modeled levels. This study clearly illustrates

the inequity in the food system: the lifestyles of many in the industrialized world cannot be supported on

a global scale. One potential development could be that as food prices increase due to strains on natural

resources, people’s motivation to decrease their food waste rises. However, food wastes in households

and retail are highest in the industrialized world – exactly where people can afford such wastage. What

this scenario study has shown is that it is possible to feed a growing world population a balanced and

adequate diet, i.e. a vegetarian diet, while respecting the limitations our natural resources pose. It is,

however, unlikely that people are willing to give up meat consumption.

This study also points out opportunities in our agricultural system. From a supply-side perspective it can

be concluded that technological development is of vital importance. Yield increases reduce land

requirements and technological development can improve the efficiencies of irrigation and fertilization.

The potential for growth in the developing countries is large, and also quite necessary in view of the

population increases that are to be expected. The current low yields in many developing countries reflect

the potential for development. Such development is not something which will happen naturally; it will

take long-term dedication of such stakeholders as governments, industry, development agencies and of

course farmers. Furthermore, the Low-Input World scenario has shown that it is absolutely necessary to

increase yields, while the Affluent World scenario shows that even maximizing yields and feeding

efficiencies is not enough if a western diet is adopted on a global scale. These results give a clear

message to decision makers on all levels of society – government, companies, retail and households.

M.Sc. Thesis

I.Y.R. Odegard

123

Because the current diet in the industrialized world cannot be sustained on a global scale, people in the

industrialized world will have to change their lifestyles if they want to have a more equitable world, or

accept the fact that their food choices have large scale consequences. This may be a tenable situation for

individuals, as long as they can afford what they consume, however, governments will have to make far-

reaching decisions if they are serious about reaching global equity and eliminating undernutrition.

M.Sc. Thesis

I.Y.R. Odegard

124

9. Discussion

It would be interesting to compare the results presented in this study with other studies. Because, by the

authors knowledge, there are no studies that assess the future use of all three resources, the scope for

comparison is limited. Other studies do not assess a complete diet, have a limited time frame or only

assess a single scenario. For example, an interesting scenario study by Wirsenius was published in August

2010, which explores the effects of increased feed-to-food efficiency, dietary changes, and decreased

wastage. Clearly, the issues identified as important correspond to the ones in this study. However,

because the scenarios are made for 2030, and include only one population growth trend, comparison

could only be done on a very basic level, and is not within the scope of this study. It does show the

relevance and popularity of the topic, and – because of the different scope – the importance of further

research of food scenarios as done in this study.

A general issue encountered in processing data was the fact that different organizations use different

definitions of regions. While the IPCC definition is useful from the perspective of economic development,

and therefore of diet change, a lot of work went into processing data to fit the IPCC regions. A solution

would be to model on a country level, which is outside the scope of this study. The need for processing

data to fit the current regions did help in getting acquainted with the data, which helped in

understanding results. The PPP data which was used for the calculation of meat consumption projections

was defined per sub-region and not per country. Using country specified data will give more detailed

results. Other data issues are related to the fact that the FAO was the prime source of agriculture data.

First of all, when this study was started, data was not available online. Halfway through, data concerning

production and consumption was put online, in Food Balance Sheets, for 184 countries, although data

was not available for every one of those countries. Furthermore, even though the FAO provides the most

complete data, their data can be very intransparant and the FAO did not respond to numerous emails

asking for clarification of different issues. Cropping intensity deserves clarification in the FAO databases,

as does fertilizer use per crop (although both may be difficult to monitor/assess/calculate/balance), total

agricultural area and fallow land, and the way in which calories are calculated from the food supply.

Furthermore, as data is still being added, the different parts of the database do not always match. For

instance, even though Singapore has a very small agricultural output, which is reported under Production,

it is not even listed as a country under Commodity Balances – which is where data is given on e.g.

production, but also import, export, domestic supply quantity and food. Other such instances have been

encountered. For example, data for a number of countries is not given at all. Data for these countries

seems to have been set at zero before (in the FBSs), however, this could be confusing as it was not clear

whether the data entry actually was zero or unknown. Now these countries seem to be eliminated from

the list, which seems odd too, as they are quite large; e.g. Afghanistan and Iraq. Also, concerning oil

crops and sugar crops, the FAO states that ‘conversion factors are applied to values when calculating

totals’, which total and which conversion factors they mean is not specified. The item ‘metadata’ to

which is referred for further information does not contain any information on these issues. This results in

issues like the following. Production of oil crops (Oilcrops + total) under Commodity Balances, for Brazil

in 2005 gives a result of ~57*106 tonnes. Under Production, Oilcrops Primary + (Total) yields a result of

M.Sc. Thesis

I.Y.R. Odegard

125

~10*106 tonnes. The sum of the produced quantities (also under Production) of individual oil crops is

~62*106 tonnes. In short, the data should be handled carefully, and this study, and studies such as these

would benefit from a sensitivity analysis, were the strength of the results are tested by varying the input

parameters. For this study, such an analysis carried too far, but it can be kept in mind that a scenario

study already gives an idea about the importance of different inputs by incorporating different trends.

Furthermore, FAO data was mainly used to provide insight into the current situation and to be able to

compare future situations to the current situation. The scenario diets were modeled for the total global

population, and compared to total global availability of resources. This way gaps in FAO data were by-

passed.

The value of the results and the limitations of the model and the data which was used will be elaborated

on by discussing a number of relevant issues; assumptions related to fertilizer use, losses, wastes, sugar

and oil, cropping intensity and yield projections will be discussed in the following sections.

Virtual Fertilizer Content

Fertilizer use was modeled using estimates of requirements recommended by the FAO [FAO, 1984].

Aggregated (over the different commodity groups) estimated requirements for N and P2O5 came quite

close to the FAO estimate. For K2O, however, estimates of requirements were much higher than the FAO

estimate of current use. Because there is reason to believe that the NPK ratio is currently not optimal,

with a preference for N and P, Virtual Fertilizer Content was modeled using the requirement estimates.

There are several issues regarding the required use rates, which will be discussed here. First, the relation

between fertilizer input and generated product was assumed linear; fertilizer use was defined as input of

N, P2O5 or K2O per ton of generated product. However, the fertilizer response curve usually approaches a

maximum value; yield response to additional input is marginal. Thus, for high yielding crops, total

fertilizer needs may be overestimated. To counter this issue to some extent, the use rates were based on

recommendations for high-yielding crops; also a valid assumption since the FAO source which was used

to estimate requirements was written in 1984, and improvements have been made since. This brings us

to the second issue; recommendations may have changed since 1984. Since the fertilizer requirements

were established and used to model the Virtual Fertilizer Content, another, more up-to-date FAO

document was found in which fertilizer requirements are discussed. It was beyond the scope of this

thesis to check whether more recent data would change the applied use rates. A third issue, also

connected to the recommended requirements, is the basis on which the rates were established for the

commodity groups sugar crops and vegetables. These were based on one of the commodities in each

group; sugar cane for sugar crops and tomatoes for vegetables. Sugar cane accounts for 84% of total

sugar crop production, and since no recommendations are given for sugar beet, on a global level this is a

reasonable assumption. For the OECD90 and REF region, however, with temperate climates and thus a

preference of sugar beet over sugar cane, this may not be the case. As requirements for sugar beet are

unknown, it is uncertain whether this leads to overestimating or underestimating fertilizer use. However,

fertilizer consumption for sugar crops is relatively low, and thus total fertilizer use will not change

substantially. For the vegetable commodity group, requirements are only given for tomatoes, cucumbers

and onions. Cucumbers and onions account for respectively 4.9% and 7.7% of total vegetable production.

Tomatoes are the single largest vegetables product; they account for 14.7% of total production.

M.Sc. Thesis

I.Y.R. Odegard

126

Recommendations for onions and tomatoes are similar. Because those for tomatoes are a little lower,

and tomatoes account for a larger share of total production, those rates provide the most reasonable

estimate. Of course, soil characteristics are important for establishing local fertilizer needs. It is assumed

that local conditions which would lead to overestimates or underestimates counter each other out.

Since assessment of the scenario results, it was found that nitrogenous fertilizer needs were by mistake

undervalued by a factor 10. Of course, in future assessments this should be adjusted. Total nitrogen

fertilizer use would increase respectively 5%, 11%, 17% and 16% in the A1, A2, B1 and B2 scenarios.

Losses

There are several issues related to losses and wastes in the food system that deserve to be discussed.

First, there is the assumption that the fraction which is used for purposes other than food or feed – seed,

waste, other utilities and processing, calculated per commodity group – is a constant relative to the total

production. For seed this is a reasonable assumption. The FAO acknowledges that waste – during storage,

transport and processing – is estimated as being a fraction of the total production; it is not measured.

While some argue [Bender, 1994] that wastes are lower in industrialized countries, it is actually the case

that wastes are estimated as being higher in developing countries because these have in general

different climates – more humid and hotter. For the use of foodstuffs for processing and other utilities it

may be more reasonable to relate production levels to either population or economic development.

Processing of food products increases with economic development. This can be reasonably assumed to

be the case for other utilities too. The current assumption does lead to the result that for scenarios in

which meat consumption, and thus subsequently feed production, is high (A1), but for which population

growth is equal and economic development similar to another (B1), food production for other purposes

is higher. For the commodity groups oil crops, sugar crops and for sugar and sweeteners and vegetable

oils, pretty much all (>94%) of the production for other purposes goes to processing and other utilities.

For fruits and vegetables, and inherently for vegetable oils and sugar and sweeteners, as they are

derived products, the FAO explicitly does not report seed. Processing accounts for little over half of the

total fruit production for other purposes (52%), for which wine production is excluded. The figure for

cereals is similar: 56%. For the commodity groups roots and tubers, pulses and vegetables, these figures

are much lower, respectively 35%, 10% and 1%. Because of the increased demand for animal products in

the A scenarios, use for other purposes per capita goes up. In the B scenarios, however, it goes down,

and because of the link to economic development, use for other utilities and processing may be

underestimated in those calculations. Also, because foodstuff use for processing and other utilities is

correlated to the level of economic development, and global average values are used, the estimates for

other uses may be overestimated for developing regions, and underestimated for industrialized regions.

The previous issue links to the production of alcoholic beverages. These are the product of the

processing of either cereal crops or fruit crops. Because of conversion issues, they are not included in the

FAO food supply and commodity balance data for cereals and fruit. It cannot be reported under

‘processing’ because this category is reported after ‘import’ and ‘export’. Production as reported by the

FAO for the year 2005 of cereals excluding alcoholic beverages is 10% lower than the reported total

production. For fruit this is only 1%, which makes inclusion less relevant.

M.Sc. Thesis

I.Y.R. Odegard

127

Wastes

Occurrence of household and retail waste has been studied by various researchers. However, a study

resulting in a clear, and for such purposes as this study useable, correlation between such wastes and

economic development and/or other factors, is lacking. Estimates for household and retail wastes in this

study were based on estimates made for the year 1995 for the USA [Kantor, 1997], as this source

provided the most detailed and clear numbers. These levels of household waste by Kantor were defined

as portion of the edible food supply. This implies that specifically for the vegetable commodity groups

fruits, vegetables and roots and tubers the household waste is underestimated, as refuse is included in

the apparent consumption. Furthermore, animal meat products are reported in slaughter-weight, which

is bone-in. Whether inclusion of refuse is also true on a caloric level, is unclear. The FAO did not answer

questions about the methodology used to calculate the caloric supply. For example, the FAO estimates

the caloric value of 100 grams of apple in the Netherlands to be 50 kcal, whereas the Dutch Nutrition

Centre estimates 100 grams of apple to contain either 58 kcal (without peel) or 60 kcal (with peel). For

bananas, the FAO estimates 100 gram to contain 70 kcal, while the Nutrition Centre gives a value of 95

kcal. Similar figures apply to roots and tubers. While the FAO estimates 100 grams of potato in the

Netherlands to contain 67 kcal, the Nutrition Centre estimates 100 grams of boiled potatoes to be 83

kcal. For the two vegetables for which both organizations give data, tomato and onion, the estimates by

the Nutrition Centre are – only slightly – lower than those by the FAO. Thus while the estimates of intake

may be overestimated because wastes are given as portion of the edible food supply, the supply or

apparent consumption may be underestimated, given that the caloric values for fruits and roots and

tubers given by the FAO are relatively low.

For determining the resource use in the scenarios, wastes play an additional role because in the A2 and

B2 scenarios, in all regions except for the OECD90 region, non-eaten food is fed to pigs. In case of lack of

availability, true for both scenarios for both the ALM region and the REF region, it is substituted by

vegetables.

Sugar and Oil

Production of sugar crops and of oil crops is characterized by the fact that their primary form is relatively

unimportant in people’s diet, but that their processed products are. This makes it necessary to make

assumptions about extraction rates. Because both commodity groups incorporate various different crops,

such extraction rates may differ between regions because of a different commodity group composition.

For example, oil content of soybean is 18% (weight basis), while it is 43% for groundnuts. Yields are

reported by the FAO in converted values for countries and regions, but in unconverted values per crop.

There are several reasons why it is necessary to know demand in unconverted values. Oil crops in their

primary form are included in the feed-mixes and by-products from vegetable oil production and sugar

production are also used in the feed-mixes. Furthermore, sugar crop and oil crop yield projections

concerning maximum attainable yields are given in unconverted values. Finally, oil crops and sugar crops

are part, although not substantial, of people’s diet in their unconverted form. Current yields of these

commodity groups were estimated by dividing total production, in unconverted values, over the

harvested area. Maximum attainable yields (MAYs) were based on the MAYs for different crops,

M.Sc. Thesis

I.Y.R. Odegard

128

proportionate to current regional and global production levels. The underlying assumption is that shares

of different crops in the total, both globally and regionally, will not change. Conversion factors were used

to convert the amount of vegetable oils and sugar and sweeteners to respectively oil crops and sugar

crops. These conversion factors are based on global data for 2005. It was assumed that all processing of

oil crops and sugar crops is done to yield vegetable oils and sugar and sweeteners. On a weight basis,

only 36% (oil) and 12% (sugar) is extracted. Because it is beyond the scope of this thesis to make regional

projections for conversion factor, and furthermore to increase transparency and bypass the trade issues

involved in regional calculations, global averages were used.

An interesting note about the area under cultivation for oil crops worldwide; two months ago this area

was reported as 2.52*108 hectare for the year 2005. Now it is reported as 0.54*108 hectare. This has a

significant influence on yields and warrants further investigation.

Cropping Intensity

The FAO reports yields in tons per harvested area, not in tons per area per year. This makes comparison

of yields per harvest possible and easy, however, in the case of multiple cropping, yields per year per

hectare may be twice or three times as high. Some data are given relating to cropping intensity. These

data are not, however, given for all countries, e.g. the only OECD90 country listed is Turkey. Furthermore,

just how the multiple cropping occurs is unclear; which crops are alternated for example. Furthermore,

multiple cropping is reported in terms of how much of the irrigated area is cultivated per year and per

crop, resulting in a higher than 100% intensity if the aggregate is higher than the area equipped for

irrigation. For some countries, the cropping intensity is lower than 100%. It is unclear whether such land

equipped for irrigation is not used for cultivation at all, or whether the irrigation systems on those lands

are unused. To retain transparency, results relating to land use are given in harvested area, and not in

land under cultivation.

Irrigation and Yield Projections

Irrigated area accounts for around 20% of arable land and permanent crops – between 10% and 12% in

the OECD90, REF and ALM region, and around 34% in the ASIA region. Because the difference between

current and historic irrigated yields and rainfed yields of crops other than cereals is unclear, a distinction

was only made for cereals. However, high yields in the OECD90 region indicate that irrigation may be

common for other crops as well, e.g. for sugar crops and roots and tubers.

Maximum attainable yields are important for establishing the yield projections, and are higher for

irrigated crops than for rainfed. As no historic data were found on the difference between irrigated and

rainfed yields for the different commodity groups in the different regions, yield projections were based

on the current yields given by the FAO, which include both rainfed and irrigated areas. The maximum

attainable yields used in these projections were based on the MAYs for rainfed agriculture. MAYs for

irrigated agriculture are higher, but, as was shown, water use already exceeds the moderate water stress

limit in the A scenarios, while the B scenarios come quite close to this limit.

M.Sc. Thesis

I.Y.R. Odegard

129

The assumption that 80% of the yield gap is closed in the A1 and B1 scenarios is a considerable challenge.

It is put somewhat in perspective by the use of the lower MAYs for rainfed agriculture. Still, it is generally

assumed that a closure of the yield gap by 80% is the highest which can possibly achieved. Furthermore,

such high yields may only be reasonably achieved on ‘very suitable’ land, while they were assumed to be

attainable across all land in the present scenarios. This results in scenario results which are on the

optimistic side.

Diet

The total caloric intake as determined for the diets in the different scenarios and the different regions

approximate the FA projections and therefore give a complete picture with regard to caloric intake. In

the A1, B1 and B2 scenarios the total food quantity in kcal/cap/day is slightly lower than the level of the

related FAO projection. In the A2 scenario this quantity is slightly higher in the OECD90 region and the

REF region, while slightly lower in the ASIA region and the REF region, which corresponds to the

difference in definition of regions between this study and the FAO definition. Overall this means that the

results as presented in Chapter 7 present a complete picture of resource use.

Caloric values of foodstuffs provide a way to add foodstuffs in a uniform way and the ability to evaluate

whether it is quantitatively sufficient. Quality cannot, however, be solely evaluated by checking the

caloric intake. While health effects and malnutrition of food intake are beyond the scope of this study,

some comments will be made here. Variety is important to receive adequate nutrition, which is

incorporated by the variety of commodity groups included in the diets. However, such variety does not

ensure that people make healthy choices, which is reflected in the fact that over half (50.1%) of the adult

population in 15 out of 27 EU countries was overweight and the average obesity rate in the EU was

15.5% in 2008 [OECD, 2010].

Fruit and vegetable consumption is not specified explicitly in the FAO projections concerning future

apparent consumption. Here, in the A scenarios, consumption of fruits and vegetables was set at the

global average consumption in 2005. This may, however, be too low an estimate. In 2005 the global

average consumption was 66 kg/cap/year for fruits and 117 kg/cap/year for vegetables. In comparison,

in the EU, these rates were on average respectively 105 kg/cap/year and 116 kg/cap/year. For vegetables,

with a slightly lower than global average supply in the EU, 10 countries exceed this level, ranging from

118 kg/cap/year for Belgium to 241 kg/cap/year for Greece [OECD, 2010]. It may therefore be

reasonable to assume higher levels of fruits and vegetables consumption. The level of consumption of

foodstuff in the miscellaneous category (undefined, but includes fruit and vegetables) increases with 51

kcal/cap/day between the year 2000 and 2050, according to the average global FAO projection for food

supply [Alexandratos, 2006]. In the B scenarios, consumption of fruits and vegetables was raised, in line

with recommendations from the European Commission to increase the consumption of fruits and

vegetables [OECD, 2010]. An important question related to meat consumption and resource use is the

type of meat consumed. The proportion between beef, pork and poultry meat was kept constant at the

2005 level. Scenario results would differ if the proportion changes. Cultural and religious beliefs play a

role in this; beef and poultry will be preferred in Muslim countries and pork and poultry in India. The FAO

projects higher growth rates for poultry meat than for beef and pork.

M.Sc. Thesis

I.Y.R. Odegard

130

10. Recommendations

In the previous chapter some methodological issues related to Virtual Fertilizer Content, losses, wastes,

sugar and oil, cropping intensity, irrigation and yield projections, and diet were addressed. Some

recommendations for further improvement of the model and its assumptions, and suggestions for

further research are given here.


The assumptions concerning fertilizer use and the recommended

requirements could be checked against more recent values.

Furthermore, as fertilizer use on pastures can account for a significant

part of total fertilizer use in a country, the relationship between

fertilizer use and pasture yields could be more thoroughly studied.

The Virtual Fertilizer Content factor of N for oil crops was miscalculated,

and should be ten times larger. This would increase the estimates

further, by around 5%-17% of total use, for the different scenarios.

The Low-Input World scenario (B2) could be extended to present an

‘Organic Agriculture’ scenario. Synthetic input use can be lowered

further by applying other, organic, inputs, or by changing agricultural

management practices. This topic is too large to fit into the scope of the

present research, but deserves attention because organic agriculture

has its advantages (as elaborated on in Section 3.3.4). In fact, research is

currently being done at Wageningen University concerning yields in

organic agriculture at a global scale [De Ponti, 2011].

Losses Currently alcoholic beverages are not included in the model or its

results. When this study was started, only information on cereal

production excluding alcohol was available. As around 10% of the total

production of cereals seems to be used to produce such drinks, it is

worthwhile to incorporate them.

Other utilities are currently included in the category losses, and are thus

assumed to increase linearly with increasing demand and thus

production. As other utilities include biofuels and other biobased

materials, this category may increase in importance in the future. It

would be interesting to include projections concerning e.g. government

policy related to biofuels into the food scenarios, or to use the basic

structure of the model to quantify scenarios related to biobased

products.

Wastes There seems to be confusion in the literature about what constitutes

‘wastes’ and ‘losses’, and this issue should be handled with care. The

terms are used loosely and they are not reported consistently. Wastes,

M.Sc. Thesis

I.Y.R. Odegard

131

thus household and retail wastes, but also wastes during storage,

processing and transport, are important factors related to food

production, and deserve the attention they are currently getting.

However, it is important that issues are clear. When the FAO estimates

‘storage, processing and transport wastes’ as a proportion of total

production in a country, based on its climate, it is nonsensical to try to

find correlations between these wastes and economic development to

make projections. It is similarly nonsensical to call losses such as seed

and other utilities ‘wastes’ as they clearly serve a valuable purpose.

Furthermore, all too often, it is unclear or dismissed that the food

supply values given by the FAO still include refuse.

Sugar and oil

There are a couple of questions related to sugar crops and oil crops

production that deserve further attention (all elaborated on in the

discussion section):

Why do the sugar yields in the OECD90 region exceed the MAY?

Will the assumed conversion factors change in case of a change in composition of the commodity group, e.g. increased demand for feed will favor soy production?

What is the correct area under cultivation for oil crops? The FAO data concerning oil crops is intransparant and deserves further

elaboration and clarification in FAOSTAT.

Cropping intensity

Cropping intensity should be reported along with area under cultivation,

for all countries and for all crops and commodity groups. Double

cropping can reduce land use, but is currently reported in a format

which is intransparant.

Furthermore, it is unclear whether significant areas in the OECD90 and

REF are left fallow; there is an inexplicable gap between the total area

under cultivation for the main commodity groups and the total

agricultural area.

Yield projections

It is reasonable to assume that different land qualities have different

maximum attainable yields, and these could be integrated in the model.

This could also be done for rainfed and irrigated agriculture for crops

other than cereals. Some of the strengths of the model and the

scenarios are their simplicity and transparency, which would be

compromised in case of much further elaboration of these matters.

However, because these matters have a great influence on future

supplies, they deserve attention, especially in combined to water use

and fertilizer use.

M.Sc. Thesis

I.Y.R. Odegard

132

Diet

Fruit and vegetable consumption are probably underestimated in the A

scenarios. Resource use in the A scenarios already exceeds our

biocapacity, adaption would further increase resource use.

Effects of changes in the composition of meat consumption pose an

interesting topic for further research. It may yield interesting results as

inputs are much low for poultry meat and pork than for beef, and

different types of meat are preferred in different cultural and religious

systems.

Recommendations regarding further research related to the Virtual Resource Content model can focus

on further elaboration of the VRC methodology, for which suggestions are pointed out above.

Furthermore, the current scenarios can be elaborated on, or other scenarios can be modeled. Finally, it

might be possible to link the methodology to other tools that assess resource use and its impacts.

Regional results were modeled for the A2 and B2 scenarios. This could also be done for the A1 and B1

scenarios. The regional assessment can be further elaborated on by estimating the resources available

per capita in the four regions. This, together with supply and demand data can be used to optimize trade

schemes. Furthermore, as pastures are an important part of the animal diet, and fertilizer inputs are very

important in the A1 scenario to achieve the high yields necessary to maintain low land use, it is

worthwhile to explore different scenarios concerning pasture utilization. Additionally, a lot of interesting

data can be extracted from the model, e.g. the inputs needed to produce a kg of protein of a kcal, the

efficiency of meat production, the influence on resource use of different types of meat.

Also interesting would be the creation of other scenarios, and quantifying their effects on resource use.

For example, bio-based materials or fuels have been mentioned, which could become more important in

the future. Other scenarios can be used for different purposes, for example increasing consumer

awareness by evaluating current consumption patterns. For instance, the resource use in agriculture of

an average Dutch diet could be calculated and compared to the global average, or the resource

requirements, would the global population consume a Dutch diet. Linking this model to a calorie check,

such as the one at Voedingscentrum.nl, may be interesting and give further incentive to change

unhealthy habits. However, it is important to be cautious, as food is essential and one would not want to

give some people further reason to reduce their intake. Addition of such a model to footprint calculators

may thus be more appropriate. In fact, one was recently put online by the Water Footprint Network.

Linked to this are the options for policymakers. The results can be useful to stimulate or discourage

certain consumer behavior, for example through higher taxes on meat, or for industrialized countries to

support foreign aid projects.

M.Sc. Thesis

I.Y.R. Odegard

133

Bibliography

Alexandratos, N., Bruinsma, J., Bödeker, G., Schmidhuber, J., Broca, S., Shetty, P., Grazia Ottaviani, M., World agriculture: towards 2030/2050, Prospects for food, nutrition, agriculture and major commodity groups, Interim report, Global Perspective Studies Unit, Food and Agriculture Organization of the United Nations, Rome, June 2006 Badgley, C., Moghtader, J., Quintero, E., Zakem, E., Chappell, M.J., Avile´s-Va´zquez, K., Samulon, A., Perfecto, I., Organic agriculture and the global food supply, Renewable Agriculture and Food Systems, 22(2), 86-108, 2006 Bender, W.H., An end use analysis of global food requirements, Food Policy, Vol. 19 (4), pp. 381-395, 1994 Börjeson, L., Höjer, M., Dreborg, K-H., Ekvall, T., Finnveden, G., Scenario types and techniques: Towards a

user’s guide, Futures 38, 723-739, 2006

Bruinsma, J. (ed.), World Agriculture: towards 2015/2030, An FAO perspective, FAO, Earthscan

publications Ltd, 2003

Cassman, K.G., Ecological intensification of cereal production systems: Yield potential, soil quality, and

precision agriculture, Proceedings of the National Academy of Sciences, Vol. 96, pp. 5952-5959, May

1999

Chapagain, A.K., Hoekstra, A.Y., The global component of freshwater demand and supply: an assessment of virtual water flows between nations as a result of trade in agricultural and industrial products, Water International, Vol. 33, No., 1, pp.19-32, 2008 Cuéllar, A.D., Webber, A.E., Wasted Food, Wasted Energy: The Embedded Energy in Food Waste in the United States, Environ. Sci. Technol. Vol. 44, pp. 6464-6469, 2010 De Fraiture, C., Wichelns, D., Rockström, J., Kemp-Benedict, E., Looking ahead to 2050: scenarios of

alternative investment approaches, in: Molden, D. (ed), Water for Food, Water for Life, Earthscan,

London, and International Water Management Institute, Colombo, 2007, available at

http://www.iwmi.cgiar.org/assessment/Publications/books.htm

De Fraiture, C., Wichelns, D., Satisfying future water demand for agriculture, Agricultural Water

Management, Vol. 97, p. 502-511, 2010

De Ponti, T., personal communication, June 2011,

Duarte, C. M., Holmer, M., Olsen, Y., Soto, D., Marbà, N., Guiu, J., Black, K., Karakassis, I., Will the Oceans Help Feed Humanity? BioScience, Vol. 59, No. 11, p. 967-976, December 2009


M.Sc. Thesis

I.Y.R. Odegard

134

Ewert, F., Rounsevell, M.D.A., Reginster, I., Metzger, M.J., Leemans, R., Future scenarios of European agricultural land use, I. Estimating changes in crop productivity, Agriculture, Ecosystems and Environment, Vol. 107, p. 101-116, 2005

Ewert, F., Rounsevell, M., Reginster, I., Metzger, M., Leemans, R., Technology Development and Climate Change as Drivers of Future Agricultural Land Use, in: Brouwer, F., McCarl, B.A., Agriculture and Climate Beyond 2015, p. 33-51, 2006

FAO, Food and Agriculture Organization of the United Nations, Fertilizer and Plant Nutrition Service, Land and Water Development Division, Fertilizer and plant nutrition guide, FAO Fertilizer and Plant Nutrition Bulletin 9, Rome, 1984 FAO, Food and Agriculture Organization of the United Nations, Allen, R.G., Pereira, L.,S., Raes, D., Smith, M., Crop evapotranspiration - Guidelines for computing crop water requirements, FAO Irrigation and drainage paper 56, Rome, 1998, http://www.fao.org/docrep/x0490e/x0490e00.htm FAO, Food and Agriculture Organization of the United Nations, Inter-temporal Changes of Conversion Factors, Extraction Rates, Productivity of Crops and Livestock and Related Matters: 1963-1967 to 1993-1997, August 2000, accessed December 2010, http://www.fao.org/fileadmin/templates/ess/documents/methodology/totdoc.pdf FAO, Food and Agriculture Organization of the United Nations, World Agriculture: towards 2015/2030, Summary Report, Rome, 2002, ftp://ftp.fao.org/docrep/fao/004/y3557e/y3557e.pdf

FAO, Food and Agriculture Organization of the United Nations, Fertilizer use by crop, FAO Fertilizer and Plan Nutrition Bulletin, Rome, 2006

FAO4, Food and Agriculture Organization of the United Nations, FAOSTAT, Glossary, last updated: 2010,

accessed: December 2010, http://faostat.fao.org/site/375/default.aspx

Fischer, G., Van Velthuizen, H., Medow, S., Nachtergaele, F., at IIASA, International Institute for Applied

Systems Analysis, and FAO, Food and Agriculture Organization of the United Nations, Global Agro-

ecological Assessment for Agriculture in the 21st Century CD-ROM, 2002, available at

http://www.iiasa.ac.at/Research/LUC/SAEZ/ accessed November 2010

Foley, J.A., DeFries, R., Asner, G.P., Barford, C., Bonan, G., Carpenter, S.R., Chapin, F.S., Coe, M.T., Daily, G.C., Gibbs, H.K., Helkowski, J.H., Holloway, T., Howard, E.A., Kucharik, C.J., Monfreda, C., Patz, J.A., Prentice, I.C., Ramankutty, N., Snyder, P.K., Global Consequences of Land Use, Science, Vol. 309, July 2005

Gerbens-Leenes, W., Nonhebel, S., Food and land use. The Influence of consumption patterns on the use

of agricultural resources, Appetite, Vol. 45, p. 24-31, 2005

Grigg, D., The Pattern of World Protein Consumption,Geoforum, Vol. 26, No. 1, pp. 1-17, 1995

Hoekstra, A.Y., Chapagain, A.K., Globalization of Water: Sharing the Planet’s Freshwater Resources, Wiley-Blackwell, Oxford, UK, 2008

http://www.fao.org/docrep/x0490e/x0490e00.htm

http://www.fao.org/fileadmin/templates/ess/documents/methodology/totdoc.pdf

ftp://ftp.fao.org/docrep/fao/004/y3557e/y3557e.pdf

http://faostat.fao.org/site/375/default.aspx

http://www.iiasa.ac.at/Research/LUC/SAEZ/

M.Sc. Thesis

I.Y.R. Odegard

135

Hoekstra, A.Y., Human appropriation of natural capital: A comparison of ecological footprint and water footprint analysis, Ecological Economics, 68, p. 1963-1974, 2009

Howarth, R.W., Boyer, E.W., Pabich, W.J., Galloway, J.N., Nitrogen Use in the United States from 1961-

2000 and Potential Future Trends, Ambio, Vol. 31, No. 2, March 2002

IPCC, Intergovernmental Panel on Climate Change, Nakicenovik, N., Alcamo, J., Davis, G., de Vries, B.,

Fenman, J., et al., Special Report on Emission Scenarios: A Special Report of Working Group III of the

Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, 2000, available at:

http://www.ipcc.ch/ipccreports/sres/emission/index.php?idp=87

IFA, International Fertilizer Association, Production and Trade Statistics, last updated: February 22nd,

2011, accessed April 2011, available at: http://www.fertilizer.org/ifa/HomePage/STATISTICS/Production-

and-trade , 2011

Kantor, L.S., Lipton, K., Manchester, A., Oliveira, V., Estimating and Addressing America’s Food Losses,

FoodReview, pp. 2-12, January-April, 1997

Keyzer, M.A., Merbis, M.D., Pavel, I.F.P.W, Wesenbeeck, C.F.A., Diet shifts towards meat and the effects

on cereal use: can we feed the animals in 2030? Ecological Economics, Vol. 55, p. 187-202, 2005

Lotze-Campen, H., Müller, C., Bondeau, A., Smith, P., Lucht, W., Rising Food Demand, Climate Change

and the Use of Land and Water, in: Brouwer, F., McCarl, B.A., Agriculture and Climate Beyond 2015,

Springer, The Netherlands, pp. 109-129, 2006

Lutz, W., Sanderson, W., Scherbov, S., IIASA's 2007 Probabilistic World Population Projections, IIASA

World Population Program Online Database of Results, 2008, accessed October 2009, available at:

http://www.iiasa.ac.at/Research/POP/proj07/index.html?sb=5,

http://www.iiasa.ac.at/Research/POP/proj07/ptotr_all.xls,

Nijdam, D.S., Wilting, H.C., Environmental load due to private consumption, RIVM rapport 771404004, in Dutch, 2003

Mäder, P., Flieβbach, A., Dubois, D., Gunst, L., Fried, P., Niggli, U., Soil Fertility and Biodiversity in Organic Farming, Science, Vol. 296, May 2002

Molden, D., Frenken, K., Barker, R., De Fraiture, C., Mati, B., Svendsen, M., Sadoff, C., Finlayson, C.M.,

Trends in water and agricultural development, in: Molden, D. (ed), Water for Food, Water for Life,

Earthscan, London, and International Water Management Institute, Colombo, 2007, available at


Penning de Vries, F.W.T., Van Keulen, H., Rabbinge, R., Natural resources and limits of food production in

2040, in: Bouma, J. et al. (eds), Eco-regional approaches for sustainable land use and food production, pp.

65-87, Kluwer Academic Publishers, the Netherlands, 1995

http://www.ipcc.ch/ipccreports/sres/emission/index.php?idp=87

http://www.fertilizer.org/ifa/HomePage/STATISTICS/Production-and-trade

http://www.fertilizer.org/ifa/HomePage/STATISTICS/Production-and-trade

http://www.iiasa.ac.at/Research/POP/proj07/index.html?sb=5

http://www.iiasa.ac.at/Research/POP/proj07/ptotr_all.xls


M.Sc. Thesis

I.Y.R. Odegard

136

OECD, Health at a Glance: Europe 2010, OECD Publishing, accessed May 2011, available at

http://dx.doi.org/10.1787/health_glance-2010-en, 2010

PBL, Planbureau voor de leefomgeving, SRES Scenarios, last updated: December 23rd 2009, accessed June

7th 2010, http://www.pbl.nl/en/themasites/image/scenarios/sres_scenarios/index.html

Pimentel, D., Environmental and Social Implications of Waste in the U.S. Agriculture and Food Sectors, Journal of Agricultural Ethics, Vol. 3, No. 1, 1990 Pimentel, D., hepperly, P., Hanson, J., Douds, D., Seidel, R., Environmental, Energetic, and Economic Comparisons of Organic and Conventional Farming Systems, BioScience, Vol. 55, No. 7, July 2005 Pollan, M., In Defense of Food: An Eater's Manifesto, Penguin Press, New York, 2008 Pollan, M., The Omnivore’s Dilemma, A Natural History of Four Meals, Penguin Press, New York, 2006 Poudel, D.D., Horwarth, W.R., Lanini, L.T., Temple, S.R., Van Bruggen, A.H.C., Comparison of soil availability and leaching potential, crop yields and weeds in organic, low-input and conventional farming systems in Northern California, Agriculture, Ecosystems and Environment, Vol. 90, pp. 125-137, 2002 Ringland, G., Scenario Planning, Managing for the Future, John Wiley & Sons Ltd, England, 1998

Rosegrant, M.W., Paisner, M.S., Meijer, S., Witcover, J., Global Food Projections to 2020, Emerging Trends and Alternative Futures, International Food Policy Research Institute, Washington, D.C., August 2001a

Rosegrant, M.W., Paisner, M.S., Meijer, S., Witcover, J., 2020 Global Food Outlook, Trends, Alternatives and Choices, A 2020 Vision for Food, Agriculture, and the Environment Initiative, International Food Policy Research Institute, Washington, D.C., August 2001b

Schoemaker, P.J.H., Scenario Planning: A Tool for Strategic Thinking, Sloan Management Review, Winter,

1995

Schneider F., Consideration of food losses in life cycle approach of food supply chain, Institute of Waste

Management, BOKU-University of Natural Resources and Applied Sciences Vienna, Switzerland, 2007

Shearer, A.W., Approaching scenario-based studies: three perceptions about the future and

considerations for landscape planning, Environment and Planning B: Planning and Design 2005, Vol. 32,

pp. 67-87

Smil, V., Feeding the World; A Challenge for the Twenty-First Century, The MIT Press, Cambridge, USA,

2001

SOW-VU, Can we feed the animals? Centre for World Food Studies, Amsterdam, 2005.

Tilman, D., Fargione, J., Wolff, B., D’Antiono, C., Dobson, A., Howarth, R., Schindler, D., Schlesinger, W.H., Simberloff, D., Swackhamer, D., Forecasting Agriculturally Driven Global Environmental Change, Science, Vol. 292, April 2001

http://dx.doi.org/10.1787/health_glance-2010-en

http://www.pbl.nl/en/themasites/image/scenarios/sres_scenarios/index.html

M.Sc. Thesis

I.Y.R. Odegard

137

UN, United Nations, UN Population Division, retrieved October 2009, from World Population Prospects:

The 2008 Revision Population Database: http://esa.un.org/unpp, 2009

UN, United Nations, The Millennium Development Goals Report 2010, New York, 2010 http://www.un.org/millenniumgoals/pdf/MDG%20Report%202010%20En%20r15%20-low%20res%2020100615%20-.pdf#page=13

USGS, U.S. Geological Survey, Mineral Commodity Summaries 2009, 2009

Van Vliet, J., PBL, SRES PPP data, Personal Communication, 2010

Vinnari, M., Tapio, P., Future images of meat consumption in 2030, Futures, Vol. 41, 269-278, 2009

Vitousek, P.M., Mooney, H.A., Lubchenco, J., Melillo, J.M., Human Domination of Earth’s Ecosystems, Science 277, 494, 1997

Wirsenius, S., Human Use of Land and Organix Materials, Modeling the Turnover of Biomass in the Global Food System, Department of Physical Resource, Theory, Chalmers University of Technology and Göteborg University, Göteborg, Sweden, 2000

Wirsenius, S., The Biomass Metabolism of the Food System, A Model-Based Survey of the Global and Regional Turnover of Food Biomass, Journal of Industrial Ecology, Vol. 7, No. 1, p. 47-80, 2003

Wirsenius, S., Azar, C., Berndes, G., How much land is needed for global food production under scenarios of dietary changes and livestock productivity increases in 2030?, Agricultural Systems, Vol. 103, p. 621-638, 2010

WWAP, World Water Assessment Programme, The United Nations World Water Development Report 3: Water in a Changing World, Paris: UNESCO, London: Earthscan, 2009

WWF, Living Planet Report 2006, edited by Halls, C., in corporation with the Zoological Society of London and the Global Footprint Network, 2006

FAO Databases:

FAOSTAT, FAO, Time-series and cross sectional data relating to food and agriculture, last updated: 2011,

accessed: 2010-2011, http://www.faostat.fao.org

AQUASTAT, FAO/NRL, FAO’s Information System on Water and Agriculture, last updated 2011, accessed 2010-2011, http://www.fao.org/nr/water/aquastat/main/index.stm

FERTISTAT, Food and Agriculture Organization of the United Nations, Plant Production and Protection Division, Fertilizer Use Statistics, updated 2007, accessed: February 2011, http://www.fao.org/ag/agl/fertistat/index_en.htm

http://esa.un.org/unpp

http://www.un.org/millenniumgoals/pdf/MDG%20Report%202010%20En%20r15%20-low%20res%2020100615%20-.pdf#page=13

http://www.un.org/millenniumgoals/pdf/MDG%20Report%202010%20En%20r15%20-low%20res%2020100615%20-.pdf#page=13

http://www.faostat.fao.org/

http://www.fao.org/nr/water/aquastat/main/index.stm

http://www.fao.org/ag/agl/fertistat/index_en.htm

M.Sc. Thesis

I.Y.R. Odegard

138

Appendix 1 World Regions

The definition of the four regions as used in this study [based on IPCC, 2000 and FAO, 2010].

Table 40: Definition of the regions [IPCC, 2000; FAO, 2010].

OECD90 REGION

North America (NAM) Canada United States of America

Western Europe (WEU) Austria Germany Madeira Sweden

Belgium Greece Malta Switzerland

Cyprus Iceland Netherlands Turkey

Denmark Ireland Norway United Kingdom

Finland Italy Portugal

France Luxembourg Spain

Pacific OECD (PAO) Australia Japan New Zealand

Note: The following countries present in the IPCC list were excluded because they are not listed in the FAO country list. These

are part of other countries (overseas) territories: Guam, Puerto Rico, Virgin Islands, Azores, Canary Islands, Faeroe Islands,

Gibraltar, Greenland, and Isle of Man. Also missing are: Liechtenstein, Monaco and Andorra.

REF REGION (countries undergoing economic reform)

Central and Eastern Europe (EEU) Albania Czech Republic Romania Yugoslavia Bosnia and Herzegovina Hungary Slovakia

Bulgaria Poland Slovenia

Croatia Republic of Macedonia Serbia and Montenegro

Newly independent states (NIS) of the former Soviet Union (FSU) Armenia Georgia Lithuania Turkmenistan

Azerbaijan Kazakhstan Republic of Moldova Ukraine

Belarus Kyrgyzstan Russian Federation Uzbekistan

Estonia Latvia Tajikistan

Note: ‘Serbia and Montenegro’ was substituted for ‘The Former Yugoslav’ (as it is named in the IPCC list), as was ‘Slovakia’ for

the ‘Slovak Republic’.

ASIA REGION

Centrally planned Asia and China (CPA) Cambodia Korea (DPR) Mongolia

China Laos (DPR) Vietnam

South Asia (SAS) Afghanistan Bhutan Nepal Sri Lanka

Bangladesh India Pakistan

Other Pacific Asia (PAS) American Samoa Malaysia Republic of Korea Tonga

Brunei Darussalam Myanmar Singapore Vanuatu

Fiji New Caledonia Solomon Islands

French Polynesia Papua New Guinea Timor-Leste

Indonesia Philippines Thailand

Note: ‘Hong Kong’ is not listed separately in the FAO country-list, as well as the ‘Maldives’, ‘Gilbert-Kiribati’, ‘Taiwan, province of

China’ and Western Samoa. ‘Timor-Leste’ was added, this country became independent in 2002.

M.Sc. Thesis

I.Y.R. Odegard

139

ALM REGION (Africa and Latin America)

Middle East and North Africa (MEA) Algeria Israel Morocco Syria (Arab Republic)

Bahrain Jordan Oman Tunisia

Egypt (Arab Republic) Kuwait Qatar United Arab Emirates

Iran (Islamic Republic) Lebanon Saudi Arabia Yemen

Iraq Libya (SPLAJ) Sudan Occupied Palestinian Territory

Latin America and the Caribbean (LAM) Antigua and Barbuda Costa Rica Honduras Saint Vincent and the

Grenadines

Argentina Cuba Jamaica Santa Lucia

Bahamas Dominica Martinique Suriname

Barbados Dominican Republic Mexico Trinidad and Tobago

Belize Ecuador Nicaragua Uruguay

Bolivia El Salvador Panama Venezuela

Brazil Guatemala Paraguay

Chile Guyana Peru

Colombia Haiti Saint Kitts and Nevis

Sub-Saharan Africa (AFR) Angola Democratic Republic of

Congo Madagascar Seychelles

Benin Equatorial Guinea Malawi Sierra Leone

Botswana Eritrea Mali Somalia

Burkina Faso Ethiopia Mauritania South Africa

Burundi Gabon Mauritius Swaziland

Cameroon Gambia Mozambique Tanzania

Cape Verde Ghana Namibia Togo

Central African Republic Guinea Niger Uganda

Chad Guinea-Bissau Nigeria Zambia

Comoro Kenya Rwanda Zimbabwe

Congo Lesotho Sao Tome and Principe

Cote d’Ivoire Liberia Senegal

Note: The following countries are not included in the FAO factsheets and are part of other countries (overseas) territories:

Bermuda, French Guyana, Guadeloupe, Netherlands Antilles, British Indian Ocean Territory, Reunion and Saint Helena. The

Democratic Republic of Congo was added. Also missing are Grenada and Djibouti.

The ‘missing’ countries together accounted for 0.1% of the total world population in the year 2005.

M.Sc. Thesis

I.Y.R. Odegard

140

Appendix 2 Commodity Group Assumptions

Table 41: Commodity group assumptions

Commodity Group Caloric Value

(kcal per kg)

Protein Content

(g per kg)f

Dry weight

fraction (% dry

weight per

harvested

weight)a

Cereals

3208.9 79.7 88

Roots and tubers

824.4 12.7 30

Sugar crops

289 1.7 26

Sugar and sweeteners 3484.6 0.6

Oil crops

2871.7 141.8 90

Vegetable oils 8735.1 1

Pulses 3418.8 215.4 100

Meat 1982.6

122.7

-

Milk

558.7 33.3 -

Vegetables

242.5

12.3d 10

Fruit

458.2

5c

20

Eggs

1426.7 111.0 -

Note: All caloric values and protein contents were based on world averages reported by FAOSTAT under Food Supply – Crops

Primary Equivalent and Livestock and Fish Primary Equivalent. Caloric values of commodity groups may vary between regions

because of different compositions. a [Wirsenius, 2000]

M.Sc. Thesis

I.Y.R. Odegard

141

Table 42: Items included in the commodity groups.

Items Included in Commodity Groups

Cereals

Wheat Rice, paddy (milled equivalent)

Maize

Oats

Sorghum

Barley

Rye

Millet

Cereals, other

Roots and tubers

Potatoes Sweet potatoes

Cassava Yams

Roots, other

Sugar crops and sweeteners

Sugar cane Sugar beet

Sugar non-centrifugal Sugar (raw equivalent)

Sweeteners, nes Honey

Oil crops and vegetable oils

Soybeans and soybean oil Groundnuts (shelled equivalent) and groundnut oil

Sunflower seed and sunflower seed oil Rape and mustard seed and rape and mustard oil

Cottonseed and cottonseed oil Coconuts (including copra and copra oil) and coconut

oil

Sesame seed and sesame seed oil Palm oil, palm kernels and palm kernel oil

Olives Rice bran oil

Maize germ oil Oilcrops, other and oilcrops oil, other

Pulses

Beans Peas

Pulses, other

Vegetables

Tomatoes Onions

Vegetables, other

Fruit

Oranges and mandarins Lemons and limes

Grapefruit Citrus, other

Bananas Plantains

Apples Pineapples

Dates Grapes

Fruit, other

M.Sc. Thesis

I.Y.R. Odegard

142

Appendix 3 Waste

Table 43: Waste (during transport, storage and processing) in 2007 [based on FAOSTAT, 2010].

Commodity Waste during transportation and storage

tons in 2007

% of total production

World OECD90 REF ASIA ALM

Cereals

% of total 85,738,291

3.6%

8,240,231

1.1%

6,494,882

2.8%

39,480,964

3.8%

31,522,211

9.0%

Roots and tubers

% of total 58,613,974

8.4%

5,239,392

6.3%

3,897,906

4.2%

19,069,122

7.3%

30,407,553

11.7%

Sugar crops

% of total 59,891,852

3.3%

173,920

0.08%

364,710

0.5%

8,540,126

1.3%

50,813,096

5.8%

Oil crops

% of total 10,438,441

7.0%

996,481

3.3%

576,728

6.3%

6,269,687

8.5%

2,595,545

7.3%

Pulses

% of total 2,644,214

4.7%

257,643

2.2%

133,308

4.7%

991,193

4.2%

1,262,070

7.1%

Vegetables

% of total 7,572,981

8.3%

14,499,732

10.7%

2,468,639

4.4%

45,921,942

7.8%

12,838,967

10.0%

Fruits

% of total 50,176,340

6.7%

7,197,473

7.2%

1,099,082

0.5%

20,964,282

9.2%

20,915,502

10.3%

Eggs

% of total 2,891,421

4.2%

271,233

2.6%

46,183

0.3%

1,869,300

5.6%

704,705

2.1%

Meat

% of total 1,142,940

0.4%

273,935

0.3%

85,410

0.5%

197,20

0.02%

763,875

1.4%

Milk

% of total 15,767,789

2.2%

1,137,084

0.4%

819,936

0.8%

9,048,976

4.8%

4,761,794

4.0%

M.Sc. Thesis

I.Y.R. Odegard

143

Appendix 4 Feed

Table 44: Feed in 2007 [based on FAOSTAT, 2010].

Commodity Feed

tons in 2007



Cereals

% of total 745,878,305

31.7%

352,962,020

48.5%

97,664,273

42.3%

166,096,091

15.9%

129,155,921

36.8%

Roots and tubers

% of total 1,61E+08

23.1%

7,431,405

9.0%

27,543,302

29.5%

64,167,500

24.5%

62,207,062

24.0%

Sugar crops

% of total 36,903,992

2.0%

193,309

0.09%

10,000,373

13.2%

6,761,432

1.0%

12,497,560

1.4%

Oil crops

% of total 26,827,777

18.0%

10,002,061

32.8%

2,536,372

27.6%

10,924,250

14.8%

3,365,092

9.5%

Pulses

% of total 10,685,440

19.1%

3,492,036

29.9%

1,784,584

63.4%

3,848,975

16.2%

1,559,844

8.8%

Vegetables

% of total 40,221,451

4.4%

5,191,192

3.8%

6,378,496

11.3%

27,704,983

4.7%

946,780

0.7%

Fruits

% of total 5,216,606

0.7%

78,743

0.07%

503,298

0.2%

752,088

0.3%

3,882,477

1.9%

Eggs

% of total 55,465

0.09%

165

0.00001%

55,300

1.1%

0

0.0%

0

0.0%

Meat

% of total 8,829,595

3.6%

8,815,756

10.0%

6,700

0.04%

770

0.000009%

6,369

0.01%

Milk

% of total 71,450,531

10.5%

11,335,223

4.2%

21,667,066

21.4%

25,945,623

13.6%

1,250,219

10.4%

M.Sc. Thesis

I.Y.R. Odegard

144

Appendix 5 Seed

Table 45: Seed in 2007 [based on FAOSTAT, 2010].

Commodity Seed

tons in 2007



Cereals

% of total 67,421,309

2.9%

15,066,469

2.1%

22,830,215

10.0%

21,930,682

2.1%

7,593,943

2.2%

Roots and tubers

% of total 34,477,598

4.9%

4,598,908

5.6%

17,618,798

18.9%

6,687,945

2.6%

5,571,947

2.1%

Sugar crops

% of total 27,481,119

1.5%

1,703,000

0.8%

0

0.0%

24,915,916

3.7%

862,203

0.09%

Oil crops

% of total 10,917,741

7.3%

3,164,796

10.4%

950,543

10.3%

3,914,465

5.3%

2,887,936

8.2%

Pulses

% of total 3,873,360

6.9%

790,797

6.8%

369,234

13.1%

1,388,926

5.9%

1,324,403

7.5%

Vegetablesa

% of total 111,537

0.01%

26,882

0.02%

30,242

0.05%

19,501

0.003%

34,912

0.03%

Fruits

% of total 0

0.0%

0

0.0%

0

0.0%

0

0.0%

0

0.0%

Eggs

% of total 3,939,412

6.2%

1,324,409

9.2%

251,666

4.8%

1,238,984

3.7%

3,939,412

11.0% a ‘Only those vegetables which are cultivated principally for human consumption belong to this group. Consequently, vegetables

grown principally for animal feed should be excluded, as should vegetables cultivated for seed’ *FAO5, 2010].

M.Sc. Thesis

I.Y.R. Odegard

145

Appendix 6 Other Utilitization

Table 46: Other utilization in 2007 [based on FAOSTAT, 2010].

Commodity Global ‘other utilization’

tons in 2007


Cereals

% of total 79,266,054

3.4%

Roots and tubers

% of total 41,096,189

5.9%

Sugar crops

% of total 18,467,045

1.0%

Oil crops

% of total 13,805,594

9.3%

Pulses

% of total 574,688

1.0%

Vegetables

% of total 462,375

0.05%

Fruits

% of total 1,728,398

0.2%

Eggs

% of total 613,743

1.0%

Meat

% of total 953,594

0.4%

Milk

% of total 17,228,369

2.5%

M.Sc. Thesis

I.Y.R. Odegard

146

Appendix 7 Yield Projections

Most yield projections are made solely for cereals. Ewert et a. give a metholodogy for making yield

projections for different commodity groups, which can only reasonably be applied to the OECD90 region

(see also Appendix 15), and can thus only be used as for comparison. The FAO gives productivity

projections (see Appendix 16) for the commodity groups cereals, oil crops and sugar crops, but it is

unclear to what extent cropping intensity and land expansion play a role. Because this study gives an

holistic picture, and includes the 7 commodity groups in four different regions, different yield projections

have to be made. First, the cereal yield projections will be introduced. The yield projections made for the

other commodity groups are based on the same methodology.

Cereal yield projections were extracted from [De Fraiture, 2010]. De Fraiture assumes that in an

“optimistic scenario” 80% of the yield gap is bridged, while in a “pessimistic” scenario 20% of the yield

gap is bridged. Economic and technological development is high in the A1 and B1 scenarios, thus for

these scenarios a bridging of 80% of the yield gap was chosen. Development in the A2 scenario is low,

which makes bridging the yield gap with 20% reasonable. Table 47 shows the cereal yield projection for

the four scenarios, and for the four regions in the A2 and B2 scenarios, for both rainfed and irrigated

cereal yields.

Table 47: Cereal yield projection [based on De Fraiture, 2010].

Scenario Region Cereal Yield (ton/ha)

Rain-fed Irrigated

2005 World 3.33 -

A1 World 3.88 5.74

A2 OECD90 5.20 7.20

REF 2.50 4.20

ASIA 2.60 4.90

ALM 2.25 4.30

B1 World 3.90 4.94

B2 OECD90 2.60 3.60

REF 1.25 2.10

ASIA 1.30 2.45

ALM 1.13 2.15

In line with the reasoning behind the cereal yield projections [De Fraiture, 2010], yield projections for the

other commodity groups were based on the exploitable yield gap and the maximum attainable yield

(MAY). Data from the FAO and the IIASA were used to estimate maximum attainable yields [Fisher, 2010].

The potential yield (or maximum attainable yield) depends on the ecological zone, the crop and the

management practice. This means that within a region and within a commodity group yields can differ.

One of the assumptions underlying the yield projections made here for the commodity groups is that the

composition of the production of crops within a commodity group does not change. This is important

M.Sc. Thesis

I.Y.R. Odegard

147

because crop yields within a commodity group can differ widely. For example, the yield of ‘beans, dry’ in

2005 in The Netherlands was 3.6 ton/ha, while the yield of ‘broad beans, horse beans, dry’ was 6.4

ton/ha. If production was shifted to only this latter type, yields would necessarily be higher. Maximum

attainable yields for the OECD90 region and the REF region were based on the data for the ‘temperate’

zone. For the ASIA region and the ALM region the average between ‘tropical’ and ‘temperate’ was used.

The global MAYs were set at the same value as those for the ASIA region and the ALM region, as these

are slightly higher than the OECD90 and REF region. Another issue which need to be kept in mind in the

definition of global average yields is that the potential to increase yields is much higher in those regions

where the yield gap is still high: the less developed regions. Thus the yield increases in these regions

accounts for a larger part of the total increase than the increase in the industrialized regions.

Current regional average yields were calculated using FAOSTAT data. Total production in the region was

divided by the total harvested area. No data concerning maximum attainable yields for fruits and

vegetables was available. Therefore, an estimate of the maximum attainable yield was made using

historic data. On a regional level, the fruits and vegetables yields of the top three yielding countries was

averaged. This was taken as the maximum attainable yield for these commodity groups for the regions.

On a global level, the yields of those 12 countries was averaged, and that value was taken as an estimate

of the global MAY. Some adjustments were made as in some cases the countries in the top three yield

range do not offer much variety in terms of production, which greatly influences the average yield. For

instance, in the OECD90 region, Iceland has the highest vegetable yield, but it produces only 5 out of 25

crops in the vegetable commodity group, while the Netherlands produces 20 out of 25. Because Iceland

produces the high yielding crops ‘cucumbers and gherkins’ and ‘tomatoes’, the average yield in Iceland is

33% higher than in the second leading country for vegetable yields: the Netherlands. It was checked that

the countries from which a maximum attainable yield was calculated either produce a significant number

of crops or have a yield comparable to those of the other countries in the top. MAYs for sugar beet were

significantly underestimated by the FAO and the IIASA [Fisher, 2002], as current yields in the OECD90

region are much higher that the estimated MAY. The same is true for the roots and tubers yield in the

OECD90 region, which was higher than the estimated MAY in the year 2005. Part of the explanation for

the difference can be that sugar crops may be irrigated in the OECD90 region, but this does not account

for the complete difference. The MAYs for these commodity groups were adjusted, using the same

methodology which was used to estimate the MAYs for fruits and vegetables. The oil crop production

data given by the FAO is given in ‘converted’ values, to account for differences in oil fractions of different

oil crops. To estimate unconverted values yields were converted using the extraction rate for oil crops as

established in Appendix 9.

M.Sc. Thesis

I.Y.R. Odegard

148

Table 48: Top three yielding countries per region for fruits and vegetables [based on FAOSTAT, 2011].

Top three yielding countries per region for fruit and vegetables

(number of crops grown in commodity group)

Fruit 1st

2nd

3rd

OECD90 Belgium (10) Netherlands (10) USA (27)

REF Slovenia (13) Armenia (11) Macedonia (15)

ASIA Republic of Korea (12) Indonesia (8) Malaysia (10)

ALM Costa Rica (12) Israel (27) Honduras (13)

Vegetables

OECD90 Netherlands (20) Austria (19) Luxembourg (6)

REF Armenia (10) Poland (15) Slovenia (13)

ASIA Republic of Korea (16) China (24) Singapore (6)

ALM Kuwait (18) United Arab Emirates (9) Jordan (20)

Virtual Land Content

Yield projections (ton/ha) can be seen as an inverse of Virtual Land Content (m2/kg). Because higher

inputs, better management practices and technological development (different strains) lead to higher

yields, such projections are adapted to the scenarios. The methodology of closure of the yield gap, as

explained above, can be used for other scenarios, for different ‘closures’. Table 49 shows the maximum

attainable yields, the yields in 2005, and the projected yields in 2050 for the four scenarios as used in this

study.

Table 49: Maximum attainable yields (MAYs) and yields in 2005 and in 2050 [based on Fisher, 2002; FAOSTAT, 2011; De Fraiture, 2010]. Yield projections for 2050 take scenario characteristic into account.

Maximum Attainable Yields and Yields in 2005 and 2050


Scenario

Fruit MAY 28.12 10.5 14.88 25.37 21.25

Yield – 2005 13.06 5.10 9.81 10.06 10.04

Yield – 2050 (A2) 16.07 6.18 10.83 13.12 -

Yield – 2050 (B2) 8.03 3.09 5.41 6.56 -

Yield – 2050 (A1/B1) - - - - 19.01

Oil crops MAY 4.40 4.64 6.03 4.81 5.21

Yield – 2005 1.56 1.34 2.02 1.12 1.58

Yield – 2050 (A2) 2.13 2.00 2.83 1.86

Yield – 2050 (B2) 1.06 1.00 1.41 0.93

Yield – 2050 (A1/B1) - - - - 4.48

Pulses MAY 3.7 3.7 3.5 3.87 3.70

Yield – 2005 1.86 1.73 0.75 0.62 0.86

Yield – 2050 (A2) 2.23 2.12 1.30 1.27 -

Yield – 2050 (B2) 1.11 1.06 0.65 0.64 -

Yield – 2050 (A1/B1) - - - - 3.13

M.Sc. Thesis

I.Y.R. Odegard

149

Sugar crops MAY 71.06 50.09 65.27 76.77 70.92

Yield – 2005 64.90 31.88 60.21 69.08 62.23

Yield – 2050 (A2) 66.13 35.52 61.22 70.62 -

Yield – 2050 (B2) 33.06 17.76 30.61 35.31 -

Yield – 2050 (A1/B1) - - - - 69.18

Roots and

tubers

MAY 43.26 34.33 41.30 48.60 43.30

Yield – 2005 36.00 13.55 17.04 9.74 13.64

Yield – 2050 (A2) 37.45 17.70 21.89 17.51

Yield – 2050 (B2) 18.73 8.85 10.95 8.76

Yield – 2050 (A1/B1) - - - - 37.37

Vegetables MAY 41.84 26.14 23.24 45.45 29.50

Yield – 2005 27.84 16.15 16.81 13.64 17.28

Yield – 2050 (A2) 30.64 18.15 18.09 20.00

Yield – 2050 (B2) 15.32 9.08 9.05 10.00

Yield – 2050 (A1/B1) - - - - 27.06

Notes: 1) Regional yields are based on assumption that 20% of the yield gap is closed (A2 scenario), while global yields are based

on the assumption that 80% of the yield gap is closed (A1 and B1 scenarios).

2) MAYs are based on maximum attainable crop yields, with high input levels under rainfed conditions as defined by the FAO

and the IIASA [Fisher, 2002]. They are averaged according to the proportion of production of main crops in 2005 in the regions.

As these crops were assumed to be grown under rainfed conditions, the upper boundary (representing the most productive

cultivar) was chosen to compensate for the lower maximum yields under such conditions. Values were converted from values in

dry weight, which can be found in Appendix 3. No information was given for fruits and vegetables; maximum attainable yields

were based on the average of the highest three yields (by three countries, for the whole commodity group) achieved in a region.

3) B2 yields are set to half the A2 yields. This corresponds to a level of between 51% and 102% of current yields; OECD90:

between 51% and 68% of current, REF: between 56% and 75% of current, ASIA: between 51% and 86% of current, ALM:

between 65% and 102% of current. The higher values in the ALM region indicate that that region has a higher potential for

improvement; the yield gaps are higher than in the other regions.

Feed Yields

The projected feed crop yields shown in Table 50 – for pasture, harvested-conserved grass-legumes,

cropland pasture – are based on data from Wirsenius [Wirsenius, 2003, p.245], and on FAOSTAT and the

global agro-ecological zones study by the FAO and IIASA for whole cereals [Fisher, 2002; FAOSTAT 2011].

Table 50: Feedcrop yields in 2005 and 2050 [based on Wirsenius, 2003; Fisher, 2002, FAOSTAT, 2011].

Feedcrop yields in 2005 and 2050 (ton/ha)


Pasture Yield – 2005

Yield – 2050 (A2)

Yield – 2050 (B2)

Yield – 2050 (A1)

Yield – 2050 (B1)

-

6.4

3.2

-

-

-

6.4

3.2

-

-

-

6.4

3.2

-

-

-

6.4

3.2

-

-

6.4

-

-

12.8

6.4

Harvested-conserved grass-

legume

Yield – 2005

Yield – 2050 (A2)

Yield – 2050 (B2)

Yield – 2050 (A1)

Yield – 2050 (B1)

-

12.8

6.4

-

-

-

12.8

6.4

-

-

-

12.8

6.4

-

-

-

12.8

6.4

-

-

6.4

-

-

12.8

6.4

M.Sc. Thesis

I.Y.R. Odegard

150

Cropland pasture Yield – 2005

Yield – 2050 (A2)

Yield – 2050 (B2)

Yield – 2050 (A1)

Yield – 2050 (B1)

-

12.8

6.4

-

-

-

12.8

6.4

-

-

-

12.8

6.4

-

-

-

12.8

6.4

-

-

6.4

-

-

12.8

6.4

Whole cereals MAY

Yield – 2005

Yield – 2050 (A2)

Yield – 2050 (B2)

Yield – 2050 (A1)

Yield – 2050 (B1)

62.67

13.92

21.3

10.7

-

-

62.67

11.40

19.5

9.7

-

-

63.33

4.51

14.7

7.3

-

-

63.33

7.75

17.0

8.5

-

-

64.0

8.1

-

-

47.7

47.7

Notes: Pasture, cropland pasture and harvested-conserved grass-legume are based on the world average ‘permanent pasture

phytomass per total area of permanent grassland’ of 1.6 Mg DM/ha (linked to a pasture DM of 0.25, thus yielding 6.4 ton/ha)

when not intensively managed, and the ‘West Europe’ average of 3.2 Mg DM/ha ((linked to a pasture DM of 0.25, thus yielding

12.8 ton/ha) when intensively managed [Wirsenius, 2003, p. 245]. B2 yields are assumed half of the A2 yields. Whole cereals

yields are based on 90% availability of whole maize yields [Wirsenius, 2003], MAY includes the non-available 10%.

M.Sc. Thesis

I.Y.R. Odegard

151

Appendix 8 Feed-mixes and Feeding Efficiency

Five animal foodstuffs are included in this study: beef, pork, poultry meat, eggs and milk. These

categories account for 79% of the global animal caloric supply in the year 2005 [based on FAOSTAT,

2011]. The remaining 21% constitutes of by-products such as butter and offals, and are thus implicitly

included. ‘Feed’ is included in FAOSTAT, but this only refers to the part of the feed-mix which consists of

edible-type foodstuffs; it is assumed that ‘feed’ as reported by the FAO can also be used as food. Feed-

mixes, however, contain a number of different components other than foodstuffs. The feed-mixes for

the 5 animal product systems in this study were obtained from Wirsenius, as well as the feeding

efficiencies [Wirsenius, 2003]. The feeding-efficiency is defined as the feed intake per fresh weight

(carcass-weight) produced. The data are quite detailed and therefore provide a good basis for the

calculations done in this study. All phases of animal husbandry are included, e.g. reproduction,

replacement, gestation and lactation. Milk used as feed (lactation) is viewed as a system internal input,

and is therefore subtracted from the overall feeding requirements. A little over 11% of milk is considered

‘feed’ by the FAO, this should therefore be considered a loss. In the research done by Wirsenius, data is

given for the year 1993, which is also the starting point in his article written in 2010 [Wirsenius, 2010].

The feed-mixes are composed of 14 different ingredients. Not all of these are included in the feed-mix

for each of the five animal systems; e.g. cattle have a preference for pasture, poultry have a preference

for cereals, while pigs prefer forage crops. To include a measure of development in the scenarios, four of

the regions defined by Wirsenius were chosen to represent the regions in the A2 and B2 scenarios, and 1

region was chosen to represent the A1 and B1 scenario. In the A1 and B1 scenario, the feed mixes

defined for the ‘North America and Oceania’ region were chosen to represent the global feeding

efficiency in 2050 because it is the most efficient in terms of the weight of the feed input in dry matter.

Four of the Wirsenius regions were chosen to represent the four regions in the A2 and B2 scenarios,

based on geographical correspondence combined with highest efficiency.

Table 51: Choices for correspondence of Wirsenius' regions to IPCC regions.

Region Wirsenius Region

OECD90 North America and Oceania

REF Eastern Europe

ASIA East Asia

ALM Latin America and Caribbean

Buffalo, sheep and goat meat are grouped under ‘cattle, beef’, as these animal systems are similar in

their requirements and efficiency. For the year 2005, feed-mixes and efficiencies were represented by

the global average in 1993. Some progress and development has taken place since then, but it was found

that these data are a reasonable approximation to the situation in the year 2005. Overall, in DM, the

calculated total feed was only 0.1% higher than the FAO estimate. Cereals provide the bulk of foodcrop-

feed: 86.7% on DM-basis. The modeled value was only 1.73% lower than the FAO estimate. The other

values differ more widely: pulses were modeled 13.6% higher, roots 2.3% lower and oil crops 45.7%

M.Sc. Thesis

I.Y.R. Odegard

152

higher. This may be due to the fact that feed-mixes have changed. Because the total is so close, as well

as the cereals value, and the more efficient regions were chosen to represent the 2050 situation, the

values presented below provide a good approximation.

Table 52: Feed-mixes for the five animal products per region [based on Wirsenius, 2003].

Feed type Cattle – Milk, Feed input (kg DM) per kg producta

OECD90b REF

c ASIA

d ALM

e WORLD

Crops, cereals 0.361 0 0 0.090 0.093

Crops, oil 0 0 0 0 0

Crops, pulses 0 0 0 0 0

Crops, roots 0 0 0 0 0

Pasture 0.240 1.066 1.816 2.971 1.213

Forage- vegetables 0 0 0 0 0

Edible-type crops by-products 0.013 0.088 0.832 0.685 0.521

Conversion by-products 0.034 0.014 0 0.040 0.034

Non-eaten food 0 0 0 0 0

Whole-cereals - forage 0.196 0.248 0 0 0.147

Harvested-conserved grass-legume 0.147 0.663 0.252 0.114 0.417

Cropland pasture - forage 0.208 0.020 0 0 0.074

Meat and bone meal 0 0 0 0 0

System external inputs 0 0 0 0 0

Total 1.2 2.1 2.9 3.9 2.5

Feed type Cattle – Beef, Feed input (kg DM) per kg product


Crops cereals 4.620 0 0 1.573 2.195

Crops, oil 0 0 0 0 0

Crops, pulses 0 0 0 0 0


Pasture 12.778 14.691 70.359 62.981 34.453

Forage-vegetables 0 0 0 0 0

Edible-type crops by-products 0.859 2.390 31.227 14.907 13.465

Conversion by-products 0.378 0.263 2.208 0.579 0.808


Whole-cereals - forage 2.699 4.882 0 0 1.154


Cropland pasture - forage 3.508 0.751 0 0 1.154


System external inputs 0 0 0 0 0

Total 27 38 108 85 59

Feed type Pork, Feed input (kg DM) per kg product


Crops, cereals 2.838 1.791 0.617 2.475 1.249

Crops, oil 0.016 0.111 0.146 0.360 0.213

Crops, pulses 0 0.046 0 0.089 0.053

Crops, roots 0.021 0.251 0.328 0.876 0.462

Pasture 0 0 0 0 0

Forage vegetables 0 1.071 2.406 0 1.234

Edible-type crops by-products 0 0.368 0.826 0.613 0.545


M.Sc. Thesis

I.Y.R. Odegard

153

Non-eaten food 0 0.375 1.289 2.553 0.810

Whole-cereals - forage 0 0 0 0 0

Harvested-conserved grass-legume 0 0 0 0 0

Cropland pasture - forage 0 0 0 0 0


System external inputs 0 0 0 0.096 0.057

Total 3.7 4.6 6.2 8.6 5.3

Feed type Poultry, Feed input (kg DM) per kg product


Crops, cereals 1.890 3.278 3.213 2.601 2.423

Crops, oil 0.173 0.038 0.161 0.106 0.178

Crops, pulses 0.152 0.031 0.132 0.093 0.146


Pasture 0 0 0 0 0


Edible-type crops by-products 0 0 0 0 0






Meat and bone meal 0.049 0.042 0.044 0 0.039

System external inputs 0.081 0.085 0.178 0.161 0.078

Total 2.9 3.9 4.1 3.6 3.5

Feed type Eggs, Feed input (kg DM) per kg product


Crops, cereals 1.776 2.646 2.562 2.206 2.300

Crops, oil 0 0 0.01 0 0.005

Crops, pulses 0 0 0.008 0 0.004

Crops, roots 0 0 0.059 0 0.029

Pasture 0 0 0 0 0


Edible-type crops by-products 0 0 0 0 0






Meat and bone meal 0.052 0.033 0.032 0.030 0.032

System external inputs 0 0 0.097 0 0.064

Total 2.3 3 3 2.7 2.9 a Units in kg of feed-intake in dry weight per kg of animal product produced.

b Based on the ‘North America and Oceania’ region *Wirsenius, 2003+.

c Based on the ‘East Europe’ region *Wirsenius, 2003+.

d Based on the ‘East Asia’ region *Wirsenius, 2003+.

e Based on the ‘Latin America and Caribbean’ region *Wirsenius, 2003+.

M.Sc. Thesis

I.Y.R. Odegard

154

The Feed-Mix

As stated above, the FAO gives data on feed. The use of primary commodity groups as feed is only

significant for cereals, oil crops, pulses and roots and tubers. As can be seen in Table 52 these are the

first four mentioned in the feed-mix table above and correspond to the primary commodity groups

under the same names mentioned throughout the report.

Permanent pasture has four subgroups, grouping native or oversown grass-legume systems to either

temperate or tropical systems. While native systems receive no additional management, oversown

systems have a relatively high management intensity; e.g. they are fertilized and irrigated.

Three different inputs are grouped under the heading animal forage crops: forage vegetables, whole

cereals, and cropland pasture. Wirsenius refers to forage crops as ‘grasses and legumes cultivated for

harvest (that is, not for grazing), whole cereals and other fodder crops’ [Wirsenius, 2003, p.6]. Forage

vegetables are only used in pork production in the REF region and in the ASIA region. As can be seen in

Appendix 11, vegetables are used as feed input, even though they are not mentioned separately in the

table above. Forage vegetables are assumed equal to vegetables (in terms of yield and inputs) as

mentioned throughout this report. Whole-cereals refers to ‘whole-maize’, eaten as silage. The recovery

rate for silage produced from whole-maize is 90%, in all regions. Whole-maize is assumed to be equal to

‘maize, green’ *FAOSTAT, 2011+ and ‘maize (silage)’ *Fisher, 2002+, in terms of production characteristics,

i.e. yield. Water input data was provided by the Water Footprint Network [Hoekstra, 2008], and fertilizer

requirements are based on green maize yield [FAOTSTAT, 2011] in USA and fertilizer proportion per

hectare of 150:70:90 NPK in US in ’98 *FERTISTAT, 2007+.

Cropland pasture consists of grass and legumes grown on land that is suitable for growing food-crops.

Cropland pasture resembles oversown permanent pasture, and will be considered as such regarding

inputs and management practices. Depending on the scenario yields are equal to intensively managed

pastures Harvested-conserved grass-legume consists of ‘Grass-legume hay, grass-legume silage and

whole-cereals silage’ [Wirsenius, 2003, p.88]. Its yield per hectare in dry matter is assumed equal to that

of cropland pasture or permanent pasture, depending on the scenario. Data on yields of feedcrops is

given in Appendix 7.

Meat and bone meal and system external inputs constitute an insignificant part of the feed-mix. Meat

and bone meal are animal-type conversion by-products. Since meat consumption will increase

availability will not pose problems, as only poultry in general and pork in the ALM region have this input

as part of their feed-mix. System external inputs are composed of fish, cotton oil and cotton meal. Since

these flows are not considered in this study, and availability is not an issue in the calculations made by

Wirsenius, their availability is not considered to be a problem here.

Non-eaten food is only part of the pork feed-mix and only for the REF region, the ASIA region and the

ALM region. Still, it constitutes between 8% (REF region) and 30% (ALM region) of the pork feed-mix. Of

course, this input can only be an input if enough non-eaten food is available. If not, forage crops will

balance the difference, as this is the preferred feed-input for pork.

M.Sc. Thesis

I.Y.R. Odegard

155

Edible-type crops by-products are the ‘left-overs’ after harvest has taken place and/or crops have gone

through a first cleaning process. Cereals, roots and tubers, sugar crops and oil crops are included in

Wirsenius’ calculations: ‘cereals straw and stover’, ‘starchy roots tops’, ’sugar crops tops and leaves’, ‘oil

crops by-products’. The amount available of such edible-type crops by-products depends on the harvest

index; this factor determines which part of the harvest is useful as food, and which part is not. The

harvest index was assumed the same as calculated by Wirsenius. The total amount of edible-type crops

generated was decreased by the amount not recovered (which varies between 0% and 34%) and the

amount of food crops generated was subsequently divided by the former value. This calculation yields

factors indicating how much edible-type crops by-products are generated per unit of edible-type crops.

The results are shown in Table 53.

Table 53: Availability of edible-type crops by-products in terms of fraction of production [based on Wirsenius, 2003].

Edible-type crops by-products Availability

Cereals straw and stover 1.30 * cereals production

Starchy roots tops 0.90 * roots and tubers production

Sugar crops tops and leaves 0.63 * sugar crops production

Oil crops by-products 1.08 * oil crops production

Conversion by-products are limited to the oil crops and sugar crops. When the oil and the sugar is

extracted from the oil crops and sugar crops, these commodities are converted to vegetable oils and to

sugar and sweeteners. This process yields significant amounts of by-products that are very suitable as

animal feed because all protein ends up in the oil cake, while hardly any remains in the vegetable oils.

Appendix 9 shows the process flows for oil crops and sugar crops. The availability of oil-type and sugar-

type conversion by-products is determined by the fractions given in these diagrams, and were

determined on the basis of oil crop and sugar crop production and processing, and vegetable oil and

sugar and sweetener production in the year 2005.

Physical and Economic Availability

Even though, on a regional or global scale, inputs of the feed-mixes may be available, it may not be

physically possible to ensure the availability where it is necessary, or it may not be economical to make

certain inputs available in certain places. Therefore, only a part of certain flows, where availability is an

issue, will be taken into account. This method is also used by Wirsenius [Wirsenius, 2010]. The flows in

question are: edible-type crops by-products, conversion by-products and non-eaten food. If the feed

requirements are less than half of the actually available amount, either globally or regionally depending

on the scenario, the feed-mix is considered ‘ok’. If not, the missing flow will be substituted with the

‘balancing flow’; i.e. the flow that is considered most appropriate for the specific animal: pasture for

cattle, forage for pigs and cereals for poultry.

M.Sc. Thesis

I.Y.R. Odegard

156

Appendix 9 Production of Oil and Sugar Crops

Production oil crops (ton/year)

Processing (76w-% of

production)

Other utilities, 3% of Production

Food, 10% of Production

Feed, 6% of Production

Seed, 2.3% of Production

Waste, 2.7% of Production

Vegetable oils36 w-% of processing

Losses6 w-% of vegetable oils

Other Utilities37 w-% of vegetable oils

Food57 w-% of vegetable oils

Conversion by-productsPotential feed

Figure 54: Processing scheme oil crops [based on FAO, 2010, on data for the year 2005].

Production sugar crops (ton/year)

Processing (94w-% of

production)

Other utilities, 0.7% of Production

Food, 1.7% of Production

Feed, 1.4% of Production

Seed, 1.3% of Production

Waste, 0.9% of Production

Sugar and

sweeteners12 w-% of processing

Conversion by-productsPotential feed

Losses5 w-% of sugar and sweeteners

Other Utilities10 w-% of sugar and sweeteners

Food85 w-% of sugar and sweeteners

Figure 55: Processing scheme sugar crops [based on FAO, 2010, on data for the year 2005].

M.Sc. Thesis

I.Y.R. Odegard

157

Appendix 10 Land Potential

The extent of land available in the four regions was estimated using data from Global Agro-Ecological

Zones study by the FAO and the IIASA [Fisher, 2002]. Gross extents of land with rain-fed cultivation

potential are given for 22 regions or countries. These were grouped to correspond most closely to the

four regions in this study, as shown in Table 54.

Table 54: Division of the ‘global agro-ecological zones-study’ regions.

Region FAO/IIASA regions

OECD90 North America, Northern Europe, Southern Europe, Western Europe, Oceania, Japan

REF Eastern Europe, Russian Federation, Central Asia

ASIA Polynesia, Western Asia, Southeast Asia, South Asia, East Asia

ALM Caribbean, Central America, South America, Eastern Africa, Middle Africa, Northern Africa,

Southern Africa, Western Africa

The extents of cultivable land suitable are shown in Table 55. These values give a ‘gross extent’, which

does not mean this is actually available. According to the Fisher et al, between 10 and 30% of gross

suitable areas may not be available for agriculture *Fisher, 2002+. The ‘net’ estimate is also shown

between brackets in Table 55.

Table 55: Global and regional gross and net extents of cultivable land [based on Fisher, 2002].

Region Land with cultivation potential – gross extent (106 ha)

(net extent range – 70-90% of gross extent)

VS+S MS VS+S+MS

OECD90 482.7

(337.9-434.4)

224.3

(157.0-201.9)

707

(494.9-636.3)

REF 226.3

(158.2-203.4)

154.5

(108.2-139.1)

380.8

(266.6-342.7)

ASIA 490.2

(343.1-441.2)

100.2

(70.1-90.2)

590.4

(413.3-531.4)

ALM 1658.2

(1160.7-1492.4)

315.3

(220.7-283.8)

1973.5

(1381.4-1776.2)

WORLD 2,857.4

(2,000.2-2,571.7)

764.3

(535.0-687.9)

3,621.7

(2,535.2-3,259.5)

Notes: It is assumed that similar yields can be attained on very suitable, suitable and moderately suitable areas which, however,

leads to overestimation of attainable production quantities, because yields are most likely lower on less suitable areas.

The extent of land with cultivation potential which is deemed suitable for agricultural purposes differs per scenario; the

VS+S+MS areas fit the A scenarios, while only the VS+S areas fit the B scenarios.

M.Sc. Thesis

I.Y.R. Odegard

158

Appendix 11 Virtual Water Content

Regional averages are needed to calculate future water use. These are, however, not available as such.

The Water Footprint Network provides data on the global average water requirements (in terms of crop

evapotranspiration) of primary crops. Furthermore, they provide data on the water needs of foodstuffs

and primary crops for different countries (see also Section 3.3.2 in Chapter 2). When the regional water

appropriation of agriculture is calculated using the global average water requirements of commodity

groups, the results deviate substantially from the data given in Table 13 and Table 14. Because certain

crops are not taken into account, lower estimates of appropriations are acceptable. The OECD90 region,

however, shows an appropriation of over 25% more than what is shown in Table 13 and Table 14. As

shown in Table 14 the total global water use in agriculture is 6,189 billion m3 per year; while the water

use of the seven main vegetable commodity groups comes to 5,706 billion m3 per year. To see if more

appropriate (regionally specified) values could be obtained, the water needs of primary crops in different

countries were used to obtain regional averages for five crops with high global total use: rice (21.3% of

total global water use in agriculture), wheat (12.4%), maize (8.6%), soybeans (4.5%) and sugar cane

(3.4%). Together they account for a little over half of the total water use in agriculture. These crops are

part of the commodity groups cereals, oil crops and sugar crops. This method can only be reasonably

applied to staple crops that account for high global uses and are grown in all regions. This means that for

the commodity groups roots and tubers, pulses, vegetables and fruits, the global average water

appropriations (m3/ton) will be assumed to be reasonable estimates for regional water use.

With the regionally specified data (the global averages) the OECD90 region has a water use which is 5%

lower than the value given in Table 13, clearly a better choice than the result with the original

calculations. The REF region, however, rose to being 43% higher than the value given in Table 13 with the

regionally specified data, instead of being 20% short with the original data. This could be due to the fact

that only 10 out of 27 countries in the REF region are accounted for. Values for the ASIA region did not

change much; from being 3% short with the original data to being 5% short with the regionally specified

data. In the ALM region, the gap between the calculated appropriation and the appropriation given in

Table 13 decreased from 38% to 28%. If the regionally specified data are chosen for the OECD90 region

and the ALM region, while the original global averages are maintained for the REF region and the ASIA

region, 95% of the water appropriation given in Table 13 is accounted for. Crops that were not taken into

account in this study (stimulants, nuts, fibre crops and some fodder crops), account for about 10% of

global water use according to Hoekstra. Because irrigation efficiency is not taken into account for crops

other than cereals, the values below provide a realistic approximation.

M.Sc. Thesis

I.Y.R. Odegard

159

Table 56: Virtual Water Content per commodity group (m3 per ton) for the four regions [based on Hoekstra, 2008]

Commodity Group Virtual Water Content

Per region per commodity

(in m3 per ton)


Cereals 1,099 1,571 1,571 2,069 1,571

Fruits 844 844 844 844 844

Oil Crops 2,226 2,002 2,002 2,002 2,209

Pulses 3,790 3,790 3,790 3,790 3,790

Roots and tubers 375 375 375 375 375

Sugar crops 122 165 165 151 165

Vegetables 264 264 264 264 264

Total (in billion m3) 1,043 383 2,732 1,291 5,669

Actual total (see Table 13, in billion m3)

Difference in % (total to actual total)

1,099

-5%

481

-20%

2,828

-3%

1,781

-28%

5,707

-1%

The deviations in the REF region and the ALM region seem big, but because some commodity groups are

left out (e.g. stimulants and nuts) gaps are to be expected. The global ‘actual total’ in Table 56 shows the

total water requirements for the commodity groups produced in 2005. The difference between the total

(calculated with the regional adjustments) and the original total water use shows a deviation of only 5%.

The Virtual Water Content of ‘whole maize’ was set to 143.4 m3/ton, as defined by Hoekstra for ‘maize

for forage and silage’ *Hoekstra, 2008+. These regional averages can be used in further calculations.

M.Sc. Thesis

I.Y.R. Odegard

160

Appendix 12 Water Potential

Table 57 shows the global and regional renewable water resources, and corresponding water thresholds.

Moderate water stress occurs at a use of 20% of the total renewable resources, while critical water

stress occurs at a use of 40% of the total. Of course, if the global or regional water use does not reach

either threshold, it does not mean that water stress is not experienced locally.

Table 57: Global and regional renewable water resources and water stress thresholds [based on Hoekstra, 2008].

Region Renewable water resources (km3)

Total 40% threshold –

critical water stress

20% threshold –

moderate water stress

OECD90 9,535.58 3,814.2 1,907.1

REF 5,728.18 2,291.3 1,145.6

ASIA 14,773.37 5,909.3 2,954.7

ALM 22,926.14 9,170.5 4,585.2

WORLD 52,963.27 21,185.30 10,592.60

Data from the Water Footprint Network was supplemented by data from the FAO [AQUASTAT, 2011].

Together, they account for 170 out of 182 countries.

Table 58: Countries for which data was used from AQUASTAT.

Region Countries for which AQUASTAT data was used

OECD90 Ireland, New Zealand

REF Bosnia and Herzegovina, Croatia, Estonia, Slovakia, Slovenia, Tajikistan

ASIA Brunei Darussalam, Timor-Leste

ALM Bahamas, Comoros, Congo, Equatorial Guinea, Eritrea, Guinea, Guinea-Bissau, Lesotho, Niger,

Occupied, Palestinian, Territories, Saint Kitts and Nevis, Sao Tome and Principe, United

Republic of Tanzania, Togo, Uganda, United Arab Emirates, Uruguay, Venezuela.

The countries for which no data are given by either AQUASTAT or WFN are listed in Table 59. Their

contribution is insignificant on a global and regional scale.

Table 59: Countries for which data on renewable water resources is lacking.

Region Countries for which no data on renewable water resources is given

OECD90 (0)

REF Serbia and Montenegro (1)

ASIA American Samoa, French Polynesia, New Caledonia, Samoa, Tonga, Vanuatu (6)

ALM Antigua and Barbuda, Dominica, Saint Lucia, Saint Vincent and the Grenadines, Seychelles (5)

M.Sc. Thesis

I.Y.R. Odegard

161

Appendix 13 Meat consumption

Keyzer et al. [Keyzer, 2005] determined the effect of income growth (GDP measured in PPP) on meat

consumption, based on the Engel curve. In general, the Engel curve describes the relationship between

individual income (on the x-axis) and consumption of a good (on the y-axis). While for some commodity

groups this relation may be simply positively or negatively linear, the relation could also be S-shaped,

indicating that beyond a first threshold, consumption increases more steeply with increasing income,

and beyond a second threshold this relative consumption increase is reduced again. This is shown in

Figure 56 [Keyzer, 2005]. This model is a simplification, based on a nonlinear Engel curve which was

based on data for 125 countries, for the years 1975 to 1997 [Keyzer, 2005]. Figure 56 shows that there

are three different regimes related to two income thresholds and two consumption thresholds.

Figure 56: Engel curve for meat consumption. On the x-axis, y and y represent the income thresholds. On the y-axis, c1 and

c2 represent the consumption thresholds [Keyzer, 2005].

The model which fits the curve above and the data on which it was based is represented by the following

system of linear equations:

2 2 1

2 2

2 2 3

( ), if ,

( ) , if ,

( ), if

i i i

i i i i

i i i

a b y y y y y

c y a b y y y y

a b y y y y y

The parameters that correspond to the parameters in the equation are shown in Table 60.

Table 60: Parameters of the Engel curve [Keyzer, 2005].

Parameter Estimate

2a

-1.182

2b

8.070

1

-4.820

3

-7.090

Income thresholds: In per capita GDP in PPP, corresponding to US$-1992

M.Sc. Thesis

I.Y.R. Odegard

162

y 2,200 US$

y 9,700 US$

yi In ‘000 US$

Consumption thresholds: Meat consumption per capita

1c 16.6 kg/year

2c 77.1 kg/year

Table 61 shows the number of countries in the different regions in the different income regimes in 2050.

Table 61: Number of countries in income regimes - related to meat consumption - in 2050 [based on Keyzer, 2005; Van Vliet, 2010].

Regions

(number of countries)

Scenarios

Number of countries in income regimes in 2050

1st

regime

< 2,200 US$

2nd

regime

2,200-9,700 US$

3rd

regime

> 9,700 US$

OECD90 (26) A1/A2/B2 - - 26

REF (27) A1/A2/B2 - - 27

ASIA (31) A1/B2 - - 31

A2 - 22 9

ALM (98) A1 - 30 68

A2/B2 - 46 52

Table 62 shows the average global and regional meat consumptions in for the 3 scenarios in which meat

is consumed. The meat consumption levels in B2 are halved relative to the projection made by the

‘Keyzer equation’, as explained in Chapter 6.

Table 62: Global and regional meat consumption (kg/cap/year) [based on Keyzer, 2005; Van Vliet, 2010].

Scenario Meat Consumption

(kg per capita per year, margin between brackets for regions)


2005 89.34 51.06 26.74 31.92 38.81

A1 129.68 99.07 90.66 84.76 93.47

(99.18-141.38) (95.79-107.91) (84.76-110.26) (62.57-104.57)

A2 106.94 79.02 53.32 57.41 61.95

(82.30-116.19) (77.27-83.76) (36.59-93.21) (25.97-84.97)

B2 57.34 43.19 41.25 30.52 39.67

M.Sc. Thesis

I.Y.R. Odegard

163

Appendix 14 Virtual Fertilizer Content

As was shown in Section 3.3.3, recommended fertilizer use varies with crop, region, and local soil

characteristics and management practices. It was also shown that actual fertilizer use may depend on

factors that have nothing to do with proper agricultural management, e.g. subsidies raising fertilizer use

well above recommended values. Another issue important to fertilizer use rates, as was also discussed in

Chapter 3, is fertilizer efficiency. Application timing and methods can significantly reduce use rates,

without reducing yields.

Fertilizer use is reported in FAOSTAT, separately for N, P2O5 and K2O fertilizers, but not specified per

commodity group, and thus only valuable as a measure for comparison. Fertilizer use per country per

crop is reported by the FAO in their FERTISTAT database. Data for 37 crops or commodity groups is given

for countries in all regions for a year (or period) between 1995 and 2004. A total of 1022 instances of

fertilizer use for a specific crop for a specific country are given (including for each of those 1022

instances, use of N, P2O5 and K2O fertilizer) [FERTISTAT, 2011]. This gives insight into regional differences

in current fertilizer application rates. It does not, however, give insight into requirements and differences

in requirements. Data on ‘nutrient removal by crops’, reported by the FAO in the Fertilizer and plant

nutrition guide, are given in ‘kg/ha’ with values corresponding to high yields and to low yields, although

not for all crops. Furthermore, data is given for specific crops, in terms of output per input of fertilizer,

for specific crops. Two different levels of nutrient removal by crops (for high yield and low yield) are

given for wheat, maize, rice, potato, sweet potato, cassava and sugar cane, other data is given for onions,

tomatoes, cucumber, soybeans, beans and groundnuts.

Here, recommended fertilizer use rates were based on estimates of fertilizer requirements per

generated product. The method is similar to that used to estimate feed requirements and water

requirements. This means that requirements are expressed in input per output, on a weight basis. This

implicates a linear relationship between fertilizer input and generated output, which cannot be used for

individual crops in individual countries, but does provide a reasonable approximation on a regional and

global scale. As the guide, on which the projections were based, was written in 1984, and the scenarios

are designed for 2050, requirements are calculated using the requirements given for the high yield

variety. These are lower per generated output than the requirements for low yields, and may thus

underestimate fertilizer use. On the other hand, fertilizer use in the REF region and in the ALM region

seems to be much lower than the rates recommended by the FAO. Furthermore, in over 60 years

(compared to the ‘80s), a lot of progress can be made in terms of agricultural management, e.g. in

application methods and proper timing, reducing the required application rates. The assumptions on

which the estimates for the requirements of the commodity-groups are based are given in Table 63. The

data were extracted from the ‘Fertilizer and Plant Nutrition Guide’ by the FAO *FAO, 1984]. Differences

between regions in fertilizer requirements were based solely on differences in crops grown, e.g. it was

assumed that no rice is grown in the OECD90 and in the REF region. The levels of nutrient removal by

crops give a baseline indication of fertilizer requirements, although some adjustments need to be made.

Pulses and soybeans (oil crops) are both leguminous, and thus the extraction of nitrogen is much higher

than the actual fertilizer requirements. Regional production of different cereals crops varies, for which

M.Sc. Thesis

I.Y.R. Odegard

164

adjustments were made. No adjustments were made for potential regional differences in soil conditions

and subsequent variances in fertilizer requirements, because it is outside the scope of this thesis.

Table 63: Basis of estimates for fertilizer requirements [based on FAO, 1984; Fertistat, 2007].

Commodity group Basis of estimate

Cereals Requirements are weighted per region, based on the production proportions of maize,

rice and wheat.

Fruit Requirements are based on those for ‘fruit trees’, which are given in kg/ha.

Yield assumption: fruit yield in USA in 2005

Pulses Requirements equal to ‘soybeans’ for N, as pulses are leguminous crops.

Requirements for ‘beans’, for P2O5 and K2O.

Oilcrops N requirements based on average oil crop yield and average N use in the OECD90 in

2005.

P2O5 and K2O requirements based on ‘soybeans’.

Sugar crops Requirements are based on those for ‘sugar cane’.

Roots and tubers Requirements are weighted per region, based on the production proportions of

cassava, potato and sweet potato. In the OECD90 and REF region only production of

potatoes was assumed.

Vegetables Requirements are based on those for ‘tomatoes’.

Fodder Requirements given for intensive management of grassland, yield based on Wirsenius’

yield on average world yield of cropland phytomass (5.1 kg DM/ha)*.

Whole Maize Requirements based on green maize yield [FAOTSTAT, 2011] in USA in and fertilizer

proportion per hectare of 150:70:90 NPK in US in ’98 *FERTISTAT, 2007].

*Note: Fertilizer requirements on intensively managed grasslands are based on the world average yield of cropland phytomass.

This value is higher than the yields in all regions related to permanent grassland phytomass. Such a value corresponds to the

values given for Puerto Rico in Table 22 [FAO, 1984], for the cutting interval of 40 days. Further research concerning fertilizer

use requirements for growth of fodder crops is recommended.

Because fertilizer efficiency and agricultural management also play a role in fertilizer uptake and

therefore in fertilizer requirements, rates were adjusted in scenarios were efficient use of natural

resources is high on the agenda, i.e. scenario B1, or technological development increases efficiencies, i.e.

A1. These choices are shown in Table 35 in Chapter 5, along with their rationale. No allowances were

made for the fact that the combination of irrigation and fertilization raises yields further. However, this is

implicitly incorporated in the cereal yield projections.


Table 64 shows the Virtual Fertilizer Content values, based on the methodology described above, for all

commodity groups and all regions. These values represent a baseline, and improvements in efficiency

will result in lower applications.

M.Sc. Thesis

I.Y.R. Odegard

165

Table 64: Virtual Fertilizer Content (kg fertilizer/kg generated product)

OECD90 REF ASIA ALM

N

Cereals 0.036533 0.0384 0.0446 0.038

Fruit 0.009302 0.009302 0.009302 0.009302

Oil*

0.0027 (0.027) 0.0027 (0.027) 0.0027 (0.027) 0.0027 (0.027)

Pulses 0.0125 0.0125 0.0125 0.0125

Roots 0.004375 0.004375 0.00347 0.0032

Sugar 0.0011 0.0011 0.0011 0.0011

Veggies 0.00275 0.00275 0.00275 0.00275

Fodder 0.019608 0.019608 0.019608 0.019608

Whole Maize 0.0088 0.0088 0.0088 0.0088

P

Cereals 0.00944 0.00952 0.0184 0.0118

Fruit 0.003721 0.003721 0.003721 0.003721

Oil 0.018333 0.018333 0.018333 0.018333

Pulses 0.020833 0.020833 0.020833 0.020833

Roots 0.002 0.002 0.0016 0.00174

Sugar 0.0009 0.0009 0.0009 0.0009

Veggies 0.00075 0.00075 0.00075 0.00075

Fodder 0.004902 0.004902 0.004902 0.004902

Whole Maize 0.0041 0.0041 0.0041 0.0041

K

Cereals 0.014275 0.0176 0.0191 0.0144

Fruit 0.006977 0.006977 0.006977 0.006977

Oil 0.040417 0.040417 0.040417 0.040417

Pulses 0.05 0.05 0.05 0.05

Roots 0.00775 0.00775 0.00712 0.00833

Sugar 0.0034 0.0034 0.0034 0.0034

Veggies 0.00375 0.00375 0.00375 0.00375

Fodder 0.019608 0.019608 0.019608 0.019608

Whole Maize 0.0053 0.0053 0.0053 0.0053

* Erratum: Since processing of the results with the values given in this table, it was discovered that the Virtual Fertilizer Content

value for nitrogenous fertilizer for oil crops was calculated as being a factor 10 too small, and should actual be set to 0.027 kg/kg.

This means the results concerning fertilizer use in 2050 are estimated too low. The mistake was discovered too late to make

adjustments. Since these values were calculated, the FAO seems to have adjusted their data concerning oil crops; the area

under cultivation seems to have doubled. This is further elaborated on in the discussion.

Note: Pasture, cropland pasture and harvested-conserved grass-legume are all grouped under ‘fodder’.

M.Sc. Thesis

I.Y.R. Odegard

166

Appendix 15 Yield Projection Calculations OECD90 Region

Ewert et al. have shown that even though yield may differ between crops and countries, the difference

between relative yield changes (% change from one year to the next) tend to be small, and converge

over time [Ewert, 2005]. In the future, these relative yield changes are expected to merge. Productivity

estimations based on the relative yield changes thus has an important advantage: ‘yield changes can be

compared and averaged across crops and countries to avoid unnecessary complexity’ [Ewert, 2005,

p.106]. Technology was identified as the most important driver of productivity change. Technology

development, as used by Ewert et al., refers to all measures related to crop management.

Ewert et al. solely focus on food production in Europe (EU15 member countries, Norway and

Switzerland). Their interpretation of the IPCC SRES is somewhat different than the one made in this study,

and therefore the yield projections can only be used as a Here, the equations established by Ewert et al.

were used to calculate potential productivity increases for the OECD90. It is assumed that the

technological development parameters generated by Ewert are also representative for the whole of the

OECD90 region. Relative yield changes in the baseline year t0 (the year 2007) were calculated as the

average relative yield change for the years 2000-2007. For pulses this period was increased to include

the years 1995-2007, because of fluctuations. The future change in productivity can be calculated with

the equation shown in Table 65.

Table 65: Future change in productivity equation [Ewert, 2005, p.105]

( ) ∫ (( ( ) ( ))

)

future change in productivity

( ) relative yield change at t0

yearly increment in the relative yield change with

reference to the baseline year t0 (calculated as

( ) )

( ) represents the potential yield as a relative fraction of

the current yield

( ) represents the actual yield as a relative fraction of

potential yield in the future

As becomes clear from the equation and parameters in Table 65, future yields can be increased by

increasing the potential yield and reducing the yield gap. Table 66 below show the values for the

parameters which are used in the equation above. These values represent the effect that technology will

have on the potential yield and the yield gap. Both are dependent on time and the scenario. Table 67

shows the relative changes in crop productivity as a result of technology development.

M.Sc. Thesis

I.Y.R. Odegard

167

Table 66: Values of parameters that represent the effect of technology on potential yield ( ( )) and yield gap ( ( )) for

different scenarios and time slices [Ewert, 2005].

Parameter Year Scenario

A1 A2 B1 B2

( ) 2020 0.90 0.80 0.60 0.20

2050 0.80 0.60 0.40 0.00

( ) 2020 0.85 0.85 0.85 0.60

2050 0.90 0.90 0.90 0.60

Table 67: Estimated relative changes in crop productivity due to technology development for the OECD90 region, the four scenarios and for the year 2050 [based on Ewert, 2005; FAO, 2010]

OECD90 Region Relative yield changea

A1 A2 B1 B2

Cereals (incl. feed) 1.7076 1.5653 1.3988 1.0516

Oil Crops 1.6388 1.5103 1.3600 1.0466

Roots and Tubers 1.3679 1.2939 1.2074 1.0268

Sugar Crops 1.6712 1.5362 1.3783 1.0490

Vegetables 1.4488 1.3585 1.2529 1.0327

Fruitsb

1.4488 1.3585 1.2529 1.0327

Pulses 1.0283 1.0226 1.0160 1.0021 a Based on relative yield changes averaged over the years 2000-2007, except for pulses for which the relative yield changes were

averaged over the years 1995-2007 (due to extreme variations). Extrapolated for OECD90, based on the equation in Table 65

[from Ewert, 2005, p.105] and data from the FAO [FAO, 2010]. b Relative yield changes for fruit were assumed equal to those of vegetables.

The interpretation made by Ewert et al. of the IPCC SRES is somewhat different than the one made in this

study, and therefore the yield projections can only be used as a reference for comparison.

M.Sc. Thesis

I.Y.R. Odegard

168

Appendix 16 FAO Productivity Projections

Table 68: FAO productivity projections

Region Growth rate (percent p.a.)

(relative yield change)

2000-2030 2030-2050

OECD90 Yield Cereals (incl. feed) 0.5

(1.1614)

0.7

(1.1497)

Oil Cropsc 1.5

(1.5631)

1.3

(1.2948)

Sugar Crops

-0.1 -0.4

REF Yield Cereals (incl. feed) 0.8

(1.27)

0.4

(1.0831)

Oil Cropsc 1.6

(1.6099)

1.4

(1.3206)

Sugar Crops 0.0 -1.5

ASIA Yield Cereals (incl. feed) 1.0-1.3

(1.3478-1.4733)

0.7-0.2

(1.2328-1.0408)

Oil Cropsc 1.8-2.1

(1.7078-1.8654)

1.4

(1.3206)

Sugar Crops 2.2

(1.9210)

1.6

(1.3736)

ALM Yield Cereals (incl. feed) 1.3-2.5

(1.4733-2.0976)

0.7-1.8

(1.1497-1.4287)

Oil Cropsc 1.7-3.7

(1.6582-2.9741)

1.1-2.0

(1.2446-1.4859)

Sugar Crops 2.2

(1.9210)

1.6

(1.3736) c Includes productivity increases due to non-food uses such as biofuel.

M.Sc. Thesis

I.Y.R. Odegard

169

Appendix 17 Results – Data

Table 69: Total production (in million tons per year and kg/cap/year), losses (kg/cap/year), feed (kg/cap/year), apparent consumption (kg/cap/year) and household and retail waste (kg/cap/year), per commodity group per scenario.

Commodity group Total production

(million tons per year)

2005 A1 A2 B1 B2

Cereals 2,048.2 5,475.4 3,697.4 2,216.1 2,744.6

Fruit 525.2 628.5 810.6 1,409.4 1,651.4

Pulses 63.9 77.7 101.8 89.7 81.4

Roots and tubers 728.3 577.7 1,235.9 725.7 1,007.0

Vegetables 1,728.4 999.2 2,567.7 1,263.9 2,114.7

Oil crops 475.8 1,052.1 928.1 750.5 881.5

Sugar crops 1,504.2 2,453.7 2,615.7 1,056.1 1,250.6

Eggs 61.7 70.2 90.6 87.8 82.3

Meat 258.5 730.1 624.2 0.0 362.7

Milk 643.6 2,117.6 1,075.1 1,212.7 1,091.8

Total 8,037.8 14,182.2 13,747.1 8,811.9 11,268.0

Commodity group Total production

(kg per capita per year)

2005 A1 A2 B1 B2

Cereals 314.51 703.78 373.47 284.85 299.96

Fruit 80.65 80.78 81.88 181.15 180.48

Pulses 9.81 9.99 10.28 11.53 8.89

Roots and tubers 111.84 74.26 124.84 93.28 110.05

Vegetables 265.41 128.43 259.36 162.46 231.11

Oil crops 73.06 135.23 93.74 96.46 96.34

Sugar crops 230.98 315.39 264.21 135.75 136.68

Eggs 9.47 9.03 9.15 11.29 9.00

Meat 39.69 93.84 63.05 - 39.64

Milk 98.82 272.18 108.60 155.88 119.32

Total 1,234.25 1,822.92 1,388.58 1,132.65 1,231.48

Commodity group Total losses


2005 A1 A2 B1 B2

Cereals 54.41 121.75 64.61 49.28 51.89

Fruit 14.76 14.78 14.98 33.15 33.03

Pulses 1.31 1.33 1.37 1.53 1.18

Roots and tubers 21.92 14.55 24.47 18.28 21.57

Vegetables 23.62 11.43 23.08 14.46 20.57

Oil crops 29.72 55.01 38.13 39.24 39.19

Sugar crops 32.75 44.72 37.47 19.25 19.38

Eggs 1.08 1.03 1.04 1.29 1.03

Meat 0.36 0.84 0.57 - 0.36

Milk 16.40 45.18 18.03 25.88 19.81

Total 196.33 310.64 223.75 202.36 208.01

M.Sc. Thesis

I.Y.R. Odegard

170

Commodity group Total feed per capita


2005 A1 A2 B1 B2

Cereals 113.48 426.03 141.51 73.57 86.67

Fruit - - - - -

Pulses 2.60 4.66 2.37 - 1.28

Roots and tubers 24.25 2.70 24.99 - 13.40

Vegetables 125.21 - 117.70 - 63.09

Oil crops 6.03 6.55 5.77 - 3.11

Sugar crops - - - - -

Eggs - - - - -

Meat - - - - -

Milk - - - - -

Total 271.57 439.94 292.34 73.57 167.55

Commodity group Total apparent consumption


2005 A1 A2 B1 B2

Cereals 146.62 156.00 167.35 162.00 161.39

Fruit 65.89 66.00 66.89 148.00 147.45

Pulses 5.91 4.00 6.54 10.00 6.43

Roots and tubers 65.67 57.00 75.38 75.00 75.08

Vegetables 116.58 117.00 118.58 148.00 147.45

Oil crops 37.31 73.67 49.84 57.22 54.04

Sugar crops 198.23 270.67 226.75 116.50 117.30

Eggs 8.39 8.00 8.11 10.00 7.97

Meat 39.33 93.00 62.48 - 39.28

Milk 82.42 227.00 90.57 130.00 99.52

Total 766.35 1,072.33 872.49 856.72 855.92

Commodity group Total household and retail waste


2005 A1 A2 B1 B2

Cereals 23.46 49.92 26.78 25.92 25.82

Fruit 8.24 16.50 8.36 18.50 18.43

Pulses 0.47 0.64 0.52 0.80 0.51

Roots and tubers 5.25 9.12 6.03 6.00 6.01

Vegetables 15.16 30.42 15.42 19.24 19.17

Oil crops 2.98 11.79 3.99 4.58 4.32

Sugar crops 15.86 43.31 18.14 9.32 9.38

Eggs 1.30 2.48 1.26 1.55 1.24

Meat 3.15 14.88 5.00 - 3.14

Milk 13.19 72.64 14.49 20.80 15.92

Total 89.05 251.69 99.98 106.71 103.95

M.Sc. Thesis

I.Y.R. Odegard

171

Table 70: Total land use per scenario and total cropland use in the A2 and B2 scenarios (million hectares).

Commodity group Total Land Use

(million hectare)

2005 A1 A2 B1 B2

Cereals – irrigated 0 340.5 339.8 272.2 272.2

Cereals – rainfed 615.1 907.5 712.7 223.4 1,502.8

Fruit 52.3 33.1 69.7 74.1 284.6

Oil crops 301.1 234.8 411.7 167.5 774.0

Pulses 74.3 24.8 73.5 28.7 119.2

Roots and tubers 53.4 15.5 61.7 19.4 99.4

Sugar crops 24.2 35.5 42.2 15.3 40.4

Vegetables 100.0 36.9 151.2 46.7 227.3

Pasture 1,985.9 883.5 4,179.2 151.5 5,003.7


Cropland pasture 77.7 321.2 60.8 131.7 76.4

Whole cereals (Whole Maize) 67.5 59.7 58.8 13.9 74.9

Total 3,520.0 2,979.7 6,309.9 1,183.1 8,656.8

Commodity group Cropland Use in A2

(million hectare)

OECD90 REF ASIA ALM

Cereals – irrigated 37.5 27.75 228.00 46.50

Cereals – rainfed 116.32 121.52 47.24 427.65

Fruit 5.76 5.31 36.60 22.01

Oil crops 73.68 23.35 143.46 171.19

Pulses 5.55 2.38 35.41 30.20

Roots and tubers 2.44 5.78 24.04 29.47

Sugar crops 5.47 4.39 19.83 12.52

Vegetables 4.81 26.66 83.75 35.93

Whole Maize 21.31 37.51 0 0

Total 272.84 254.65 618.33 775.47

Commodity group Cropland Use in B2

(million hectare)

OECD90 REF ASIA ALM

Cereals – irrigated 33.75 25.50 177.75 35.25

Cereals – rainfed 179.95 159.04 470.11 693.73

Fruit 23.83 214.2 153.85 85.46

Oil crops 130.03 417.3 291.26 310.94

Pulses 7.52 2.72 62.18 46.75

Roots and tubers 4.12 7.75 43.34 44.19

Sugar crops 4.12 4.19 19.67 11.47

Vegetables 11.21 28.97 131.07 56.00

Whole Maize 28.07 46.79 0 0.00

Total 422.60 906.46 1,349.23 1,283.79

M.Sc. Thesis

I.Y.R. Odegard

172

Table 71: Total water use in the scenarios and water use in the A2 and B2 scenarios (billion m3).

Commodity group Total Water Use

(billion m3)

2005 A1 A2 B1 B2

Cereals – irrigated 0 5,117.5 4,414.4 3,250.6 1,628.6

Cereals – rainfed 3,217.7 5,531.4 3,325.8 1,368.7 3,401.4

Fruit 443.3 530.4 684.1 1,189.5 1,393.8

Oil crops 1,051.1 2,324.1 1,893.1 1,657.8 1,795.8

Pulses 242.2 294.5 385.9 340.1 308.4

Roots and tubers 273.1 216.6 463.5 272.2 377.6

Sugar crops 248.2 404.9 403.7 174.3 193.4

Vegetables 456.3 263.8 756.3 333.7 575.6

Whole Maize 78.2 408.2 169.9 94.8 108.2

Total 6,010.1 15,091.4 12,496.7 8,681.7 9,782.8

Commodity group Total Water Use in A2

(billion m3)

OECD90 REF ASIA ALM

Cereals – irrigated 494.5 305.2 2,925.2 689.5

Cereals – rainfed 664.7 477.3 193.9 1,990.8

Fruit 78.2 27.7 334.5 243.7

Oil crops 349.4 93.5 812.8 637.5

Pulses 46.9 19.1 174.5 145.3

Roots and tubers 34.3 38.4 197.3 193.5

Sugar crops 44.1 25.7 200.3 133.5

Vegetables 38.9 127.7 400.0 189.7

Whole Maize 65.1 104.8 0.0 0.0

Total 1,816.1 1,219.4 5,238.5 4,223.5

Commodity group Total Water Use in B2

(billion m3)

OECD90 REF ASIA ALM

Cereals – irrigated 205.4 129.4 1,052.5 241.2

Cereals – rainfed 514.2 312.3 960.1 1,614.7

Fruit 161.6 55.9 703.1 473.2

Oil crops 308.2 83.5 825.1 578.9

Pulses 31.8 10.9 153.2 112.5

Roots and tubers 28.9 25.7 177.9 145.1

Sugar crops 20.6 12.3 99.3 61.2

Vegetables 45.3 69.4 313.0 147.8

Whole Maize 42.9 65.4 0.0 0.0

Total 1,358.9 764.8 4,284.2 3,374.6

M.Sc. Thesis

I.Y.R. Odegard

173

Table 72: Total fertilizer use (N, P2O5 and K2O) in the four scenarios (million tons per year).

Commodity group Total Fertilizer – N


2005 A1 A2 B1 B2

Cereals – irrigated 0.00 71.78 71.76 46.65 24.48

Cereals – rainfed 83.57 129.30 75.80 30.22 69.40

Fruit 4.88 5.26 7.54 11.14 13.06

Oil crops 1.28 2.56 2.51 1.72 2.02

Pulses 0.80 0.87 1.27 0.95 0.86

Roots and tubers 2.64 1.88 4.32 2.23 2.99

Sugar crops 1.65 2.43 2.88 0.99 1.17

Vegetables 4.75 2.47 7.88 2.95 5.10

Fodder 0.00 291.72 52.53 0.00 0.00

Whole Maize 4.80 22.55 10.43 4.95 5.65

Total 104.37 530.82 236.92 101.8 124.73

Commodity group Total Fertilizer – P2O5


2005 A1 A2 B1 B2


Cereals – rainfed 28.87 44.67 22.22 10.44 22.75

Fruit 1.94 2.09 3.02 4.43 5.22

Oil crops 8.71 17.33 17.01 11.67 13.74

Pulses 1.33 1.46 2.12 1.59 1.44

Roots and tubers 1.27 0.91 2.13 1.08 1.47

Sugar crops 1.35 1.99 2.35 0.81 0.96

Vegetables 1.30 0.67 2.15 0.81 1.39

Fodder 0 72.93 13.13 0.00 0.00

Whole Maize 2.24 10.50 4.86 2.30 2.63

Total 47.01 177.34 95.56 49.24 58.57

Commodity group Total Fertilizer Use – K2O


2005 A1 A2 B1 B2


Cereals – rainfed 34.80 53.84 25.14 12.58 24.67

Fruit 3.68 3.96 3.32 8.39 5.75

Oil crops 19.22 38.25 20.48 25.77 16.34

Pulses 3.20 3.5 2.48 3.81 1.65

Roots and tubers 5.57 3.97 2.65 4.71 1.84

Sugar crops 5.11 7.51 3.26 3.05 1.32

Vegetables 6.48 3.37 2.59 4.03 1.83

Fodder 0.00 291.72 25.46 0.00 0.00

Whole Maize 2.89 13.58 5.40 2.98 2.94

Total 80.95 449.58 118.66 84.74 65.82

master of science thesis the future of...

Documents