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ARTICLES
PUBLISHED ONLINE: 31 AUGUST 2014 | DOI: 10.1038/NCLIMATE2353
Importance of food-demand management for
climate mitigation
Bojana Bajželj1*, Keith S. Richards2, Julian M. Allwood1, Pete Smith3, John S. Dennis4,
Elizabeth Curmi1and Christopher A. Gilligan5
Recent studies show that current trends in yield improvement will not be sucient to meet projected global food demand in
2050, and suggest that a further expansion of agricultural area will be required. However, agriculture is the main driver of
losses of biodiversity and a major contributor to climate change and pollution, and so further expansion is undesirable. The
usual proposed alternative—intensification with increased resource use—also has negative eects. It is therefore imperative
to find ways to achieve global food security without expanding crop or pastureland and without increasing greenhouse gas
emissions. Some authors have emphasized a role for sustainable intensification in closing global ‘yield gaps’ between the
currently realized and potentially achievable yields. However, in this paper we use a transparent, data-driven model, to show
that even if yield gaps are closed, the projected demand will drive further agricultural expansion. There are, however, options
for reduction on the demand side that are rarely considered. In the second part of this paper we quantify the potential for
demand-side mitigation options, and show that improved diets and decreases in food waste are essential to deliver emissions
reductions, and to provide global food security in 2050.
Over 35% of the Earth’s permanent ice-free land is used for
food production and, both historically and at present, this
has been the greatest driver of deforestation1and associated
biodiversity loss. Food demand has increased globally with the
increase in global population and its affluence. Globally, the demand
for food will undoubtedly increase in the medium-term future.
The United Nations’ Food and Agriculture Organization (FAO) has
projected that cropland and pasture-based food production will see
a 60% increase by 2050, calculated in tonnages weighted by crop
prices2. Another study3projected a ∼100% increase in cropland-
based production, measured in calories, and including both food
and livestock feed. The difference between the two studies can be
partly explained by shifts towards more cropland-grown livestock
feed (as opposed to pasture-based), as countries become richer.
Because agriculture is not on track to meet this demand,
according to current trends in yields4, it has been widely
suggested that we should strengthen global efforts in sustainable
intensification of agriculture5–8. This involves an increase in crop
yields while also improving fertilizer, pesticide and irrigation use-
efficiency. The existence of yield gaps—the difference between
yields achieved in best-practice agriculture and average yields in
each agro-climatic zone—suggests that the scope for sustainable
intensification is large. Yield gaps are wide in some developing
countries, notably in Sub-Saharan Africa, but also exist in developed
countries9,10. However, to complement these supply-side options,
demand-side measures may also be necessary6–8,11–13.
The objectives of this paper are to estimate the environmental
consequences of the increasing food demand by 2050, and to
quantify the extent to which sustainable intensification and
demand reduction measures could reduce them. Previous
quantitative studies have examined future food systems and
their impacts on land use14. However, few have touched on
sustainable intensification3or demand-side reductions12,15,16. The
types of model used in these studies include multiple regression
analysis3, partial equilibrium models (such as the IMPACT (ref. 17)
and GLOBIOM (ref. 18) models), and Integrated Assessment
models (such as IMAGE; ref. 19). We based our calculations
on a transparent, data-based biophysical analysis, which allows
us to vary the key drivers of future land use, including those
on the demand side. Our scenario based on current trends
predicts a higher need for agricultural expansion than previous
models20. Reasons include using less optimistic projections for
future agricultural productivity4, and not including barriers for
land-use conversions. Our methodology is described in more
detail in Supplementary Notes 1–2, Figs 1–8, and Tables 1–20.
A comparison between our approach and previous studies is
detailed in Supplementary Notes.
Analysis of current land use as a baseline
Our approach uses a model of the current global land system, with
2009 as a base year, based on empirical data. Two key components
of this model are: an analysis of land distribution, which enables
us to allocate land-use change, and determine natural ecosystem
losses and GHG emissions; and a map of agricultural biomass flows,
which is required to represent the demand-side options. In Fig. 1
we visualize the land system in 2009 with two Sankey diagrams,
one for each component: Fig. 1a shows the distribution of land use,
which connects to a representation of agricultural biomass flows
(Fig. 1b). Sankey diagrams act as a visual accounting system and
facilitate communication to a wide array of stakeholders in land use
and management, by illustrating magnitudes, flows and efficiencies.
The analysis of land distribution overlays agricultural suitability10
with global biomes21 and current land use22,23 in each region
(Fig. 1a). This shows in which biomes cropland and pasture
1Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK, 2Department of Geography, University of Cambridge, Cambridge,
CB2 3EN, UK, 3Scottish Food Security Alliance-Crops and Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen,
AB24 3UU, UK, 4Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, CB2 3RA, UK, 5Department of Plant
Sciences, University of Cambridge, Cambridge, CB2 3EA, UK. *e-mail: bb415@cam.ac.uk
NATURE CLIMATE CHANGE | ADVANCE ONLINE PUBLICATION | www.nature.com/natureclimatechange 1
ARTICLES NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE2353
Harvesting
Tropical
forest
Temperate
forest
Boreal
forest
Tropical
grassland
Temperate
grassland
Dense shrubland
Open shrubland
Tundra
Desert,
rock/ice Barren
Shrubland
Grasslands
Forestry:
plantation
Forestry:
semi-natural
Pristine
forest
Settlements
Cropping
Pasture
Tundra
Fertilizer
irrigation
Supporting
Supporting
SOC and nut.
recycling
SOC and nut.
recycling
Best
Suitable
Unsuitable
Harvest
Residues
Food
Fuel
Seed
Other plants
Processing
Livestock
feed
Manure
Respir.
Ungrazed plants
Roots
Erosion, timing
Overgrazing
Roots
Forage
15.2
22.6
16.5
17.6
15.6
32.8
25.5
76.6
28.3
7.7
2.5
7.4
7.9
19.2
14.3
6.0
11.9
7.0
8.5
8.0
2.0
4.1
8.0
15.2
6.0
3.7
1.1
8.4
10.311.0
9.5
2.4
2.0
0.7 0.7
0.3
1.1
0.6
2.7 4.6
1.1
Land-use and suitability distribution (area)
Global biomes
(Mkm2)
ab
Land-use Natural
NPP potential
(Pg C yr−1)
Actual
NPP
(Pg C yr−1)(Pg C yr−1)
Processing Ecosystem
services
(Pg C yr−1)(Pg C yr−1)
(Mkm2)
Land agricultural
suitability (Mkm2)
Agricultural biomass flow (biomass carbon/year)
Pests,
weeds
Figure 1 | Distribution of terrestrial biomes, suitability and land use and its connection to the global agricultural annual biomass flows for 2009. a, Major
global biomes are traced onto three classes of land for agricultural suitability. 40% of the total ice-free land area is suitable for agriculture, of which about
half is already in agricultural use for either pasture or cropping. b, Pasture and cropland areas support agricultural biomass growth, which we follow through
harvesting and processing stages, to the delivery of final services. In both panels the width of each line is proportional to the magnitude of flow. Black lines
show losses.
expansion have happened in the past, and where they are likely to
occur in the future. For example, further cropland expansion is likely
in tropical forests and savannahs, where approximately 75% of their
area is suitable for agriculture.
Where possible, we base the agricultural biomass flow analysis
for the base year of 2009 (Fig. 1b) on FAO agricultural statistics24.
These are supplemented where necessary by other data sources25–29:
for example on pre-harvest losses, livestock feeds, crop residues and
their uses. Given the uncertainty in the data, subsistence farming
is likely to be under-represented. Food sourced from forests and
aquatic systems is not included. Net primary productivity potential
of cropland and pasture is a starting point for biomass flows. Some
productivity potential is lost (∼5 PgC yr−1) to soil erosion (caused
by overgrazing on pasture) and to the use of cropping systems
that do not achieve the productivity of all-year natural vegetation.
On the other hand, humans artificially improve productivity with
irrigation30,31 and fertilization32 (adding ∼4.3 PgC yr−1).
It is striking how small the amount of food actually delivered
is (0.7 PgC yr−1, or 2,490 kcal person−1d−1), compared with overall
cropland productivity (8.3 PgC yr−1), or compared to harvest
(2.4 PgC yr−1). The discrepancies are mainly due to the inefficiency
of supplying food calories as livestock products, and to losses in
every step in the system (shown in Fig. 1b as black curved lines).
Livestock globally consume 4.6 PgC yr−1as feed (1.2 PgC yr−1of
crop products, 0.7 PgC yr−1of crop residues and 2.7 PgC yr−1of
pasture forage). The main outputs, meat and dairy, contain only
about 0.12 PgC yr−1or 2.6% of that carbon mass, before losses
(contributing 410 kcal person−1d−1). These results are confirmation
of both the trophic energy inefficiency and the land-intensiveness of
animal-based food products. We estimate that grazing on pasture
unsuitable for cropping, whose natural climax vegetation is grass
or shrubs, contributes approximately 14% of total livestock feed
measured in carbon mass (0.6 PgC yr−1). Such land use has no
opportunity cost in cropping and does not cause deforestation,
but can still have negative consequences for carbon storage and
biodiversity. The latter is particularly true for ‘improved’ pastures,
which, as opposed to semi-natural pastures, are sown and require
artificial inputs. If we also add the crop residue feeds and processing
co-products as efficient contributions to the livestock production
system, together these support about 30% of current livestock
production; the remaining 70% has to be seen as a very inefficient
use of land to produce food.
Losses due to pests and weeds account for 1.0PgC yr−1, or
13% of plant growth on cropland (Fig. 1). This calculation is
based on a single study29 and is highly uncertain, highlighting
the need for new world-wide studies of preventable pre-harvest
losses. Losses further down the chain are smaller in mass, but
nevertheless represent significant fractions of their representative
flows (agricultural losses 0.18 PgC yr−1(12%), processing losses
0.06 PgC yr−1(8%) and food waste losses 0.08 PgC yr−1(12%); these
are calculated on the basis of a previous top-down study of losses in
agriculture26). Importantly, the later in the chain the loss of biomass
occurs, the more wasteful is the loss, as the biomass has already
undergone previous transformation stages that required inputs of
resources and energy.
From our analysis shown on Fig. 1, it is clear that if the demand
for inefficient pathways of food supply (that is, livestock products)
disproportionally increases, the whole system becomes not only
larger, but also less efficient. Previous studies3,17,33 directly link the
demand for food commodities to agricultural production without
considering possible changes in the supply chain that connect the
two, and put most emphasis on yields. Our biomass flow map
highlights that opportunities to reduce waste and improve efficiency
are equally important.
Future scenarios
The interplay between intensification, waste reduction and dietary
preferences, informed our choice for six parameter combinations
for scenarios in 2050 (Table 1). The probabilities of these key
variables are unknown. We examine sustainable intensification to
the point of yield-gap closures as the scenario that best represents
the collection of supply-side management changes that improve
food supply and reduce environmental impact. It includes improved
irrigation efficiency and eliminates over-fertilization. Food waste
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NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE2353 ARTICLES
Table 1 | Main parameters for the six core scenarios, split into
two groups.
Scenarios Yields Demand-side reductions
Current
trends
in yields
Yield gap
closures
(sustainable
intensification)
50% food
waste
reduction
Healthy
diets
CT1 ×
CT2 × ×
CT3 × × ×
YG1 ×
YG2 × ×
YG3 × × ×
The Current Trends (CT) scenarios assume yields in each region will continue to increase at
current rates4. The Yield Gap (YG)scenarios assume that sustainable intensification will achieve
yield gap closures10 in all regions. Both yield scenarios are set against three dierent options on
the demand-side: no changes to the system; a 50% reduction in food and agricultural waste; and
waste reduction as above plus a move towards healthy diets, meaning the average consumption
of sugar, oil, meat and dairy is limited accordingto expert health recommendations37–40 .
and dietary change are the two most prominent demand-side
measures proposed in previous studies12,34,35 and have been shown
to have a large potential, so we have selected these two for closer
examination in our study. Changes in agricultural biomass flows and
land distributions in the six scenarios are shown in Supplementary
Fig. 9. For each scenario we estimated four indicators: forest
losses, carbon emissions (from land-use change and agricultural
production), fertilizer use and irrigation use (Table 2).
Baseline scenarios assume that global population increases
to 9.6 billion by 2050 (ref. 36), and that dietary preferences
change with socio-economic transitions2. The average per capita
consumption increases to 2,710kcal d−1(including 470 kcal of
livestock products). Large conversion (+42%) to cropland will be
necessary if yield improvements at current rates, combined with
livestock intensification, are the only changes to the agricultural
system (CT1 scenario, see Table 2). A predicted increase in food
demand would result in an overall ∼77% increase in agriculture-
related GHG emissions, due to increased deforestation rates (a 78%
increase to 7.1 GtCO2e yr−1; mostly in Sub-Saharan Africa and
South-East Asia) and increased emissions from livestock, fertilizer
and higher agricultural energy use associated with mechanized
agriculture (a 76% increase to 13.0 GtCO2e yr−1). There would
also be large losses of tropical forests (3Mkm2) and other valuable
ecosystems. This scenario, which represents ‘business-as-usual’,
would, therefore, have a number of very detrimental consequences.
The YG1 scenario (‘yield gap closure’) fares a lot better
(Table 2). Previous studies3,33 have already established that decreased
deforestation more than offsets any increase in emissions associated
with sustainable intensification. Here we confirm this, while also
including some relevant emission sources omitted in previous
studies (fertilizer production and agricultural energy use). However,
without demand reductions, cropland would still need to expand
by ∼5%, pasture by ∼15%, and GHG emissions would increase by
∼42% compared with current levels, even with currently-attainable
yields being achieved world-wide. Our results indicate that yield-
gap closures achieved with sustainable intensification would not
meet projected future demands without an increase in agricultural
area and in GHG emissions. Sustainable intensification is crucial;
however, it is unlikely to be sufficient.
Demand-side reductions show further promise. Here we
quantify potential savings from cutting food and agricultural
waste by half, which has previously been suggested as a promising
mitigation strategy26,34,35. These scenarios (CT2 and YG2) reduce
the area of cropland by ∼14% and GHG emissions by 22–28%
(∼4.5GtCO2e yr−1)compared with their respective baseline
scenarios for 2050 (CT1 and YG1; Table 2). Along with the reduced
cropping area, reducing waste would also reduce fertilizer and
irrigation water demand and associated environmental impacts.
Improvement potentials are similar in scale in all regions; improving
crop storage in developing countries while raising awareness and
setting policy targets for food-waste reduction worldwide could be
viable climate mitigation strategies.
We also tested dietary adaptation as a demand-side measure,
by assuming average diets that are considered to be ‘healthy’ on
the basis of nutritional evidence37–40. Their parameterization is
described in detail in the notes to Supplementary Table 3. The
main alteration from the projected dietary preferences is a reduction
in the consumption of energy-rich food commodities (sugars and
saturated fats, including livestock products) in regions where diets
projected for 2050 exceed established health recommendations. The
necessary alterations vary by regions. For example, in industrialized
regions, the average consumption of livestock products (which
are high in saturated fats) largely exceeds healthy levels37, and a
reduction, or no further increase, could be desirable on health
grounds. However, we recognize that livestock can play a critical
nutritional role in many regions, societies and agricultural systems.
The model ensures that adjusted diets still provide enough protein37,
and a daily calorie intake of 2,500 kcal, through an increase in
pulses and staples. These levels are conservative to avoid potential
deficiency at an individual level. Regional cultural preferences and
crop suitability are retained where possible within these guidelines.
Such altered average diets can hardly capture the complexities of
nutritional requirements across regional populations; but for brevity
we hereafter refer to them as ‘Healthy Diets’.
Scenarios involving Healthy Diets (CT3 and YG3 in Table 2)
reduce the area necessary for cropping by ∼5%, pasture by ∼25%
and the total GHG emissions by ∼45%, compared to the CT2 and
YG2 scenarios. Almost all of these large GHG emission savings (5.6
out of ∼6 GtCO2e yr−1) are associated with livestock reductions.
There are two sources of these savings: a decrease in enteric
fermentation and manure emissions, and carbon sequestration
occurring with a return of some of crop and pasture lands to
natural vegetation. Implementation of healthy diets would therefore
greatly benefit both the environment and the general health of the
population37 in regions where excessive consumption of energy-rich
food occurs, or may develop.
The changes towards healthy diets are greatest in the
industrialized world, which, with some exceptions, also produces
most of the livestock products. Therefore the greatest reductions
in impacts are in temperate zones, rather than the tropics. All
scenarios, including the most optimistic one (YG3), incur losses of
pristine tropical forests due to the combination of large predicted
increases in population and per capita food demand in the tropics,
and the suitability of current forest land for conversion to cropland.
One of the goals of sustainable agriculture is to avoid further
expansion into tropical forests7, but this appears to be unachievable
with changes in the agricultural sector alone.
The results from our model are highly sensitive to some
assumptions, especially those about yields, total population and
livestock system developments; they are somewhat sensitive to
fertilizer assumptions and less sensitive to assumptions about trade
(Table 3 and Supplementary Note). If global population is assumed
to be 14% higher, then the resulting cropland area increases by
14%, and GHG emissions increase by 26%. Under more pessimistic
assumptions, results change even more. For example, if we assume
yield stagnation on today’s level, we would expect the resulting
cropland area to increase by about 27%, (the difference between
today’s yields and yields in CT1). However, the combination
of demand growth and stagnating yields causes expansion into
relatively unsuitable land in regions that exhaust their reserves
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ARTICLES NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE2353
Table 2 | Main indicator outputs for six 2050 scenarios.
Units 2009∗CT1 CT2 CT3 YG1 YG2 YG3
Cropland Mkm215.6 22.2 (+42%) 19.2 (+23%) 18.2 (+17%) 16.4 (+5%) 14.2 (−9%) 13.7 (−12%)
Pasture Mkm232.8 37.1 (+13%) 33.7 (+3%) 25.4 (−23%) 37.7 (+15%) 33.9 (+3%) 25.8 (−21%)
Net forest cover†Mkm226.1 22.6 (−14%) 23.9 (−8%) 26.0 (−0%) 24.0 (−8%) 25.9 (−1%) 27.2 (+4%)
Tropical pristine forests Mkm27.9 7.2 (−10%) 7.3 (−8%) 7.5 (−6%) 7.5 (−6%) 7.7 (−3%) 7.7 (−3%)
Total GHG emissions GtCO2yr−111.4 20.2 (+77%) 15.7 (+38%) 9.3 (−19%) 16.2 (+42%) 11.7 (+2%) 5.9 (−48%)
Fertilizer use Mt yr−1106 154 (+45%) 136 (+29%) 125 (+18%) 190 (+79%) 161 (+51%) 145 (+37%)
Irrigation water use km3yr−12,890 6,370 (+120%) 5,410 (+87%) 5,270 (+82%) 4,500 (+56%) 3,830 (+33%) 3,790 (+31%)
Percentages in brackets arerelative to values in 2009. In the two scenarios with no demand management, cropland area increases for 5–42%, pasture for13–15%, there is significant deforestation and an
increase in GHG emissions. YG scenarios farebetter across the indicators, with the exception of fertilizer use. Demand reduction measures on the other hand improve all indicators. ∗Showing middle
values23,24,31,49, uncertainty ranges are up to ±70%. †Excluding boreal forests.
Table 3 | One-at-a-time sensitivity analysis for population, yield trends, trade, livestock intensification and fertilizer, using the CT1
or YG1 scenario as a baseline.
Sensitivity scenario Change in inputs from the
baseline scenario
Change in key outputs Relative sensitivity index∗
Cropland
(Mkm2)
GHG emissions
(GtCO2yr−1)
Cropland GHGs
UN high population 2050 population from 9.6 to
10.9 billion (+14%)
25.3 (+14%) 25.4 (+26%) 1.05 1.90
UN low population 2050 population from 9.6 to
8.3 billion (-14%)
19.0 (−14%) 15.0 (−26%) 1.05 1.89
Stagnating yields Average yield from 1.8 to
1.3 tC ha−1(−27%)
31.2 (+41%) 28.8 (+43%) −1.44 −1.51
Two-fold increase in yield improvement rates Average yield from 1.8 to
2.3 tC ha−1(+27%)
17.9 (−19%) 16.1 (−20%) −0.72 −0.76
Increased trade from baseline scenario†Total trade from 103,300 to
162,800 tC (+58%)
21.6 (−3%) 19.7 (−2%) 0.02 0.04
Fertilizer use eciency in YG1 improved further Total fertilizer use from 189,820
to 151,748 ktN (−20%)
16.4 (0%) 15.5 (−4%) 0 0.21
Pasture
(Mkm2)
GHG emissions
(GtCO2yr−1)
Pasture GHGs
Livestock densities and feed as in 2009 Livestock products per area from
44.5 to 21.8 kgCha−1(−51%)
73.3 (+98%) 27.7 (+37%) −1.91 −0.73
Increased stocking density, but no intensification Livestock products per area from
44.5 to 33.5 kgCha−1(−25%)
47.9 (+29%) 23.1 (+15%) −1.18 −0.59
Intensification, but 2009 stocking density Livestock products per area from
44.5 to 34.4 kgCha−1(−23%)
50.5 (+36%) 24.5 (+22%) −1.59 −0.95
We varied the inputs based on alternative projectionsin the literature, or if such explicit projections are missing, by what we consider to be plausible levels. The larger the relativesensitivity index
(last two columns, either positive or negative), the more sensitive the model outputs are.∗Calculated as the ratio between the change in the input parameter and the relative change in the output.
†The increased trade scenario assumes that any surplus cropland in land-rich countries(N. America, W. Europe) will not be abandoned, but used for exports into regions with largest cropland deficits.
Without accounting for increased GHG emissions from transport, this incursa small net emission saving.
of suitable land, resulting in a higher, 41% increase in cropland
area required.
Our results show that only when strategies include significant
elements of demand reduction is it possible to prevent an increase
in agricultural expansion and agriculture-related GHG emissions.
As previously suggested, the reduction of meat consumption could
be tackled with economic incentives (such as a carbon tax) and
the livestock sector should be included into a comprehensive
climate mitigation policy11. Defining appropriate incentives may
require some policy innovation and experimentation, but a strong
commitment for devising and monitoring them seems essential41.
Nutritional experts40 have called for healthy nutrition to be elevated
to the highest priority in national agendas, and that health
requirements should dictate agricultural priorities, not vice versa.
Our results are consistent with the findings of the recent IPCC
report which reported a significant, but uncertain, potential for
GHG reduction in agriculture from demand-side measures such as
dietary change and waste reduction42; at the same time, this delivers
better outcomes for food security and environmental impacts.
This study focuses on the overall global picture, but it is
important to be aware of the demand differences between regions,
and farming systems within regions. The South Asian and Sub-
Saharan African regions are predicted to be the most critical in terms
of the agricultural land expansion needed to meet the demand, in
all scenarios. Water is a local issue, but even on regional levels the
estimated amount of irrigation needed to support higher yields is
challenging. The irrigation demand in South Asia, for example, is
projected to increase by 80% in the YG3 scenario, and up to 200%
in the CT1 scenario (Supplementary Table 12). Such large increases
in irrigation water supply may not be possible, given that today
the use of groundwater is already excessive in many places. For
example, the extraction from the Upper Ganges aquifer is already
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NATURE CLIMATE CHANGE DOI: 10.1038/NCLIMATE2353 ARTICLES
GHG emissions from agriculture and LUC
2° target 2009 emissions
GtCO2e yr−1
5
10
15
20
25
CT1 CT2 CT3 YG1 YG2 YG3
Figure 2 | Diagram showing the total GHG emissions from agriculture and
land-use change due to agricultural expansion, for the six scenarios. The
2009 emissions from these sources are shown for comparison, as is the
target in 2050 for avoiding dangerous climate change45 (which should also
accommodate energy, industry, and land-use-change emissions from other
non-agricultural sources, such as settlement expansion). Agricultural
energy use is already included and represents 2–3GtCO2e.
50 times larger than its estimated recharge rate43. Yield increases
from increased irrigation may not be fully realized, implying that, to
meet the demand, even greater expansion of cropland into natural
landscapes would be necessary.
The model presented here would benefit from further
developments to include yield as a function of availability of water
and fertilizer, and the inclusion of climate change as a driver of
yield changes and irrigation demand. This would enable estimation
of how shortfalls in irrigation water availability might affect future
food production. Bioenergy scenarios also lie outside the scope of
this paper; unless food demand patterns change significantly, there
seems to be little spare land for bioenergy developments without a
reduction of food availability. It is important to note that the model
results we present here are conservative in estimating the extent of
agricultural land use and its associated emissions in the absence of
these model limitations.
Although it is theoretically possible to decarbonize energy supply,
such complete reductions are unattainable in the livestock part
of the agricultural sector. Although there are many mitigation
options in agriculture44, our study indicates that a decrease in overall
agriculture-related emissions can only be achieved by employing
demand-side reductions. The agriculture-related emissions in our
business-as-usual scenario (CT1) alone almost reach the full 2◦C
target emissions allowance in 2050 (21 ±3 GtCO2e yr−1; ref. 45).
Even scenario YG2, with yield-gap closures coupled with halving of
food waste, reaches more than a half of the target, leaving only the
other half for all other energy and industrial processing emissions
(Fig. 2). The share of emissions related to agriculture may therefore
increase in the future. However, to date, global food and land-
use scenarios have received relatively little consideration in climate
change mitigation policies compared with the consideration given
to the energy supply and end-use sectors.
Reducing emissions from agriculture is essential to reduce
the risks of dangerous climate change. The agricultural industry
must strive to improve yields and food distribution, but improved
diets and reductions in food waste are also essential to deliver
emissions reductions, and to provide enough food for the global
population of 2050.
Methods
Future land-use predictions are based on a model that describes the physical
characteristics of global land-use and agricultural systems. This model was
composed by collecting and fitting together the empirical data from many global
datasets. It has two crucial components: the land-use distribution analysis and the
agricultural biomass flow map. The analysis of land-use distribution was achieved
by overlaying data on global biomes21, current land use22,23,46 and agricultural
suitability10 in a Geographical Information System.
The agricultural biomass flow map allows us to model changes in food
supply chains explicitly, together with livestock management systems, agricultural
waste, food waste and dietary preferences. It is constructed in the manner of a
material flow analysis, so that the flows always add up to the total vegetation
growth on cropland and pasture, measured as net primary productivity (NPP) in
grams of carbon. It follows the allocation of agricultural vegetation biomass to
harvest, residues, losses and ecosystems in the first instance, and then to food,
feed, fibre, fuel, soil recycling, losses and intermediate steps. This biomass flow
map is first parameterized with 2009 data. FAOSTAT statistics24 provide most of
the data, supplemented by some characterization of livestock feed systems25,
agricultural residue quantification and uses25,47, and losses at each stage26,29 .
The model with these two major components was used to assess the
consequence of future food demands and changes in the agricultural systems in
12 global regions. Calculations can be described conceptually as the
following sequence:
Future consumption for each commodity in a region was calculated as a
product of the per capita future dietary preferences associated with
socio-economic changes as projected by the FAO (ref. 2) and regional population
from the UN mid-range projections36. Aggregated by carbon mass, these add up
to a 57% increase in food consumption, underpinned by a 75% increase in
cropland productivity. Healthy dietary preferences37–40 are taken as an alternative.
Required future production is calculated on the basis of the predicted future
consumption and the characterized agricultural biomass flow map. We assume
that agricultural systems in 2050 are different from those of today, in terms of the
increased share of cropland-grown feed for livestock, and improved livestock
efficiency. Trade between regions is assumed to remain the same. Changes in
agricultural waste are implemented at this stage.
Future cropland area is a result of the required future production and yields.
The Current Trends (CT) scenarios assume yields in each region will continue to
increase linearly at current rates, which are taken from a recent global yield
study4. The Yield Gap (YG) scenarios assume that sustainable intensification will
achieve yield gap closures in all regions, achieving the current potentially
attainable yields for their agro-ecological zone. Yield gaps for each region and
crop are taken from the GAEZ study10.
Future pasture area is a result of future demand for grazing and the assumed
livestock stocking densities. Unfortunately there are no statistics that could be
used to estimate possible stocking densities on global levels. We compared results
from a global dynamic vegetation model, a previous livestock energy model25 ,
and livestock product statistics24, to determine that some regions can significantly
increase densities (Latin America, SE Asia), whereas in others they are already
very high (W. Europe, N. America). Because of many unknowns (about stocking
densities as well as livestock management systems), pasture areas are
highly uncertain.
The location of future cropland and pasture expansions (or retractions) is
based on the land suitability component of the land distribution analysis,
described above. Losses of ecosystems and GHG emissions are also dependant on
the distribution of agricultural expansion over current land use and biomes in
each region.
Fertilizer and irrigation use is estimated on the basis of current trends in
their uses and total cropland area for each scenario. The YG scenarios assume an
increase in irrigation use efficiency, whereas fertilizer use is set at high enough
levels to support optimum yields.
GHG emissions from land-use change (LUC) are calculated on the basis of
the ‘before and after’ land carbon pools, which depend on the biome and land
use. We used the published methodology and parameters to obtain GHG values
of ecosystems48. Only emissions from agriculture expansion and contraction
are included.
GHG emissions from agriculture associated with fertilizer use and
production, rice paddy methane emissions, emissions from enteric fermentation
and manure management, as well as energy use in mechanization, are also
calculated. Calculations are based on scaling up today’s emissions49,50 linearly with
emission sources.
Received 7 April 2014; accepted 26 July 2014;
published online 31 August 2014
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Acknowledgements
This work was funded by a grant to the University of Cambridge from BP as part of their
Energy Sustainability Challenge.
Author contributions
B.B., J.M.A., K.S.R., C.A.G., J.S.D. and E.C. developed the model, B.B., P.S., J.M.A. and
K.S.R. designed the study/scenarios, B.B., K.S.R. and C.A.G. analysed the outputs, and all
authors wrote the paper with B.B. leading.
Additional information
Supplementary information is available in the online version of the paper. Reprints and
permissions information is available online at www.nature.com/reprints.
Correspondence and requests for materials should be addressed to B.B.
Competing financial interests
The authors declare no competing financial interests.
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