Ecological Modelling 288 (2014) 103–111
Contents lists available at ScienceDirect
Ecological Modelling
journal homepage: www.elsevier.com/locate/ecolmodel
Feeding 10 billion people under climate change: How large is the
production gap of current agricultural systems?
Boris Sakschewski a , Werner von Bloh a,∗ , Veronika Huber a ,
Christoph Müller a , Alberte Bondeau b
a
Potsdam Institute for Climate Impact Research, Telegraphenberg A 31, D-14473 Potsdam, Germany
Mediterranean Institute of Biodiversity and Ecology (IMBE), Aix-Marseille University/CNRS/IRD, Bâtiment Villemin, Europole de l’Arbois—BP 80, F-13545
Aix-en-Provence cedex 04, France
b
a r t i c l e
i n f o
Article history:
Received 21 January 2014
Received in revised form 23 May 2014
Accepted 26 May 2014
Available online 21 June 2014
Keywords:
Population growth
Food production
Dynamic global vegetation model
Climate change
LPJmL
a b s t r a c t
The human population is projected to reach more than 10 billion in the year 2100. Together with changing consumption pattern, population growth will lead to increasing food demand. The question arises
whether or not the Earth is capable of fulfilling this demand. In this study, we approach this question
by estimating the carrying capacity of current agricultural systems (KC ), which does not measure the
maximum number of people the Earth is likely to feed in the future, but rather allows for an indirect
assessment of the increases in agricultural productivity required to meet demands. We project agricultural food production under progressing climate change using the state-of-the-art dynamic global
vegetation model LPJmL, and input data of 3 climate models. For 1990 to 2100 the worldwide annual
caloric yield of the most important 11 crop types is simulated. Model runs with and without elevated
atmospheric CO2 concentrations are performed in order to investigate CO2 fertilization effects. Countryspecific per-capita caloric demands fixed at current levels and changing demands based on future GDP
projections are considered to assess the role of future dietary shifts. Our results indicate that current population projections may considerably exceed the maximum number of people that can be fed globally
if climate change is not accompanied by significant changes in land use, agricultural efficiencies and/or
consumption pathways. We estimate the gap between projected population size and KC to reach 2 to 6.8
billion people by 2100. We also present possible caloric self-supply changes between 2000 and 2100 for
all countries included in this study. The results show that predominantly developing countries in tropical
and subtropical regions will experience vast decreases of self-supply. Therefore, this study is important
for planning future large-scale agricultural management, as well as the critical assessment of population
projections, which should take food-mediated climate change feedbacks into account.
© 2014 Elsevier B.V. All rights reserved.
1. Introduction
The Earth currently sustains about 7 billion people. Their
demand for food, fiber, energy, and industrial products already
now exceeds many of the provisioning and regulatory services of
the planetary system (Rockström et al., 2009). According to recent
estimates of the United Nations (2011), the world population is
expected to increase to 9.3 billion in 2050 and to reach about
10.1 billion by 2100. The question to be answered here is: can
these projected numbers be fed under climate change and changing
∗ Corresponding author. Tel.: +49 3312882603; fax: +49 3312882600.
E-mail address: bloh@pik-potsdam.de (W. von Bloh).
http://dx.doi.org/10.1016/j.ecolmodel.2014.05.019
0304-3800/© 2014 Elsevier B.V. All rights reserved.
consumer behavior without expanding cropland area or significantly improving agricultural management and technology?
We assess this crucial question by revisiting the long-standing
debate on the human carrying capacity of planet Earth (K). According to the analysis of Cohen (1995a,b) the median of all estimations
of K published up to 1995 ranged between 7.7 to 12 billion people.
Acknowledging that K is effectively determined by many different
factors ranging from biophysical boundaries (e.g., land and energy
availability) to socio-economic developments (e.g., the rate of technological progress and wealth distribution), we consider caloric
food supply as the sole limiting factor here (similar to Franck et al.,
2010) but constrain our estimates to current (year 2000) land use
patterns and management intensity, which we refer to as the carrying capacity of current land use systems, KC . Our estimates of KC
should not be understood as direct predictions of how many people
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B. Sakschewski et al. / Ecological Modelling 288 (2014) 103–111
the Earth will be able to support in the future. Instead, they point
to potential future gaps between food supply and demand, which
may result from climate change, population growth and increasing
per-capita consumption, if agricultural practices are not adapted
accordingly.
We compute KC for different global warming and diet shift
scenarios spanning 1990–2100. We do not apply yield transfer
functions (regression functions of crop yield responses on climate
based on previous simulations; e.g. Parry et al., 2004), but we compute global caloric production using the dynamic global vegetation
model LPJmL which includes crops and pasture (Bondeau et al.,
2007; Sitch et al., 2003). Recent studies show that projections of
agricultural production are sensitive to differences in climate projections of various climate models, which is often explained by
distinct precipitation patterns (Gornall et al., 2010). Therefore, we
use projections of 3 different climate models to estimate the importance of uncertainty in climate change projections for estimates of
KC . Furthermore, since yield projections can depend strongly on the
effectiveness of CO2 fertilization (Parry et al., 2004; Nelson et al.,
2009; Gornall et al., 2010) we explore these uncertainties by calculating all scenarios with and without the direct effects of rising
atmospheric CO2 concentrations on plant growth.
To assess the role of diet shifts with increasing income (e.g. Popp
et al., 2010), KC is estimated using country-specific present-day
food demands (according to FAOSTAT) as well as changing demands
e.g. of animal products based on future projections of countryspecific gross domestic product (GDP). Population growth, effects
of economic growth on lifestyles, and impacts of climate change
on yields will differ strongly around the globe, especially between
developed and developing countries (e.g. Reilly et al., 2001; Darwin
and Kennedy, 2000; Parry et al., 2004). To examine national and
regional differences we also present possible caloric self-supply
change between 2000 and 2100 for all countries included in this
study.
2. Methods
2.1. Dynamic global vegetation model LPJmL
LPJmL is a process-based ecosystem model that simulates the
growth, production and phenology of 9 plant functional types (PFTs
representing natural vegetation at the level of biomes; Sitch et al.,
2003), 11 crop functional types (CFTs) and managed grass (Bondeau
et al., 2007). Plant productivity is modeled via leaf-level photosynthesis that responds to the photosynthetic pathway (C3/C4),
climate conditions, atmospheric CO2 concentrations and canopy
conductance (Farquhar et al., 1980; Collatz et al., 1991, 1992;
Haxeltine and Prentice, 1996), autotrophic respiration, phenology
(Bondeau et al., 2007; Sitch et al., 2003) and management intensity. The phenology and management dates (sowing and harvest)
of the different crop types are simulated dynamically based on
crop-specific parameters and past climate experience, allowing
for adaptation of varieties and growing periods to climate change
(Waha et al., 2012). Technical coefficients for the representation
of management intensity and production efficiency in this study
are based on Fader et al. (2010). We assumed limited irrigation
constrained by surface water supply (Rost et al., 2008).
2.2. LPJmL settings and runs
For all model runs land-use patterns and local specification
of agricultural management levels were set to the year 2000
(Fader et al., 2010). This approach keeps agricultural area and
management levels constant and implies that KC depends on
climate impacts, CO2 fertilization and diet shifts only. Climatic
parameter inputs for LPJmL were obtained from 3 general circulation models (GCMs): CCSM3 (Collins et al., 2005; National
Center for Atmospheric Research), Echam5 (Roeckner et al., 2003,
Max-Planck-Institute for Meteorology) and HadCM3 (Gordon et al.,
2000; UK Meteorological Office), which were chosen for their ability to accurately reproduce current temperatures and precipitation.
Climate scenarios corresponded to the relatively high-emission
scenario SRES A2 of the IPCC (Nakicenovic et al., 2000), with global
mean temperatures rising by 4.6–4.9 ◦ C above pre-industrial levels
until 2100.
To investigate a possible CO2 fertilization effect, each LPJmL
model run was performed twice: One run with rising CO2 concentrations (according to the SRES A2 scenario) and the other with fixed
CO2 concentrations of the year 2000 (disabling additional CO2 fertilization). LPJmL delivered an annual harvested caloric amount for
each country in the study period from 1990 to 2100 by summing
up the produced calories in the respective grid cells. Crop yields
were converted into caloric yields as in Franck et al. (2010) after
Wirsenius (2000) and FAO (2001).
2.3. Caloric demand calculations
For each country i, the per-capita caloric demand (Ci ,
kcal cap−1 d−1 ) was calculated according to
Ci = (1 − ai )Si + vai Si
(1)
where Si is the country-specific total per-capita caloric consumption (kcal cap−1 d−1 ), ai is the country-specific share of animal
products in per-capita caloric consumption, and v is the conversion
factor of transforming vegetal into animal calories. Ci is expressed
as vegetal calories (as derived from crops and pasture; see Section
2.4), accounting for the vegetal calories consumed both directly,
and indirectly as meat and/or other animal products. Ci is always
larger than Si due to the conversion losses related to meat production. The conversion factor v was fixed at 5, which is a rough average
from a variety of conversion factors for pork, cattle and poultry
under different feeding treatments (Smil, 2000), weighted by the
respective global meat production in 2000 (FAOSTAT, 2011b).
When food demand was fixed at present-day levels, Ci was set
to the value of the year 2000. When food demand was assumed
to change, projections of Si and ai were derived from projections
of country-specific per-capita GDP (Gi ) for 1990 to 2100 using the
following log–linear relationships:
Si = 729.2 + 587.8 log10 (Gi )
(2)
ai = −0.255 + 0.132 log10 (Gi )
(3)
These relationships were established by fitting pooled countryspecific data for 1961–2007, and explain approximately 58% and
60% of the observed variability in globally pooled Si and ai , respectively (Fig. 1). Data of per-capita GDP expressed as current US$ was
taken from the World Bank data base (World Bank Indicators, 2011)
and converted into 1990 US$ to match with GDP projections. Food
consumption data was taken from FAOSTAT (2011a). It represents
the calories available at the retail level, thus potentially including
calories wasted by the consumer. Projections of per-capita GDP for
1990–2100 corresponded to the SRES B2 scenario downscaled and
aggregated at country-level by Columbia Earth Institute (Center
for International Earth Science Information Network, 2002). SRES
B2 was chosen because its underlying assumption on population
growth (with around 10 billion people in 2100) corresponded well
with the most recent UN population projections (United Nations,
2011) also used in this study. Projections of Si , ai , and Ci are shown
in Figs. 2 and 3.
105
a)
b)
Food consumption (kcal person−1 day−1)
Share of animal products in food consumption
B. Sakschewski et al. / Ecological Modelling 288 (2014) 103–111
y = −0.25 + 0.13 x
2
0.5
R = 0.6
0.4
0.3
0.2
0.1
0
1
2
3
4
5
log10(GDP per capita) (1990 US$ MEX)
4000
y = 729.2 + 587.8 x
R2 = 0.58
3500
3000
2500
2000
1500
1000
1
2
3
4
5
log10(GDP per capita) (1990 US$ MEX)
Fig. 1. Log–linear relationships between (a) country-specific per-capita GDP (Gi ) and country-specific caloric consumption (Si ) (Eq. (2)), and (b) Gi and country-specific share
of animal products in per-capita caloric consumption (ai ) (Eq. (3)) based on yearly data of 1961–2007.
2.4. Estimating the carrying capacity of current agricultural
systems KC
entering Y in every year. This is consistent with constant pasture
use efficiency (technical and biological). Multiplying z with the corresponding projected world population Pw yielded KC :
Y
z=
=
W
B
i i
i
Di
with Di = Ci Pi
(4)
Food consumption (kcal person−1 day−1)
where B represents the caloric production, P the projected population size (medium fertility variant; United Nations, 2011; Center for
International Earth Science Information Network (CIESIN)), C the
per capita demand, and D the national caloric demand per country i. Y indicates the caloric production and W the caloric demand
of the world. Y was split into 2 subcategories Ycrop and Ypasture .
We assumed a worldwide coverage of animal caloric demand by
about 50% crops and 50% pasture (Gilland, 2002). Therefore, the
percentage value of pasture calories needed to cover 50% of the animal caloric demand in 2000 was used to constrain Ypasture calories
a)
4000
3500
3000
2500
2000
1500
1000
1950
2000
2050
Years
2100
KC = zPw =
P
i i
Y
i
(5)
Di
Data of 143 countries were available to be included in projections of KC . In 2000, these countries accounted for over 99% of
the world population and the same is predicted for 2100 (United
Nations, 2011).
2.5. Potential self-supply change of each country
To assess the caloric self-supply change s between 2000 and
2100 for each country i, the country crop caloric yields Bi (reduced
by 50% of the respective animal caloric demand) from LPJmL (average outcome of the model runs for the 3 GCM climate forcing and
for the time span 2096–2100) were divided by the crop caloric
demands per country Di (vegetal and crop-based feed, average for
the time span 2096–2100). This yielded a self-supply factor z for
each country i. We used all possible combinations for the quotient of
Share of animal product in food consumption
To calculate future KC under global warming, we combined the
caloric yield data of LPJmL with our caloric demand projections.
First we introduced a factor z ≥ 0 indicating the fraction of projected
population size which can be supplied in a certain year as follows
(for a calculation scheme see also Fig. 4):
b)
0.6
0.5
0.4
0.3
0.2
0.1
0
−0.1
1950
2000
2050
2100
Years
Fig. 2. Observed (green), hind-casted (blue), and projected (red) (a) country-specific caloric consumption (Si ), and (b) country-specific share of animal products in per-capita
caloric consumption (ai ), based on Eqs. (2) and (3) (see also Fig. 1). (For interpretation of the references to color in this figure legend, the reader is referred to the web version
of this article.)
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B. Sakschewski et al. / Ecological Modelling 288 (2014) 103–111
Bi and Di regarding CO2 fertilization and per capita caloric demand
described above. Putting the self-supply for 2100 into a ratio with
the self-supply of 2000 yielded the potential self-supply change si :
Total caloric demand (kcal person−1 day−1)
10000
9000
8000
si =
7000
zi (2100) − zi (2000)
zi (2000)
(6)
Furthermore, we calculated si for “fixed population” scenarios,
using the population sizes of 2000 in order to investigate pure climate effects.
6000
5000
4000
3. Results
3000
3.1. Global carrying capacities
2000
Assuming no change in per-capita caloric demand after 2000,
KC increases over the 21st century when CO2 fertilization occurs
(Fig. 5a). In this case, KC reaches an average of 7.3 billion in 2050
and 8 billion in 2100 (Table 1). Assuming no effectiveness of CO2
fertilization KC reaches 6.3 billion in 2050 and drops to 5.7 billion
in 2100. By contrast, assuming a correlation of per-capita caloric
demand and GDP, KC decreases until 2100 (Fig. 5b). Here, under no
CO2 fertilization KC declines to an average of 4.2 billion in 2050 and
3.3 billion in 2100 (Table 1).
The gap between projected human population size (United
Nations (2011); medium fertility variant) and KC amounts to be
1000
1980
2000
2020
2040
Years
2060
2080
2100
Fig. 3. Projected per-capita caloric demand (Ci ) based on Eq. (1). Underlying projections of country-specific caloric consumption (Si ) and country-specific share of
animal products in per-capita caloric consumption (ai ) are shown in Fig. 2.
Fig. 4. Calculation pathway to derive KC for one time step. For each country i, calorie demands D are calculated separately, by multiplying the projected calorie demand per
capita C and the respective projected population size P (United Nations, 2011). By summing all D, the calorie demand of the world is obtained. At this point meat and crop
calorie demand are still separated. The ratio of Y and W yields a factor z indicating the calorie demand coverage of the world. 50% of the meat calorie demand is here covered
by pasture and 50% by crops, what is not shown here for reasons of visual clarity. The product of z and worlds projected population size Pw (medium fertility variant; United
Nations, 2011) represents the KC in comparison to the projected population size P.
B. Sakschewski et al. / Ecological Modelling 288 (2014) 103–111
107
Fig. 5. 5-year running means of KC estimates from 1990 to 2100 (orange corridor) under different GCM-climate forcing (CCSM3, ECHAM5, HadCM3) and different CO2
fertilization effect (fert: maximal CO2 fertilization; nofert: CO2 levels of 2000) based on caloric demands of 2000 (a) and changing caloric demands (b). The blue corridor
indicates the low and high fertility variant boundaries of the population projections of the United Nations (2011) with the medium fertility variant highlighted as black solid
line. The red line in panel (a) indicates KC under constant yields and per capita demands of the year 2000. (For interpretation of the references to color in this figure legend,
the reader is referred to the web version of this article.)
between 2 and 5 billion people by 2050, and 2 and 6.8 billion
people by 2100 (Fig. 5, Table 1). Under optimistic assumptions of
food production (assuming full effectiveness of CO2 fertilization)
and fixed per-capita caloric demands, the medium UN projection is about 27% higher in 2050 and 2100 than our estimates
of KC (Table 1). The most pessimistic calculations with changing per-capita caloric demands and no further CO2 fertilization
reveal medium UN projections to be 120% higher in 2050 and 210%
higher in 2100. KC reaches 6.5 billion in 2100 under the assumption of no further CO2 fertilization, constant crop yields and fixed
per-capita caloric demands of the year 2000 (Fig. 5a). This value
is about 8% greater than the world population in 2000 and 14%
greater than the average KC under climate forcing and no CO2
fertilization.
Table 1
Summary of the different calculated KC (in billion) for the years 2050 and 2100 and the percent difference to the population projection of United Nations (2011); medium
fertility variant).
CO2 fertilization
Yes
No
No
Yes
No
GCM
HadCM3
Echam5
CCSM3
HadCM3
Echam5
CCSM3
Fixed climate
Arithmetic mean
Arithmetic mean
UN projection
Yes
No
2050
2100
Demand of 2000
Diet shift
Demand of 2000
Diet shift
7.16
7.41
7.45
6.09
6.36
6.35
6.44
7.34
6.27
4.84
5.01
5.03
4.12
4.30
4.29
/
4.96
4.24
7.63
7.94
8.33
5.38
5.87
5.87
6.48
7.97
5.71
4.36
4.54
4.76
3.07
3.35
3.36
/
4.55
3.26
9.31
% difference to UN projection
% difference to UN projection
27
49
10.10
88
120
27
77
122
210
108
B. Sakschewski et al. / Ecological Modelling 288 (2014) 103–111
Fig. 6. Political world maps to visualize the relative self-supply change from 2000 to 2100 (average value for the time spans 1996–2000 and 2096–2100) with (b, d and f)
and without (a, c and e) CO2 fertilization, with fixed caloric demand per capita of 2000 (a–d) and changing per capita caloric demand (e and f), and fixed world population of
2000 (a and b). Panels (a) and (b) indicate pure climate effects. The color of each country corresponds to its self-supply change s indicated by the color scale. Countries with
insufficient data are shown hatched.
3.2. Potential self-supply change of each country
4. Discussion
Under our most optimistic assumptions (fixed per-capita caloric
demand and full CO2 fertilization), self-supply of current landuse systems (s) is declining in Africa (except South Africa), many
countries in Asia (with the exception of Russia, Kazakhstan, China
and Thailand), parts of South and Central America, and small parts
of Europe (Fig. 6d). The largest demand–supply gaps are found in
African countries. Considerable improvement of self-supply over
the 21st century is only projected in Russia, Ukraine, Bulgaria,
Latvia, Moldavia, Japan and to some smaller extent in China and
South Africa. If CO2 fertilization is assumed to be ineffective on crop
yields, a self-supply increase is only observed in Portugal, Russia,
Latvia, Japan and China (Fig. 6c). The majority of countries show a
decrease in self-supply.
Under our most pessimistic assumptions (increasing per-capita
caloric demand and no CO2 -fertilization), the capacity of selfsupply deteriorates in almost all countries over the 21st century,
especially in tropical and subtropical regions (Fig. 6e). Japan and
Portugal are the only countries slightly increasing their selfsupply. Under full effectiveness of CO2 fertilization, few additional
countries experience improvement of their capacity of self-supply
(Russia, Moldavia, Bulgaria, Portugal and Japan; Fig. 6f). The African
continent shows again the strongest decrease.
Under fully effective CO2 fertilization and constant population
sizes, self-supply is decreasing only in some tropical and subtropical countries of Africa and America (Fig. 6b). Almost all other
countries show neutral to positive effects as the CO2 fertilization
outweighs negative climate change impacts. Assuming no effectiveness of CO2 fertilization on crop yields, climate change impact
becomes clearly evident. Only few countries show increasing selfsupply, whereas the majority shows decreases (Fig. 6a).
4.1. Closing the production gap by increasing agricultural
productivity
Our results point to the possibility of considerable food scarcities, if future population growth, climate change and changing
consumption pattern are not accompanied by strong measures
to increase global food production and availability. To analyze
whether agricultural production kept pace with the increasing
demands of a growing and richer world population in the most
recent past, we compared model output to real observations for
2000–2010. During this time period, worldwide total cereal production increased from about 2.06 billion metric tons in 2000 to
2.48 billion metric tons in 2010 (FAOSTAT, 2013), an increase of
about 20%. At the same time, we calculated a gap between KC in
2010 and the respective observed population in 2010 of 0.39–1.09
billion people. In other words, the observed population in 2010 is
6–19% higher than our estimates of KC depending on the specific
assumptions about diet shifts and CO2 fertilization. The gap of 19% –
resulting from our most pessimistic assumptions – has barely been
compensated by the 20% increase in cereal production from 2000
to 2010. Thus, this comparison highlights the importance of sustaining past agricultural production increases into the future—at
least as long as no demand-side measures (such as decreasing ai in
developed countries) are taken (see Section 4.5).
The two major levers to increase agricultural production are
improvements of land productivity and expansion of agricultural
lands. Since land for agricultural expansion is becoming increasingly scarce – at least if some of the remaining fertile land is set
aside for the purpose of nature conservation and carbon sequestration (Rockström et al., 2009) – much of the needed increase in
B. Sakschewski et al. / Ecological Modelling 288 (2014) 103–111
agricultural production will have to come through increases in land
productivity (crop yields). Over the past half century crop yield
gains kept pace with population growth (Huber et al., 2014), giving rise to some optimism. By contrast, the large potential gaps
between food demand and production found here, together with
the need for making intensive agricultural production systems
more environmentally friendly (Foley et al., 2011), show that producing enough food for all will remain one of the major challenges
of the coming decades.
This challenge will turn out to be especially important on
the African continent, as mirrored in the regional patterns of
self-supply change found in our study and in accordance with
recent empirical studies. Among others, population growth and
low agricultural productivity have been found as main reasons for
the African continent becoming a net food importer in the last
decades (Rakotoarisoa et al., 2011). Between 1989 and 2009 Africa’s
cereal imports increased about 89% while total cereal production
increased by only 56% (FAOSTAT, 2013). In consideration of ongoing
climate change and population growth this trend could strengthen,
supporting the drastic negative self-supply change until 2100 found
for most African countries in this study.
4.2. The role of population composition
Compared with today developing countries are predicted to contribute a higher proportion to the world population in the future
(United Nations, 2011). The result is a lower worldwide average
caloric demand per capita under the assumption of lower percapita caloric demands in developing countries. This relationship
increased KC in this study, since it depends on the average caloric
demand per capita of the world. Therefore, KC in 2100 was 8% higher
than KC in 2000, if caloric demand per capita was set constant and
climate forcing was ignored (Fig. 5a).
4.3. The role of climate forcing
Accounting for the effects of changing population composition
in this study, climate change was responsible for a reduction of
KC of 24% in 2100 compared to 2000. Climate impact on selfsupply change was largest in tropical and subtropical regions,
including mainly emerging and developing countries (Fig. 6a and
b) with a projected high population growth in the future (United
Nations, 2011). Hence, we argue that climate change could lower
the projected population growth for these countries, since it is
not explicitly regarded in the UN projections. Malnutrition leads
to lower life-expectancy and reduced fertility, which is especially
problematic in low-income countries (Popkin, 1994). Recent efforts
to better assess the range of possible future demographic development as e.g. in the Shared Socio-economic Pathways (SSPs) reflect
the importance of varied population projections as well as various fields of interaction between climate science and demography
(Hunter and O’Neill, 2014; Moss et al., 2010). Based on our results
KC would decrease directly through the reduction of the world population and indirectly through the increment of the average caloric
demand per capita of the world (see Section 4.2).
109
(2001) CO2 fertilization could still be important under rather high
CO2 concentrations of 800 to 1000 ppm. Assuming a linear relation
between yield and atmospheric CO2 concentration, the possible
crop yield increase, according to the SRES scenarios, could be about
12–22% in 2100, with SRES A2 at about 20%, neglecting climatic
influences. These values are considerably lower than the 40% caloric
yield increase projected by LPJmL. Since LPJmL does not directly
account for nutrient limitations simulated caloric yields under CO2
fertilization are most likely overestimated. On the other hand,
increased CO2 can have a positive effect on plants growing under
water stress due to a significant decrease in stomatal conductance
at elevated CO2 levels (Maroco et al., 1999; Adams et al., 2000)
resulting in an increased water-use efficiency (Huber et al., 1984).
This plant trait is implemented in LPJmL and leads to higher yields
especially in water-stressed regions. Whether or not the CO2 fertilization effect is overestimated in our settings, it cannot compensate
for the detrimental effects of climate change and increasing caloric
demands per capita on KC . When future diets shift, even under full
CO2 fertilization, the UN population estimates remain higher than
KC (Fig. 5b) and national self-supply deteriorates in most countries
until 2100 (Fig. 6f). These results suggest that current agricultural
area and management intensities, even if assuming a rather high
CO2 fertilization effect, are not enough to fulfill the demands of
growing and increasingly rich societies worldwide under climate
change.
4.5. The role of diet shifts
Global diet shifts decrease KC about 43% in 2100 (Table 1), making it the factor with the highest influence in our calculations.
Our GDP-based approach to project country specific per-capita
caloric demands does not account for the projected increment of
global food prices in the future (IAASTD, 2009), possibly leading
to decreased caloric consumption pattern in many regions of the
world. The economic and political impact of this scenario is beyond
the scope of this study. However, feeding parts of the global population with less-than-average calories would increase KC . Most
developed countries today have the theoretical ability to restrict
their per-capita caloric demand in the future, since it is beyond a
healthy diet (Smil, 2000; Parfitt et al., 2010). For many developing
countries, however, “less-than-average calories” would mean an
unhealthy diet or even undernourishment.
4.6. Additional factors not accounted for in this study
In addition to the assumptions made in this study, it should be
kept in mind that KC is undoubtedly affected by many other factors
contributing to closing or widening the supply gaps found in this
study.
4.6.1. Post-harvest food loss
We are not accounting for post-harvest food losses other than
at the retail and consumer level. Taking food loss at the immediate
post-harvest stages into account would decrease KC . Its prevention
is one of the key solutions for improving the nutritional situation
in many developing countries (Parfitt et al., 2010).
4.4. The role of CO2 fertilization
The uncertainty in the effectiveness of CO2 fertilization on crop
yields strongly affects KC and the national self-supply (Figs. 5 and 6).
Simulated CO2 fertilization increases KC about 28% in 2100
(Table 1). According to Long et al. (2006) wheat, rice and soybean yield at elevated CO2 (550 ppm) under fully open-air field
conditions increases by ∼12–14%, relative to CO2 concentration
of 380 ppm. Hardly any fertilization for C4 crops and grasses was
shown (Ainsworth and Long, 2005). According to Prentice et al.
4.6.2. Non-food products
Non-food products derived from agricultural land (e.g. natural
fibers) draw from resources that could be devoted to food production. Accounting for these losses would decrease KC .
4.6.3. Water consumption
The LPJmL version used in this study does not account for
any other anthropogenic water extraction from rivers other than
for agriculture. The amount of available water could effectively
110
B. Sakschewski et al. / Ecological Modelling 288 (2014) 103–111
decrease as the population grows, e.g. due to personal or industrial needs. Therefore, yields in water scarce regions could decrease.
On the other hand, some regions withdraw their irrigation water
from ground water reserves (Tiwari et al., 2009), which are not
taken into account in this study, so the amount of water used for
irrigation is significantly underestimated here (Rost et al., 2008).
Another problem associated with irrigation is, that many semi-arid
areas show severe salinization of soils (Dregne, 2002). We do not
account for any soil degradation processes. Certainly, improvement
of irrigation methods is possible (Neumann et al., 2010) and can be
seen as part of improvement of agricultural efficiency.
4.6.4. World food trade and food distribution
Our approach assumes an optimal distribution of all calories
produced worldwide to cover all caloric demands equally. This represents a fictitious market system from which any deviation could
decrease KC in 2100, especially since possible gain in overproduction of calories is not located in all population hotspots (Fig. 6).
Generally, world food trade is projected to increase. Cereal trade
for example is projected to increase about 156% from the year
2000 to 2050 (IAASTD, 2009). Vast amounts of caloric demands
of developing countries are expected to be covered by USA, Brazil
and Argentina in the future (IAASTD, 2009). For these countries,
not even our optimistic calculation shows a possible gain in production of exportable calories in 2100 (Fig. 6d), pointing once
again to the important role of future agricultural productivity
gains.
5. Conclusion
Our findings show that the caloric demand of the projected
population of Earth until 2100 (United Nations, 2011) under climate change can only be satisfied by the additional improvement
of agricultural methods (i.e. use of fertilizer, pest control, breeding of new varieties), expansion of agricultural area, and a more
sustainability-oriented consumer behavior. On the other hand, if
agricultural production were not to keep pace with growing food
demands, common population projections would turn out to be
highly overestimated. It is, thus, highly important that population
projections better account for potential climate change feedbacks
via food supply-demand imbalances. In addition, our results reveal
that the realization of beneficial effects of CO2 fertilization in global
agricultural production will have to be facilitated by adjusted management (e.g. additional nutrient supply). Fertilization alone cannot
increase production on current agricultural land with current management intensities sufficiently to meet future demand. To this
point it is clear that most developing, fast growing nations in tropical and subtropical regions will have to improve their technology
level to avoid massive expansion of agricultural areas or stronger
dependence on food imports. Besides these supply-side measures,
our study underlines that changes in consumption pattern may
strongly influence the potential mismatch between agricultural
production and demand. Limiting meat consumption and avoiding
food waste are two widely discussed options that especially developed nations will need to pursue in order to guarantee enough food
for all—across a more populated and warmer world.
Acknowledgments
This article is dedicated to Siegfried Franck who passed away
unexpectedly on 16 May 2011. His ideas, effort and knowledge gave
rise to this study and will inspire our future work. The authors thank
Alice Boit (Potsdam Institute for Climate Impact Research) for her
constructive comments on the manuscript and Dennis Drechsler
for helping with the figure design.
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