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Reserves and trade jointly determine exposure to food supply shocks
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2016 Environ. Res. Lett. 11 095009
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Environ. Res. Lett. 11 (2016) 095009
doi:10.1088/1748-9326/11/9/095009
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Reserves and trade jointly determine exposure to food supply shocks
Philippe Marchand1, Joel A Carr2, Jampel Dell’Angelo1, Marianela Fader3, Jessica A Gephart2,
Matti Kummu4, Nicholas R Magliocca1, Miina Porkka4, Michael J Puma5, Zak Ratajczak2,
Maria Cristina Rulli6, David A Seekell7, Samir Suweis8, Alessandro Tavoni9 and Paolo D’Odorico1,2
1
2
ACCEPTED FOR PUBLICATION
26 August 2016
PUBLISHED
14 September 2016
3
4
5
6
Original content from this
work may be used under
the terms of the Creative
Commons Attribution 3.0
licence.
7
8
9
National Socio-Environmental Synthesis Center (SESYNC), Annapolis, MD 21401, USA
Department of Environmental Sciences, University of Virginia, Charlottesville, VA 22904, USA
International Centre for Water Resources and Global Change (UNESCO), German Federal Institute of Hydrology, PO Box 200253,
D-56002 Koblenz, Germany
Water and Development Research Group (WDRG), Aalto University, FI-00076 Aalto, Finland
Columbia University Center for Climate Systems Research, NASA Goddard Institute for Space Studies, New York, NY 10025, USA
Department of Hydraulics, Roadways, Environmental and Surveying Engineering, Politecnico di Milano, Milan I-20133, Italy
Department of Ecology and Environmental Science, Umeå University, SE-901 87 Ume, Sweden
Department of Physics and Astronomy, University of Padova, I-35131 Padova, Italy
Grantham Research Institute on Climate Change and the Environment, London School of Economics, London WC2A 2AE, UK
Keywords: food systems, resilience, food crises
Any further distribution of
this work must maintain
Supplementary material for this article is available online
attribution to the
author(s) and the title of
the work, journal citation
and DOI.
Abstract
While a growing proportion of global food consumption is obtained through international trade,
there is an ongoing debate on whether this increased reliance on trade benefits or hinders food
security, and specifically, the ability of global food systems to absorb shocks due to local or regional
losses of production. This paper introduces a model that simulates the short-term response to a food
supply shock originating in a single country, which is partly absorbed through decreases in domestic
reserves and consumption, and partly transmitted through the adjustment of trade flows. By applying
the model to publicly-available data for the cereals commodity group over a 17 year period, we find
that differential outcomes of supply shocks simulated through this time period are driven not only by
the intensification of trade, but as importantly by changes in the distribution of reserves. Our analysis
also identifies countries where trade dependency may accentuate the risk of food shortages from
foreign production shocks; such risk could be reduced by increasing domestic reserves or importing
food from a diversity of suppliers that possess their own reserves. This simulation-based model
provides a framework to study the short-term, nonlinear and out-of-equilibrium response of trade
networks to supply shocks, and could be applied to specific scenarios of environmental or economic
perturbations.
1. Introduction
Country-scale food availability depends on domestic
production, reserves, and trade. About 24% of the
food that is consumed worldwide is available through
international trade (e.g., D’Odorico et al 2014); likewise, 24% of the global agricultural land (Weinzettel
et al 2013) and 23% of the freshwater resources used
for food production (D’Odorico and Rulli 2013) are
accessed through trade. Trade dependency has substantially increased in the last few decades and more
than doubled since the mid-1980s (Porkka et al 2013,
D’Odorico et al 2014) likely as a result of liberalization
© 2016 IOP Publishing Ltd
and the associated removal of subsidies and trade
protections in developing countries (e.g., Shafaeddin 2005). While international food trade increases the
variety of food products available to customers and
helps buffer the impact of local supply shocks (e.g.,
crop failures), the effect of the liberalization of trade
on economic development and food security in the
developing world is the subject of a vigorous debate
(Schanbacher 2010, Oliveira and Schneider 2016). It
has been argued that it may allow for an influx of cheap
subsidized food commodities from more developed
countries, thereby displacing smallholder farmers,
undermining rural livelihoods, and enhancing trade
Environ. Res. Lett. 11 (2016) 095009
dependency in the global south (Shafaeddin 2005, De
Schutter 2014, Godar et al 2015), as some countries
increasingly rely on resources they do not control (e.g.,
Carr et al 2013, Suweis et al 2013).
Recent studies have stressed the environmental
implications of food trade in terms of loss of environmental stewardship resulting from the tele-coupling
between agricultural production and consumer behavior or market volatility (DeFries et al 2010, Schmitz
et al 2012, Meyfroidt et al 2013). Other studies hint at
the emergence of patterns of ecological unequal
exchange, whereby an unbalanced distribution and
flow of resources and environmental impacts perpetuates conditions of uneven economic development
around the world (e.g., Rice 2007, MartinezAlier 2014). In particular, the globalization of food
through trade has been associated with the exportation
and externalization of environmental impacts (Galloway et al 2007, O’Bannon et al 2014), virtual water and
land trade (Hoekstra and Chapagain 2008, Fader
et al 2011), and the overall geographic disconnection
between consumers and the environment that supports
them (Carr et al 2013, MacDonald et al 2015).
In contrast with the number of studies on the economic and environmental impacts of food trade, the
joint effects of economic and environmental changes
on the resilience of the global food system, i.e., its
capacity to meet food demand in spite of supply
shocks, remain poorly understood (D’Odorico
et al 2010, Prakash et al 2011, Fader et al 2013, Suweis
et al 2015). In conditions of food crisis, prices dramatically increase, leaving the poor with no or limited
access to food (e.g., De Schutter 2014). Moreover, during the recent food crises (e.g., in 2008 and 2011) the
governments of exporting countries have responded
to food price spikes by banning food exports, thereby
leaving trade dependent countries in a state of insecurity (e.g., Fader et al 2013, Puma et al 2015). Export bans
increase the uncertainty and unreliability of the global
food markets, thereby eroding food security in tradedependent countries.
The impact of the intensification of trade on food
security is difficult to evaluate, particularly the short
term response of food systems to shocks in production
and the way such a response propagates through the
global trade network. These effects are hardly captured
by state-of the art economic models accounting for
changes in supply and price fluctuations. In fact, such
models typically assume (general or partial) equilibrium and market-clearing conditions (e.g., Hatfield
et al 2013, Gouel 2013, Gouel and Jean 2015) that are
unlikely attained in the course of a food crisis when
hoarding and speculations occur while consumers
scramble and suppliers make short-term arrangements
(Piesse and Thirtle 2009, Headey 2011, Jones and Hiller 2015). A shock to production induces a short-term
out-of-equilibrium condition in which shortfalls in
food supply are addressed through either local adjustments or trade relationships. From a mass balance
2
point of view, this shock may be partly absorbed at the
country level by tapping on reserves or reducing
domestic consumption, and partly transmitted to other
countries as affected regions decrease their exports or
increase their imports. The outcome of these processes
cannot be predicted through a linear stability/reactivity
analysis (e.g., Suweis et al 2015), as large perturbations
cause nonlinear responses (such as threshold effects)
and the system may not recover to its original state.
In this study, we develop a model that simulates
the propagation of a food supply shock through the
processes described above (changes in reserves, trade
and consumption) while preserving mass balance
at the country level. We share this approach with
other ‘cascading shock’ models applied to specific
food commodities (Puma et al 2015, Gephart
et al 2016), virtual water (Tamea et al 2016), industrial
sectors linked by input–output relationships (Contreras and Fagiolo 2014) and aggregate economic production (Lee et al 2011). Our model differs from
previous work by its inclusion of food reserves, which
empirical research has shown may play a major role in
the resilience of food systems (Fraser et al 2015).
We apply our model to a major food commodity
group (cereals) using publicly-available data on production, trade and reserves over the last two decades.
We make parsimonious assumptions about countrylevel response to shocks that are consistent with the
historical record, and simulate the propagation of
shocks under different versions of the trade network to
assess: (1) how food reserves and trade patterns interact to increase or decrease exposure to supply shocks;
(2) how systemic changes in the cereals trade network
over the last 20 years affect the risk (frequency and
severity) of national food shortages following supply
shocks; and (3) which countries may bear a relatively
greater risk due to their position in the trade network.
2. Methods
2.1. Simulation model
The model simulates the impact of a shock to the
supply of some food commodity on the global trade
network for that commodity. The shock is initiated as
a drop in production in one country and propagates
through the network over multiple iterations of the
simulation loop (figure 1).
At each iteration, countries affected by a shock first
tap into their reserves. When reserves are depleted,
countries absorb a fraction of the residual shock by
reducing domestic consumption, then reduce their
trade balance by decreasing exports and increasing
imports, with the impact spread to each trade link proportionally to the volume on that link10. This
10
The exception to this rule, as illustrated in figure 1, is that
countries cannot import more from a partner country that already
reduced its trade balance following a shock, i.e.such trade links are
‘blocked’ from further increases.
Environ. Res. Lett. 11 (2016) 095009
Figure 1. Simulation model flow chart. See table 1 for a list of variables.
propagates the shock from affected countries to their
trade partners. Finally, any shock that could not be
propagated is absorbed by reducing domestic consumption. These steps are repeated until all shocks
have been absorbed (see the supplementary materials
for a detailed description of the model and table 1 for a
list of symbols and variables used in this paper).
As a portion of the shock is absorbed by domestic
reserves and consumption at each iteration, the residual shock monotonically decreases towards zero. To
avoid arbitrarily small shocks being propagated, countries will absorb (through consumption) any shock
smaller than a fraction α of their current supply; we set
α = 0.001% for all our simulations. With this adjustment, the model converges within 10 iterations for all
parameter sets considered in this paper. At convergence, we verify that the mass balance equation:
DP + DI - DE = DR + DC is satisfied for
each country, and that the sum of DR and DC over all
countries matches the magnitude of the initial shock
(both within a tolerance level of α).
The assumption that production shocks are absorbed first by reserves is supported by the FAO commodity balance data, showing that interannual
changes in cereals production (DP ) are most closely
associated with changes in R. In contrast, all components of C, except animal feed, show little interannual
3
Table 1. List of variables and parameters of the shock propagation
model.
Symbol
Description
Nc
P
R
F
E
Number of countries in network
Production by country
Reserves by country
Trade matrix ( Fjk = exports from country j to country k )
Exports by country i.e. åk Fjk
I
Imports by country i.e. åk Fkj
C
S
α
Domestic consumption by country (for any use)
Net supply, S = P + I - E
Minimum threshold (as fraction of S) for a shock to be
propagated
Fraction of residual shock absorbed by C if R is depleted
Fraction of actual reserves that are available to absorb
shocks
Magnitude of initial shock as a fraction of the affected
country’s P
fc
fr
fp
variation and little to no association with DP (see supplementary table 3). Both E and I are more variable
than C over time, but these changes are mostly uncorrelated with DP , as trade dynamics are affected by
other factors than immediate changes in production.
We further discuss these assumptions and alternatives
at the end of the paper.
Environ. Res. Lett. 11 (2016) 095009
2.2. Data
We initialize our model with historical data on the
international trade in cereals. As a major component
of global food trade and food stocks, cereals provide a
natural starting point to study how food security is
impacted by the distribution of trade flows and
reserves. By focusing our analysis on a commodity
group rather than a single commodity, we avoid the
need to consider substitution effects between functionally-similar crops in that group.
We use cereals production and trade data (detailed
trade matrix exports) from the Food and Agricultural
Organization of the United Nations’ online database
(FAOSTAT, faostat3.fao.org, data acquired in January
2016). Production and trade quantities of individual
primary and secondary commodities in the cereals
group are converted into kcal equivalents (FAO 2001)
and aggregated by country and year (see supplementary table 2 for the list of included crops and conversion factors). We use population data from FAOSTAT
to subset the network so that only countries with a
population exceeding half a million people during the
period 1986–2011 are considered. We rectify the data
as described in Carr et al (2013) to account for the
merging and splitting of countries between years.
We obtain data on countries’ cereals reserves from
the Production, Supply and Distribution database of
the United States Department of Agriculture’s Foreign
Agricultural Service (USDA-PSD, apps.fas.usda.gov/
psdonline/, data acquired in October 2015). End-ofyear reserves for nine commodities (barley, corn,
millet, mixed grain, oats, rice, rye, sorghum and
wheat) were converted in kcal equivalents and aggregated by country and year, and political entities were
rectified to match the FAO data above. While the
USDA-PSD data does not include some minor crops
covered by FAOSTAT (including buckwheat, fonio
and quinoa), those nine commodities account for over
90% of the total grain production reported by FAOSTAT over the 25 year period. Note that since USDAPSD reports aggregated reserves for the European
Union (pre-1998) and EU-25 (1998 and after), we
divide these reserves between EU countries for each
year in proportion to their share of the EU cereals
production.
For model input, we average the cereals production, reserves and trade over three five-year periods
(1994–1998, 2001–2005 and 2007–2011), which we
refer to by their median years (1996, 2003 and 2009).
The set of countries was constant over each period (no
merge/split event). The averaging process smooths
out annual perturbations in the data and ensures that
simulated shocks are applied to a typical state of the
network rather than, e.g., one where some countries
were already experiencing a shock.
The evolution of the cereals trade across these
three time periods shows a decrease of global reserves
and an increase in global trade, both expressed as a
fraction of total production (table 2).
4
To separate the effects of changes in reserves and
trade flows on model outcomes, we perform two different scalings of the 1996 and 2003 reserves: (1) in the
R-scaled version, all countries’ reserves are scaled by a
common factor so that the ratio of global R to global P
matches that of 2009 (e.g., from table 2, the global R/P
ratio is 0.262 in 1996 and 0.192 in 2009, so the 1996 Rscaling would multiply each country’s reserves by
0.192/0.262); (2) in the R/S-scaled version, each
country’s reserves are adjusted so that the ratio of R to
the net supply S matches that of 2009 for that country
(e.g., the R/S ratio for Australia in 2009 is 0.435, so its
actual 1996 reserves would be replaced with 0.435
times its 1996 supply). Comparison between simulations based on the original data and results from these
scaled versions allow us to identify the impact of (1)
changes in global reserves or (2) changes in the
distribution of these reserves among countries,
respectively.
2.3. Simulation parameters and response variables
In this study, we refer to a model run as a set of Nc
simulations, to observe the effect of a production
shock initiated (separately) at each country in the
dataset. For each run, we select one of the three time
periods, a specific scaling of the reserves (see above)
and three global parameters: the initial shock magnitude as a fraction of the target country’s production
( fp), the fraction of reserves that are available to absorb
a shock ( fr), as well as the fraction of a shock absorbed
by consumption after reserves are depleted ( fc, as
defined in our model above). In general, we expect
fr < 1 as the reported reserves include not only
strategic stocks, but also temporary storage of goods
along the supply chain.
For each model run, we report the number of
simulations where the initial shock was transmitted
(Ns), i.e.excluding those where the target country has
no production or has available reserves that exceed the
loss of production. We calculate the following summary metrics for each country: the number of hits, or
simulations where the country receives a shock (Nh);
the number of hits where domestic consumption is
affected (Nhc); the relative change in net supply (Dsrel )
and consumption (Dc rel ) over all simulations; and the
evenness ( J) of impact across simulations (see below).
To compare the average impact of a shock across
countries and model runs, the total changes in supply
(DSj ) and consumption (DCj ) for a given country j are
scaled by its initial supply (S0, j ) and the simulation
parameter fp, i.e.if DSj(k ) is the impact on Sj of a shock
initiated at country k, then:
Dsrel, j =
åk DSj(k)
f p S 0, j
and Dc rel, j =
åk DC j(k) ,
f p S 0, j
(1)
where the sum is taken over all simulations (initiated
at each country) in a given model run. These metrics
are always negative, so we usually refer to their
Environ. Res. Lett. 11 (2016) 095009
Table 2. Summary statistics of the cereals trade data for each time period considered in our analysis (Nc = number of countries in network, P = production, R = reserves, F = trade volume).
Med. year
Nc
å P (kcal)
å R (kcal)
# trade links
å F (kcal)
åR åP
åF åP
1996
2003
2009
162
164
165
6.59 × 1015
7.04 × 1015
8.13 × 1015
1.73 × 1015
1.49 × 1015
1.56 × 1015
5985
7580
8358
7.93 × 1014
9.88 × 1014
1.18 × 1015
0.262
0.211
0.192
0.120
0.140
0.146
magnitude, e.g.which countries receive a greater
shock or impact.
The average of Dsrel for all affected countries may
be less or greater than −1, depending on the covariance between Dsrel and the initial supply S0. Starting
from equation (1), we obtain (E denotes the expected
value):
⎤
⎡
E ⎢åDS (k)⎥ = E [Dsrel ] E [ fp S 0] + cov (Dsrel , f p S 0) ,
⎦
⎣k
(2)
E [Dsrel ] =
E ⎡⎣åk DS (k) ⎤⎦
f p E [S 0 ]
-
1
cov (Dsrel , S 0).
E [S 0 ]
(3)
Since the mean supply equals the mean production,
both the numerator and the denominator of the first
term on the right-hand side are equal in magnitude to
the mean initial shock over simulations, and thus:
E [Dsrel ] = - 1 -
1
cov (Dsrel , S 0).
E [S 0 ]
(4)
Based on Pielou’s measure of community evenness in
ecology (Pielou 1966), J measures the degree to which
the total impact on a country is spread out across
multiple simulations. It is calculated as:
Jj = -
1
log Nc
åpkj log pkj ,
(5)
k
where pkj is the proportion of the total DSj that is due
to a shock initiated at country k:
pkj =
DS j(k)
ål DSj(l)
.
(6)
Note that terms with pkj = 0 are excluded from the
sum in equation (5). When the whole impact on
country j occurs in a single simulation, Jj = 0; if it is
due equally to shocks originating from all countries,
Jj = 1. Since a single shock is spread out across many
countries through trade, we expect this metric to
increase with the number of links and trade volume in
the network.
2.4. Model implementation
We performed the simulations and all data processing
steps in R (R Core Team 2015), using the FAOSTAT
package (Kao et al 2015) to facilitate data acquisition
from the FAOSTAT database. The necessary code to
reproduce all results in this paper is available on
GitHub (http://github.com/pmarchand1/cerealsnetwork-shocks).
5
3. Results
3.1. Effects of global changes in the trade network
To compare simulation results across different versions of the trade network, we fix the global simulation
parameters to fp = 0.2 (20% production decrease in
the country initiating the shock), fr = 0.5 (50% of
reserves available to buffer shocks) and fc = 0.01 (1%
of residual shock absorbed by consumption before it is
passed through trade). Our sensitivity analysis (in the
supplementary materials) shows that the number of
countries to which a shock spreads depends primarily
on the ratio of fp to fr, whereas the impact on domestic
consumption is most affected by fp (supplementary
table 1).
Our simulation results indicate that the most
recent trade network (2009) has a greater capacity to
absorb shocks compared with those of 1996 and 2003,
as evidenced by a decrease in hits by country (Nh, Nhc)
and a lesser impact on consumption (Dc rel ) (table 3).
However, this pattern is largely driven by the distribution of reserves rather than increased trade. Despite
the total reserves being greater in 1996 and 2003—
which explains why the impact metrics are even higher
when scaling these total reserves to 2009 levels—they
are less evenly distributed, with a few countries (such
as China) holding a very large proportion of their net
supply in reserve and more countries having no reported reserves. By scaling relative reserves by country to
their 2009 values (R/S-scaling), we see that the number of hits by country monotonically increases over
time, with a small increase in the mean evenness of the
impact across simulations, all factors consistent with
an increase of the number and volume of trade
connections.
While a more even distribution of reserves lessens
the average impact on domestic consumption, it
increases the average relative shock felt by countries
(Dsrel ). To understand this pattern, we note that the
mean of Dsrel is greater than −1 for all our model
runs, which, based on equation (3), means that countries with a larger S0 receive a proportionally greater
impact from the shocks. This can be in turn related to
the structure of the cereals trade network. A few large
producers account for most of the net exports and
receive more shocks due to their central position in the
network (i.e. as each of their many trade partners will
increase their imports when affected by a shock).
These main producers/exporters also tend to have
proportionately higher reserves, allowing them to
Environ. Res. Lett. 11 (2016) 095009
Table 3. Summary statistics by input data version with global parameters set at fp = 0.2, fr = 0.5 and fc = 0.01. Each row
aggregates results from a set of simulations, each with a shock originating in a different country. Ns is the number of simulations where a shock is passed; Nh (resp., Nhc ) is the number of times a countryʼs supply (resp., consumption) are affected
across simulations; Dsrel (resp., Dc rel ) is a relative measure of the total change in a countryʼs supply (resp., consumption)
across simulations; J is the evenness of impact between simulations. Means and standard deviations are calculated across
affected countries.
Year (version)
1996 (original)
2003 (original)
2009 (original)
1996 (R -scaled)
2003 (R -scaled)
1996 (R S -scaled)
2003 (R S -scaled)
141
139
137
148
142
138
140
Dsrel
Nh
Ns
J
mean
s.d.
mean
s.d.
mean
s.d.
71.71
85.71
70.36
74.93
88.74
56.86
66.25
32.77
28.39
31.51
34.12
28.59
32.04
33.12
−0.82
−0.88
−1.00
−0.83
−0.88
−0.91
−0.95
0.59
0.67
0.72
0.60
0.67
0.55
0.67
0.30
0.32
0.34
0.32
0.33
0.31
0.33
0.16
0.16
0.15
0.17
0.16
0.15
0.14
absorb most of these shocks (see supplementary figure
2). The fact that the mean Dsrel approaches −1 in 2009
shows that these discrepancies are becoming less
important. Once again, a large portion of the change
between 1996 and 2009 can be explained by the distribution of reserves, with the residual differences
(shown in the R/S-scaled version) reflecting the intensification of trade.
Although the previous tables do not indicate the
standard deviations for the consumption impact
metrics (Nhc and Dc rel ), their distributions are highly
skewed with most countries receiving little to no
impact. As such, we focus on the most impacted countries in the next section.
3.2. Country-level impacts
Figures 2 and 3 present the total impact on the supply
and consumption (respectively) of each country for
three different model runs: original 1996 data, 1996
data scaled with R/S ratios from 2009, and original
2009 data. Once again, results are aggregated over
simulated shocks initiated at each country. As stated in
the previous section, major exporters tend to absorb a
disproportionate share of shocks relative to their base
supply (figure 2), due to their large reserves and high
number of trade links. However, these large reserves
also ensure that the impact on domestic consumption
is negligible (figure 3). The larger impact on DS for
Argentina, Australia and Paraguay in 2009 (figure 2)
reflects the growing relative importance of their
exports. Some countries with low production and high
trade can also exhibit a high aggregate DS , such as
Oman, which increased both its reserves and trade
volume between 1996 and 2009.
Countries where the simulated shocks produce
substantial decreases in domestic consumption are
concentrated in a few regions: Central America, the
Sahel and East Africa, South and South-East Asia and
(in 1996 only) the former Soviet Union (figure 3). The
set of most affected countries is not sensitive to variation of the global simulation parameters fp, fr and fc
(supplementary table 2). Compared with the original
6
Nhc
Dc rel
17.67
15.09
7.02
18.71
15.94
5.93
6.34
−0.19
−0.13
−0.13
−0.23
−0.14
−0.15
−0.12
1996 data, simulations using the R/S-scaled reserves
led to smaller impacts to consumption overall. Changes in the trade network itself greatly reduced the DC
impact on many countries, including most of the former Soviet Union, Afghanistan, Sudan and Tanzania,
but led to larger impacts in others, notably in Central
America.
We can gain additional insights on these results by
separating the share of DC in a country due to the
shock initiated at that same country (internal shock)
and that which is due to shocks initiated at other countries (external shocks). Figure 4(a) compares the
impact on the 40 countries that experience a >1%
decrease in consumption due to an internal shock in at
least one model run. Using either the original or R/Sscaled reserve levels, most countries lie above the 1:1
dividing line and are thus less impacted in the 2009
trade network. This is consistent with the additional
trade links and volume, which result in a greater capacity to transfer an internal shock to trade partners.
Conversely, a majority of the 24 countries receiving a substantial (>1% of S0) external shock are more
impacted under the 2009 trade network (figure 4(b)),
reflecting an increased reliance on food imports from
one or a few trade partners. A look at the specific external shocks causing these DC show that they originate
from nine source countries (figure 5), with 14 of these
shocks—including the four greatest in magnitude—
caused by an initial production drop in the United
States. While the other target countries in the
graph experience this risk from one or two simulations, the DC in Singapore is spread over five sources;
it also has the lowest aggregate DC , only slightly above
the 1% threshold.
Contrasting with the overall trend towards a more
globalized food trade network, our results show that
the vulnerabilities to external shocks occur mostly at a
regional scale, with American, South Asian / Indian
Ocean and East Asian clusters clearly visible in figure 5
(the link from the United States to Japan being a notable exception).
Environ. Res. Lett. 11 (2016) 095009
Figure 2. Total change in net supply (å DS , summed over independent simulations of shocks initiated at each country) as a fraction
of the affected country’s initial supply (S0), for three versions of the input data. The middle panel (R/S-scaled) uses the 1996
production and trade data, but scales the reserves to match the 2009 reserves/supply ratio. Global simulation parameters (see table 1)
are set at fp = 0.2, fr = 0.5 and fc = 0.01.
4. Discussion and conclusion
In this study, we presented a dynamic simulation
model that complements previous approaches aimed
at understanding the effect of increasingly globalized
trade networks on the resilience of national food
supply systems. Initialized with historical food production and trade data, the model describes how a
local production shock is propagated as countries use
their reserves and trade links to buffer the loss in food
supply.
Based on data for a specific commodity group—
cereals and cereal products—spanning a 17 year period from 1994 to 2011, we identified two global trends
7
affecting the model’s dynamics: an increase in both the
number and volume of trade links (relative to production), but also a decrease and a more even distribution
of global reserves (still relative to production). This latter point is particularly relevant to the ongoing discussion on the importance of food stocks (Fraser
et al 2015, Laio et al 2016), as our results suggest that
the distribution of reserves matters more than their
aggregate quantity in terms of conferring resilience to
shocks. Trade and reserves also interact: as more
countries have the reserves to absorb production losses or the capacity to import more from countries with
such reserves, both trends contribute to reducing the
number and severity of cases where a local drop in
Environ. Res. Lett. 11 (2016) 095009
Figure 3. Total change in domestic consumption (å DC , summed over independent simulations of shocks initiated at each country)
as a fraction of the affected country’s initial supply (S0), for three versions of the input data. The middle panel (R/S-scaled) uses the
1996 production and trade data, but scales the reserves to match the 2009 reserves/supply ratio. Global simulation parameters (see
table 1) are set at fp = 0.2, fr = 0.5 and fc = 0.01.
production forces a decrease in domestic consumption. However, a greater reliance on imports increases
the risk of critical food supply losses following a foreign shock, notably in the case of several Central
American and Caribbean countries that import grains
from the United States.
Since we simulated independent production
shocks of the same relative size originating in
each country, our aggregated results do not
account for variation in the probability of production shocks across countries, or for the possibility
of simultaneous shocks in multiple countries.
The latter is of particular significance as, according to our analysis, the countries most at risk of
8
food shortages from external shocks tend to be
concentrated in regional blocks. Further research
on this topic could thus focus on developing
more realistic shock scenarios where the impact is
distributed across a region.
By focusing on the impact of global reserve distribution and trade network structure, we necessarily
ignore the particulars of each country’s domestic policies and trade agreements that may affect the national
response to food supply shocks. While our results
indicate the relative vulnerabilities of countries along
the dimensions considered in the model, this analysis
alone cannot serve as an assessment of the actual level
of food security in each country.
Environ. Res. Lett. 11 (2016) 095009
Figure 4. Decrease in consumption (DC ) as a fraction of initial net supply (S0) from (a) internal shocks (i.e. shock was initiated at the
target country) and (b) external shocks (initiated at other countries), compared between the 1996 and 2009 trade networks. Two
outcomes are given in 1996 based on whether the original reserves or those scaled to the 2009 reserves/supply ratio are used. Each plot
includes all countries where the decrease exceeded 1% in at least one simulation. Global simulation parameters (see table 1) are set at
fp = 0.2, fr = 0.5 and fc = 0.01. Country labels correspond to their ISO alpha-3 codes.
To avoid introducing too many adjustable parameters, we chose a parsimonious model of the agents’
(in this case, national economies) behavior: all countries are willing to spend the same proportion of their
reserves, and any shock transmitted to trade partners
is partitioned equally among all trade links. The latter
assumption is shared with other models of ‘contagion’
in economic networks (e.g., Lee et al 2011). A few
recent models of shock propagation in food commodity networks (Puma et al 2015, Gephart et al 2016) use
a GDP-weighted partitioning, based on the assumption that countries with a higher purchasing power
will have a greater ability to sustain their imports from
production-stressed countries. A key challenge in the
development and parametrization of more complex
model versions is the coarse, aggregate nature of available production and trade data, which limits our ability to follow the propagation of individual shocks in
the empirical record.
We can contrast our simulation-based approach
with previous studies aimed at evaluating the global
food trade network’s resilience to supply shocks.
9
Using an aggregated virtual water trade network, Sartori and Schiavo (2015) analyzed the distribution of
historical supply shocks as well as changes in the network structure over time, to support the thesis that the
global food supply became more stable as the reliance
of trade increased. However, the data alone does not
suffice to isolate the effect of increased trade from that
of other trends present in the historical record, such as
a change in the distribution of food reserves. Our
model not only differentiates the effect of these two
trends, it also highlights the uneven impact of these
changes among the most vulnerable countries, showing how relative risks may shift from one region to
another.
By shedding light on the complex interactions that
determine the link between trade and food security,
our model also suggests different paths through which
national economies can reduce the risk of food shortages, such as diversifying the sources of staple food supplies and ensuring that trading partners have the
reserves to withstand a shock. We recognize however
that these country-level metrics constitute only one
Environ. Res. Lett. 11 (2016) 095009
Figure 5. Graph of countries having to reduce consumption for a shock initiated at source countries (orange nodes). The width of each
edge is proportional to log DC S0 . Simulations initialized with 2009 input data and global parameters (see table 1) are set at fp = 0.2,
fr = 0.5 and fc = 0.01.
dimension of food security, and that a more complete
assessment requires consideration of within-country
inequality in income, nutrition and access to food.
Furthermore, while patterns of trade-dependency can
be studied within the context of specific food commodity networks, their origin is intrinsically linked to
larger socio-environmental issues, including differences in access to water or land, the intensity of
their use (Fader et al 2016), and the geographical
distribution of pollution and other environmental
externalities.
Acknowledgments
We thank Roberto Patricio Korzeniewicz and Christina Prell for their participation in early discussions on
this project. This work was supported by the National
Socio-Environmental Synthesis Center (SESYNC)
under funding received from the National Science
Foundation (NSF) grant DBI-1052875. M Kummu
received support from Academy of Finland SRC
project Winland and Academy of Finland project
SCART. M J Puma is supported by a fellowship from
the Columbia University Center for Climate and Life
and the Interdisciplinary Global Change Research
under NASA cooperative agreement NNX08AJ75A. D
A Seekell was supported by the Carl Trygger Foundation for Scientific Research. Z Ratajczak received
support from NSF grant DBI-1402033. A Tavoni is
supported by the Centre for Climate Change Economics and Policy, funded by the ESRC, and the Grantham
Foundation for the Protection of the Environment.
10
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