HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-24-3015-2020Changing global cropping patterns to minimize national blue water scarcityChanging global cropping patterns to minimize national blue water scarcityChouchaneHatemhatemchouchane1@gmail.comhttps://orcid.org/0000-0002-1129-7968KrolMaarten S.HoekstraArjen Y.Twente Water Centre, University of Twente, Enschede, the
NetherlandsInstitute of Water Policy, Lee Kuan Yew School of Public Policy,
National University of Singapore, Singapore
Feeding a growing population with global natural-resource constraints
becomes an increasingly challenging task. Changing spatial cropping patterns
could contribute to sustaining crop production and mitigating water scarcity.
Previous studies on water saving through international food trade focussed
either on comparing water productivities among food-trading countries or
on analysing food trade in relation to national water endowments. Here,
we consider, for the first time, how both differences in national average
water productivities and water endowments can be considered to analyse
comparative advantages of countries for different types of crop production.
A linear-optimization algorithm is used to find modifications in global
cropping patterns that reduce national blue water scarcity in the world's
most severely water-scarce countries, while keeping global production of
each crop unchanged and preventing any increase in total irrigated or
rainfed harvested areas in each country. The results are used to assess
national comparative advantages and disadvantages for different crops. Even
when allowing a maximum expansion of the irrigated or rainfed harvested area per
crop per country of only 10 %, the blue water scarcity in the world's most
water-scarce countries can be greatly reduced. In this case, we could
achieve a reduction of the global blue water footprint of crop production of
21 % and a decrease of the global total harvested and irrigated areas of
2 % and 10 % respectively. Shifts in rainfed areas have a dominant
share in reducing the blue water footprint of crop production.
Introduction
Water scarcity poses a major societal and economic risk (WEF, 2019) and
threat to biodiversity and environmental sustainability
(Vörösmarty et al., 2010). Population
growth and climate change are expected to worsen the situation and impose
more pressure on freshwater resources everywhere (Vörösmarty et
al., 2000; Parry et al., 2004). Since water consumption already exceeds the
maximum sustainable level in many parts of the world
(Hoekstra et al., 2012) and population growth
in water-scarce countries alone could enforce global international trade in
staple crops to increase by a factor of 1.4 to 18 by 2050
(Chouchane et al., 2018), solutions are urgently needed for a
more sustainable allocation of the world's limited freshwater resources
(Hoekstra, 2014; Konar et al., 2016).
Considerable debate has arisen over the last few decades on the pathways to
overcome the problem of water scarcity and its implications
(Gleick, 2003), especially for agriculture, the largest
consumer of freshwater, accounting for 92 % of water consumption globally
(Hoekstra and Mekonnen, 2012). A growing number of studies addresses the
question of how to mitigate problems related to blue water scarcity (Wada
et al., 2014; Kummu et al., 2016). Some proposed solutions focus on better
water management in agriculture (Evans and Sadler, 2008), for
instance, by improving irrigation efficiency and precision irrigation (Sadler
et al., 2005; Greenwood et al., 2010), using better agricultural practices like
mulching and drip irrigation (Mukherjee et al., 2010; Chukalla et al.,
2015; Nouri et al., 2019), improving irrigation scheduling (Jones,
2004) and enhancing water productivity (Bouman, 2007; Molden et al.,
2010; Pereira et al., 2012). Other suggested solutions focus on changing
diets (Vanham et al., 2013; Jalava et al., 2014; Gephart et al., 2016)
and reducing food losses (Munesue et al., 2015; Jalava et al., 2016)
to diminish water consumption. Yet another category of studies focusses on
spatial cropping patterns (Davis et al., 2017a, b) and the
role of international trade in saving water and in bridging the gap between
national water demand and supply in water-short countries (Chapagain et
al., 2006; Hoekstra and Hung, 2005). The volume of fresh water used to
produce a traded product, measured at the place where it was produced, also
known as virtual water trade, is the hidden flow of water if food or other
commodities are traded from one place to another (Allan, 1998).
According to international trade theory, countries can profit from trade by
focussing on the production and export of goods for which they have a
comparative advantage. What precisely constitutes a comparative advantage is
still subject to debate. Whereas Ricardo's theory of comparative advantage
says that a country can best focus on producing goods for which they have
relatively high productivity, the Heckscher–Ohlin (H–O) theory states that a
country can best specialize in producing and exporting products that use
production factors that are comparatively most abundant. When focussing on
the role of water in trade, the first theory would consider relative water
productivity (crop per drop), while the second theory would look at relative
water abundance (Hoekstra, 2013). Part of the literature on
water saving through international food trade has focussed on comparing
water productivities among food-trading countries (Chapagain et al.,
2006; Yang et al., 2006; Oki et al., 2017), while other studies have
concentrated on analysing food trade in relation to water endowments
(Yang et al., 2003; Oki and Kanae, 2004; Chouchane et al., 2018). In a
study for China, Zhao et al. (2019), evaluated spatiotemporal differences
in the regional water, land and labour productivity of agricultural and
non-agricultural sectors across Chinese provinces and defined comparative
advantage on that basis. These comparative advantages were used to track the
driving force of the virtual water regional trade. Their findings suggest that
differences in land productivity were the main forces shaping the pattern of
virtual water flows across Chinese regions, while neither labour nor water
productivity had a significant influence.
In the current study, we consider, for the first time, how both differences
in water productivity and water endowment can be considered to analyse
comparative advantages of countries for different types of crop production.
While doing so, we also consider differences between countries in land
productivities (crop yields) and land endowments (available cropland areas).
Studies on the spatial allocation of crop production, given differences in land
and water productivity and endowments, are sparse, particularly large-scale
studies. In local or regional studies that study best crop choices given
land and water constraints, the focus is generally to maximize food
production or agricultural value, without the requirement of fulfilling
overall crop demand. Osama et al. (2017), for example, employ
a linear-optimization model for some regions in Egypt to maximize the net
annual return by changing the cropping pattern, given water and land
constraints, and conclude that some crops are to be expanded, while others
are to be reduced. Another example of a regional study is Ye et
al. (2018), who used a multi-objective-optimization model, considering the
trade-offs between economic benefits and the environmental impact of water use
when changing the cropping pattern in a case study for Beijing.
In a study for the US, Davis et al. (2017b) investigated alternative crop distribution that saves water and improves productivity
while maintaining crop diversity, protein production and income. The only
global study on changing cropping patterns in order to reduce water use, to
our knowledge, is Davis et al. (2017a), who combine data on water
use and productivity for 14 major crops and show that changing the
distribution of these crops across the world's currently cultivated lands could decrease blue water use by 12 % and feed an additional 825 million people. However, the current study has a number of differences compared with
Davis et al. (2017a). First, we are only changing cropping patterns while
maintaining the same global production per crop, whereas Davis et al. (2017a)
aim for a higher caloric and protein production while reducing water use;
that also results in a different global consumption pattern, which hampers
the identification of potential water-saving effects of just production
shifts amongst countries. Second, we consider a larger number of crops (125
crops including vegetables, fruits and pulses which were not considered in
Davis et al., 2017a).
Although it has been widely acknowledged that the spatial water scarcity
pattern in the world can be explained by where crops are grown and how much
they are irrigated (Wada et al., 2011; Mekonnen and Hoekstra, 2016), it
has not yet been studied how differences between countries in water and land
productivities and endowments can be used to derive comparative advantages
of countries for specific crops and how a change in the global cropping
pattern can reduce water scarcity in the most water-scarce places. Here, we
explore how we can stepwise minimize the highest national water scarcity in
the world by changing cropping patterns and the related blue water
allocation to crops. The spatial resolution of the country level reflects
the coarse resolution at which the Food and Agriculture Organization of the United Nations (FAO) monitors and reports water stress in the Sustainable Development Goal (SDG) framework (FAO, 2018); subnational heterogeneity in water scarcity, which
is significant in countries like the US or China, is not covered at this
resolution. With cropping pattern we mean the allocation of crops to rainfed
and irrigated land in all countries of the world, where both rainfed and
irrigated areas of each crop in each country are allowed to expand up to a
modest maximum rate (factor α), while respecting the bounds of
current total rainfed and total irrigated area per country as well as the
global production per crop. For this purpose, we develop and apply a linear-programming-optimization algorithm considering a number of constraints.
First, total rainfed and irrigated harvested areas in each country should
not grow beyond their extent in the reference period of 1996–2005. Second, the
harvested area per country per crop can only expand by a limited rate (which
will be varied), both for the rainfed and irrigated area. Third, the global
production of each crop must remain the same as in the reference period. The
optimization takes into account both factor endowments (blue water
availability, rainfed land availability and irrigated land availability) in
each country and factor productivities (blue water productivity in
irrigation and land productivities in rainfed and irrigated lands) for each
crop in each country. In order to focus on aspects of natural-resource
endowment and productivity in relation to water scarcity, other important
aspects such as socioeconomic or national food self-sufficiency goals were
not considered.
Methods and data
We developed a linear-optimization algorithm in MATLAB. In the optimization
we allow the global cropping pattern to change, that is to grow crops in countries other than in the reference situation. In the optimization,
the cropping areas by crop, country and production system are the
independent variables, and the following constraints are considered. First,
both total rainfed and total irrigated harvested areas per country are not
allowed to expand. Second, both crop-specific rainfed and irrigated
harvested area per country are allowed to expand but not beyond a factor
α (whereby we stepwise increase α from 1.1 to 2.0 in a
number of subsequent experiments). Third, the global production of each crop
should remain equal to the global production of the crop in the reference
situation. For any cropping pattern, the water scarcity in each country is
computed, and the country with the highest water scarcity is identified. The
objective of the optimization is to minimize the highest water scarcity.
The algorithm continuously tries to reduce the blue water scarcity in the
countries with the highest blue water scarcity while disallowing blue water
scarcity in any country to increase. The algorithm will thus tend to reduce
and equalize blue water scarcity in the most water-scarce countries.
We considered 125 crops of the main crops groups (cereals, fibres, fruits,
nuts, oil crops, pulses, roots, spices, stimulants, sugar crops and
vegetables; for an extensive list of crops used, see Chouchane et al.,
2020); the optimization was performed using the linear-optimization routine
from the Optimization Toolbox of MATLAB.
Given the cropping pattern, production is computed per country and crop,
both for rainfed and irrigated lands based on the harvested area and crop
yields:
∀i,j:Prfi,j=Arfi,j×Yrfi,j∀i,j:Piri,j=Airi,j×Yiri,j∀i,j:Pi,j=Prfi,j+Piri,j,
whereby Prf(i,j), Pir(i,j) and
P(i,j) are the rainfed, irrigated and total production in
country i of crop j; Arf(i,j) and
Air(i,j) are the rainfed and irrigated harvested area in
country i for crop j; and Yrf(i,j) and
Yir(i,j) are the rainfed and irrigated crop yield in
country i for crop j.
Blue water scarcity (BWS) is defined per country i as the total blue water
footprint divided by the blue water availability in the country (Hoekstra et
al., 2012). The blue water footprint (BWF) refers to the volume of
consumptive freshwater use for irrigation that comes from surface water and
groundwater. Blue water availability is taken from FAO (2015) and refers to
the total renewable amount (internal and external resources), which is the long-term
average annual flow of rivers (surface water) and sustainably available
groundwater (FAO, 2003).
BWS(i)=∑jPir(i,j)×BWF(i,j)BWA(i),
where Pir(i,j) is the irrigated production in country
i of crop j, BWF(i,j) is the blue water footprint per unit of
crop j in country i, and BWA(i) is the blue water
availability in country i. A country is considered to be under low, moderate,
significant or severe water scarcity when BWS (expressed as a percentage) is
lower than 20 %, in the range of 20 %–30 %, in the range of 30 %–40 % and larger
than 40 % respectively (Hoekstra et al., 2012).
The optimization can be presented as follows:
minArf,Airmaxi(BWS(i)),
subject to
∀i:∑jArf(i,j)≤∑jArf,ref(i,j)∀i:∑jAir(i,j)≤∑jAir,ref(i,j)∀i,j:Arf(i,j)≤α×Arf,ref(i,j)∀i,j:Air(i,j)≤α×Air,ref(i,j)∀j:∑iP(i,j)=∑iPref(i,j)∀i:BWS(i)≤BWSref(i),
where Arf(i,j) and Air(i,j) are
the rainfed and irrigated harvested areas in country i of crop j in the
cropping pattern that is varied in order to minimize the highest national
blue water scarcity, Arf,ref(i,j) and
Air,ref(i,j) are the rainfed and irrigated
harvested areas in the reference situation, P(i,j) is the
total (rainfed plus irrigated) production in country i of crop j in the new
cropping pattern, Pref(i,j) is the total (rainfed
plus irrigated) production in country i of crop j in the reference situation,
and BWSref(i) is the blue water scarcity in country
i in the reference situation. Parameter α is the factor of the maximally
allowed expansion of the harvested area per crop and country and production
system (rainfed or irrigated), which is varied in the optimization
experiments between 1.1 and 2. Note that total national croplands (both
rainfed and irrigated) are not allowed to expand but that reductions in
land use are always allowed.
A country is considered to have a comparative advantage for producing a
certain crop or crop group when the following criteria are met: (1) the
relative change (production in the optimized cropping pattern divided by the
production in the reference situation) of that crop or crop group continues
to increase in that country when we gradually increase the maximally allowed
expansion of harvested area per crop per country (the factor α) and
(2) the share of the country in the global production of the crop or crop
group exceeds 5 % (in the optimized cropping pattern at α=1.1).
In order to test the sensitivity of the optimization results to the allowed
changes in irrigation, we run the optimization without allowing any
expansion of the irrigated area. In this case, the factor α will be only
applied to the rainfed area, while the irrigated area per country per crop
will be below or equal to the irrigated area of the same crop in the same
country in the reference situation. The optimization objective and
constraints remain the same except that the following constraint was added:
∀i,j:Air(i,j)≤Air,ref(i,j).
The sources of the data used to perform the optimization are summarized in
Table 1.
Overview of data used.
VariableSpatial resolutionTemporal resolutionSourceBlue water availabilityCountry (internal and external renewable water resources)Average for 1961–1990FAO (2015)Harvested irrigated and rainfed land per crop in the reference situationCountryAverage for 1996–2005Mekonnen and Hoekstra (2011), FAO (2015)Rainfed and irrigated produc- tion per crop in the reference situationCountryAverage for 1996–2005Mekonnen and Hoekstra (2011), FAO (2015)BWF per unit of crop in irrigated production per cropCountryAverage for 1996–2005Mekonnen and Hoekstra (2011)Yield in rainfed and irrigated production per cropCountryAverage for 1996–2005Mekonnen and Hoekstra (2011)ResultsChanges in blue water scarcity and blue water consumption
When α is 1.1, that means when we allow a maximum of 10 %
expansion of the reference harvested areas for each individual crop, in
every country, both for rainfed and irrigated production, blue water
scarcity in the world's seven most water-scarce countries, Libya, Saudi
Arabia, Kuwait, Yemen, Qatar, Egypt and Israel (with current scarcities
ranging from 54 % to 270 %), is reduced to a scarcity of 39 % or less
(Table 2). In this scenario, the aggregated blue water footprint of crop
production in the world is reduced by 21 %, while the total global
harvested and irrigated areas are reduced by 2 % and 10 % respectively.
Current versus optimized blue water consumption (BWC) and blue
water scarcity (BWS) for countries currently having a water scarcity value higher
than 15 %.
When α is equal to 1.3, 1.5 and 2.0 (i.e. when the maximally
allowed expansion of harvested area per crop per country is equal to 30 %,
50 % and 100 %), the world's maximum national blue water scarcity
is further reduced to 6 %, 4 % and 2 % respectively. In these
scenarios, global blue water consumption gets reduced by 38 %, 48 % and
60 % respectively; the total global harvested area gets reduced by 6 %,
7 % and 9 % respectively, and the total global irrigated area gets
reduced by 23 %, 27 % and 37 % respectively.
Current and optimized (α=1.1) blue water
scarcity.
Most countries with severe water scarcity (BWS >40 %) in the
reference situation show a moderate (BWS in the range of 20 %–30 %) to low
water scarcity (BWS <20 %) in the optimized situation with α=1.1 (Fig. 1). However, not all countries would benefit similarly in
the optimized situation. China and India, major crops producers in the
reference situation, only start to have a decrease in their BWS when α≥1.3.
Current blue water consumption depth (in mm yr-1) and blue water
saving as a percentage of current BWC in the case of an optimized cropping
pattern (α=1.1).
In the case of α=1.1, Pakistan, the third-largest consumer of
blue water in the reference situation, has the largest reduction in its blue
water consumption in absolute terms, viz. 60 000 m3 yr-1, which
represents 80 % of its current BWC and 35 % of the global blue water
saving. Other countries that have a significant reduction in their BWC in
absolute terms include Iran, Egypt, Iraq, Syria, Saudi Arabia, Sudan and
Turkmenistan (Fig. 2). However, not all countries would benefit similarly
in the optimized set. India and China, the first- and second-largest consumers
of blue water in the reference situation, will only start to have a decrease
in their blue water scarcity when the allowed expansion rate α is
larger than 1.2; this is due to the optimization of water scarcity at the
level of countries, where India and China have modest national water
scarcity.
The changing global cropping pattern for the case of α=1.1
The reduction of global blue water consumption is achieved by reallocating
the most resource-intensive crops from countries that have lower
productivity in terms of land and water to countries with significantly
higher productivities, both for rainfed and irrigated production, thus
reducing irrigation in countries that initially have a high BWS value. In the
optimized cropping pattern, cereal production is reduced most significantly
in Africa, relative to the reference situation, and South America and
expanded in North America and Europe (Table 3). Irrigated cereal production
is reduced in most of the world's regions (except for a small expansion in Europe and
South America), whereas global rainfed production increases. For individual
countries, Pakistan and Egypt have the largest decrease in total cereal
production. The most significant expansions in cereal production are found
in the US and China for maize; in China, India, the Russian Federation and
France for wheat production; and in India, Indonesia and Vietnam for rice
production. In terms of harvested area, the largest areal decrease in
cereals is found in Asia, with a reduction of 8×106 ha in total
(Table S1 in the Supplement), which represents 3 % of the current harvested area
of cereals in Asia. The irrigated area of cereals in Asia is reduced by
6 % compared to the reference situation, while the rainfed area has an
increase of 1 %. Africa has the second-largest decrease of the irrigated area
of cereals with 3×106 ha and the largest increase of rainfed area
of cereals with 2.6×106 ha. Changes in the global pattern of
cereal production for the case of α=1.1 contribute 50 % to the
total global reduction in the blue water footprint and 46 % to the total
global reduction in the irrigated area.
Change in production per product group per continent in absolute
terms (106 t yr-1) when shifting from the cropping pattern in the
reference period (1996–2005) to the optimized cropping pattern (with α=1.1).
Fruit production is reduced most significantly in Asia and Africa and
expanded in the Americas (Table 3). Major fruit production reductions
include the decrease of apple production in Iran; banana production in
Thailand; orange production in Egypt, Iran and Pakistan; and grape production
in France. In North America, the most significant expansion in fruit
production is the increase in orange, grape and apple production in the US;
in South America, the largest fruit production increases are for oranges in
Brazil and bananas in Ecuador. Although the reduction in fruit production in
Asia and Africa mainly concerns irrigation, the irrigated production of
fruits increases in North America and Europe. The largest share of
increase in the irrigated production in North America comes from the increase in
irrigated production of oranges, apples and grapes in the US. The world's
harvested area of fruits reduces by 2 %. The irrigated area reduces by
19 %, while the rainfed area increases by 4 %. Changes in fruit
production contributed 12 % to global blue water savings and 9 % to
total global reductions in irrigated area.
The production of oil crops is reduced most significantly in Africa (e.g.
oil palm in Nigeria) and expanded in the Americas (e.g. soybeans in the US,
Brazil and Argentina). The harvested area shrinks globally by 3 % in
total. The irrigated area reduces by 30 %, although the global rainfed area remains
the same as the reference situation. Asia and Africa have the most
significant shrinkage in harvested areas of oil crops. Reallocating oil
crops contributed 7 % to global reductions in blue water footprint and
19 % to total global reductions in irrigated area.
Root production partly moves from South America to Africa, Asia and Europe.
At the country level, the most significant reduction is due to the decrease of
potato production in Poland and Iran and cassava production in Brazil, China
and Vietnam. The largest expansions are sweet potato production in China,
potato production in the Russian Federation and cassava and yam production in Nigeria.
Globally, the harvested area of roots is reduced by 4 % (11 % for
irrigated and 3 % for rainfed croplands).
Sugar crop production is reduced most significantly in Asia and Africa and
expanded in the Americas. Sugar cane production is mainly reduced in
Pakistan, India and Egypt and expanded in Brazil. The global irrigated
harvested area of sugar crops is reduced in total by 10 %, while the global
rainfed area increases by 8 %. Changes in sugar crop production
contribute 10 % to the total blue water savings globally.
Vegetable production is reduced most significantly in Europe and Africa and
expanded in Asia. Major reductions in vegetable production are for tomato
production in Iran and Egypt. The most significant expansions are the
increases in tomato and watermelon production in China. The global harvested
area of vegetables is reduced by 4 %, with a reduction of 17 % for
irrigated croplands, while the rainfed area remains the same as reference
situation. Reallocating vegetables contributed 5 % to global reductions in
the blue water footprint and 7 % to global reductions in the total irrigated
harvested area globally.
Although the cereal rainfed harvested area is reduced in North America when
α=1.1, for example (Table S1), rainfed cereal
production will increase by 11.6×106 t yr-1. This illustrates that by
allocating production to more productive countries, we can reduce water and
land use and increase production at the same time.
Comparative advantages
We explore the comparative advantages of countries to contribute to the goal of
relieving global water scarcity. In the following, we use the term
“comparative advantage” to indicate a comparative advantage for this specific
goal, as that is where results from the study provide insight in;
comparative advantages to e.g. contribute to increasing agroeconomic revenue
or reducing the agricultural carbon footprint could result in different
conclusions. Our exploration of comparative advantage is considering which
crops in a country are expanding when we gradually move from α=1.1 to α=1.5. As a summary, Fig. 3 shows at the level of
continents and crop groups, the relative change in total production when we
move from the reference cropping pattern (1996–2005) along the
optimized cropping pattern, considering a stepwise increase in the maximally
allowed expansion rate in harvested area per crop per country from α=1.1 to α=1.5. Most of the changes in production that already
occur for the modest areal-expansion rate per crop of 10 % (Table 3) will
continue under larger expansion rates, with some exceptions. This is, for
example, the case for fibres in Europe and oil crops in North America.
Fibre production expands for the case of α=1.1, 1.2 and
1.3 in Europe but again reduces for higher expansion rates. This can be
explained by the fact that even more suitable regions, namely Oceania, North
America and to a lesser extent Africa, continue expanding fibre production,
allowing Europe to rather focus on cereal, sugar crop and stimulant
production (Fig. 3). North America expands oil crop production when
α=1.1 (Table 3) but decreases oil crop production when α=1.2 and has the largest reduction in oil crop production for α=1.5 (Table S1). The reason behind this is that for the
smallest expansion rate, the US still needs to produce oil crops, and the
global production could not be reached without the expansion of oil crops in
the US, which limits the allocation of harvested areas to more suitable crops
in the US such as maize and sugar crops. From α=1.2, the US will
focus on producing maize in which they have a comparative advantage and give
up a part of oil crop production. This example for North America shows that
it is hard to have a robust conclusion on comparative advantages by looking
at the level of continents. In order to explore comparative advantages, we
will need to look at the country level. Figures 4 and 5 show the absolute and
relative changes in production per crop group per country when we move from
the cropping pattern in the reference situation to the optimized cropping
pattern with α=1.5.
Ratio of total production in the optimized cropping pattern to
total production in the reference cropping pattern (1996–2005), per
crop group and per continent, for α=1.1 to α=1.5.
Absolute change in production for cereals, fruits, oil crops,
sugar crops and vegetables per country (in 106 t yr-1; maps on the left) and relative production (ratio of production in optimized and
reference situation) for the same crops groups for the case of an optimized
cropping pattern with α=1.5 (maps on the right), all
compared to the reference cropping period (1996–2005). Relative production
=1: no change; relative production <1: national production is
reduced; relative production >1: national production is
expanded.
Absolute change in production for fibres, nuts, pulses, roots,
spices and stimulants per country (in 106 t yr-1; maps on the left)
and relative production (ratio of production in optimized and reference
situation) for the same crops groups for the case of an optimized cropping
pattern with α=1.5 (maps on the right), all compared to the
reference cropping period (1996–2005). Relative production =1: no change;
relative production <1: national production is reduced;
relative production >1: national production is expanded.
Cereal production
The US and to a lesser extent Indonesia and France have large absolute
and relative changes (Fig. 4) for cereals and thus a comparative advantage
(given the combination of their water endowments and water productivities
compared to other countries). In the case of α=1.5, the cereal
production of the US, Indonesia and France will increase by 30 %, 26 % and
23 % respectively compared to the reference situation. India has a
comparative disadvantage in cereals and will reduce its production by 40 %
in the optimized cropping pattern with α=1.5. Looking at the
main cereal crops separately (wheat, barley, maize and rice) and combining
information on relative and absolute changes, we find that France and the
Russian Federation have a comparative advantage in wheat production, with
large absolute increases when we optimize the global cropping pattern
(Fig. S1). India and China, contributing 12 % and 17 %
respectively of global wheat production in the reference period, have a
comparative disadvantage and shrink their wheat production by 41 % (for
China) and 26 % (for India) when α=1.5. For barley, we find Canada,
France, Spain and Turkey to have a comparative advantage. Germany and the
Russian Federation, contributing 9 % and 11 % respectively to the global
barley production in the reference period, have a comparative disadvantage
and will decrease their barley production respectively by 28 % and 88 %
when α=1.5. For maize, the US is found to have a comparative
advantage, while, Brazil, contributing 6 % to global maize production in
the reference period, has a comparative disadvantage and will reduce its
maize production with 64 % in the optimized situation (α=1.5). For
rice, China, Indonesia and Vietnam have a comparative advantage, with shares
in global rice production raising from 32 %, 9 % and 5 % respectively
in the reference situation to 22 %, 29 % and 27 % in the optimized
situation (when α=1.5). India, contributing 22 % to global rice
production in the reference period, has a comparative disadvantage and will
decrease its rice production by 43 % when α=1.5 compared to the
reference situation.
Fruit production
Comparative advantages for fruit production are found for Brazil and the US,
which will increase their respective shares in global fruit production from
7 % and 6 % in the reference situation to 11 % and 9 % in the
optimized cropping pattern (when α=1.5). China and India,
contributing 14 % and 10 % respectively to global fruit production in
the reference period, appear to have a comparative disadvantage and will
reduce their fruit production by 13 % and 31 % respectively in the
optimized situation (when α=1.5). Zooming in to the top four produced
fruits – apples, bananas, grapes and oranges – we find the following. For
apples, the US has a comparative advantage; the country will increase its
share in global apple production from 8 % (reference) to 12 % (when
α=1.5). China, contributing 35 % to the global apple production in
the reference period, has a comparative disadvantage and will decrease its
apple production by 12 % in the optimized cropping patterns (when α=1.5). For bananas, Ecuador, Brazil and the Philippines have a comparative
advantage. India, contributing 22 % to global banana production in the
reference period, has a comparative disadvantage. For grapes, Italy, the US and
China have a comparative advantage, with shares in global grape production
rising from 15 %, 9 % and 7 % (reference) to 22 %, 13 % and 10 %
(α=1.5). France and Spain, contributing 13 % and 9 %
respectively to global grape production in the reference situation, have a
comparative disadvantage and will entirely abandon grape production when
α=1.5. For oranges, Brazil and the US have a comparative advantage,
while Mexico, Spain and Iran have a comparative disadvantage (Fig. S2).
Oil crops
For oil crops, we find Indonesia, Brazil and Argentina to have a
comparative advantage. Their shares in global oil crop production will
raise from 13, 9 % and 6 % respectively (reference) to 16 %, 13 % and
9 % (α=1.5). The US and Malaysia, contributing 17 % and 12 %
respectively to global oil crop production in the reference situation, have
a comparative disadvantage and will reduce their oil crop production by
32 % and 14 % respectively in the optimized cropping pattern (when
α=1.5). Focussing on soybean, which contributes 36 % to the global
oil crop production, we find the comparative advantage for Argentina and
Brazil. The share of Argentina and Brazil in global soybean production will
rise from 14 % and 22 % respectively (reference) to 21 % and 33 %
(α=1.5). China and the US have a comparative disadvantage in
soybean production. While the US, contributing 43 % to the global soybean
production in the reference period, will reduce its production by 31 %,
China, contributing 9 % in the reference period, will entirely stop its
soybean production in the optimized pattern (when α=1.5; Fig. S3).
Sugar crops
Brazil and China have a comparative advantage in sugar crop production,
with shares in global sugar crop production rising from 23 % and 6 %
respectively (reference) to 35 % and 9 % (optimized cropping pattern
with α=1.5). India, currently contributing 18 % to the global
sugar crop production, has a comparative disadvantage and will quit sugar
crop production almost entirely. Considering sugar beet and sugar cane
separately, we find that France, Poland, the Russian Federation and the US
have a comparative advantage in sugar beet production. Germany, Turkey and
Ukraine, contributing 11 %, 7 % and 6 % to the global sugar beet
production (reference), have a comparative disadvantage and will decrease
their sugar beet production by 72 %, 100 % and 94 % respectively (when
α=1.5). For sugar cane, Brazil and China have a comparative
advantage; their shares in global sugar cane production will increase from
28 % and 6 % respectively (reference) to 42 % and 10 % (optimized
cropping pattern with α=1.5). India, contributing 22 % to global
sugar cane production in the reference period, has a comparative
disadvantage and will decrease its sugar cane production by almost 100 %
(Fig. S3).
Vegetables
China and India have a comparative advantage in vegetable production. Their
shares in global vegetable production will rise from 45 % and 9 %
respectively (reference) to 52 and 12 % respectively (optimized cropping
pattern with α=1.5). Turkey, contributing 4 % to global
vegetable production in the reference, has a comparative disadvantage and
will reduce its vegetable production by 83 % in the optimized pattern
(when α=1.5) compared to the reference situation. Looking at the
most produced vegetable crop, tomato, which contributes 15 % to global
vegetable production, we find that China and the US have a comparative
advantage (Fig. S3). The share of China and the US in the
global production of tomatoes will increase from 21 % and 11 %
respectively (reference) to 30 % and 16 % respectively (when α=1.5). Egypt and Turkey, contributing 6 % and 8 % to global tomato
production in the reference, have a comparative disadvantage and will stop
their production almost entirely in the optimized situation.
Sensitivity to restricting expansion to rainfed areas
By allowing only rainfed areas per crop to expand up to 10 % and
the irrigated area per crop only to shrink, global blue water consumption of
crop production is reduced by 16 %. When α is equal to 1.3, 1.5
and 2.0 (i.e. when harvested area per crop per country can expand by up to
30 %, 50 % and 100 %), global blue water consumption gets reduced by 31 %, 41 % and 54 % respectively. The maximum blue water scarcity is reduced to a scarcity of 62 %, 14 %, 5 % and 3 % when α is equal to 1.1, 1.3, 1.5 and 2.0 respectively (Table 4).
Current versus optimized maximum BWS when allowing both irrigated
and rainfed areas to expand and when allowing only rainfed areas to expand
and the share of rainfed areas shifts in reducing maximum BWS for the case
when α is equal to 1.1, 1.3, 1.5 and 2.0 respectively.
Factor αMaximum BWS Reduction in maximum BWS Share of rainfedCurrent∗Optimized compared to reference situation shifts in reducingExpansion in bothExpansion onlyExpansion in bothExpansion onlymaximum BWSirrigated andin rainfed areasirrigated andin rainfed areasrainfed areasrainfed areasα=1.1272 %39 %62 %-86 %-77 %90 %α=1.3272 %6 %14 %-98 %-95 %97 %α=1.5272 %4 %5 %-99 %-98 %99 %α=2.0272 %2 %3 %-99 %-99 %99.6 %
∗ Independent of α.
The shifts in only the rainfed area give a dominant contribution to the
reduction of the maximum BWS value when allowing both rainfed and
irrigated areas to expand. Contributions from only rainfed areas shifts amount to
90 % of the total reduction when α is equal to 1.1 to 97 %, 99 % and
99.6 % when α is equal to 1.3, 1.5 and 2.0 respectively. The
dominance effect of shifts in rainfed areas proves that the optimization
results are not very sensitive to modestly allowed expansion in irrigated
areas per crop.
Discussion
Our study has some limitations that need careful consideration when
interpreting results. Limited by the availability of some of the required data
and operational computational limitations of optimization software, we
analyse the global cropping pattern at the country scale rather than at
subnational or grid scale. However, having a high average yield for a
specific crop in a certain country does not necessarily mean that everywhere
in that country the same performance in terms of land and water productivity
is achieved, due to spatial differences in crop suitability. This could
result in reallocating crops to countries that have a very limited suitable
production area but are productive in terms of water and land in the
reference situation. To constrain this effect, we do not allow total
cropland per country to expand so that areal expansion for one crop
replaces the land use of another crop with a shrinking area; also, we limit
the expansion in cropland by a certain maximum rate for each crop per
country (the factor α). The analysis at the country level also has
implications for the interpretability of water scarcity indicators.
Assessing water scarcity at the country level for an average year
hides the water scarcity that manifests itself in particular places within
countries or in particular periods (Mekonnen and Hoekstra,
2016). We minimize average water scarcity in countries; within countries scarcity
differences will still appear, both in the reference situation and in the
case of the optimized cropping patterns. Still, water scarcity indicators at
national levels provide insight; within the framework of the Sustainable
Development Goals, indicator 6.4.2 (“Level of water stress”) is used to
monitor Goal 6 (“Ensure availability and sustainable management of water and
sanitation for all”). It is defined similar to water scarcity in our study,
also at the resolution of countries, but based on water extractions rather
than consumptive water use. While lowering the water stress level is a goal
for each country, from a global equity perspective lowering stress in
countries with the highest water scarcity is prioritized. This is
operationalized by choosing the maximum national water scarcity as an
objective function in the optimization. Relieving water scarcity in specific
hotspots within countries by changing cropping patterns could be studied
using the current approach but is beyond the scope of this paper. The
sensitivity analysis did show that by far the largest impact on water
scarcity relief emerges from shifts in cropping patterns of rainfed crops,
not depending on the heterogeneity of blue water availability; therefore
water scarcity reduction in countries with the highest scarcity at the national
level in the current study does not rely on worsening water scarcity in
countries with heterogeneous conditions.
Another limitation of this study is the focus on water and land endowments
and productivities and on global water scarcity reduction as a shared goal,
while other production factors such as labour, knowledge, technology and
capital can be limiting factors to expanding the production of certain crops in
some countries, and certainly, agroeconomic aspects may play a role in
considering comparative advantages as well. Other factors could be included
in a future study by refining the optimization model; other objective
functions could emphasize trade-offs between economic and environmental
goals. Moreover, agricultural, trade and food security policies could be
other factors that drive cropping patterns rather than water and land
availability (Davis et al., 2018). Here, we purposely limited our
analysis to considering comparative advantages from a perspective of land
and water resource use to understand the specific role of these two
particular factors. By no means do we suggest that the optimized cropping
patterns found here are better than the reference pattern, because what is
best depends on many more factors than are included here, including political
preferences. Rather, our results are instrumental in illustrating directions
of change if we would put emphasis on the factors of land and water endowment
and productivity and put particular value on reducing water scarcity in the
most water-scarce places.
The scope of the current study is restricted to the exploration of
alternative cropping patterns to reduce water scarcity in the reference
situation; we therefore use reference resource efficiencies. We do not take
into consideration the future increase in food demand due to population
growth, agronomic developments that may increase resource use
efficiencies or climate change that will affect the future ability of
countries to produce crops. The current study supports the findings of Davis
et al. (2017a) on the benefits of crop redistribution on water saving which
could be a potential strategy for sustainable crop production and an
alternative to the large investments that are usually needed to close up the
technological and yield gaps in developing nations. Besides reducing water
and land use, changing cropping patterns will also have an impact on reducing greenhouse gas emission that results from extensive energy activities in irrigation,
such as groundwater pumping, which accounted for around 61 % of total
irrigation emissions in China (Zou et al., 2015).
The results suggest that Asia, for example, could contribute to global water
scarcity mitigation by reducing its production of fruits and sugar crops
while increasing its cereal and vegetable production. This implies that Asia
will move to economically less attractive crops. This illustrates the
possible trade-off between the goal of reducing water scarcity in the most
water-scarce countries and the goal of economic profit by producing cash
crops by individual countries or regions. The optimization results do not
pretend that the changes in production patterns are likely to occur but
merely that these changes reduce water scarcity most; national and
international policies would be required to promote such water-saving
changes to be implemented (Klasen et al., 2016).
Changing cropping patterns could reduce the global blue water footprint by
21 % and global irrigated area by 10 %. These findings prove that
current high scarcity levels in a serious number of countries are shown to
be caused by the current crop allocation pattern rather than by an
inevitability of those scarcities to occur; that suggests that water
endowment is insufficiently driving crop allocation to avoid water scarcity.
This in consistent with Zhao et al. (2019) who find in their study for
China that comparative advantages with respect to labour and water were not
reflected in the regional distribution of agricultural production. However,
not all countries would benefit similarly in the optimized set. India and
China, the main crop producers in the reference situation, will only start to
have a decrease in their blue water scarcity when the allowed expansion rate
is larger than 20 %. This is in line with the findings of Davis et al. (2017a), who find in their simulations that water scarcity persists in many
important agricultural areas (the US Midwest, northern India and Australia's
Murray–Darling basin, for example), indicating that extensive crop
production in these places prohibits water sustainability, regardless of
crop choice (Davis et al., 2017a).
Findings suggest that China, one of the world's main producers of the major crop, will abandon soybean production and halve wheat irrigation area.
This will relieve some of the pressure on the northern part of China, where
water scarcity is the most severe (Ma et al., 2020). China will
increase the harvested area of rice and rapeseed, the crops with the most
significant comparative advantage in terms of land and water. Similarly, our
results suggest that the US, another major crop producer, would restrict
soybean production to rainfed systems, abandoning irrigation, in the
optimized set in the US. The US focuses on producing maize, mainly rainfed,
for which the US has a comparative advantage in terms of water and land
productivities. This may be a great relief to the US Corn Belt, where most of the
irrigated soybean and maize crops are located (Zhong et al., 2016), and could be a
remedy to the projected water shortage of that region resulting from
population growth and climate change (Brown et al., 2019). We also find that
India, another of the world's major producers of crops, will move away from
sorghum production and shift a large share of its rice and wheat production
to rainfed conditions. Moving to rainfed production in India could mitigate
the effect of the intensive use of irrigation from groundwater and surface
water which caused groundwater degradation in many districts of Haryana and
Punjab, the largest contributing states to rice and wheat production in
India (Singh, 2000).
For some of the most water-scarce countries, results show that blue water
consumption in crop production is reduced by more than 70 % compared to
the reference situation: Cyprus, Egypt, Iran, Jordan, Kuwait, Libya,
Pakistan, Saudi Arabia, Syria, Turkmenistan and Yemen. This means that these
countries, with modest rainfed agricultural areas, will rely more heavily on
imports and thus become highly dependent on other countries. Most of these
countries already have a high dependency on crop imports in the reference
situation. This reflects a trade-off between reducing water scarcity and
increasing food security on the one hand and shows the important role of
food trade in relieving water scarcity in many places in the world on the
other.
Conclusion
When allowing a 10 % maximum expansion of harvested area per crop and per
country, while not allowing an increase in the total rainfed or irrigated
cropland per country, a global blue water saving of 17×1010 m3 yr-1 is achievable, which is 21 % of the current global blue
water footprint. Changes in the cropping pattern of rainfed production have
a dominant effect, relieving irrigated areas to contribute to production;
the total global harvested area would decrease by 2 %, and the total
global irrigated area would decrease by 10 %. The blue water scarcity in
the seven countries with highest national water scarcity, Libya, Saudi Arabia,
Kuwait, Yemen, Qatar, Egypt and Israel (with current scarcities ranging
from 54 % to 270 %), can be reduced to a scarcity of 39 % or less.
Optimizing the global cropping pattern to reduce the highest national water
scarcity comes with trade-offs, where severely water-scarce countries reduce
water scarcity at the expense of decreased food self-sufficiency.
When considering how to change the global cropping pattern in order to
reduce water scarcity in the world's most severely water-scarce countries,
we specifically find the following major shifts. Cereal production is
reduced in Africa and South America and increased in North America and
Europe. Fruit production is reduced most significantly in Asia and Africa
and expanded in the Americas. Oil crop production is reduced most
significantly in Africa and expanded in the Americas. Sugar crop production
is reduced most significantly in Asia and Africa and expanded in the
Americas. Vegetable production is reduced most significantly in Europe and
Africa and expanded in Asia. Reallocating cereal crops is the main
contributor to global blue water saving with a contribution of 50 % for
the case of α=1.1, followed by fruit, sugar crops and fibres
with 12 %, 10 % and 9 % respectively.
From a water and land perspective and aiming for global water scarcity
reduction, comparative advantages for cereal production are found for the US
and to a lesser extent Indonesia and France, whereas India has a comparative
disadvantage. A comparative advantage exists for the US for maize, for
France for wheat and barley and for Indonesia for rice. India has a comparative
disadvantage in cereal production, particularly for wheat and rice. For
fruit production, Brazil and the US are found to have a comparative
advantage, whereas China and India have a comparative disadvantage. More specifically, the US has a comparative advantage for apples, grapes and
oranges, and Ecuador and Brazil have one for bananas, while China has a comparative
disadvantage for apples and India for bananas. For oil crops, Indonesia,
Brazil and Argentina have a comparative advantage, and the US and Malaysia have a
comparative disadvantage. Argentina and Brazil have a comparative advantage
for soybean, while the US and China have a comparative disadvantage. For
sugar crop production, Brazil and China are found to have a comparative
advantage, while India has comparative disadvantage. Brazil
and China have a comparative advantage for sugar cane, while India has a
comparative disadvantage. For vegetables, we find China and
India to have a comparative advantage and Turkey to have a comparative
disadvantage. China has a comparative advantage for tomatoes, and Turkey has a
comparative disadvantage.
By considering differences in national water and land endowments, following
the Heckscher–Ohlin theory of comparative advantage, as well as
differences in national water and land productivities, following Ricardo's
theory of comparative advantage, we combine two rationales that are both
relevant. With the optimization exercises carried out in this study, we show
that blue water scarcity can be reduced to reasonable levels throughout the
world by changing the global cropping pattern while maintaining current
levels of global production and reducing land use. Future research could
refine the current study by taking the subnational heterogeneity of water
scarcity into account and by interpreting resulting changes in international
trade patterns.
Data availability
The datasets generated and/or analysed during the current study are
available in the Supplement and the 4TU.ResearchData
repository (CC-BY-NC-ND) at
10.4121/uuid:64e7f59a-03f3-4e25-83c8-06745e9216d2 (Chouchane et al., 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/hess-24-3015-2020-supplement.
Author contributions
The three authors designed the research, analysed the data and wrote the paper. HC carried out the calculations.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
Hatem Chouchane and Maarten S. Krol dedicate this work to their co-author, Arjen Y. Hoekstra, who passed away unexpectedly just before the revision of this paper
and whose ideas and ideals greatly influenced and will still influence a
generation of scientists.
Financial support
The work by Maarten S. Krol and Arjen Y. Hoekstra was partially funded by the European Research Council (ECR) under the European Union's Horizon 2020 research and innovation programme through the project “Moving Towards Adaptive Governance in Complexity: Informing Nexus Security” (MAGIC; EU H2020 grant no. 689669) and Earth @lternatives (grant agreement no. 834716).
Review statement
This paper was edited by Gerrit H. de Rooij and reviewed by three anonymous referees.
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