Virtual water trade A quantification of virtual water flows between ...

A.Y. Hoekstra

Virtual water trade

P.Q. Hung September 2002

A quantification of virtual water flows between nations in relation to international crop trade

Value of Water

Research Report Series No.11

VIRTUAL WATER TRADE A

QUANTIFICATION OF VIRTUAL

WATER FLOWS BETWEEN NATIONS IN RELATION TO INTERNATIONAL CROP TRADE

A.Y. HOEKSTRA P.Q. HUNG

SEPTEMBER 2002

VALUE OF WATER RESEARCH REPORT SERIES NO. 11

IHE D ELFT

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A.Y. Hoekstra

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E-mail [email protected]

Contents

Summary...................................................................................................................................................................................7 1. Introduction .........................................................................................................................................................................9 1.1. The economics of water use........................................................................................................................................9 1.2. Virtual water trade......................................................................................................................................................10 1.3. The objective of this study........................................................................................................................................11 2. Method................................................................................................................................................................................ 13 2.1. Calculation of specific water demand per crop type ............................................................................................13 2.2. Calculation of virtual water trade flows and the national virtual water trade balance....................................14 2.3. Calculation of a nation’s ‘water footprint’.............................................................................................................15 2.4. Calculation of national water scarcity, water dependency and water self-sufficiency ...................................16 3. Data sources ...................................................................................................................................................................... 19 4. Specific water demand per crop type per country................................................................................................. 23 5. Global trade in virtual water....................................................................................................................................... 25 5.1. International trade in virtual water...........................................................................................................................25 5.1.1. Overview of international virtual water trade.............................................................................................. 25 5.1.2. Virtual water trade balance per country....................................................................................................... 28 5.1.3. International virtual water trade by product................................................................................................ 34 5.2. Inter-regional trade in virtual water.........................................................................................................................35 5.2.1. Inter-regional virtual water trade relations................................................................................................. 35 5.2.2. Virtual water trade balance per world region ............................................................................................. 40 5.2.3. Gross virtual water trade between countries within regions..................................................................... 50 5.3. Intercontinental trade in virtual water.....................................................................................................................51 5.3.1. Intercontinental virtual water trade relations.............................................................................................. 51 5.3.2. Virtual water trade balance per continent.................................................................................................... 53 5.3.3. Gross virtual water trade between countries within continents................................................................ 54 6. Virtual water trade of nations in relation to national water needs and availability..................................... 55 6.1. Water footprints, water scarcity, water self-sufficiency and water dependency of nations ..........................55 6.2. The relation between water scarcity and water dependency ...............................................................................60 7. Concluding remarks....................................................................................................................................................... 63 References .............................................................................................................................................................................. 65

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Appendices

I.

Crop water requirements (m3 /ha)

II.

Actual crop yields (ton/ha) in 1999

III.

Specific water demands (m3 /ton) in 1999

IV.

FAO guidelines on crop water requirements in mm [=10 m3 /ha]

Va.

Gross virtual water import per country for the years 1995-1999 (106 m3 )

Vb.

Gross virtual water export per country for the years 1995-1999 (106 m3 )

Vc.

Net virtual water import per country for the years 1995-1999 (106 m3 )

VI.

Classification of countries into thirteen world regions

VII.

Gross virtual water trade between and within regions (Gm3 )

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Summary The water that is used in the production process of an agricultural or industrial product is called the 'virtual water' contained in the product. A water-scarce country might wish to import products that require a lot of water in their production (water-intensive products) and export products or services that require less water (waterextensive products). This implies net import of ‘virtual water’ (as opposed to import of real water, which is generally too expensive) and will relieve the pressure on the nation’s own water resources. Until date little is known on the actual volumes of virtual water trade flows between countries.

The objective of this study is to quantify the volumes of all virtual water trade flows between nations in the period 1995-1999 and to put the virtual water trade balances of nations within the context of national water needs and water availability. The study has been limited to the quantification of virtual water trade flows related to international crop trade.

The basic approach has been to multiply international crop trade flows (ton/yr) by their associated virtual water content (m3 /ton). The required crop trade data have been taken from the United Nations Statistics Division in New York. The required data on virtual water content of crops originating from different countries have been estimated on the basis of various FAO databases (CropWat, ClimWat, FAOSTAT).

The calculations show that the global volume of crop-related virtual water trade between nations was 695 Gm3 /yr in average over the period 1995-1999. For comparison: the total water use by crops in the world has been estimated at 5400 Gm3 /yr (Rockström and Gordon, 2001). This means that 13% of the water used for crop production in the world is not used for domestic consumption but for export (in virtual form). This is the global percentage; the situation strongly varies between countries.

Considering the period 1995-1999, the countries with largest net virtual water export are: United States, Canada, Thailand, Argentina, and India. The countries with largest net virtual water import in the same period are: Sri Lanka, Japan, the Netherlands, the Republic of Korea, and China.

For each nation of the world a ‘water footprint’ has been calculated (a term chosen on the analogy of the ‘ecological footprint’). The water footprint, equal to the sum of the domestic water use and net virtual water import, is proposed here as a measure of a nation’s actual appropriation of the global water resources. It gives a more complete picture than if one looks at domestic water use only, as is being done until date. In addition to the water footprint, indicators are proposed for a nation’s ‘water self-sufficiency’ and a nation’s ‘water dependency’.

In studying global virtual water trade flows, it is recommended to start working on other products than crops as well, for instance livestock products such as meat. Another next step is to start interpreting the data and to study how governments can deliberately interfere in the current national virtual water trade balances in order to achieve higher global water use efficiency.

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8

1. Introduction 1.1. The economics of water use

Water should be considered an economic good. Ten years after the Dublin conference this sounds like a mantra for water policy makers. The sentence is repeated again and again, conference after conference. It is suggested that problems of water scarcity, water excess and deterioration of water quality would be solved if the resource ‘water’ were properly treated as an economic good. The logic is clear: clean fresh water is a scarce good and thus should be treated economically. There is an urgent need to develop appropriate concepts and tools to do so.

In dealing with the available water resources in an economically efficient way, there are three different levels at which decisions can be made and improvements be achieved. The first level is the user level, where price and technology play a key role. This is the level where the ‘local water use efficiency’ can be increased by creating awareness, charging prices based on full marginal cost and by stimulating water-saving technology. Second, at a higher level, a choice has to be made on how to allocate the available water resources to the different sectors of economy (including public health and the environment). Water is used for the production of several ‘goods’ and ‘services’. People allocate water to serve certain purposes, which generally implies that other, alternative purposes are not served. Choices on the allocation of water can be more or less ‘efficient’, depending on the value of water in its alternative uses. At this level we speak of ‘water allocation efficiency’. Water is a public good, so water allocation at the country or catchment level is principally a governmental issue. The question is here how all demands for water can best be met and where – in case of water shortage – supply should be restricted.

Beyond ‘local water use efficiency’ and ‘water allocation efficiency’ there is a level at which one could talk about ‘global water use efficiency’. It is a fact that some regions of the world are water-scarce and other regions are water-abundant. It is also a fact that in some regions there is a low demand for water and in other regions a high demand. Unfortunately there is no general positive relation between water demand and availability. Until recently people have focussed very much on considering how to meet demand based on the available water resources at national or river basin scale. The issue is then how to most efficiently allocate and use the available water. There is no reason to restrict the analysis to that. In a protected economy, a nation will have to achieve its development goals with its own resources. In an open economy, however, a nation can import products that are produced from resources that are scarcely available within the country and export products that are produced with resources that are abundantly available within the country. A water-scarce country can thus aim at importing products that require a lot of water in their production (water-intensive products) and exporting products or services that require less water (water-extensive products). This is called import of virtual water (as opposed to import of real water, which is generally too expensive) and will relieve the pressure on the nation’s own water resources. For water-abundant countries an argumentation can be made for export of virtual water. Import of water-intensive products by some nations and export of these products by others includes what is called ‘virtual water trade’ between nations.

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Global water use efficiency

virtual water trade between water-scarce and water-abundant regions

Water allocation efficiency

value of water in its alternative uses

Local water use efficiency

technology, water price, environmental awareness of water user

In summary, the overall efficiency in the appropriation of the global water resources can be defined as the ‘sum’ of local water use efficiencies, meso-scale water allocation efficiencies and global water use efficiency. So far most attention of scientists and politicians has gone to local water use efficiency. There is quite some knowledge available and improvements have actually been achieved already. More efficient allocation of water as a means to improved water management has got quite same attention as well, but if it comes to the implementation of improved allocation schemes there is still a long way to go. At the global level, it is even more severe, since basic data on virtual water trade and water dependency of nations are generally even lacking. This has been the incentive for this study.

1.2. Virtual water trade

For the production of nearly all goods water is required. The water that is used in the production process of an agricultural or industrial product is called the 'virtual water' contained in the product. For example, for producing a kilogram of grain, grown under rain-fed and favourable climatic conditions, we need about one to two cubic metres of water, that is 1000 to 2000 kg of water. For the same amount of grain, but growing in an arid country, where the climatic conditions are not favourable (high temperature, high evapotranspiration) we need up to 3000 to 5000 kg of water.

If one country exports a water-intensive product to another country, it exports water in virtual form. In this way some countries support other countries in their water needs. For water-scarce countries it could be attractive to achieve water security by importing water-intensive products instead of producing all water-demanding products domestically. Reversibly, water-rich countries could profit from their abundance of water resources by producing water-intensive products for export. Trade of real water between water-rich and water-poor regions is generally impossible due to the large distances and associated costs, but trade in water-intensive products (virtual water trade) is realistic. Virtual water trade between nations and even continents could thus be used as an instrument to improve global water use efficiency and to achieve water security in water-poor regions of the world.

World-wide both politicians and the general public increasingly show interest in the pros and cons of ‘globalisation’ of trade. This can be understood from the fact that increasing global trade implies increased

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interdependence of nations. The tension in the debate relates to the fact that the game of global competition is played with rules that many see as unfair. Knowing that economically sound water pricing is poorly developed in many regions of the world, this means that many products are put on the world market at a price that does not properly include the cost of the water contained in the product. This leads to situations in which some regions in fact subsidise export of scarce water.

1.3. The objective of this study

The objectives of this study are:

1.

To estimate the amount of water needed to produce crops in different countries of the world;

2.

To quantify the volume of virtual water trade flows between nations in the period 1995-1999;

3.

To put the virtual water trade balances of nations within the context of national water needs and water availability.

This report is primarily meant as a data report. We do not pretend to give an in-depth interpretation of the results. Besides, we limit ourselves to virtual water trade in relation to international crop trade, thus excluding virtual water trade related to international trade of livestock products and industrial products.

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2. Method 2.1. Calculation of specific water demand per crop type

Per crop type, average specific water demand has been calculated separately for each relevant nation on the basis of FAO data on crop water requirements and crop yields:

SWD[n, c] =

CWR[n, c] CY [n, c]

(1)

Here, SWD denotes the specific water demand (m3 ton -1) of crop c in country n, CWR the crop water requirement (m3 ha-1) and CY the crop yield (ton ha-1). The crop water requirement CWR (in m3 ha-1) is calculated from the accumulated crop evapotranspiration ETc (in mm/day) over the complete growing period. The crop evapotranspiration ETc follows from multiplying the ‘reference crop evapotranspiration’ ET0 with the crop coefficient Kc:

ETc = K c × ET0

(2)

The concept of ‘reference crop evapotranspiration’ was introduced by FAO to study the evaporative demand of the atmosphere independently of crop type, crop development and management practices. The only factors affecting ET0 are climatic parameters. The reference crop evapotranspiration ET0 is defined as the rate of evapotranspiration from a hypothetical reference crop with an assumed crop height of 12 cm, a fixed crop surface resistance of 70 s m-1 and an albedo of 0.23. This reference crop evapotranspiration closely resembles the evapotranspiration from an extensive surface of green grass cover of uniform height, actively growing, completely shading the ground and with adequate water (Smith et al., 1992). Reference crop evapotranspiration is calculated on the basis of the FAO Penman-Monteith equation (Smith et al., 1992; Allen et al., 1994a, 1994b; Allen et al., 1998):

ET0 =

900 U (e − e ) T + 273 2 a d ∆ + γ (1 + 0 .34U 2 )

0.408 ∆( Rn − G ) + γ

(3)

in which:

ET0

= reference crop evapotranspiration [mm day-1];

Rn

= net radiation at the crop surface [MJ m-2 day -1];

G

= soil heat flux [MJ m-2 day -1];

T

= average air temperature [°C];

U2

= wind speed measured at 2 m height [m s -1];

ea

= saturation vapour pressure [kPa]; 13

ed

= actual vapour pressure [kPa];

ea -e d

= vapour pressure deficit [kPa];

∆

= slope of the vapour pressure curve [kPa °C-1];

γ

= psychrometric constant [kPa °C-1].

The crop coefficient accounts for the actual crop canopy and aerodynamic resistance relative to the hypothetical reference crop. The crop coefficient serves as an aggregation of the physical and physiological differences between a certain crop and the reference crop.

The overall scheme for the calculation of specific water demand is drawn in Figure 1.1. This figure also shows the next step: the calculation of the virtual water trade flows between nations.

Climatic parameters

Ref. crop evapotransp. E0 [mm day-1]

Crop coefficient Kc [-]

Crop evapotranspiration Ec [mm day-1]

Crop water requirement CWR [m 3 ha-1]

Crop yield

Specific water demand

CY [ton ha-1]

SWD [m 3 ton-1]

Global crop trade

Global virtual water trade

-1

CT [ton yr ]

VWT [m 3 yr-1 ]

Figure 1.1. Steps in the calculation of global virtual water trade.

2.2. Calculation of virtual water trade flows and the national virtual water trade balance

Virtual water trade flows between nations have been calculated by multiplying international crop trade flows by their associated virtual water content. The latter depends on the specific water demand of the crop in the exporting country where the crop is produced. Virtual water trade is thus calculated as: VWT [n e , ni , c, t ] = CT [ne , n i , c, t ]× SWD[ne , c]

(4)

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in which VWT denotes the virtual water trade (m3 yr-1) from exporting country n e to importing country n i in year t as a result of trade in crop c. C T represents the crop trade (ton yr-1) from exporting country n e to importing country ni in year t for crop c. SWD represents the specific water demand (m3 ton -1) of crop c in the exporting country. Above equation assumes that if a certain crop is exported from a certain country, this crop is actually grown in this country (and not in another country from which the crop was just imported for further export). Although a certain error will be made in this way, it is estimated that this error will not substantially influence the overall virtual water trade balance of a country. Besides, it is practically impossible to track the sources of all exported products.

The gross virtual water import to a country n i is the sum of all imports: GVWI [ ni , t ] = ∑ VWT [n e , n i , c, t ]

(5)

ne ,c

The gross virtual water export from a country n e is the sum of all exports: GVWE [n e , t ] = ∑ VWT [n e , n i , c, t ]

(6)

n i ,c

The net virtual water import of a country is equal to the gross virtual water import minus the gross virtual water export. The virtual water trade balance of country x for year t can thus be written as: NVWI [x, t ] = GVWI [x, t ] − GVWE [x, t ]

(7)

where NVWI stands for the net virtual water import (m3 yr-1 ) to the country. Net virtual water import to a country has either a positive or a negative sign. The latter indicates that there is net virtual water export from the country.

2.3. Calculation of a nation’s ‘water footprint’

The total water use within a country itself is not the right measure of a nation’s actual appropriation of the global water resources. In the case of net import of virtual water import into a country, this virtual water volume should be added to the total domestic water use in order to get a picture of a nation’s real call on the global water resources. Similarly, in the case of net export of virtual water from a country, this virtual water volume should be subtracted from the volume of domestic water use. The sum of domestic water use and net virtual water import can be seen as a kind of ‘water footprint’ of a country, on the analogy of the ‘ecological footprint’ of a nation. In simplified terms, the latter refers to the amount of land needed for the production of the goods and services consumed by the inhabitants of a country. Studies have shown that for some countries the ecological footprint is smaller than the area of the nation’s territory, but in other cases much bigger (Wackernagel and Rees, 1996; Wackernagel et al., 1997). The latter means that apparently some nations need land outside their own territory to provide in their goods and services. 15

The ‘water footprint’ of a country (expressed as a volume of water per year) is defined as:

(8)

Water footprint = WU + NVWI

in which WU denotes the total domestic water use (m3 yr-1) and NVWI the net virtual water import of a country (m3 yr-1). As noted earlier, the latter can have a negative sign as well.

Total domestic water use WU should ideally refer to the sum of ‘blue’ water use (referring to the use of groundand surface water) and ‘green’ water use (referring to the use of precipitation). However, since data on green water use on country basis are not easily obtainable, we have provisionally chosen in this report to limit the definition of water use to blue water use. It should be noted that ‘net virtual water import’ as defined in the previous section includes both ‘blue’ and ‘green’ water.

2.4. Calculation of national water scarcity, water dependency and water self-sufficiency

At the start of this study we expected to find a relation between national water scarcity and net virtual water import. One would logically assume that a country with high water scarcity would seek to profit from net virtual water import. On the other hand, countries with abundant water resources could make profit by exporting water in virtual form. In order to check this hypothesis we need indices of both water scarcity and virtual water import dependency. Plotting countries in a graph with water scarcity on the x-axis and virtual water import dependency on the y-axis, would expectedly result in some positive relation.

As an index of national water scarcity we use the ratio of total water use to water availability:

WS =

WU ×100 WA

(9)

In this equation, WS denotes national water scarcity (%), WU the total water use in the country (m3 yr-1) and WA the national water availability (m3 yr-1). Defined in this way, water scarcity will generally range between zero and hundred per cent, but can in exceptional cases (e.g. groundwater mining) be above hundred per cent. As a measure of the national water availability WA we take the annual internal renewable water resources, that are the average fresh water resources renewably available over a year from precipitation falling within a country’s borders (see for instance Gleick, 1993). As noted in the previous section, total water use WU should ideally refer to the sum of blue and green water use, but for practical reasons we have provisionally chosen in this report to define water scarcity as the ratio of blue water use to water availability, which is generally done by others as well.

Next, we have looked for a proper indicator of ‘virtual water import dependency’ or ‘water dependency’ in brief. The indicator should reflect the level to which a nation relies on foreign water resources (through import

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of water in virtual form). The water dependency WD of a nation is in this report calculated as the ratio of the net virtual water import into a country to the total national water appropriation:

 NVWI WU + NVWI × 100  WD =   0  

if NVWI ≥ 0

(10)

if NVWI < 0

The value of the water dependency index will per definition vary between zero and hundred per cent. A value of zero means that gross virtual water import and export are in balance or that there is net virtual water export. If on the other extreme the water dependency of a nation approaches hundred percent, the nation nearly completely relies on virtual water import.

As the counterpart of the water dependency index, the water self-sufficiency index is defined as follows: WU  WU + NVWI × 100  WSS =   100  

if NVWI ≥ 0

(11)

if NVWI < 0

The water self-sufficiency of a nation relates to the water dependency of a nation in the following simple way:

WSS = 1 − WD

(12)

The level of water self-sufficiency WSS denotes the national capability of supplying the water needed for the production of the domestic demand for goods and services. Self-sufficiency is hundred per cent if all the water needed is available and indeed taken from within the own territory. Water self-sufficiently approaches zero if a country heavily relies on virtual water imports.

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3. Data sources Data on crop water requirements are calculated with FAO’s CropWat model for Windows, which is available through the web site of FAO (www.fao.org). The CropWat model uses the FAO Penman-Monteith equation for calculating reference crop evapotranspiration as described in the previous chapter (Clarke et al., 1998). The CropWat model calculates crop water requirement of different crop types on the basis of the following assumptions:

(1) Crops are planted under optimum soil water conditions without any effective rainfall during their life; the crop is developed under irrigation conditions. (2) Crop evapotranspiration under standard conditions (ETc), this is the evapotranspiration from disease-free, well-fertilised crops, grown in large fields with 100% coverage. (3) Crop coefficients are selected depending on the single crop coefficient approach, that means single cropping pattern, not dual or triple cropping pattern.

Climatic data The climatic data needed as input to CropWat have been taken from FAO’s climatic database ClimWat, which is also available through FAO’s web site. The ClimWat database contains climatic data for more than hundred countries. For many countries climatic data are available for different climatic stations. As a crude approach, the capital climatic station data have been taken as the country representative. For the countries, where the required climatic input data are not available in ClimWat, the crop water requirement is taken from the guideline of FAO as reported by Gleick (1993) (Appendix IV). Depending on the country, the authors made an estimate somewhere between the minimum and maximum estimate given in the FAO guideline. If still data were lacking, data were taken from a neighbouring country.

Crop parameters In the crop directory of the CropWat package sets of crop parameters are available for 24 different crops (Table 3.1). The crop parameters used as input data to CropWat are: the crop coefficients in different crop development stages (initial, middle and late stage), the length of each crop in each development stage, the root depth, and the planting date. For the 14 crops where crop parameters are not available in the CropWat package, crop parameters have been based on Allen et al. (1998).

Crop yields Data on crop yields have been taken from the FAOSTAT database, again available through FAO’s web site.

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Table 3.1. Availability of crop parameters. Crops for which crop parameters have been taken from FAO’s Crops for which crop parameters have been CropWat package taken from Allen et al. (1998) Banana

Maize

Sugar beet

Artichoke

Onion dry

Barley

Mango

Sugar cane

Carrots

Peas

Bean dry

Millet

Sunflower

Cauliflower

Rice

Bean green

Oil palm fruit

Tobacco

Citrus

Safflower

Cabbage

Pepper

Tomato

Cucumber

Spinach

Cotton seeds

Potato

Vegetable

Lettuce

Sweet potato

Grape

Sorghum

Watermelon

Oats

Groundnut

Soybean

Wheat

Onion green

Global trade in crops As a source for the global trade in crops, we have used the 1995-1999 data contained in the Personal Computer Trade Analysis System (PC-TAS), a cd-rom produced by the United Nations Statistics Division (UNSD) in New York in collaboration with the International Trade Centre (ITC) in Geneva. These data are based on the Commodity Trade Statistics Data Base (COMTRADE) of the UNSD. Every year individual countries supply the UNSD with their annual international trade statistics, detailed by commodity and partner country. These data are processed into a standard format with consistent coding and valuation. Commodities are classified according the Harmonised System (HS) classification of the World Customs Organization.

Link between two crop classifications Specific water demand is calculated for 38 crop types as distinguished by the FAO in CropWat. The Harmonised System (HS) classification used in the COMTRADE database is a much more detailed classification. For our purpose we therefore have to link the two classifications, which has been done as shown in Table 3.2.

Table 3.2. The link between FAO’s crop types and the Harmonised System classification. FAO crop types

Commodities in the Harmonised System classification

Artichoke

Global artichoke, fresh or chilled

Banana

Banana, including plantains

Barley

Barley

Bean dry

Bean dried Bean, small red, dried

Bean green

Bean, frozen

Cabbage

Cabbage lettuce, fresh or chilled

Bean, shelled or unshelled, fresh or chilled

Cabbages, konrabi Carrots

Carrot, fresh or chilled

Cauliflower

Cauliflower and headed broccoli, fresh or chilled

Citrus

Citrus fruit, fresh or dried Grapefruit, fresh or chilled

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FAO crop types

Commodities in the Harmonised System classification

Cotton seeds

Cotton seed, whether or not broken

Cucumber

Cucumber and gherkins provisionally preserved but not immediately consumption Cucumber and gherkins, fresh or chilled

Sorghum

Grain sorghum

Grape

Grape dried Grape fresh

Groundnut

Groundnut in shell whether or not broken Groundnuts in shell or roasted

Lettuce

Lettuce, fresh or chilled

Maize

Maize (corn)

Millet

Millet

Oats

Oats

Onion dry

Onion dried, but not further prepared

Onion green

Onion and shallots, fresh or chilled Onion, provisionally preserved

Oil palm fruit

Palm nut

Peas

Peas, dried, shelled Peas, frozen Peas, shelled or unshelled, fresh or chilled

Pepper

Pepper of the genius capsuis

Potato

Potato, fresh or chilled Potatoes, frozen

Sugar beet

Raw sugar beet

Sugar cane

Raw sugar can

Rice

Rice, broken Rice, husked, (brown) Rice, in the husk (paddy or rough)

Safflower

Safflower seed, whether or not broken

Soybean

Soybean

Spinach

Spinach, N-Z spinach orache spinach

Sunflower

Sunflower seed

Sweet potato

Sweet potatoes, fresh or dried

Tobacco

Tobacco, unmanufactured, not stemmed Tobacco, unmanufactured, partly or wholly stemmed

Tomato

Tomatoes, fresh or chilled

Vegetable

Vegetable, fresh or chilled vegetable, frozen

Wheat

Wheat Durum wheat Buck wheat

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4. Specific water demand per crop type per country The calculated crop water requirements for different crops in different countries are shown in Appendix I. The crop water requirements as calculated here refer to the evapotranspiration under optimal growth conditions (see Chapter 3). This means that the calculated values are overestimates, because in reality there are often water shortage conditions. On the other hand, the calculated values can also be seen as conservative, because they exclude inevitable losses (e.g. during transport and application of water) and required losses such as drainage. The calculated crop water requirements differ considerably over countries, which is mainly due to the differences in climatic conditions.

Data on actual crop yields in the year 1999 have been retrieved from the FAOSTAT database. The data, which are country averages, are shown in Appendix II. Where country specific crop yield data are lacking in FAOSTAT, regional averages have been taken. The values that have been assessed in this way are presented in grey-shadow cells in Appendix II. The differences between countries are here even larger than in the case of the crop water requirements. This is due to the impact of the human factor on the actual crop yields. Specific water demand (m3 /ton) per crop type has been calculated for different countries by dividing the crop water requirement (m3 /ha) by the crop yield (ton/ha). The results are shown in Appendix III. Because both crop water requirements and crop yields strongly vary between countries, specific water demands vary as well.

It is noted here that the specific water demand data for 1999 will be used to calculate the virtual water trade flows in the whole period 1995-1999 (see Chapter 5). This is acceptable because country crop yield data appear not to vary considerably over years.

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5. Global trade in virtual water 5.1. International trade in virtual water

5.1.1. Overview of international virtual water trade

The calculation results show that the global volume of crop-related virtual water trade between nations was 695 Gm3 /yr in average over the period 1995-1999. For comparison: the global water withdrawal for agriculture (water use for irrigation) was about 2500 Gm3 /yr in 1995 and 2600 Gm3 /yr in 2000 (Shiklomanov, 1997, p.61). Taking into account the use of rainwater by crops as well, the total water use by crops in the world has been estimated at 5400 Gm3 /yr (Rockström and Gordon, 2001, p.847). This means that 13% of the water used for crop production in the world is not used for domestic consumption but for export (in virtual form). This is the global percentage; the situation strongly varies between countries.

Considering the period 1995-1999, the top-5 list of countries with net virtual water export is: 1st. United States, 2nd. Canada, 3rd. Thailand, 4th. Argentina, and 5th. India. The top-5 list of countries in terms of net virtual water import for the same period is: 1st. Sri Lanka, 2nd. Japan, 3rd. Netherlands, 4th. Republic of Korea, and 5th. China. Top-30 lists are given in Table 5.1. The ranking lists do not considerably change if we look into particular years within the five-year period 1995-1999.

Net virtual water import, Gm3 -100- -800 -10- -100 -1- -10 0- -1 0- 1 1- 10 10- 50 50- 100 100- 500 No Data

Figure 5.1. National virtual water trade balances over the period 1995-1999. Green coloured countries have net virtual water export. Red coloured countries have net virtual water import.

25

National virtual water trade balances over the period 1995-1999 are shown in the coloured world map of Figure 5.1. Countries with net virtual water export are green and countries with net virtual water import are red. Appendix V presents the complete set of calculated data with respect to gross import, gross export and net import of virtual water for all countries of the world for the years 1995 up to 1999.

Some countries have net export of virtual water over the period 1995-1999, but net import of virtual water in one or more particular years in this period (Table 5.2). There are also countries that show the reverse (Table 5.3).

Table 5.1. Top-30 of virtual water export countries and top-30 of virtual water import countries (over 1995-1999). Country

Net export volume (109 m3)

Country

Net import volume (109 m3)

United States

758.3

1

Sri Lanka

428.5

Canada

272.5

2

Japan

297.4

Thailand

233.3

3

Netherlands

147.7

Argentina

226.3

4

Korea Rep.

112.6

India

161.1

5

China

101.9

Australia

145.6

6

Indonesia

101.7

Vietnam

90.2

7

Spain

82.5

France

88.4

8

Egypt

80.2

Guatemala

71.7

9

Germany

67.9

Brazil

45.0

10

Italy

64.3

Paraguay

42.1

11

Belgium

59.6

Kazakhstan

39.2

12

Saudi Arabia

54.4

Ukraine

31.8

13

Malaysia

51.3

Syria

21.5

14

Algeria

49.0

Hungary

19.8

15

Mexico

44.9

Myanmar

17.4

16

Taiwan

35.2

Uruguay

12.1

17

Colombia

33.4

Greece

9.8

18

Portugal

31.1

Dominican Republic

9.7

19

Iran

29.1

Romania

9.1

20

Bangladesh

28.7

Sudan

5.8

21

Morocco

27.7

Bolivia

5.3

22

Peru

27.1

Saint Lucia

5.2

23

Venezuela

24.6

United Kingdom

4.8

24

Nigeria

24.0

Burkina Faso

4.5

25

Israel

23.0

Sweden

4.2

26

Jordan

22.4

Malawi

3.8

27

South Africa

21.8

Dominica

3.1

28

Tunisia

19.3

Benin

3.0

29

Poland

18.8

Slovakia

3.0

30

Singapore

16.9

26

Table 5.2. Countries with net export of virtual water in the period 1995-1999 that have however net import in particular years. A ‘minus’ indicates a negative virtual water trade balance (i.e. net export of virtual water). A ‘plus’ indicates a positive virtual water trade balance (i.e. net import of virtual water). Country

1995

1996

1997

1998

1999

Brazil

-

+

-

-

-

Syria

-

-

-

-

+

Greece

-

-

-

+

-

Sudan

-

+

+

+

-

United Kingdom

+

+

-

+

+

Burkina Faso

-

+

+

-

-

Benin

+

-

-

-

-

Slovakia

-

-

+

-

-

Ecuador

-

-

-

+

-

Bulgaria

-

+

+

-

-

Cuba

+

-

-

+

-

Finland

-

-

-

-

+

Yugoslavia

-

-

+

-

-

Uganda

-

-

+

+

+

Papua N. Guinea

-

-

+

-

+

Bahamas

+

-

-

-

-

Montserrat

-

-

-

-

+

Tajikistan

+

-

-

-

-

Cameroon

-

-

+

+

-

Martinique

-

+

+

+

+

Pakistan

-

-

+

-

+

Solomon Islands

-

+

-

+

+

Central Africa

-

+

-

+

-

Samoa

-

-

-

+

-

Wallis Island

-

+

+

+

+

Table 5.3. Countries with net import of virtual water in the period 1995-1999 that have however net export in particular years. A ‘minus’ indicates a negative virtual water trade balance (i.e. net export of virtual water). A ‘plus’ indicates a positive virtual water trade balance (i.e. net import of virtual water). Country

1995

1996

1997

1998

1999

St. Kitts & Nevis

-

+

-

+

+

Guinea Bissau

+

+

-

-

+

Burundi

+

+

-

+

+

Tonga

+

+

-

+

+

Mongolia

-

+

+

+

+

Nepal

+

+

+

-

+

Kyrgyzstan

+

+

-

-

-

27

Country

1995

1996

1997

1998

1999

Macedonia

-

+

+

+

+

Lithuania

+

+

+

-

-

Bermuda

+

+

+

-

-

Bahrain

+

+

+

+

+

Gambia

+

+

+

-

+

Bosnia

+

-

+

+

+

Madagascar

+

-

-

+

+

George

+

+

+

+

-

Croatia

-

-

+

+

+

Nicaragua

+

+

-

+

+

Uzbekistan

+

+

+

+

-

Czech Republic

-

+

+

+

-

Philippines

-

-

+

+

+

Russian Fed.

-

-

+

-

+

Mexico

+

+

-

+

+

5.1.2. Virtual water trade balance per country

In this section we present the virtual water trade balances of a few selected countries. For each country, we give the annual balances for the individual years 1995-1999 and the overall five-year balance. Figures 5.2-5.11 show the virtual water trade balances for the ten biggest net export countries: United States, Canada, Thailand, Argentina, India, Australia, Vietnam, France, Guatemala and Brazil. Figures 5.12-5.21 show the balances for the ten biggest net import countries: Sri Lanka, Japan, Netherlands, Korea Rep., China, Indonesia, Spain, Egypt, Germany and Italy. The Figures 5.22-5.29 show the balances for a few other countries which have been chosen a bit arbitrarily. For the balances of those countries that are not shown here, the reader is referred to the data in Appendix V.

It is not the intention of this report to make an in-depth analysis and interpretation of the calculated national virtual water trade balances. Instead, we limit ourselves here to make just a few observations. First, the data show that developed countries generally have a more stable virtual water trade balance than the developing countries. Peak years in virtual water export were for instance found for Thailand, India, Vietnam, Guatemala and Syria. The opposite, the occurrence of peak years with relatively high virtual water import, was found for Sri Lanka and Jordan.

Second, we see that countries that are relatively close to each other in terms of geography and development level can have a rather different virtual water trade balance. While European countries such as the Netherlands, Belgium, Germany, Spain and Italy import virtual water in the form of crops, France exports a large amount of virtual water. In the Middle East we see that Syria has net export of virtual water related to crop trade, but Jordan and Israel have net import. In Southern Africa, Zimbabwe and Zambia had net export in the period 199528

1999, but South Africa had net import. [It should be noted that the trade balance of Zimbabwe has recently turned due to the recent political and economic developments.] In the regions of the Former Soviet Union, countries such as Kazakhstan and the Ukraine have net export of virtual water, but the Russian Federation has net import.

It is hard to put the data presented here in the context of earlier studies, for the simple reason that few quantitative studies into virtual water trade between nations have been carried out. A few interesting studies have been done for the Middle East and Africa (Allan, 1997, 2001; Wichelns, 2001; Nyagwambo, 1998; Earle, 2001). One study was done by Buchvald for Israel and is available in Hebrew only. The main results of this study are cited in Yegnes-Botzer (2001). According to Buchvald’s estimation Israel exported 377 million m3 of virtual water in 1999 and imported more than 6900 million m3 . The current study calculates for Israel an export of 700 million m3 of virtual water in 1999 and an import of 7400 million m3 .

3.5E+11

1.0E+12 9.0E+11 8.0E+11

Export

Export 3.0E+11

Import

Import

2.5E+11

7.0E+11 6.0E+11

2.0E+11

5.0E+11 1.5E+11

4.0E+11 3.0E+11

1.0E+11

2.0E+11 5.0E+10

1.0E+11 0.0E+00

0.0E+00 1995

1996

1997

1998

1999

1995

Total

Figure 5.2. Gross virtual water import into and export 3 -1 from the United States in the period 1995-1999 (m yr ).

1997

1998

1999

Total

Figure 5.3. Gross virtual water import into and export from Canada in the period 1995-1999 (m 3yr-1). 2.5E+11

3.0E+11 Export 2.5E+11

1996

Export

Import

2.0E+11

Import

2.0E+11 1.5E+11 1.5E+11 1.0E+11 1.0E+11 5.0E+10

5.0E+10 0.0E+00

0.0E+00 1995

1996

1997

1998

1999

1995

Total

1996

1997

1998

1999

Total

Figure 5.5. Gross virtual water import into and export from Argentina in the period 1995-1999 (m 3yr-1).

Figure 5.4. Gross virtual water import into and export 3 -1 from Thailand in the period 1995-1999 (m yr ).

29

2.0E+11 1.8E+11 1.6E+11

1.6E+11 Export

Export 1.4E+11

Import

Import

1.2E+11 1.4E+11 1.2E+11

1.0E+11

1.0E+11

8.0E+10

8.0E+10

6.0E+10

6.0E+10 4.0E+10 4.0E+10 2.0E+10

2.0E+10 0.0E+00

0.0E+00 1995

1996

1997

1998

1999

Total

1995

1.0E+11

8.0E+10

1997

1998

1999

Total

Figure 5.7. Gross virtual water import into and export from Australia in the period 1995-1999 (m 3yr-1).

Figure 5.6. Gross virtual water import into and export 3 -1 from India in the period 1995-1999 (m yr ).

9.0E+10

1996

1.6E+11 Export

Export 1.4E+11

Import

Import

1.2E+11 7.0E+10 6.0E+10

1.0E+11

5.0E+10

8.0E+10

4.0E+10

6.0E+10

3.0E+10 4.0E+10 2.0E+10 2.0E+10

1.0E+10 0.0E+00

0.0E+00 1995

1996

1997

1998

1999

Total

1995

1997

1998

1999

Total

Figure 5.9. Gross virtual water import into and export from France in the period 1995-1999 (m 3yr-1).

Figure 5.8. Gross virtual water import into and export 3 -1 from Vietnam in the period 1995-1999 (m yr ). 9.0E+10 8.0E+10

1996

1.8E+11 Export

1.6E+11

Import

Export Import

7.0E+10

1.4E+11

6.0E+10

1.2E+11

5.0E+10

1.0E+11

4.0E+10

8.0E+10

3.0E+10

6.0E+10

2.0E+10

4.0E+10

1.0E+10

2.0E+10

0.0E+00

0.0E+00 1995

1996

1997

1998

1999

Total

1995

Figure 5.10. Gross virtual water import into and export 3 -1 from Guatemala in the period 1995-1999 (m yr ).

30

1996

1997

1998

1999

Total

Figure 5.11. Gross virtual water import into and export from Brazil in the period 1995-1999 (m 3yr-1).

3.5E+11

5.0E+11 4.5E+11 4.0E+11

Export

Export 3.0E+11

Import

Import

2.5E+11

3.5E+11 3.0E+11

2.0E+11

2.5E+11 1.5E+11

2.0E+11 1.5E+11

1.0E+11

1.0E+11 5.0E+10

5.0E+10 0.0E+00

0.0E+00 1995

1996

1997

1998

1999

1995

Total

Figure 5.12. Gross virtual water import into and export 3 -1 from Sri Lanka in the period 1995-1999 (m yr ). 2.0E+11 1.8E+11 1.6E+11

1996

1997

1998

1999

Total

Figure 5.13. Gross virtual water import into and export from Japan in the period 1995-1999 (m 3yr-1). 1.2E+11

Export

Export

Import

1.0E+11

1.4E+11

Import

8.0E+10

1.2E+11 1.0E+11

6.0E+10

8.0E+10 4.0E+10

6.0E+10 4.0E+10

2.0E+10

2.0E+10 0.0E+00

0.0E+00 1995

1996

1997

1998

1999

Total

1995

Figure 5.14. Gross virtual water import into and export 3 -1 from the Netherlands in the period 1995-1999 (m yr ). 1.8E+11 1.6E+11

1996

1997

1998

1999

Total

Figure 5.15. Gross virtual water import into and export from the Korea Republic in the period 1995-1999 (m 3yr-1). 1.2E+11

Export

Export

Import

1.0E+11

Import

1.4E+11 1.2E+11

8.0E+10

1.0E+11 6.0E+10 8.0E+10 6.0E+10

4.0E+10

4.0E+10 2.0E+10 2.0E+10 0.0E+00

0.0E+00 1995

1996

1997

1998

1999

Total

1995

Figure 5.16. Gross virtual water import into and export 3 -1 from China in the period 1995-1999 (m yr ).

31

1996

1997

1998

1999

Total

Figure 5.17. Gross virtual water import into and export from Indonesia in the period 1995-1999 (m 3yr-1).

1.2E+11

9.0E+10 Export

1.0E+11

8.0E+10

Import

Export Import

7.0E+10 8.0E+10

6.0E+10 5.0E+10

6.0E+10 4.0E+10 4.0E+10

3.0E+10 2.0E+10

2.0E+10 1.0E+10 0.0E+00

0.0E+00 1995

1996

1997

1998

1999

Total

1995

Figure 5.18. Gross virtual water import into and export 3 -1 from Spain in the period 1995-1999 (m yr ). 1.4E+11

1997

1998

1999

Total

Figure 5.19. Gross virtual water import into and export from Egypt in the period 1995-1999 (m 3yr-1). 1.2E+11

Export 1.2E+11

1996

Export

Import

1.0E+11

Import

1.0E+11 8.0E+10 8.0E+10 6.0E+10 6.0E+10 4.0E+10 4.0E+10 2.0E+10

2.0E+10 0.0E+00

0.0E+00 1995

1996

1997

1998

1999

Total

1995

Figure 5.20. Gross virtual water import into and export 3 -1 from Germany in the period 1995-1999 (m yr ).

1997

1998

1999

Total

Figure 5.21. Gross virtual water import into and export from Italy in the period 1995-1999 (m 3yr-1). 2.5E+10

3.0E+10 Export 2.5E+10

1996

Export

Import

2.0E+10

Import

2.0E+10 1.5E+10 1.5E+10 1.0E+10 1.0E+10 5.0E+09

5.0E+09 0.0E+00

0.0E+00 1995

1996

1997

1998

1999

1995

Total

Figure 5.22. Gross virtual water import into and export 3 -1 from Syria in the period 1995-1999 (m yr ).

32

1996

1997

1998

1999

Total

Figure 5.23. Gross virtual water import into and export from Jordan in the period 1995-1999 (m 3yr-1).

3.0E+10

6.0E+10 Export

2.5E+10

Export

Import

5.0E+10

2.0E+10

4.0E+10

1.5E+10

3.0E+10

1.0E+10

2.0E+10

5.0E+09

1.0E+10

0.0E+00

0.0E+00 1995

1996

1997

1998

1999

Total

1995

Figure 5.24. Gross virtual water import into and export 3 -1 from Israel in the period 1995-1999 (m yr ).

1996

1997

1998

1999

Total

Figure 5.25. Gross virtual water import into and export from Saudi Arabia in the period 1995-1999 (m 3yr-1). 3.5E+09

4.0E+10 Export 3.5E+10

Import

Export 3.0E+09

Import

3.0E+10

Import

2.5E+09

2.5E+10 2.0E+09 2.0E+10 1.5E+09 1.5E+10 1.0E+09

1.0E+10

5.0E+08

5.0E+09 0.0E+00

0.0E+00 1995

1996

1997

1998

1999

1995

Total

Figure 5.26. Gross virtual water import into and export 3 -1 from South Africa in the period 1995-1999 (m yr ).

1997

1998

1999

Total

Figure 5.27. Gross virtual water import into and export from Zimbabwe in the period 1995-1999 (m 3yr-1). 4.5E+10

8.0E+10 Export 7.0E+10

1996

4.0E+10

Import

Export Import

3.5E+10

6.0E+10

3.0E+10

5.0E+10

2.5E+10 4.0E+10 2.0E+10 3.0E+10

1.5E+10

2.0E+10

1.0E+10

1.0E+10

5.0E+09

0.0E+00

0.0E+00 1995

1996

1997

1998

1999

1995

Total

Figure 5.28. Gross virtual water import into and export 3 -1 from the Russian Federation in the period 1995-1999 (m yr ).

33

1996

1997

1998

1999

Total

Figure 5.29. Gross virtual water import into and export from Kazakhstan in the period 1995-1999 (m 3yr-1).

5.1.3. International virtual water trade by product

The total volume of crop-related virtual water trade between nations in the period 1995-1999 can for 30% be explained by trade in wheat (Table 5.4). Next come soybeans and rice, which account respectively for 17% and 15% of global crop-related virtual water trade.

Table 5.4. Global virtual water trade between nations by product (Gm 3). Product

1995

%

1996

%

1997

%

1998

%

1999

%

Total

%

Wheat

181

32.35

215

26.49

254

32.01

203

29.00

197

32.73

1049

30.20

Soybean

103

18.37

108

13.28

125

15.79

122

17.47

135

22.45

593

17.07

Rice

81

14.57

198

24.35

71

8.95

119

16.95

65

10.78

534

15.36

Maize

58

10.40

56

6.93

67

8.51

65

9.22

61

10.14

307

8.85

Raw sugar

9

1.60

68

8.35

119

14.99

42

5.99

13

2.09

250

7.20

Barley

36

6.41

30

3.67

35

4.41

29

4.15

30

5.05

170

4.88

Sunflower

12

2.17

24

2.97

20

2.50

20

2.92

18

2.94

94

2.71

Sorghum

12

2.14

26

3.21

12

1.49

10

1.39

10

1.73

70

2.01

Bananas

11

1.88

16

2.00

15

1.95

15

2.15

11

1.83

68

1.97

Grapes

12

2.07

13

1.64

13

1.65

13

1.87

13

2.24

65

1.86

Oats

9

1.67

10

1.25

11

1.41

9

1.34

10

1.61

50

1.43

Tobacco

5

0.98

10

1.19

11

1.33

13

1.90

7

1.10

46

1.31

Ground-nuts

6

1.10

7

0.84

8

1.02

6

0.90

4

0.70

32

0.91

Peppers

4

0.80

5

0.62

9

1.12

6

0.84

6

1.02

30

0.87

Cotton seeds

5

0.83

5

0.56

5

0.64

6

0.92

7

1.24

28

0.81

Peas

3

0.46

4

0.48

4

0.57

5

0.67

2

0.31

18

0.50

Beans

3

0.47

6

0.68

3

0.35

2

0.36

2

0.38

16

0.45

Potatoes

2

0.40

2

0.26

2

0.31

2

0.33

2

0.37

11

0.33

Onions

2

0.28

3

0.33

2

0.19

2

0.35

1

0.25

10

0.28

Vegetables

1

0.14

1

0.10

1

0.12

4

0.50

1

0.17

7

0.20

Millet

1

0.23

1

0.14

1

0.16

1

0.17

1

0.22

6

0.18

Tomatoes

1

0.14

1

0.12

1

0.13

1

0.17

1

0.19

5

0.15

Palm nuts

1

0.12

1

0.12

1

0.07

1

0.08

0

0.08

3

0.09

Safflower

1

0.12

1

0.09

1

0.08

1

0.09

1

0.09

3

0.09

Cucumbers

0

0.06

1

0.12

1

0.07

0

0.06

0

0.07

3

0.08

Cauliflower

0

0.06

0

0.05

0

0.05

0

0.06

0

0.07

2

0.06

Cabbages

0

0.05

0

0.04

0

0.04

0

0.05

0

0.06

2

0.05

Carrots

0

0.04

0

0.03

0

0.03

0

0.04

0

0.05

1

0.04

Citrus

0

0.04

0

0.03

0

0.02

0

0.01

0

0.01

1

0.02

Artichokes

0

0.02

0

0.01

0

0.01

0

0.01

0

0.02

1

0.01

Lettuce

0

0.01

0

0.01

0

0.01

0

0.01

0

0.02

0

0.01

Sweet potato

0

0.02

0

0.01

0

0.01

0

0.01

0

0.01

0

0.01

Spinach

0

0.00

0

0.00

0

0.00

0

0.00

0

0.01

0

0.00

Grand total

559 100.00

813 100.00

793 100.00

34

700 100.00

601 100.00

3475 100.00

5.2. Inter-regional trade in virtual water

5.2.1. Inter-regional virtual water trade relations

In order to show virtual water trade between major world regions, the world has been classified into thirteen regions: North America, Central America, South America, Eastern Europe, Western Europe, Central and South Asia, the Middle East, South-east Asia, North Africa, Central Africa, Southern Africa, the Former Soviet Union, and Oceania. A list of countries per world region is given in Appendix VI.

The gross virtual water trade between and within regions in the period 1995-1999 is presented in Table 5.5. The details of the regional trade data are presented in Appendix VII. Net virtual water trade between regions in the period 1995-1999 is presented in Table 5.6 and Figure 5.30. In the figure the largest trade flows are indicated with arrows. The regions that have net import are marked in red colour and the regions that have net export are marked in green colour.

For each world region, a ranking has been made of the most important regions for gross import and gross export of virtual water (Table 5.7). Also a ranking has been made of the most important regions for net import and net export (Table 5.8).

Western Europe

FSU Eastern Europe

North America

Central and South Asia Middle East

Central America North Africa

Central Africa South America Net virtual water import, Gm3 -1030 -240 -140 -135 -45 -22 -5 12 20 151 222 380 833 No Data

Southern Africa

South east Asia

Oceania

Figure 5.30. Virtual water trade balances of thirteen world regions over the period 1995-1999. Green coloured regions have net virtual water export; red coloured regions have net virtual water import. The arrows show the largest net virtual water flows between regions (>100 Gm3).

35

1.65

0.25

3.53

0.02

0.79

0.13

2.87

0.81

0.01

0.73

1.63

1.81

2.00

14.60

Central America

Central and South Asia

Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

Southern Africa

South America

South-east Asia

Western Europe

Total gross import

Central Africa

Central Africa

Exporter

Importer

167.30

2.26

2.14

7.16

0.68

0.33

0.40

153.24

0.15

0.13

0.15

0.67

4.62

0.00

981.76

59.53

226.63

62.29

5.38

8.00

83.26

395.21

2.46

11.56

2.82

100.40

124.52

0.11

60.16

18.97

2.56

7.83

0.50

13.06

0.07

9.51

1.14

2.54

20.40

3.07

0.78

0.12

Central Central & Eastern America South Europe Asia

205.35

20.20

25.76

20.26

0.37

29.26

9.47

63.77

3.74

25.65

10.37

21.64

0.43

0.07

Middle East

253.06

25.45

31.56

18.63

0.42

3.07

9.31

128.51

2.74

13.21

7.56

13.76

1.53

0.05

North Africa

87.62

5.08

12.97

13.37

1.74

0.96

2.69

82.78

4.18

2.35

0.56

3.32

40.37

0.05

North America

8.71

0.15

2.63

0.34

0.10

0.01

2.80

4.02

0.00

0.82

0.21

0.40

0.01

0.02

Oceania

45.53

3.89

5.98

4.85

0.26

48.68

0.06

9.65

0.22

1.21

5.23

9.88

4.29

0.01

FSU

40.11

2.03

11.81

2.75

2.78

0.00

2.84

9.84

0.43

0.03

0.12

9.44

0.17

0.64

107.24

1.59

3.45

146.73

1.31

0.06

3.66

88.67

4.61

0.48

0.08

0.87

2.45

0.00

203.03

1.78

87.20

16.50

1.21

0.40

31.56

82.80

0.16

2.72

0.55

64.89

0.41

0.05

523.28

250.46

11.08

191.21

7.66

35.00

4.41

170.27

13.79

18.37

37.42

17.77

14.33

1.99

Southern South South- Western Africa America east Asia Europe

Table 5.5. Gross virtual water trade between world regions in the period 1995-1999 (Gm 3). The grey-shaded cells refer to gross trade between countries within the regions.

2698

142.95

338.38

346.83

20.33

90.17

148.54

1118.38

30.99

54.21

65.09

149.25

189.52

3.11

Total gross export

-0.1

0.73

0.08

2.82

0.79

Eastern Europe

Middle East

North Africa

North America

Oceania

0.09

1.63

1.77

0.02

11.51

Southern Africa

South America

South-east Asia

Western Europe

Total net import

0

3.43


FSU

0.25

Central Africa

Central America

Central Africa

Exporter

Importer

-22.2

-12.07

1.73

4.71

0.51

-3.96

0.4

112.87

-1.38

-0.3

-0.62

-123.84

-0.25

832.49

41.76

161.74

61.42

-4.06

-1.89

82.86

391.89

-11.31

-10.08

-0.25

123.84

-3.43

-4.94

-18.44

2

7.75

0.38

7.83

-0.14

8.96

-6.42

-7.83

0.25

0.62

0.1

Central Central & Eastern America South Europe Asia

151.15

1.84

23.04

19.79

0.34

28.05

8.65

61.43

-9.47

7.83

10.08

0.3

-0.73

Middle East

-165.19

-69.84

-75.31

-8.1

-8.69

-1.33

-124.34

-61.43

-8.96

-391.89

-112.87

-2.82

-139.84

-4.26

-28.94

-3.32

-2.74

-0.04

1.33

-9.31

-8.65

0.14

-82.86

-0.4

-0.79

North Oceania America

222.08 -1030.77

11.66

31.41

14.02

-0.02

2.86

9.31

124.34

9.47

6.42

11.31

1.38

-0.08

North Africa

Table 5.6. Net virtual water trade between regions in the period 1995-1999 (Gm 3).

0

-44.65

-31.11

5.57

4.79

0.26

0.04

8.69

-2.86

-28.05

-7.83

1.89

3.96

FSU

19.76

-5.62

10.6

1.44

-0.26

2.74

8.1

0.02

-0.34

-0.38

4.06

-0.51

-0.09

-239.59

-189.62

-13.05

-1.44

-4.79

3.32

75.31

-14.02

-19.79

-7.75

-61.42

-4.71

-1.63

-135.33

-9.3

13.05

-10.6

-5.57

28.94

69.84

-31.41

-23.04

-2

-161.74

-1.73

-1.77

380.33

9.3

189.62

5.62

31.11

4.26

165.19

-11.66

-1.84

18.44

-41.76

12.07

-0.02

Southern South South- Western Africa America east Asia Europe

-380.33

135.33

239.59

-19.76

44.65

139.84

1030.77

-222.08

-151.15

4.94

-832.49

22.2

-11.51

Total net export


North America

South-east Asia

North America

North America

Central America

North America

North America

North America

Western Europe

South America

North America


North Africa

Southern Africa

South America

Central America

North America

Central Asia

Middle East

South-east Asia

Eastern Europe

Western Europe

Oceania

Russian Fed

North America

South-east Asia

North America

Russian Fed


Russian Fed

South-east Asia

Southern Africa

South America

North Africa

North America

South-east Asia

North America

South-east Asia

Middle East

Eastern Europe

North America

Oceania

South-east Asia

Central America

South-east Asia

Western Europe

South-east Asia


Western Europe

Western Europe

Eastern Europe


Middle East

South America

Southern Africa


Oceania

Western Europe

South-east Asia

Oceania

Oceania

South America

South-east Asia

Fourth

Western Europe



Western Europe


Western Europe

South-east Asia



Western Europe

Western Europe

Western Europe

Western Europe

First

Third

First

Second

Gross export to

Gross import from

Central Africa

Region

Table 5.7. Ranking of gross import and gross export regions for each of the thirteen world regions.

Middle East

South-east Asia

North Africa

Middle East

North Africa

North Africa

Middle East

Western Europe

North America


South and Central Asia

South America

Southern Africa

Second

Eastern Europe

Middle East

Middle East

North Africa

Middle East


Western Europe

Central America

Western Europe

Middle East

North America

North America

Eastern Europe

Third


North Africa

Eastern Europe

Russian Fed

North America

South-east Asia

North Africa

North Africa

Russian Fed

North Africa

South America

Middle East


Fourth

South America

North America

North America

Western Europe

Ocean

Russian Fed

South-east Asia

North America

Russian Fed

South America

Russian Fed

South America

Central America

Eastern Europe

South-east Asia

Ocean

Western Europe



Western Europe


North Africa

North America

South America

South America

Eastern Europe

Oceania

South-east Asia

North America


South-east Asia

Russian Fed

Oceania

Southern Africa

North America

Central America

South-east Asia

Western Europe

Western Europe

Middle East

South-east Asia

South America

Oceania

Central Africa

North Africa

North America

Central America

Oceania


Western Europe

Eastern Europe

North America

North America

South America

North America

South America

South America


South-east Asia

Southern Africa

South-east Asia

South-east Asia


North America

North Africa

North America

North America


Fourth

First

Third

First

Second

Net export to

Net import from

Central Africa

Region

Table 5.8. Ranking of net import and net export regions for each of the thirteen world regions.

Middle East

South-east Asia

North Africa

Middle East

North Africa

Central Africa

Middle East

Western Europe

Western Europe


Central America

Second

Eastern Europe

North Africa

Middle East

North Africa

Middle East

Southern Africa

North Africa

Russian Fed

Middle East

Eastern Europe

Third

North Africa

Middle East

Central Africa

Oceania

Southern Africa

Central Africa

Central Africa

North Africa

Middle East

Fourth

5.2.2. Virtual water trade balance per world region

The virtual water trade balances of the thirteen world regions are shown in Figures 5.31a and 5.31b. The former shows the gross import and export of virtual water for each region. The latter shows the difference between the two, the net import, which is positive in some cases and negative in others.

Regions with a significant net virtual water import are: Central and South Asia, Western Europe, North Africa, and the Middle East. Two other regions with net virtual water import, but less substantial, are Southern Africa and Central Africa. Regions with substantial net virtual water export are: North America, South America, Oceania, and South-east Asia. Three other regions with net virtual water export, but less substantial, are the FSU, Central America and Eastern Europe.

North America is by far the biggest virtual water exporter in the world, while Central and South Asia is by far the biggest virtual water importer. A further ranking of the world regions is given in Table 5.9.

1200 Export Import

1000 800 600 400 200 0 Central Africa

Central America


Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

South Africa

South America

South east Asia

Western Europe

Figure 5.31a. Gross virtual water import and export per region in the period 1995-1999 (Gm 3).

1000 500 0 -500 -1000 -1500 Central Africa

Central Central Eastern America and South Europe Asia

Middle East

North Africa

North America

Oceania

FSU

Figure 5.31b. Net virtual water import per region in the period 1995-1999 (Gm 3).

40

South Africa

South America

South east Asia

Western Europe

Table 5.9. Ranking of regions in terms of gross virtual water import and gross virtual water export. Gross virtual water import (1995-1999)

Ranking

Gross virtual water export (1995-1999)

Region

Gm 3


982

1

North America

1118

Western Europe

523

2

South America

347

North Africa

253

3

South-east Asia

338

Middle East

205

4

Central America

190

South-east Asia

203

5


149

Central America

167

6

Oceania

149

South America

107

7

Western Europe

143

North America

88

8

FSU

90

Eastern Europe

60

9

Eastern Europe

65

FSU

46

10

Middle East

54

Southern Africa

40

11

North Africa

31

Central Africa

15

12

Southern Africa

20

9

13

Central Africa

Oceania

Gm 3

Region

3

In the remaining part of this section, an overview will be given of the import and export of virtual water for each of the thirteen world regions. Figures 5.32a to 5.44a give the gross virtual water import and export of the thirteen world regions for the period 1995-1999. Figures 5.32b to 5.44b give the net virtual water import of the world regions for this period.

450 Export Import

400 350 300 250 200 150 100 50 0 Central Africa

Central America


Eastern Europe

Middle East

North Africa

Oceania

FSU

South Africa

South America

South east Asia

Figure 5.32a. Gross virtual water import and export of North America in the period 1995-1999 (Gm 3).

41

Western Europe

Central Africa

Central America


Eastern Europe

Middle East

North Africa

Oceania

FSU

South Africa

South America

South east Asia

Western Europe

0 -50 -100 -150 -200 -250 -300 -350 -400 -450

Figure 5. 32b. Net virtual water import of North America in the period 1995-1999 (Gm 3).

180 Export Import

160 140 120 100 80 60 40 20 0 Central Africa


Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

South Africa

South America

South east Asia

Western Europe

Figure 5.33a. Gross virtual water import and export of Central America in the period 1995-1999 (Gm 3).

150 100 50 0 -50 -100 -150 Central Africa


Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

South Africa

South America

Figure 5.33b. Net virtual water import of Central America in the period 1995-1999 (Gm 3).

42

South east Asia

Western Europe

250 Export Import

200 150 100 50 0

Central Africa

Central America


Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

South Africa

South east Asia

Western Europe

Figure 5.34a. Gross virtual water import and export of South America in the period 1995-1999 (Gm 3).

100 50 0 -50 -100 -150 -200 -250 Central Africa

Central America


Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

South Africa

South east Asia

Western Europe

Figure 5.34b. Net virtual water import of South America in the period 1995-1999 (Gm 3).

40 35

Export Import

30 25 20 15 10 5 0 Central Africa

Central America


Middle East

North Africa

North America

Oceania

FSU

South Africa

South America

South east Asia

Figure 5.35a. Gross virtual water import and export of Eastern Europe in the period 1995-1999 (Gm 3).

43

Western Europe

15 10 5 0 -5 -10 -15 -20 Central Africa

Central America


Middle East

North Africa

North America

Oceania

FSU

South Africa

South America

South east Asia

Western Europe

Figure 5.35b. Net virtual water import of Eastern Europe in the period 1995-1999 (Gm 3).

250 200

Export Import

150 100 50 0 Central Africa

Central America


Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

South Africa

South America

South east Asia

Figure 5.36a. Gross virtual water import and export of Western Europe in the period 1995-1999 (Gm 3).

250 200 150 100 50 0 -50 -100 Central Africa

Central America


Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

South Africa

Figure 5.36b. Net virtual water import of Western Europe in the period 1995-1999 (Gm 3).

44

South America

South east Asia

450 Export Import

400 350 300 250 200 150 100 50 0 Central Africa

Central America

Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

South Africa

South America

South east Asia

Western Europe

Figure 5.37a. Gross virtual water import and export of Central and South Asia in the period 1995-1999 (Gm 3).

450 400 350 300 250 200 150 100 50 0 -50 Central Africa

Central America

Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

South Africa

South America

South east Asia

Western Europe

Figure 5.37b. Net virtual water import of Central and South Asia in the period 1995-1999 (Gm 3).

70 Export Import

60 50 40 30 20 10 0 Central Africa

Central America


Eastern Europe

North Africa

North America

Oceania

FSU

South Africa

South America

South east Asia

Figure 5.38a. Gross virtual water import and export of the Middle East in the period 1995-1999 (Gm 3).

45

Western Europe

70 60 50 40 30 20 10 0 -10 -20 Central Africa

Central America


Eastern Europe

North Africa

North America

Oceania

FSU

South Africa

South America

South east Asia

Western Europe

Figure 5.38b. Net virtual water import of the Middle East in the period 1995-1999 (Gm 3).

250 Export Import

200 150 100 50 0 Central Africa

Central America


Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

South Africa

South America

Western Europe

Figure 5.39a. Gross virtual water import and export of South-east Asia in the period 1995-1999 (Gm 3).

100 50 0 -50 -100 -150 -200 Central Africa

Central America


Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

South Africa

Figure 5.39b. Net virtual water import of South-east Asia in the period 1995-1999 (Gm 3).

46

South America

Western Europe

140 Export Import

120 100 80 60 40 20 0 Central Africa

Central America


Eastern Europe

Middle East

North America

Oceania

FSU

South Africa

South America

South east Asia

Western Europe

Figure 5.40a. Gross virtual water import and export of North Africa in the period 1995-1999 (Gm 3).

140 120 100 80 60 40 20 0 -20 Central Africa

Central America


Eastern Europe

Middle East

North America

Oceania

FSU

South Africa

South America

South east Asia

Western Europe

Figure 5.40b. Net virtual water import of North Africa in the period 1995-1999 (Gm 3).

4.0 Export Import

3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 Central America


Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

South Africa

South America

South east Asia

Figure 5.41a. Gross virtual water import and export of Central Africa in the period 1995-1999 (Gm 3).

47

Western Europe

4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5 Central America


Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

South Africa

South America

South east Asia

Western Europe

South America

South east Asia

Western Europe

Figure 5.41b. Net virtual water import of Central Africa in the period 1995-1999 (Gm 3).

14 Export Import

12 10 8 6 4 2 0

Central Africa

Central America


Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

Figure 5.42a. Gross virtual water import and export of Southern Africa in the period 1995-1999 (Gm 3).

12 10 8 6 4 2 0 -2 -4 -6 -8 Central Africa

Central America


Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

South America

Figure 5.42b. Net virtual water import of Southern Africa in the period 1995-1999 (Gm 3).

48

South east Asia

Western Europe

40 35

Export Import

30 25 20 15 10 5 0 Central Africa

Central America


Eastern Europe

Middle East

North Africa

North America

Oceania

South Africa

South America

South east Asia

Western Europe

Figure 5.43a. Gross virtual water import and export of the Former Soviet Union in the period 1995-1999 (Gm 3).

15 10 5 0 -5 -10 -15 -20 -25 -30 -35 Central Africa

Central America


Eastern Europe

Middle East

North Africa

North America

Oceania

South Africa

South America

South east Asia

Western Europe

Figure 5.43b. Net virtual water import of the Former Soviet Union in the period 1995-1999 (Gm 3).

90 Export Import

80 70 60 50 40 30 20 10 0 Central Africa

Central America


Eastern Europe

Middle East

North Africa

North America

FSU

South Africa

South America

South east Asia

Figure 5.44a. Gross virtual water import and export of Oceania in the period 1995-1999 (Gm 3).

49

Western Europe

10 0 -10 -20 -30 -40 -50 -60 -70 -80 -90 Central Africa

Central America


Eastern Europe

Middle East

North Africa

North America

FSU

South Africa

South America

South east Asia

Western Europe

Figure 5.44b. Net virtual water import of Oceania in the period 1995-1999 (Gm 3).

5.2.3. Gross virtual water trade between countries within regions

The virtual water trade flows between countries in each of the thirteen world regions are shown in Figures 5.45 and 5.46. The gross trade in virtual water within a region has been calculated by summing up all virtual water imports of the countries of the region that originate from other countries in the same region. Note that it yields the same result as if we would have added all virtual water exports of the countries in a region that go to other countries in the same region.

Western Europe is the region with the biggest internal trade in virtual water. Besides, the trade volume is rather stable here. South America is second in the ranking of internal trade volume. Central and South Asia is a rather unstable region if we look at the annual volume of virtual water traded between the countries of the region. Central and South Asia is the largest region in terms of population, so food demand is higher than in the other regions. This explains why the region is the biggest virtual water importer (see Figure 5.31b). The virtual water trade between countries within the region is also high, thus the countries within the region highly depend on both countries outside and countries within the region.

70 60 1995

50

1996

1997

1998

1999

Average

40 30 20 10 0 Central Africa

Central America

Central Eastern and South Europe Asian

Middle East

North Africa

North America

Oceania

FSU

South Africa

South America

South east Asian

Western Europe

Figure 5.45. Gross virtual water trade between countries within each region in the years 1995-1999 (Gm 3).

50

300 250 200 150 100 50 0 Central Africa

Central Central America and South Asia

Eastern Europe

Middle East

North Africa

North America

Oceania

FSU

South Africa

South America

South east Asia

Western Europe

Figure 5.46. Gross virtual water trade between countries within each region in the total period 1995-1999 (Gm 3).

5.3. Intercontinental trade in virtual water

5.3.1. Intercontinental virtual water trade relations

In the previous section the world was divided into thirteen ‘world regions’. In the current section, virtual water trade will be presented at the level of ‘continents’. The six continents – Africa, America, Asia, Europe, Oceania and the Former Soviet Union (FSU) – correspond to the world regions as indicated in Table 5.10.

The volumes of gross virtual water trade between continents in the period 1995-1999 are presented in Table 5.11. The gross virtual water trade between countries within the continents is given in the same table (the greyshaded cells). The net virtual water trade between continents is presented in Table 5.12.

Table 5.10. Correspondence between the six ‘continents’ and the thirteen ‘world regions’. Continent America Europe

Region North America

Central America

Eastern Europe

South America Western Europe

Asia

Middle East


South-east Asia

Africa

North Africa

Central Africa

Southern Africa

FSU

FSU

Ocean

Oceania

51

Table 5.11. Gross virtual water trade between continents in the years 1995-1999 (Gm 3). The grey-shaded cells refer to gross trade between countries within the continents. Importer Exporter Africa

Africa

America

Asia

Europe

Oceania

FSU

1995

1.874

0.442

4.204

5.980

0.029

0.005

1996 1997 1998 1999 Total 1995 1996 1997

2.201 1.560 2.060 1.873 9.568 32.655 31.882 33.980

1.651 1.106 3.121 6.393 12.713 83.598 107.887 115.344

3.247 2.071 2.463 1.542 13.527 136.725 146.857 227.862

4.708 3.967 5.380 5.154 25.188 78.196 73.898 77.621

0.018 0.022 0.020 0.024 0.113 1.239 0.755 1.023

0.115 0.094 0.130 0.145 0.488 1.523 3.028 2.640

1998 1999 Total

35.488 32.187 166.193

1995 1996 1997 1998

16.082 12.549 16.918 20.624

114.888 117.654 539.371 4.593 6.212 5.375 6.023

138.451 116.306 766.201 73.852 243.549 90.138 108.184

79.397 84.815 393.934 8.580 12.740 12.797 11.345

0.646 0.705 4.370 0.599 0.715 0.881 0.810

3.206 8.392 18.790 0.855 3.524 3.394 7.120

1999 Total

17.804 85.951

4.175 26.379

Europe

1995 1996 1997 1998 1999 Total

9.390 5.008 7.549 7.611 7.624 37.181

1.607 1.117 1.973 2.727 2.297 9.722

43.626 566.451 8.782 8.737 52.808 13.979 10.948 95.253

9.353 55.379 61.467 65.128 65.571 67.163 67.857 327.254

0.844 3.851 0.129 0.032 0.066 0.048 0.089 0.366

2.179 17.072 1.818 1.748 1.476 1.225 2.855 9.121

Oceania

1995 1996 1997 1998 1999 Total 1995

0.178 3.028 4.889 2.799 2.064 12.958 0.694

0.363 1.847 1.950 0.949 1.641 6.751 0.003

13.247 36.723 27.316 26.480 20.527 124.293 3.813

0.474 1.021 0.921 1.139 0.935 4.482 4.757

0.547 0.646 0.476 0.393 0.738 2.796 0.000

0.000 0.023 0.013 0.018 0.002 0.057 0.545

1996 1997 1998 1999 Total

0.575 0.776 0.673 0.365 3.082

0.547 0.416 0.308 0.076 1.350

7.066 5.533 13.261 7.985 37.659

14.002 12.495 11.171 5.644 48.066

0.000 0.013 0.000 0.000 0.013

9.740 11.843 10.695 15.862 48.682

America

Asia

FSU

Table 5.12. Net virtual water trade between continents in the period 1995-1999 (Gm 3). Importer Exporter Africa America Asia Europe Oceania FSU Total net import

Africa

America -153.48

153.48 72.42 11.99 12.84 2.59 253.32

-739.82 -384.21 2.38 -17.44 -1292.57

Asia -72.42 739.82 39.87 120.44 20.59 848.3

52

Europe -11.99 384.21 -39.87 4.12 38.94 375.41

Oceania -12.84 -2.38 -120.44 -4.12 -0.04 -139.82

FSU -2.59 17.44 -20.59 -38.94 0.04 -44.64

Total net export -253.32 1292.57 -848.3 -375.41 139.82 44.64 0

5.3.2. Virtual water trade balance per continent

Figure 5.47a shows the gross virtual water import and gross virtual water export for each continent for the whole period 1995-1999. Figure 5.47b shows the net import per continent. Net import is negative - this means there is net export – for America, Oceania and the Former Soviet Union. Net import is positive for Asia, Europe and Africa.

Table 5.13 ranks the continents according their gross import and gross export of virtual water. America is by far the largest export continent, with an average gross export of 270 Gm3 per year (over the period 1995-1999). The USA takes the largest share in this total export. Gross import to the American continent as a whole is only 11 Gm3 per year in average, which results in a net export of virtual water of 259 Gm3 per year. Asia is the largest importer of virtual water. Average gross import amounts to 207 Gm3 per year (over the years 1995-1999). Average gross export amounts to 38 Gm3 per year, resulting in an average annual import of 169 Gm3 .

1600 Export Import

1400 1200 1000 800 600 400 200 0 Africa

America

Asia

Europe

Oceania

FSU

Figure 5.47a. Gross virtual water import and export per continent in the period 1995-1999 (Gm 3).

1000 500 0 -500 -1000 -1500 Africa

America

Asia

Europe

Oceania

Figure 5.47b. Net virtual water import per continent in the period 1995-1999 (Gm 3).

53

FSU

Table 5.13. Ranking of continents in terms of gross virtual water import and gross virtual water export. Gross virtual water import (1995-1999) Continent

Rank Gm

Asia

3

Gross virtual water export (1995-1999) Gm 3

Continent

1037

1

America

Europe

527

2

Asia

189

Africa

305

3

Europe

152

America

57

4

Oceania

149

FSU

46

5

FSU

90

9

6

Africa

65

Oceania

1350

5.3.3. Gross virtual water trade between countries within continents

Data on gross virtual water trade between countries within continents are shown in Table 5.11 (the grey-shaded cells). Asia and America have the biggest internal gross virtual water trade (Figures 5.48-5.49). The virtual water trade between the American countries seems to be rather stable, which is not the case for the trade between the countries of the Asian continent. If compared to Asia and America, virtual water trade between countries within the area of the Former Soviet Union, within Africa and within Oceania is very small.

300 1995

1996

1997

1998

1999

Average

250 200 150 100 50 0 Africa

America

Asian

Europe

Oceania

FSU

Figure 5.48. Gross virtual water trade between countries within each continent in the years 1995-1999 (Gm 3). 600 500 400 300 200 100 0 Africa

America

Asia

Europe

Oceania

FSU

Figure 5.49. Gross virtual water trade between countries within each continent in the period 1995-1999 (Gm 3).

54

6. Virtual water trade of nations in relation to national water needs and availability 6.1. Water footprints, water scarcity, water self-sufficiency and water dependency of nations

Using the definition given in Section 2.3, a ‘water footprint’ has been calculated for each nation. Next, given the definitions in Section 2.4, indicators of national water scarcity, water self-sufficiency and water dependency have been calculated. The basic data on national water withdrawal and water availability have been taken from Raskin et al. (1997). The data on net virtual water import per country are taken from Appendix Vc. The results are shown in Table 6.1.

The level of water self-sufficiency has been classified into six categories: 0-20%; 20-50%; 50-70%; 70-90%; 90-99%; and 100%. Table 6.2 lists the countries in each of the categories.

Table 6.1. Water footprints, water scarcity, water self-sufficiency and water dependency of nations in 1995. Country

Afghanistan

Water withdrawal (106 m 3)

Water Net virtual 1 availability water import (106 m 3) (106 m 3)

Water footprint (106 m 3)

Water scarcity (%)

Water selfsufficiency (%)

Water dependency (%)

35704

50000

29

35733

71.4

99.9

0.1

Albania

356

21300

100

456

1.7

78.1

21.9

Algeria

5042

14300

9523

14565

35.3

34.6

65.4

Angola

628

184000

224

852

0.3

73.7

26.3

Argentina

35812

994000

-36742

-930

3.6

100.0

0.0

Armenia

4109

13300

308

4417

30.9

93.0

7.0

Australia

27312

343000

-13269

14043

8.0

100.0

0.0

2424

90300

-42

2382

2.7

100.0

0.0

17061

33000

158

17219

51.7

99.1

0.9

334

290

144

478

115.2

69.8

30.2

26467

2,357,000

12391

38858

1.1

68.1

31.9

Belarus

2979

73800

142

3121

4.0

95.4

4.6

Belgium

9237

12500

11730

20967

73.9

44.1

55.9

Benin

154

25800

97

251

0.6

61.4

38.6

Bhutan

23

95000

10

33

0.0

70.2

29.8

Bolivia

1557

300000

-1409

148

0.5

100.0

0.0

Bosnia/Herzeg.

1354

265000

83

1437

0.5

94.3

5.7

Brazil

46856

6,950,000

-1933

44923

0.7

100.0

0.0

Bulgaria

13576

205000

-1128

12448

6.6

100.0

0.0

Burkina Faso

412

17500

-10

402

2.4

100.0

0.0

Burundi

127

3600

2

129

3.5

98.8

1.2

Cambodia

660

498100

201

861

0.1

76.7

23.3

Austria Azerbaijan Bahrain Bangladesh

1

Data refer to the sum of internal and external water resources.

55

Country

Cameroon




Water scarcity (%)



500

268000

-15

485

0.2

100.0

0.0

47246

2,901,000

-55330

-8084

1.6

100.0

0.0

Cape Verde

30

300000

40

70

0.0

43.0

57.0

Cent. African Rep.

85

141000

-1

84

0.1

100.0

0.0

Chad

218

43000

3

221

0.5

98.6

1.4

Chile

23203

468000

1509

24712

5.0

93.9

6.1

China

504315

2,800,000

42189

546504

18.0

92.3

7.7

6031

1,070,000

5604

11635

0.6

51.8

48.2

Comoros

13

1020

13

26

1.3

50.2

49.8

Congo

51

832000

636

687

0.0

7.4

92.6

Costa Rica

1464

95000

932

2396

1.5

61.1

38.9

Cote d'Ivoire

941

77700

578

1519

1.2

62.0

38.0

Croatia

1760

265000

-166

1594

0.7

100.0

0.0

Cuba

9585

34500

203

9788

27.8

97.9

2.1

Czech Rep.

2727

58200

-610

2117

4.7

100.0

0.0

Denmark

1210

13000

-1029

181

9.3

100.0

0.0

11

2300

102

113

0.5

9.7

90.3

Dominican R.

3483

20000

-1190

2293

17.4

100.0

0.0

Ecuador

6677

314000

-516

6161

2.1

100.0

0.0

55432

68500

15302

70734

80.9

78.4

21.6

1084

19000

918

2002

5.7

54.1

45.9

Eritrea

240

8800

27

267

2.7

90.0

10.0

Estonia

3220

17600

194

3414

18.3

94.3

5.7

Ethiopia

2156

110000

487

2643

2.0

81.6

18.4

33

28600

68

101

0.1

32.6

67.4

Finland

2243

113000

-431

1812

2.0

100.0

0.0

France

38570

198000

-18454

20116

19.5

100.0

0.0

Gabon

78

164000

64

142

0.0

55.0

45.0

Gambia

36

8000

150

186

0.5

19.3

80.7

Georgia

4054

65200

207

4261

6.2

95.1

4.9

Germany

47303

171000

12228

59531

27.7

79.5

20.5

Ghana

325

53200

229

554

0.6

58.7

41.3

Greece

7109

58700

-2989

4120

12.1

100.0

0.0

Guatemala

1501

116000

-883

618

1.3

100.0

0.0

22

27000

8

30

0.1

72.8

27.2

1501

241000

-14

1487

0.6

100.0

0.0

47

11000

364

411

0.4

11.4

88.6

Honduras

1656

63400

315

1971

2.6

84.0

16.0

Hungary

6678

120000

-5536

1142

5.6

100.0

0.0

Canada

Colombia

Djibouti

Egypt El Salvador

Fiji

Guinea-Bissau Guyana Haiti

56

Country




Iceland

167

168000

56

223

0.1

74.9

25.1

607227

2,085,000

-24610

582617

29.1

100.0

0.0

Indonesia

83061

2,530,000

25256

108317

3.3

76.7

23.3

Iran

85608

117500

5494

91102

72.9

94.0

6.0

Iraq

52259

109200

51

52310

47.9

99.9

0.1

808

50000

675

1483

1.6

54.5

45.5

Israel

2277

2200

2021

4298

103.5

53.0

47.0

Italy

56362

167000

12706

69068

33.7

81.6

18.4

414

8300

271

685

5.0

60.4

39.6

91945

547000

55416

147361

16.8

62.4

37.6

907

1700

7629

8536

53.4

10.6

89.4

44138

169400

-658

43480

26.1

100.0

0.0

2454

30200

1667

4121

8.1

59.5

40.5

Korea (DPR)

16407

67000

561

16968

24.5

96.7

3.3

Korea (Rep.)

29558

66100

18964

48522

44.7

60.9

39.1

472

758000

472

944

0.1

50.0

50.0

12953

61700

143

13096

21.0

98.9

1.1

Laos

1260

270000

86

1346

0.5

93.6

6.4

Latvia

673

34000

224

897

2.0

75.0

25.0

1178

5600

727

1905

21.0

61.8

38.2

168

232000

67

235

0.1

71.5

28.5

Libya

4751

600000

610

5361

0.8

88.6

11.4

Lithuania

4416

24200

443

4859

18.2

90.9

9.1

847

265000

-32

815

0.3

100.0

0.0

23135

337000

447

23582

6.9

98.1

1.9

971

18700

-387

584

5.2

100.0

0.0

13058

456000

9983

23041

2.9

56.7

43.3

Mali

1746

100000

67

1813

1.7

96.3

3.7

Mauritania

1851

11400

161

2012

16.2

92.0

8.0

390

2200

250

640

17.7

60.9

39.1

Mexico

84209

357400

12432

96641

23.6

87.1

12.9

Moldova

3787

13700

-210

3577

27.6

100.0

0.0

Mongolia

657

24600

-27

630

2.7

100.0

0.0

11540

30000

6710

18250

38.5

63.2

36.8

655

216000

376

1031

0.3

63.5

36.5

Myanmar

4694

1,082,000

-1477

3217

0.4

100.0

0.0

Nepal

3284

170000

129

3413

1.9

96.2

3.8

Netherlands

8039

90000

29315

37354

8.9

21.5

78.5

New Zealand

1992

327000

845

2837

0.6

70.2

29.8

India

Ireland

Jamaica Japan Jordan Kazakhstan Kenya

Kuwait Kyrgyzstan

Lebanon Liberia

Macedonia Madagascar Malawi Malaysia

Mauritius

Morocco Mozambique


57


Water scarcity (%)

Country

Nicaragua




Water scarcity (%)



1688

175000

168

1856

1.0

90.9

9.1

628

32500

106

734

1.9

85.5

14.5

Nigeria

4648

280000

628

5276

1.7

88.1

11.9

Norway

2077

392000

2548

4625

0.5

44.9

55.1

524

2103

1158

1682

24.9

31.1

68.9

Pakistan

278844

468000

-429

278415

59.6

100.0

0.0

Panama

1975

144000

68

2043

1.4

96.7

3.3

Papua New Guinea

120

801000

-81

39

0.0

100.0

0.0

Paraguay

541

314000

-6914

-6373

0.2

100.0

0.0

Peru

18726

40000

4789

23515

46.8

79.6

20.4

Philippines

49035

323000

-654

48381

15.2

100.0

0.0

Poland

12349

56200

4298

16647

22.0

74.2

25.8

7257

69600

6154

13411

10.4

54.1

45.9

226

195

49

275

115.9

82.2

17.8

25173

208000

-740

24433

12.1

100.0

0.0

116422

4,498,000

-4000

112422

2.6

100.0

0.0

809

6300

112

921

12.8

87.9

12.1

Saudi Arabia

5092

8760

10241

15333

58.1

33.2

66.8

Senegal

1702

39400

1282

2984

4.3

57.0

43.0

Sierra Leone

445

160000

324

769

0.3

57.9

42.1

Singapore

211

600

3599

3810

35.2

5.5

94.5

Slovakia

1818

30800

-1149

669

5.9

100.0

0.0

Slovenia

762

265000

1255

2017

0.3

37.8

62.2

Somalia

914

13500

138

1052

6.8

86.9

13.1

South Africa

14890

50000

6334

21224

29.8

70.2

29.8

Spain

30968

111300

17348

48316

27.8

64.1

35.9

Sri Lanka

10410

43200

1333

11743

24.1

88.6

11.4

Sudan

17800

154000

-5159

12641

11.6

100.0

0.0

518

200000

-31

487

0.3

100.0

0.0

Sweden

2990

180000

-220

2770

1.7

100.0

0.0

Switzerland

1146

50000

2045

3191

2.3

35.9

64.1

Syria

10907

53700

-8414

2493

20.3

100.0

0.0

Tajikistan

14950

101300

49

14999

14.8

99.7

0.3

Tanzania

1193

89000

606

1799

1.3

66.3

33.7

Thailand

35042

179000

-39010

-3968

19.6

100.0

0.0

Togo

115

12000

598

713

1.0

16.1

83.9

Trinidad & Tobago

163

5100

707

870

3.2

18.7

81.3

Tunisia

3391

9000

6048

9439

37.7

35.9

64.1

Turkey

36237

193100

1206

37443

18.8

96.8

3.2

Niger

Oman

Portugal Qatar Romania Russia Rwanda

Suriname

58

Country


Turkmenistan



Water scarcity (%)



26186

72000

139

26325

36.4

99.5

0.5

UAE

657

797

2282

2939

82.4

22.4

77.6

Uganda

217

66000

-338

-121

0.3

100.0

0.0

UK

11929

71000

6390

18319

16.8

65.1

34.9

Ukraine

34623

231000

-1779

32844

15.0

100.0

0.0

Uruguay

4325

124000

-998

3327

3.5

100.0

0.0

492259

2,478,000

-168000

324259

19.9

100.0

0.0

Uzbekistan

91842

129600

434

92276

70.9

99.5

0.5

Venezuela

4446

1,317,000

4031

8477

0.3

52.5

47.5

30851

376000

-2596

28255

8.2

100.0

0.0

Yemen

3397

4902

1416

4813

69.3

70.6

29.4

Yugoslavia

4248

265000

-1

4247

1.6

100.0

0.0

Zambia

1759

116000

-38

1721

1.5

100.0

0.0

Zimbabwe

1527

20000

-340

1187

7.6

100.0

0.0

3696312

50547567

USA

Viet Nam

Grand total

Table 6.2. Countries categorised into different levels of water self-sufficiency (data for 1995). Level of water self-sufficiency 0-20% Congo Djibouti Gambia Haiti Jordan Singapore Togo Trinidad Tobago

20-50 %

50-70%

70-90%

90-99 %

100%

Algeria Belgium Cape Verde Fiji Kuwait Netherlands Norway Oman Saudi Arabia Slovenia Switzerland Tunisia UAE

Bahrain Bangladesh Benin Colombia Comoros Costa Rica Côte d'Ivoire El Salvador Gabon Ghana Ireland Israel Jamaica Japan Kenya Korea (Rep.) Lebanon Malaysia Mauritius Morocco Mozambique Portugal Senegal Sierra Leone Spain Tanzania UK Venezuela

Albania Angola Bhutan Cambodia Egypt Eritrea Ethiopia Germany Guinea-Bissau Honduras Iceland Indonesia Italy Latvia Liberia Libya Mexico New Zealand Niger Nigeria Peru Poland Qatar Rwanda Somalia Southern Africa Sri Lanka Yemen

Afghanistan Armenia Azerbaijan Belarus Bosnia Burundi Chad Chile China Cuba Estonia Georgia Iran Iraq Korea Kyrgyzstan Laos Lithuania Madagascar Mali Mauritania Nepal Nicaragua Panama Tajikistan Turkey Turkmenistan Uzbekistan

Argentina Australia Austria Bolivia Brazil Bulgaria Burkina Faso Cameroon Canada Central Africa Croatia Czech Rep Denmark Dominican R. Ecuador Finland France Greece Guatemala Guyana Hungary India Kazakhstan Macedonia

59

Malawi Moldova Mongolia Myanmar Pakistan Paraguay Philippines Papua/NG Russia Syria Slovakia Suriname Sweden Thailand Uganda Ukraine USA Vietnam Yugoslavia Romania Sudan Uruguay Zambia Zimbabwe

6.2. The relation between water scarcity and water dependency

One would expect that in general terms there is a positive relationship between water scarcity and water dependency, because high water scarcity will make it attractive to import virtual water and thus become water dependent. One would logically suppose: the higher the scarcity within a country, the more dependency on water in other countries. To test this hypothesis, all countries of the world have been plotted in a scarcitydependency graph. The result is shown in Figure 6.1. Surprisingly, there seems to be no relation as hypothesised. Let us for simplicity schematise the scarcity-dependency graph into four areas or ‘classes’. See Figure 6.2. In Table 6.3 we can see that most of the countries fall in class I.

100 90 80 70 60 50 0

10

20

30

40

60 *

50

70

80

90

100

110

120

40 30 20 10 0 Water scarcity

Figure 6.1. Water dependency versus water scarcity for all countries of the world (1995).

Water dependency 100 Class II

Class III

50

Class IV

Class I

0

100

50

Water scarcity Figure 6.2. Four classes in the scarcity-dependency graph. The grey-shaded areas refer to combinations of water scarcity and water dependency that can difficult be understood at first sight: high water scarcity but low water dependency, and low water scarcity but high water dependency.

60

Table 6.3. Position of countries in the scarcity-dependency graph. The grey-shaded countries fall in one of the grey-shaded areas of Figure 6.2. Class I

Class II

Class III

Class IV

Angola

Costa Rica

India

Mexico

South Africa

Algeria

Belgium

Afghanistan

Albania

Cote d'Ivoire

Indonesia

Moldova

Spain

Cape Verde

Jordan

Azerbaijan

Argentina

Croatia

Italy

Mongolia

Sri Lanka

Congo

Saudi Arabia

Bahrain

Armenia

Cuba

Iraq

Morocco

Sudan

Djibouti

UAE

Egypt

Australia

Czech Rep.

Ireland

Mozambique

Suriname

Fiji

Iran

Austria

Denmark

Jamaica

Myanmar

Syria

Gambia

Israel

Bangladesh

Dominican R.

Japan

Nepal

Sweden

Haiti

Pakistan

Belarus

Ecuador

Kazakhstan

New Zealand Tajikistan

Kuwait

Qatar

Benin

El Salvador

Kenya

Nicaragua

Tanzania

Netherlands

Uzbekistan

Bhutan

Eritrea

Korea (DPR)

Niger

Thailand

Norway

Yemen

Bosnia

Estonia

Korea (Rep.)

Nigeria

Turkey

Oman

Bolivia

Ethiopia

Kyrgyztan

Panama

Turkmenistan Singapore

Brazil

Finland

Laos

Papua/NG

Uganda

Slovenia

Bulgaria

France

Latvia

Paraguay

UK

Switzerland

Burkina Faso Gabon

Lebanon

Peru

Ukraine

Togo

Burundi

Georgia

Liberia

Philippines

Uruguay

Trinidad

Canada

Germany

Libya

Poland

USA

Tunisia

Cambodia

Ghana

Lithuania

Portugal

Venezuela

Cameroon

Greece

Macedonia

Romania

Vietnam

Madagascar

Central Africa Guatemala

Russia

Yugoslavia

Chad

Guinea-Bissau Malawi

Rwanda

Zambia

Chile

Guyana

Malaysia

Senegal

Zimbabwe

China

Honduras

Mali

Sierra Leone

Colombia

Hungary

Mauritania

Slovakia

Comoros

Iceland

Mauritius

Somalia

61

7. Concluding remarks This study was limited to virtual water trade related to crop trade between nations. Also other goods contain virtual water, for instance meat, diary products, cotton, paper, etc. In order to get a complete picture of the global virtual water trade flows, also other products than crops have to be taken into account. For instance, the virtual water trade balance of the Netherlands drawn in the current study suggests that this country has an incredibly high net import of virtual water, due to the large import of feed for the Dutch bio-industry. The balance will look quite differently if we would take into account the export of virtual water that relates to the export of meat from the Netherlands.

As stated in the introductory chapter, the current study is primarily a data report, aimed at disclosing the numbers. A next step is of course to interpret the results and ask the question why the global virtual water trade flows are as they are. What are the explanatory factors behind changes in national virtual water trade balances? What is for instance the relative importance of year-to-year fluctuations in agricultural yields, subsidies in agriculture, national water scarcity, the development of domestic demand for agriculture products? Another next step is to go beyond ‘explanation’ and to study how governments can deliberately interfere in the current national virtual water trade balances in order to achieve a higher global water use efficiency.

Knowing the national virtual water trade balance is essential for developing a rational national policy with respect to virtual water trade. But for some large countries it might be as relevant to know the internal trade of virtual water within the country. For China for instance, relatively dry in the north and relatively wet in the south, domestic virtual water trade is a relevant issue.

The method used for the calculation of the virtual water content of different types of crops has a few weak points. As explained, the crop water requirement estimates used in this study are conservative on the one hand (due to the water losses that are not taken into account), but they are overestimates on the other hand (because they are based on the assumption of optimal growth conditions, an assumption which is generally not met in reality). Improvements to the calculated figures can be made if we could make better estimates of actual specific water use per crop.

63

References Allan, J.A. (1997) ''Virtual water': A long term solution for water short Middle Eastern economies?' Occasional Paper 3, School of Oriental and African Studies (SOAS), University of London.

Allan, J.A. (2001) The Middle East water question: Hydropolitics and the global economy I.B. Tauris, London.

Allen, R.G., M. Smith, A. Perrier, and L.S. Pereira (1994a) An update for the definition of reference evapotranspiration ICID Bulletin 43(2): 1-34.

Allen, R.G., M. Smith, A. Perrier, and L.S. Pereira (1994b) An update for the calculation of reference evapotranspiration ICID Bulletin 43(2): 35-92.

Allen, R.G., L.S. Pereira, D. Raes, and M. Smith (1998) Crop evapotranspiration: Guidelines for computing crop water requirements, FAO Irrigation and Drainage Paper 56, FAO, Rome, Italy.

Clarke, D., M. Smith, and K. El-Askari (1998) CropWat for Windows: User guide, Version 4.2, www.fao.org.

Earle, A. (2001) 'The role of virtual water in food security in Southern Africa' Occasional Paper 33, School of Oriental and African Studies (SOAS), University of London.

Gleick, P.H. (ed.) (1993) Water in crisis: A guide to the world’s fresh water resources, Oxford University Press, New York, USA.

Nyagwambo, N.L. (1998) ''Virtual water' as a water demand management tool: The Mupfure river basin case' MSc thesis DEW 045, IHE Delft, the Netherlands.

Postel, S.L., Daily, G.C., and Ehrlich, P.R. (1996) ‘Human appropriation of renewable fresh water’ Science 271:785-788.

Rockström, J. and L. Gordon (2001) ‘Assessment of green water flows to sustain major biomes of the world: implications for future ecohydrological landscape management’ Phys. Chem. Earth (B) 26: 843-851.

Shiklomanov, I.A. (ed.) (1997) Assessment of water resources and water availability in the world, Comprehensive assessment of the freshwater resources of the world, World Meteorological Organisation, Geneva.

Smith, M., R.G. Allen, J.L. Monteith, A. Perrier, L.S. Pereira, and A. Segeren (1992) ‘Report on the Expert Consultation on revision of FAO methodologies for crop water requirements’, FAO, Rome, Italy, 28-31 May 1990.

65

Wackernagel, M., Onisto, L., Linares, A.C., Falfan, I.S.L., Garcia, J.M., Guerrero, I.S., and Guerrero, M.G.S. (1997) Ecological footprints of nations: How much nature do they use? - How much nature do they have? Centre for Sustainability Studies, Universidad Anahuac de Xalapa, Mexico.

Wackernagel, M. and Rees, W. (1996) Our ecological footprint: Reducing human impact on the earth New Society Publishers, Gabriola Island, B.C., Canada.

Wichelns, D. (2001) 'The role of 'virtual water' in efforts to achieve food security and other national goals, with an example from Egypt' Agricultural Water Management 49:131-151.

Yegnes-Botzer, A. (2001) 'Virtual water export from Israel: Quantities, driving forces and consequences' MSc thesis DEW 166, IHE Delft, the Netherlands.

66

Appendix I. Crop water requirements (m3/ha). Source: CropWat model (www.fao.org).

Afghanistan Albania Algeria American Samoa Angola Antille Antigua Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium-Luxembourg Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bahrain Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi Burma Cambodia Cameroon Canary Islands Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Colombia Comoros Congo, Dem Republic Congo, Republic of Cook Islands Costa Rica Côte d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Estonia Ethiopia Falkland Fiji Islands Finland France French Guiana Gabon Gambia Georgia Germany Ghana Greece Grenada Guadeloupe Guatemala Guinea Guinea-Bissau

Banana 6800 6610 13690 9400 5840 11380 9760 6570 7120 8970 7120 7120 8130

Barley Bean dry Bean green 3770 3850 3540 5420 3970 4250 3420 8040 4260

Grapes Groundnut 6470 3890 12640 3790 14360 7370

Maize 3340 2160 6860

Mango 12680 21660

3400 5520 4510 3640 3560 4030 3560 3560 3790

2390 5920 5010 4140 2700 5290 2700 2700 4420

2500 4200 3390 2120 2200 2670 2200 2200 2760

6780 12510 10200 6530 3970 9030 3970 3542 7790

3550 7290 6060 3540 3180 5930 3180 3180 5240

3290 6780 5640 3250 3170 4380 3170 3170 4890

11400 21190 17430 11450

7050 9250 7120 3520 7010 16700 7260

3520 4180 3560 2780 3270 3420 3490

3450 4620 2700 2700 3450 3500 4200

3260 3190 2200 2120 2500 2740 2460

7640 10110 3970 2690 7410 9400 6290

4600 5570 3180 3400 4280 4450 4940

3350 5180 3170 3120 3980 4110 4620

13060 17000

5940 3520 18840 12510 7730

2960 2780 4310 2870 4220

2350 2700 2950 7180 3970

2470 2120 3570 3510 2250

7260 3110 11120 13010 7640

3430 3400 4200 6140 4830

3170 3120 3850 5700 3480

7120 13690 16810 7070 7410 13330 18060 7120 19640 9860 7930 22850 5580 5180 6560 18380 14340

3560 3420 4440 3620 3580 2870 5140 3560 4600 4520 4110 5210 2540 4100 3120 4290 3220

2700 8040 4110 3050 3660 2860 5600 2700 4830 5050 4000 5740 2620 4330 3040 3450 2910

2200 4260 3480 3470 2740 2290 3760 2200 3540 3400 3200 4120 2270 4120 2460 3580 2630

3970 14360 9130 8570 7690 7470 9170 3970 10680 10360 8340 15070 8270 5200 7390 10550 8110

3180 7370 5440 5690 4650 3660 7150 3180 6150 6080 5110 7150 3740 4960 3940 4690 3830

3170 6860 5060 4470 4330 3390 6690 3170 5720 5660 4760 6610 2510 3500 3660 4330 3540

6300 16280 5530 9430 8670 7120 7120 22760 9480 9481 6290 9680 8720 14100 7120 19170 2050

3880 3480 4350 4270 5050 3560 3560 6250 4330 4270 3050 5620 3930 3040 3560 4080 1030

4000 3310 3330 4900 5200 2700 2700 6230 4840

7000 9220 5770 9790 7650 3970 3970 12020 10020

4080 4280 5370 5820 6530 3180 3180 8060 5820

2970 3960 2460 5410 6060 3170 3170 7520 5410

2680 4520 4080 3100 2700 4470 1230

3000 2830 3530 3190 4180 2200 2200 4660 3260 3260 2480 4200 3090 2400 2200 3170 1040

7440 10250 11080 7890 3970 10550 3600

3680 6930 5060 3960 3180 5600 1880

3410 4490 4690 3670 3170 5190 1410

7120 7120 8380 11930 21520 7120 7120 16280 6610

3560 3560 4300 2630 4130 3560 3560 3480 5420

2700 2700 3730 2570 5220 2700 2700 3310 3970

2200 2200 3430 2100 3110 2200 2200 2830 4250

3970 3970 8870 6680 11750 3970 3970 9220 12640

3180 3180 5220 3320 6230 3180 3180 4280 3790

3170 3170 4860 3070 5860 3170 3170 3960 2160

8520 6300 15280 17740

3940 3880 3000 3450

4320 4000 3200 3950

2980 3000 2390 2690

8910 7000 8600 9880

5260 4080 4020 4880

4890 2970 3710 4520

Appendix I - p.1

17370

13820

12540 11530 11650 11350 16950 19570 13810

21660 15300 14130 13030 12080 16590 17880 17680 13970 21320 12250 10760 12110 16600 12980

10940 14730 16830

20810 17080 11880 20110 16630 12780 17430 6630

14620 10810 19580

14730

15200 10940 13840 16100

Millet 4580 2160 5530

Palm 11910

3360 4800 3820 2500 3690 4210 3690 3690 2580

10560 19600 16130 10550

3370 3910 3690 2720 2810 3680 1850

19630

15920 5140 12740 12190 15770 5140 11600 14100 10670

2380 2720 3050 4580 2940

10520 15700 17700 12780

3690 5530 4190 3010 2990 2980 5680 3690 4990 3860 4090 6070 2140 2930 3130 3560 3030

19630 14080 13190 12020 11230 15220 5140 16560 16360 13000 22720 11480 9980 11210 15370 12050

2000 3460 3050 3530 5100 3690 3690 6340 3720

12250 13700

3390 4210 4250 3230 3690 4670 2210

11000 18570 14570 11890

3690 3690 3810 2680 5510 3690 3690 3460 2160 3330 2000 3380 4170

15580

19130 15830

16210 4590

7080 13450 10040 18310 7080 13700

14060 12250 12920 15040


Guyana Haiti Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kampuchia Kazakhstan Kenya Kiribati Korea Korea, Republic of Kuwait Kyrgyzstan Laos Latvia Lebanon Lesotho Liberia Libyan Lithuania Macedonia Macao Madagascar Maderia Malawi Malaysia Maldives Mali Malta Martinique Mauritania Mauritius Mexico Micronesia Moldova Mongolia Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norway Oman Pakistan Panama Palestine Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Réunion Romania Russian Federation Rwanda Saint Kitts and Nevis Saint Lucia

Banana 7960 8230 6300 7120 7120 8510 8640 13070 14920 7120 13070 5530 9500 5400 8740 7720 8600 16780

Barley Bean dry Bean green 4120 3590 3270 3900 4270 2930 3880 4000 3000 3560 2700 2200 3560 2700 2200 3780 5510 5190 4180 3920 3020 8800 4160 1880 8530 3280 3560 2700 2200 8800 4160 4350 3330 3530 4400 4880 3310 3650 3070 2980 1910 5120 2470 3700 3510 3380 3200 2672 3370 3090 2800

Grapes Groundnut 8180 5020 8470 5220 7000 4080 3970 3180 3970 3180 11320 6930 9680 5010 13240 5980 14990 6180 3970 3180 13240 5980 5770 5370 9800 5910 5100 4620 9060 4340 8890 5340 3970 9680 4020

Maize 4680 4860 2970 3170 3170 4720 4850 6350 5710 3170 6350 2460 5500 3160 4030 4470 6420 3710

Mango 13620 14590 10940

Palm 12510 13460 12250 5140

15140

Millet 3420 3110 2000 3690 3690 5180 4160 3960 4400 3690 3960 3050 3610 2910 3220 3530 3180 3260

19720 15710 24940 21620 24940 16820 10540 13550 14730

18460 14500 22610 19300 7080 22610 9740 15550 9500 12240 13710 14120

6500

2460

3870

2980

5290

3370

2190

9880

3560

9230

17850 6840 7120 7560 13510 14220 10520 7120 7120 7010 20580 12380 14430 7810 8440 23300

3000 3040 3560 1710 2960 3120 2550 3560 3560 3020 5130 3430 3520 3560 4250 4270

8170 3170 2700 4300 2400 3100 6140 2700 2700 3820 4340 3880 2600 3640 3770 4900

4510 2960 2200 2040 2690 2470 3200 2200 2200 2560 4170 2490 2980 3180 3560 3380

18220 7650 3970 7840 8250 7940 11000 3970 3970 6420 11550 6270 8230 8720 9880 13140

8210 4520 3180 3590 2500 3980 5540 3180 3180 3660 5850 4900 3670 4950 5850 6010

7620 3670 3170 3330 2280 3690 5150 3170 3170 2270 4530 4590 3350 4250 5100 5560

24720 12770

23960 11880

11140 18650 11380 13030 14460 16200 21100

5980 3170 3690 2670 2300 3230 4150 3690 3690 3770 4450 3950 2670 3690 3860 5190

8530 12530 15160 5820

3820 6630 3120 3170

4210 6310 2370 3880

3000 5830 2710 2920

9270 14400 8970 6330

5160 9240 3250 4580

4790 8620 2980 3430

15560 23750 13620 12060

3600 7350 2490 3190

14420 22090 12670 11130

7120

3560

2700

2200

3970

3180

11160 7560 15170 7410 19090

5120 2400 3460 3580 4350

5710 4150 2520 3660 3000

3860 2570 2970 2740 3780

11720 8080 8880 7690 11290

6880 4300 3510 4650 4260

3170 4200 6400 4010 3230 4330 3910

8340 7120

3330 3560

4380 2700

4040 2200

8170 3970

4970 3180

6570 9130 23300 23110

3640 3900 4270 4440

4140 4420 4900 4620

2120 3020 3380 3660

6530 10710 13140 14500

2200 11130 11900 7320

3970 4490 4520 4460

3180 5960 7100 3380

3170 4300 4760 2820

7000 7880 8170 7840 7120 9000 12520 8530 15310 7120 7120

3530 4000 4170 3380 3560 3590 2870 4020 3560 3560 3560

3150 2460 3450 3860 2700 3650 7180 4330 8430 2700 2700

15610 12420

4050

3900

11780 12080 12900 16580

10650 11200 11980 15020

10290 17200 10460 12030 13430 15050 19750

3690 20000 12510 13660 13030 17160

4380 3300 2610 2990 3090

18510 11420 12660 12020 15900

2960 3170

14580

4060 3690

13670 7080

3540 5200 6010 5780

3250 4820 5560 5310

11450 17660 21100 22660

2500 4230 5190 4950

10550 16470 19750 21240

3600 12450 9900 9340

3690 7040 4540 4670

4010 6560 2430 4520

8340 19810 18370 15160

3290 5490 6490 3470

3520 18230 17150 13460

2630 3560 3400 3320 2200 2960 3510 3040 4420 2200 2200

8040 11140 9200 8250 3970 4280 13010 8740 15980 3970 3970

4390 4180 4940 4630 3180 4340 6140 5340 7750 3180 3180

4140 3850 4590 3630 3170 4270 5700 4970 7200 3170 3170

13070 16420 14800 14020

3290 3650 3390 3800 3690 3610 4580 3250 5770 3690 3690

12090 15290 13650 13040

3180

9070

5070

4710

15010

3990

13860

Appendix I - p.2

19570 14980 24010

17700 13840 21730 7080

Appendix I. Crop water requirements (m3/ha). Source: CropWat model (www.fao.org). Banana Saint Vincent/Grenadines Samoa Sao Tome and Principe Saudi Arabia Senegal Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States of America Uruguay Uzbekistan Vanuatu Venezuela Viet Nam Virgins Wallis and Futuna Is West Bank Western Sahara Yemen Yugoslavia Zaire Zambia Zimbabwe

9540 14220 19050 14220 7810 7580 7352

Barley

5640 3120 4560 3120 3560 3570

Bean dry

4080 3100 4450 3100 3640 3470

Bean green

3600 2470 3580 2470 3180 3000

Grapes

10060 7940 10490 7940 8720 8550

Groundnut

6390 3980 5780 3980 4950 4800

Maize

4120 3690 5370 3690 4250 4240 4240

Mango

18500 12900 17310 12900 14460 14070

Millet

3940 3230 4580 3230 3690 3570

Palm

16850 11980 16010 11980 13430 13040 12000

23110 8970 6230 8970 7520 15800 8740 7120 7120 9540 15340 16780 12440 8390 15440 9430 8110 7880 6700

4030 3450 5590 3080 5750 4400 3560 3560 5640 1740 3370 2440 4700 3160 4270 3710 1260 5150

5290 4200 5640 3620 8280 3950 2700 2700 4080 8790 3090 2180 4370 3130 4900 3950 4790 3820

2670 2450 4570 3030 5620 3490 2200 2200 3600 3270 2800 2050 4260 2550 3190 2860 1900 3500

9030 7430 8280 8060 17110 9260 3970 3970 10060 15350 9680 7260 8780 8760 9790 8890 8030 7330

5930 4090 6870 4480 9250 5400 3180 3180 6390 6240 4020 2850 5740 4000 5820 4860 3500 4390

4380 3800 6640 3690 8620 5030 3170 3170 4120 5760 3710 2620 4460 3700 5410 4520 3240 1540

16780 18360 9540 7120 7120 6570 5600

3370 3730 5640 3560 3560 3640 2760

3090 3600 4080 2700 2700 4140 2430

2800 3040 3600 2200 2200 2120 2580

9680 9070 10060 3970 3970 6530 8840

4020 5070 6390 3180 3180 3540 2690

3710 4270 4120 3170 3170 3250 2450

15140 16590 18500

6560 7410 7050 9840

3120 3580 3390 4570

3040 3660 3180 5060

2460 2740 3160 3440

7390 7690 8170 10180

3940 4650 5020 6140

9900 9310 5570 14220 18070 18470

3110 3590 4100 3150 4900 4060

5410 4810 4190 2960 3710 2750

3230 3340 3400 2550 4050 3570

10570 10160 4390 8010 10150 10990

5450 5490 4930 3850 5180 3910

Appendix I - p.3

17370 12130 13540 25900 15340

18500 22000 15140 11210 16870 13970 16830 14880 11730 12980

4210 3380 5620 3630 7180 3880 3690 3690 3940 4410 3260 2290 3950 3290 3530 3470 2530 2400

15920 11350 12580 24750 14130

16850 19600 14120 10460 15450 13020 15580 13800 10520 11760

11450 12590

3260 3770 3940 3690 3690 2500 2890

14120 15460 16850 5140 6340 10550 11780

3660 4330 4110 5720

12110 13030 13550 17440

3130 2990 3190 3800

11210 12020 12620 16120

5060 5100 4890 3570 4800 3580

16380 16130

4170 4270 4130 3080 3760 2860

14950 14810

12880 16380 16590

11960 15050 15400



Pepper 4200 3240 3520

Potato Sorghum Soybean Sugarbeet Sugarcane Sunflower Tobacco 2960 4450 5300 6960 12740 6300 5790 6640 5230 6870 8190 11640 6040 5730 6920 7550 5520 5710 23310 9100 5280

Tomato Vegetable 7640 4040 7700 4900 8350 4230

3690 5740 4570 4050 3130 5790 3130 3130 3120

4130 6880 5630 5290 3290 4680 3290 3290 4740

3260 5030 4070 4210 2600 4880 2600 2600 3830

4230 6660 5400 6020 3400 5810 3400 3400 3780

4850 8960 7530 6700 3990 5780 3990 3990 6550

11620 21000 17970 12850 12830 18520 12830 12830 14160

3710 6800 5770 3770 2990 5550 2990 2990 5050

3550 5710 4660 4090 2830 5350 2830 2830 3420

4610 8600 7280 4490 3740 6330 3740 3740 6320

3160 5480 4600 2430 2450 4490 2450 2450 3760

3420 4680 3130 3110 3370 4940 2250

3520 4070 3290 2610 5230 4230 4360

3270 3000 2600 2670 4280 3880 2890

5210 3970 3400 3450 5660 5730 2570

5270 5360 3990 3970 6930 6640 6180

13450 12930 12830 6670 17490 15540 11950

4030 3980 2990 2990 5370 4210 4730

4280 3440 2830 2840 4860 4620 4030

4830 5050 3740 3740 6810 6330 5860

3360 4560 2450 2440 3280 3550 3110

3520 3110 6570 3300 4400

4000 2610 4260 5750 4980

3150 2670 3010 6530 3920

4330 3450 6290 4570 5330

4980 3970 7770 5640 6170

12070 6670 17270 21130 14250

3280 2990 3360 8100 4120

3520 2840 5380 4340 4210

4080 3740 5940 6990 5120

2480 2440 5230 3460 3160

3130 3520 4360 3790 3830 3880 3410 3130 5090 4630 4820 7850 4610 4070 3790 5830 4310

3290 6920 5400 4650 3780 3570 7000 3290 6000 5650 5050 6880 5410 4320 3940 4690 3780

2600 7550 3830 2920 2950 3060 5140 2600 4900 4180 3980 6540 4290 4450 3090 3490 3050

3400 5520 4430 5460 3780 4400 4340 3400 5830 5550 5190 9500 5740 5580 4020 5840 4650

3990 5710 5340 4710 4580 5140 4530 3990 6770 7570 6040 10880 6640 6530 4790 7100 5510

12830 23310 16240 14300 13320 12470 18070 12830 18780 18190 14500 22930 13830 9870 12690 17120 13400

2990 9100 4560 3490 3590 3380 6200 2990 5580 5830 4550 7010 4330 4730 3550 3940 3390

2830 5280 3610 4470 3360 3560 3000 2830 4640 4810 4280 7590 3740 4650 3240 4900 3810

3740 8350 5030 4350 4620 4860 5450 3740 6830 7370 5690 10680 5250 6220 4510 5930 4970

2450 4230 3620 2890 3080 2850 2950 2450 3860 4720 3770 5440 3120 3710 2920 4600 3270

2380 4920 2590 4240 6000 3130 3130 5200 4470

3580 4200 5500 5350 6300 3290 3290 7280 5410

2980 3560 4340 3950 4950 2600 2600 5810 4100

2790 5460 5600 5250 6150 3400 3400 5620 5440

3320 6430 6720 7290 7790 3990 3990 6620 7250

8600 15150 10920 17240 14790 12830 12830 22320 17540

3530 3910 4940 5670 5700 2990 2990 6930 5600

3000 4460 4730 4630 5390 2830 2830 4430 4730

4280 5890 6300 7160 7330 3740 3740 6940 7080

2620 3640 4070 4630 4630 2450 2450 4350 4610

3730 4570 5060 4110 3130 5170 1730

4060 4730 4910 3860 3290 5410 2400

3190 4790 4550 3280 2600 4840 2470

4280 7790 5980 4630 3400 6250 2780

5000 7580 6230 5420 3990 7150 3060

12570 21420 17210 13230 12830 18090 5660

3490 7380 4770 3640 2990 5320 2420

3440 6110 4990 3760 2830 4940 1910

4380 5890 6110 5100 3740 7280 3550

2760 5170 4320 3000 2450 3720 960

3130 3130 3970 3470 5680 3130 3130 4920 3240 4400 3990 2380 4510 5020

3290 3290 4210 3250 6010 3290 3290 4200 6640

2600 2600 3290 2710 5940 2600 2600 3560 5230

3990 3990 5250 4530 8340 3990 3990 6430 8190

2830 2830 4390 3130 5830 2830 2830 4460 5730

3740 3740 4740 4240 9000 3740 3740 5890 7700

2450 2450 3060 2680 3670 2450 2450 3640 4900

3570 2980 3640 4490

12830 12830 14890 11200 20220 12830 12830 15150 11640 12000 15650 8600 14160 16540

2990 2990 3710 3050 6390 2990 2990 3910 6040

4920 3580 3880 4690

3400 3400 4500 3860 7500 3400 3400 5460 6870 4730 4730 2790 5420 6240

4980 3530 3900 4820

4110 3000 4350 4950

6290 4280 6030 7140

4020 2620 3130 3420

6500 3320 6220 7080

Appendix I - p.4



Pepper 3600 3730 2380 3130 3130 4720 5150 6170 2310 3130 6170 2590 4330 2790 2100 4140 2600 5430

Potato Sorghum Soybean Sugarbeet Sugarcane Sunflower Tobacco 3740 2920 3980 4670 13740 3370 4080 4870 3260 4340 6440 15040 4890 3780 3580 2980 2790 3320 8600 3530 3000 3290 2600 3400 3990 12830 2990 2830 3290 2600 3400 3990 12830 2990 2830 4920 5030 6910 8250 20300 6490 6870 5170 4030 4890 6180 16480 4440 3980 4780 3450 5820 6380 24710 9270 7000 5590 7840 3690 5460 23840 10210 3900 3290 2600 3400 3990 12830 2990 2830 4780 3450 5820 6380 24710 9270 7000 5500 4340 5600 6720 10920 4940 4730 5500 3860 5130 7330 17320 5610 4470 2410 2500 4580 5260 10100 4130 3940 4050 4670 3140 3680 14640 5770 3030 4680 3420 5400 5450 15170 4080 4420 2610 3400 3990 2990 4120 3940 3470 6000 7070 15400 3890 4950

2360

2300

3460

4270

5610

10600

4480

3350 3580 3130 2010 5230 4100 2700 3130 3130 3410 6040 3300 4630 4220 4720 6950

7550 3860 3290 3360 2600 3900 5200 3290 3290 2480 5850 4790 3670 4480 5350 5750 4200 4890 9070 3250 4260

8070 3080 2600 3860 2680 3280 5720 2600 2600 3660 4170 3630 2540 3580 3740 5730

5400 4740 3400 2680 4810 4600 4130 3400 3400 3830 5950 4180 4430 5110 5700 8610

6760 4900 3990 3470 6080 5390 4470 3990 3990 5720 7270 4370 5470 5640 5900 9790

3870 6890 2600 3620

5110 8940 5440 4550

6370 7980 6620 4700

27860 13180 12830 12750 12130 13360 17810 12830 12830 12010 19500 12380 13460 15020 16470 21490 15672 16050 24430 13760 12300

12150 3680 2990 4840 3480 3650 6940 2990 2990 4350 4860 4340 3200 4190 4350 6050 5670 4870 7290 2960 3980

2600

3400

3990

12830

5350 2570 5150 3830 6770

3290 3200 6400 4110 3550 3780 4330

4730 4000 2590 2950 3000

6270 3560 5030 3780 6370

8550 3570 6170 4580 7930

3320 3130

3100 3290

3930 2600

6490 3400

4050 5080 6950 8130

5290 4900 5750 5560

4210 5000 5730 5620

3400 4880 4360 5940

3990 6810 2640 6130

4110 5400 4210 3780 3130 4220 3300 3900 3940 3130 3130 4490

4310 6920 5400 4250 3130

Tomato Vegetable 4320 2830 6160 3810 4280 2620 3740 2450 3740 2450 7470 5460 5830 3620 8700 4400 7130 2880 3740 2450 8700 4400 6300 4070 7080 4450 5270 2810 4940 2400 5040 3380 6200 6250 3920

3550

5060

3790

7000 5460 3890 2830 2530 4250 3730 3950 2830 2830 3150 4950 3360 3740 4180 4680 6880

9410 4530 3740 4090 4110 5100 6280 3740 3740 5290 6270 3900 4390 5240 5490 9630

4170 3050 2450 2020 4190 3050 3160 2450 2450 3620 4960 2560 3800 3460 3590 4630

4370 7580 4620 3840

6180 10290 5210 5270

4090 6590 4060 3510

2990

2830

3740

2450

20580 13270 13960 13320 17490

6580 4720 3020 3590 3380

5440 3240 4270 3360 5500

8320 4850 4870 4620 6090

5330 2680 4070 3080 5360

6490 3990

15050 12830

5160 2990

5370 2830

5850 3740

4350 2450

6020 6590 8610 9130

6700 6580 9790 11160

12850 18050 21490 22830

3770 5250 6050 5810

4090 5660 6880 7870

4490 6740 9630 10360

2430 4990 4630 5490

7000 5990 6300 4830

2990 6300 8140 5870

2830 6440 10230 7200

4580 20790 19660 16670

2450 6810 8180 5620

2990 5520 6540 3540

3740 7900 9330 6960

4680 6920 4500

4380 6720 4640 3840 3290 4440 5750 5010 7240 3290 3290

3190 5330 3640 3680 2600 3500 6530 3380 8210 2600 2600

4240 7550 4690 5290 3400 4590 4570 4490 5720 3400 3400

4950 8480 5810 5870 3990 5400 5640 6560 6640 3990 3990

13630 18060 15340 14600 12830 8680 21130 15470 29090 12830 12830

3450 5620 4570 4340 2830 3820 4340 3890 5460 2830 2830

4650 6110 4970 5400 3740 5060 6990 6250 8810 3740 3740

2950 3460 3130 3720 2450 3300 3460 3870 4350 2450 2450

5000

3790

4760

5670

15770

3590 5020 3940 4460 2990 4030 8100 4960 10090 2990 2990 5810 4400

3880

5330

3530

Appendix I - p.5

Appendix I. Crop water requirements (m3/ha). Source: CropWat model (www.fao.org). Pepper Saint Vincent/Grenadines Samoa Sao Tome and Principe Saudi Arabia Senegal Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States of America Uruguay Uzbekistan Vanuatu Venezuela Viet Nam Virgins Wallis and Futuna Is West Bank Western Sahara Yemen Yugoslavia Zaire Zambia Zimbabwe

Potato

Sorghum

Soybean

Sugarbeet Sugarcane

Sunflower

Tobacco

Tomato

Vegetable

5420 4100 5230 4100 4220 4230

4820 3900 5680 3900 4480 4480 4480

4680 3280 4410 3280 3580 3460

6910 4600 5610 4600 5110 4820

6900 5390 6660 5390 5640 5420

18270 13360 18150 13360 15020 14620 13564

6360 3650 5080 3650 4190 3980

6120 3730 4560 3730 4180 3940

7020 5100 6290 5100 5240 5060

4630 3050 4200 3050 3460 3280

5790 3960 6600 3890 6370 4350 3130 3130 5420 2130 5430 4210 5200 4700 4240 4160 1470 4210

4680 4140 6930 3900 8910 4510 3290 3290 4820 5630 3940 3000 5310 3900 5350 4630 3200 4390

4880 3280 5460 3520 8110 3520 2600 2600 4680 8100 3470 2450 4120 3460 3950 3760 4200 5160

5810 4230 5100 4900 8150 5030 3400 3400 6910 3590 6000 4550 6330 5330 5250 4870 2260 5210

5780 4660 8450 5560 8820 5550 3990 3990 6900 5380 7070 5410 6990 6230 7290 6000 3030 5330

18520 12170 15790 14110 27190 15700 12830 12830 18270 24320 15400 11350 17390 14320 17240 15340 12850 12030

5550 3700 6270 4170 9430 4080 2990 2990 6360 10610 3890 2640 5540 3760 5670 4590 5370 5450

5350 3680 5920 4010 7190 4440 2830 2830 6120 3870 4950 3800 5150 4340 4630 4250 2290 5650

6330 4540 7910 5150 10400 5190 3740 3740 7020 7210 6250 4600 5950 5770 7160 5840 4020 5970

4490 3390 5140 3450 6060 3390 2450 2450 4630 2810 3920 3020 4600 3400 4630 3930 1740 3950

5430 5800 5420 3130 3130 4050 4500

3940 4520 4820 3290 3290 5290 5730

3470 3950 4680 2600 2600 4210 4550

6000 6430 6910 3400 3400 6020 6430

7070 7570 6900 3990 3990 6700 7210

15400 16960 18270 12830 12830 12850 14260

3890 4290 6360 2990 2990 3770 4190

4950 5290 6120 2830 2830 4090 4410

6250 6790 7020 3740 3740 4490 5030

3920 4180 4630 2450 2450 2430 2780

3790 3830 3760 4550

3940 3780 4300 5710

3090 2950 3090 4000

4020 3780 5040 5300

4790 4580 4950 7600

12690 13320 13910 17990

3550 3590 3700 5810

3240 3360 4120 4590

4510 4620 4570 7320

2920 3080 3060 4570

3460 4020 4810 4220 5240 6630

5200 5300 5070 3790 5250 3970

5130 4720 4010 3120 3380 2890

4550 4960 5240 4620 4840 6420

5090 5430 6160 5450 6030 7910

17430 16990 9200 13310 17220 16800

6130 5480 4610 3460 4040 3170

4100 4300 4370 3770 4050 5510

6150 6170 5770 5000 5010 6470

3400 3660 3770 3180 4580 5150

Appendix I - p.6



W.melon 5600 6130 6250

Wheat Cotton seed Cabbage 4350 7410 2700 8010 5900 6060 2930

3660 6630 5600 4670 2990 5750 2990 2990 4910

3540 5480 4440 3030 3740 4730 3740 3740 3120

5470 8960 7180 5110

3240 1180 3740 3020

8710 3880

3200 1600 1800 2780

3980 3880 2990 3000 5150 4960 4660

4260 4660 3740 3540 3270 4560 2470

6360 7380

3770 3000 5440 5190 4530

2920 3540 3830 4890 3320

6090

2990 6250 3830 3470 3450 3810 3420 2990 5040 5640 4490 8230 4240 4990 3430 5060 4030

3740 5900 3800 4260 3440 3520 4620 3740 5180 4570 4250 7770 3720 4040 3310 4070 3520

3280 4730 5020 5450 5780 2990 2990 4850 5400

2300 4180 6540 4340 8400 3740 3740 5530 4480

11020 7080

3680 6740 4580 4000 2990 5410 2170

3170 6380 4910 3690 3740 5450 1240

6000 11430 7900 5900 3880 8520 3730

2990 2990 4490 3340 6500 2990 2990 4730 6130

3740 3740 3470 3060 6810 3740 3740 4180 8010

6680 4910 10070 5600 3880 6440 10168

4830 3280 4690 5400

3900 2300 4350 5250

6280 4280 6310 7710

4970

5290 6840 4330

5940 5680 6660

6060 7400 6290 5480 5500 9710 4872 8920 7300 6590 11280 7660 6780 5670 6670 5620

4280 6440 6770

3660 1960 1800 2790 3500 2550 3200 1800 5150 2540 4000

Carrots

Cauliflower Cucumber

2470 3620 2790 3460 3960 2020 1720 4980 3610 3130 4580 3790 2970

Oats

Onion green

3000

6730

6100

4360

4150

5740

5330

6730 3760

6100 3410

5010 3000 4600 4860

4150 2300 3120

5740 3220

5330 2980 3200

3760 3760

3410

2220 3220

2980 2980

3000 3000

2300

4440

6120

5400

1960 2930 3960 3200 2860 3660 1642 4080 3760 3870 5270 3440 2250 2940 4640 3320

Lettuce

3670 3000

3190

4410

3850

3220

3000

5040

4840

2300

2980

4220

4720

5280

4110

5790

4450

3180 4560 4660

5090 4640 4910

3980 4410 3390

3730 3440 2860

5450 5470 3760

4860 4610 3600

3410

2670

3540

2160

2590

2590

3410

3000

2300

3220

2980

3410 3410

5620 3000 3000

4430 2300 2300

3220 3220

5040 2980 2980

3600 3600 5790

3100 4200

3410 3760 3760

4250 3860

3000

6240 5290

3890 5570

3220

3800 1760 1800 1960 3970 2610 3670 1960 1940 3620 3100 3290 3800 2300 3060 3360

3760 3600

4540 4540

3790 3790

3110 3110

3510 3510

3450 3450

3600

4540

3790

3110

3510 3510

3450

5510

6150

5670

3550

6500

5010

5080

5460

4200

3830

4020

5760

Appendix I - p.7



W.melon 4120 4780 3280 2990 2990 6390 4410 6860 5110 2990 6860 5020 5450 4170 3660 4040 5190

Wheat Cotton seed Cabbage 3180 6000 3690 3570 5840 3150 2300 4280 3740 1960 3740 6910 9190 3660 3950 7080 4080 6100 13080 3210 4780 4590 1560 3740 1960 6100 11920 4200 6540 8752 2800 4220 6810 3600 3760 6510 2700 3450 3690 1650 4330 6930 6440 3880 1800 4310 6210 3770

4400

3820

7020

6860 3640 2990 3030 4160 3980 4700 2990 2990 4390 5150 3410 3840 4150 4320 7450

6440 3840 3740 2850 3490 3710 4430 3740 3740 3380 4490 3320 2970 4150 4550 6950

6250 6210

4710 8080 4700 3930

4200 7650 3370 4030

6770 10440 4860 6270

2990 6360 3690 4350 3450 5510

3740 6460 5160 3480 3200 3440 3750

8270 4210 5020 5480 5970

5080 2990

5440 3740

8730 5600

4670 4910 7450 8300

3030 5450 6950 7220

5110 8180 9730 9540

6090 7240 3950

3672 5760 7040 5160

7660 10500 7290

3570 6220 4770 4410 2990 4040 5190 4850 6550 2990 2990

3410 4200 3600 4370 3740 5200 4890 3690 6170 3740 3740

5860 9910 7300 7410

4110

3960

7130

3470 3490 5920 4660

6620 8080 6760 5020 6930 7350 9730

Carrots


4540

4570 3790

3570 3110

6160

5090 5600 6260

6120 4390 8000

6010 3530 3920

6570 4910

4720

3790 6250 4720 5480 3990 5350

3110 2540 3870 4630 3310 4450

3510

4220 4680 6380 5450 4470 5820

5770 4860 5990

5200

3672

4590

4230

2530 1800

4220

4600

5150 2400 3890 3878

Oats

3600

4220

1720 1520 3950 3060 2200 1960 1960

Lettuce

3580

3510

5010 5720

4960

3600

4720

5480

5240

4620 3450 4400 4980 5780 4330 3450 4210 4300 5670 4230 5580

5600

3450 5100

4910

4590

3450 3220

6110

Onion green

3670

3000 3000

3510

5230

3940

4200

3700

3450 4760

6630 3450 3450 6030

4790

4460

4390 2590 3430 6030 3830 3280

4220

1960

6790 5680 6090 6790

5200

6780

4720

3000

2440 4110

5790

3190 5550 3450

4270 4350 4020 3200 5210

3800

4450

4030

3780

1960

3600

3410

3000

2300

3220

2980

2600 3020

6730

6100

4360

4150

5740 5120

5330 4480 8940 5130

6130

5570 5460

6200

5080 1960 4070

2300 4840

3780 6550 8650

4320

5150 4140 1960 2230 3300 3110

3760 4710

1960 1960 3730

Appendix I - p.8

4720 4650

3760

3790 3220

3000 3830

3150

3510 3980

3570 4410 4630 2980 3850

3000 3600

2300

3510 3510

2980 3570

Appendix I. Crop water requirements (m3/ha). Source: CropWat model (www.fao.org). W.melon Saint Vincent/Grenadines Samoa Sao Tome and Principe Saudi Arabia Senegal Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States of America Uruguay Uzbekistan Vanuatu Venezuela Viet Nam Virgins Wallis and Futuna Is West Bank Western Sahara Yemen Yugoslavia Zaire Zambia Zimbabwe

Wheat

Cotton seed Cabbage

6450 3980 4850 3980 4150 3950

5650 3710 4660 3710 4150 3890

8830 5920 8210 5920 6930 6560

5750 3650 6270 4130 7980 4540 2990 2990 6450 5140 5190 3940 5280 4610 5450 4430 2910 5880

4730 3450 8730 4020 7540 3830 3740 3740 5650 4810 4310 3100 5140 4110 4340 4090 2730 4270

8710 5090

5190 5560 6450 2990 2990 4670 5000


5400

5120

6200

3060 4026 1960 1960

5320 3670 3760

4850 4720

3000

4560 3440 3950

5960 4390

3040 4120

1960

3670

8830 4390 6210 4400 8010 6140 6770 6540 2750 8720

3650 1960 2840 3690 3320

4310 4740 5650 3740 3740 3030 3430

6210 7070 8830

4010 2660 3650 1960 2100 4050 2430

3430 3450 3670 5650

3310 3440 4020 4360

5670 5480 6350 7110

3030 3200 3760

4670 4740 4620 4000 4190 5560

4400 4480 5730 3580 3560 3800

5630 6310

2800 3270 2390 3210 4950 4990

6830 10140 6920

6210 5110 8640

5690 6860 5640

3650 4240

Carrots

Lettuce

Oats

5290 5840

4570

3410

4410 3510

4200 2980

5990 4210

3860 4960 3570

5100 6770

4460 6350 4350 6240

4720

3000

2300

3510

2980

4150 3670

4410

4150

4030

6020

5080

5110

4580 3430

5570

4620 2980 3950 4550

5210 4010

3460 2260 2150 2080

Onion green

4900 4140

5490 4200

2640

5610 6260

5940 3670 5400 3670 4220

6200 4720 5390

3670 5410 5080

5280 5490 3000 5280

3510 3110 4110

3510 6370 6110

4600 4400 5240

4030

5060

3920 3620

4160

4870 5220 5380 2980 5340 2980 5600 3450 4450 3450 2980 4280 3780

5410 4200

5570

Appendix I - p.9

4000

Appendix I. Crop water requirements (m3/ha). Source: CropWat model (www.fao.org). Onion dry Afghanistan Albania Algeria American Samoa Angola Antille Antigua Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium-Luxembourg Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bahrain Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi Burma Cambodia Cameroon Canary Islands Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Colombia Comoros Congo, Dem Republic Congo, Republic of Cook Islands Costa Rica Côte d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Estonia Ethiopia Falkland Fiji Islands Finland France French Guiana Gabon Gambia Georgia Germany Ghana Greece Grenada Guadeloupe Guatemala Guinea Guinea-Bissau

Peas

Safflower

Spinach

Sweet potato

Artichoke

Citrus 8660

Rice 5900

14490

6620

4490

5970

6620 3970

4490 2730

5970 3370

7780 3000 11180 7680

6430 3700 2330

6430 3500

10850 4680

7680

8600 6200 8600 5900 5900

9400 3670 5480 3970

2730 2730 3600

3370

2330

3500

11630 4680

7670 4910 11420 13020 9400

2730

6530

4100

3970

2730

4910 8550 10380 7870

5760

6700 8200 8950 5900 5900 6780 7200 6570 6800

7600 5900

10300 8200 3000 5920

3400

8260 11190 6120

3280

6200

3430

12190 2570 9570 16780 8390 7360 8230 11220 8840

3350

3000

8770

3970

2730

3970 3970

5180 2730 3780

6670 5580

4020 4170 4020

5490 5680

3900

3370

2330

3370 3370

4400 2330 2330

3780 5260 6720 6800

4210 5420 4020

4960

3500

11370 9370 10940

4680

3500 3500

4680 4680

6810

3780 3780

5900 5900 14040 11660

10710 13800

3780 4020

5590 5590

7340 8450 5900

11500 9740

6940

6800

7340 6800 7200

8050 13650 11390 8740

6870 9600 8200 5900

11960 3320 4680 4680

3610 3610

4370 4370

7490 7490

4020 5590

3780

4680

3610

8190

5550

6820

4710

3370

5680

4370 5090

7490 11040

4250 9820 7380 13580 4250 10060 8370 10230 11940

5900 5900 6250 9600 5900 5900 9200 7820 9120 7650

9530 11120

Appendix I - p.10

Appendix I. Crop water requirements (m3/ha). Source: CropWat model (www.fao.org). Onion dry Guyana Haiti Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kampuchia Kazakhstan Kenya Kiribati Korea Korea, Republic of Kuwait Kyrgyzstan Laos Latvia Lebanon Lesotho Liberia Libyan Lithuania Macedonia Macao Madagascar Maderia Malawi Malaysia Maldives Mali Malta Martinique Mauritania Mauritius Mexico Micronesia Moldova Mongolia Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norway Oman Pakistan Panama Palestine Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Réunion Romania Russian Federation Rwanda Saint Kitts and Nevis Saint Lucia

5590 6400

Peas

4130 3780 6020 3680 8210

5590 5170 6760

3780 8120 4190

5710

3560 5420

Safflower

Spinach

Sweet potato

Artichoke

4680

3610

4370

7490

8080 4850

7100 5670

18410

3190

3870 4260 3210

5620 4030 6820 5050

9200 5640

9550

Citrus 9130 9920 8260

Rice 8820

13600 12400 16300

9880 9300 10210

14800 7360 11460 7060 9010

3970 4570 6190

3760

3860

3570

13920

3520

3960

3970

4020 3780 3780

7030

4170

6220

4570 3900

5860

10290

5900 12000 6400 7600 8210

10370

6200 9230

6880

6570

17640 7600

3970

7920 6200

7820 8120 8810 11080

6200 8720 5900 5900

5900 5900

3320

12560 7710 8760 10100

5450

11200

8900

14610 7450

6000

4020 4800

6200

3260

10700

10620 16330 9250 10230

6410 7980

3790

5140

4200 5670 3670

3370

3000

13640 8450 9230

4810

12000 7620 8720

11560

3970

2730

3370

6620

4490

5970

8920

5500 5490

2930

3500 6430

6300

2730 6040

4680

3910 7680 12170 15680

13440 9530

5490 4970 3970 5450

4180

3970 3970

2730 3790

3790 4300

7580

6180

4550

5860 3500 4860

3120

11110 9960

5870 10180 16000

10420 12300 12600 5900 11670 10970 7620

7840 8230 8720 5900 6800

5900 5900

3370 10160

Appendix I - p.11

9800 5900

Appendix I. Crop water requirements (m3/ha). Source: CropWat model (www.fao.org). Onion dry Saint Vincent/Grenadines Samoa Sao Tome and Principe Saudi Arabia Senegal Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States of America Uruguay Uzbekistan Vanuatu Venezuela Viet Nam Virgins Wallis and Futuna Is West Bank Western Sahara Yemen Yugoslavia Zaire Zambia Zimbabwe

Peas

Safflower

Spinach

Sweet potato

5640 6140

16480

5740 3970

4600 3790

4520

6570

4010 5330 6200

5210 4200

8710

Artichoke

4830

5630 4600

11060 13790

6690

3790

7120 3970

8410

7140

3270 4640

7100 7910

3850 4800 4860

11600 10600

9570

8720 5900 5900

11750 8330 10520

3370

7260 8350 7600 12600 8900 5900

18830

5080

Rice

12400 11740

18220 10340

5320 3970

Citrus

3840

5860

14410 7670 11400 9570

12000 6200 6800 9600 7230

10170 7740 8610

9200 7200 7680

11360

9850 6200 9340 5900 8600 7680 6200

5300

7120 3790 4630

4500 3780 6200 3790 4750 4770

10170 6640

6130

6070

6800

2680 3620

6860

4910 7260 8560

3970

5140

5220

3770 3670

8830 9270 11870

4810

11000 10900 10520 8780 10940 11210

3490 4370 4490

Appendix I - p.12

7680 8720

8670 8960 9000 10340

Appendix II. Actual crop yields (ton/ha) in 1999. Source: FAOSTAT database (www.fao.org).

Banana Afghanistan Albania Algeria American Samoa Angola Antille Antigua Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia Botswana Bahrain Brazil Brunei Bulgaria Burkina Faso Burundi Burma Cambodia Cameroon Canary Islands Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Colombia Comoros Congo, D Congo, R Cook Islands Costa Rica Côte d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Estonia Ethiopia Falkland Fiji Islands Finland France French Guiana Gabon Gambia Georgia Germany Ghana Greece Grenada Guadeloupe Guatemala Guinea Guinea-Bissau

3.2

Barley Bean dry Bean green 1.2 1.8 1.1 1.2 0.9 0.5 3.7

2.5 9.7 11.0 2.6 19.7

2.2 9.3 14.3 5.6 24.0 18.8 5.2 16.5 11.2

16.0 4.3

2.3 1.3 1.9 4.7 1.0 0.9

1.6 2.5 0.9 1.3 2.7

0.6

0.7

1.5 7.4

0.9 3.4 1.0 0.7

1.0 1.0 0.6 2.6

1.0 2.0

2.3

0.7

2.6

0.8

4.6 14.4

0.4 0.9 3.2

3.7 2.5 2.2 0.6

1.0 12.3 33.2 37.5 1.8 4.5 15.9

2.0

0.6 1.6 1.4 1.3

2.1 3.9 5.2

3.6 7.0 5.1 12.3

0.6

16.6

3.3 1.4

1.2 0.9

4.4

1.0 1.6 0.6

3.7

0.7

0.5

1.0 2.5 2.6 12.2 2.1 1.1 4.1 1.6 2.2 3.9 0.3

15.8

1.8

2.8

7.4

3.3

1.0 1.0 0.8

3.8 1.4 1.1

5.0

4.3 18.9 7.2

7.6 3.6

3.0 2.0 4.0 24.5 29.3 3.8 9.5

2.5

0.9

1.1

1.2 1.0 1.1 1.0 4.0 1.1 1.1

3.4 0.4 1.5 1.3 1.3

14.0 5.0 8.0

0.8 0.5 2.6 0.9

6.2 1.5 12.5 2.9

12.0 12.0 4.0 19.2 11.0

1.2 0.7 3.5 1.0

2.2 0.8

4.6

3.3 4.0

1.1 1.3

12.0 1.7

1.0

2.5 4.1 9.0 0.8 1.7 1.5 2.2 6.7 1.4 9.3 1.0

5.8

0.6 0.9 1.3 2.7 1.0 0.7

2.3 11.2 11.1

7.2 9.3

5.2 1.7 8.5 8.2 11.1 2.9

3.5 16.4 9.3

3.0

Appendix II - p.1

1.1 1.0 1.1 0.3 1.1 1.3 2.9

0.9 1.3 1.2

1.8 1.4 1.0

14.1

0.7

8.7

9.9

1.1

2.2

1.2

16.7

4.8 4.9

1.0 0.4

8.0

12.5 12.7

1.7

14.7 19.2

16.8

0.6

5.0 12.5

45.0

6.3

1.6

11.2

8.3 0.3

6.3

11.2 11.4

0.7 1.1

3.0

11.4 12.4 12.9

1.5

0.2

1.4

1.6 0.7 8.5 4.9 1.8 2.4 0.8 0.7

11.4

0.9

6.2 1.0

1.1 1.0 2.0 3.0 1.4 1.0 0.8 1.1

Palm

9.4

1.3 6.9

1.9 0.8 5.6 2.9 4.1 6.6 12.0 1.6 1.4 1.2 1.3 8.9 2.5

2.3

5.9 5.2

1.6 1.9

7.0 7.0 7.2 7.0 5.9 6.4 6.0

6.2 6.4 1.8 6.2

1.1 2.3 1.0 1.0

0.8 0.6

7.6 2.6 6.2

1.6 0.9

9.0

1.7 0.7 2.1

0.5

7.3

6.3 11.2

Millet 0.8

5.7 9.0

1.5

0.8

4.0

Mango

1.7 5.2 4.0 5.3 9.6 3.6 2.2

11.8 7.3 13.3 7.6 3.4 3.2 2.6

4.7

0.5 0.8 0.5

2.9 11.7 43.5

0.3

4.0

1.0

12.0 2.8 31.2 5.9 3.7 1.4 3.3 54.0 14.7

Maize 1.5 3.7 2.2

0.4

5.1

12.0 3.0 1.3 6.5

Grapes Groundnut 6.9 13.9 0.6 3.9 1.1

27.0 1.4 1.5

0.6 3.6

16.9 9.6

34.3 2.0 1.6 1.0 7.8 1.5 9.8 6.7

15.1 1.6 0.9 15.0 1.0 0.8 0.9

12.0 0.8

11.4

4.7

0.9

8.0 1.0

8.0

0.9

3.8

11.4 8.8 8.8

1.0 0.7 1.8 6.4

0.8 0.9

21.3 2.7 8.6



Banana 6.8 6.4 38.6

Barley

3.1 31.2 12.5 26.1

51.8 26.7 8.1 4.4 21.6

5.5 5.2 4.2

1.9 1.4 0.5 6.7 1.5 3.8 4.0 0.7

Bean green

0.7 0.5 1.3

2.9 2.9 8.4

0.4 1.7 1.6 1.0 4.6

2.7 5.9 2.3 2.3 1.0 9.0 8.8

2.0 1.2 1.8

1.3 2.4

0.9

2.0 4.4 2.5 2.0

0.8 1.9

11.0 25.7

Bean dry

Grapes

Groundnut 0.9 0.9 6.0 1.0 5.8 0.9

7.0 9.6 7.1 2.8

2.9 6.2

1.6 0.6

3.2 1.5

9.0

2.0 1.8

15.7 8.1

13.3 13.3 0.6 2.5

2.5 3.9 23.0 5.3 2.4

1.6

4.3 9.9

6.9 1.1 1.2 2.3

1.0 1.3

5.4

0.9

3.3

5.3

0.7

0.9

11.4

0.5 0.6

6.0

7.0

0.9 3.6

1.8 2.1

5.6 4.8

0.6 2.3

6.0

11.4

0.4 0.6

8.3

2.3 0.9 1.7 2.0 12.0 4.4

1.0

1.7

0.8 2.1 1.5

0.6 4.5 2.6 1.2 2.8 3.3 1.9 0.4 1.8 1.7 0.5

1.2 0.9 0.9

5.0

12.2 0.5 19.8 2.5 0.8

5.8 1.0

4.4

0.9 2.2

0.6

3.5 6.5

1.7 0.7

0.8

3.0

3.2

0.7

0.6

11.9

7.1 6.3

10.0 12.4

1.9 0.6 1.1 0.6

1.2

0.7

1.0 6.3

0.6 2.9

8.7

6.7

1.1

5.7

1.3 0.7 0.7

7.6 3.0 7.0

9.1

9.0

0.4 9.4

0.7

8.8 8.3

0.5 6.7 7.3

1.1 3.0 0.4 1.5

1.7 7.1 4.2 1.1 1.2 1.3 1.4

6.0

1.1 1.8 0.8 1.1

12.0 4.0 1.5 1.6

2.7 9.9 6.7

0.8 0.9 1.8 0.9 1.3 2.5

5.4 2.3 2.4 1.7 5.8 5.3 4.0 12.5 6.8 3.6 2.0 0.8

2.0

1.0

11.4 16.9 11.4

1.0 1.0

8.0 0.9

1.6

21.4 14.1

11.4 11.4 11.4 1.0

5.0 4.4 8.5

0.6 1.7 1.1

0.8 0.6

Palm

7.5

3.0 1.0 3.0

2.9

0.7

11.2

0.5 1.8 2.5

28.7

44.0 7.0

12.3 5.1 8.8

1.6 2.4 0.3

3.4

4.5 32.0 6.5

1.7 2.7 6.2 1.9 12.0 12.6 9.7 1.2 2.4 11.9

0.8 1.8 2.3 3.8

Millet

1.4

0.9 1.4 2.4 0.8

5.9

19.9 24.4 5.2

Mango 6.3 7.3 6.7

23.5 7.5 8.9 5.9 7.0 16.3 1.7 7.9 11.8 2.8

1.6

5.1 17.6 2.5 6.0

Maize 1.2 0.8 1.2 6.4

1.3 0.6 0.7 0.2

11.4

1.1 0.8

11.4

0.4 1.6

11.4 26.5 10.5 2.7

2.0 14.0 3.6 39.5

3.5 0.8 1.2

0.6 0.6 0.5 0.5

7.8

7.0

7.9 6.0

8.4 6.8 6.7

0.4 14.3 20.0 22.0 2.0 29.2 21.1 20.0 8.3 23.0 23.0

1.5 1.2 3.7 1.3

0.7 0.9 0.9 1.9 0.5 0.6

1.8 3.0 2.8 2.8 2.0 8.4

3.0 2.5 1.4

0.7 1.6 0.8 0.6

1.4 4.4

17.8 9.5 4.4 4.3 4.4 3.2 1.0 4.4 4.3

1.0

0.9 1.1 0.6 0.9

9.6 14.8 9.6 26.0 12.5

7.5 17.6 6.2

2.9 12.5 0.8 0.9 0.8 7.5

Appendix II - p.2

0.5

11.4


Saint Vincent/Grenadines Samoa Sao Tome and Principe Saudi Arabia Senegal Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States of America Uruguay Uzbekistan Vanuatu Venezuela Viet Nam Virgins Wallis and Futuna Is West Bank Western Sahara Yemen Yugoslavia Zaire Zambia Zimbabwe

Banana 1.0 4.5 6.9

Barley

Bean dry

4.7 17.2 4.9 25.0 22.0 21.3 2.9 16.1 15.4 44.7 33.0 33.9 21.4 4.2 22.0 4.1 8.4 12.8 1.7 1.5 5.1 26.9

0.6

1.7 3.0 3.2

0.9 2.4

3.8 5.3 0.4 0.9 2.3 0.7

0.9 1.9 0.4

1.8 8.0

11.9 19.4 15.6

Grapes

3.0 7.9

13.7

3.0 3.4

7.5 3.5 6.6

Groundnut 1.0

Maize 3.3

4.0 1.7

2.1 1.6 0.9

Mango 5.7 8.4

Millet

Palm

12.8 6.8

0.8 1.6 1.1

1.2 8.0 6.0 6.9

1.6

0.7 1.7 2.3 0.6 0.7 1.4 1.4 1.1

0.7 2.2 9.5 1.9 0.6 2.3 1.8 9.2 9.2 3.2 3.5 1.4 3.4 2.0

7.2 11.4

2.9

6.6

2.0 0.6 1.0 0.9 1.9 1.5

1.0 8.3 16.5

16.0

4.6

17.7 21.0

3.0 1.5 2.5

Bean green

5.6 3.2 2.2 0.8 1.1 0.9

0.3 1.2 1.1 0.5 1.9

5.0 12.9 4.8 5.0 1.6

1.2 1.8 1.6 0.8 0.8 0.3

8.7 8.2 7.0 7.0 4.0

13.5 4.7 7.9 11.0

11.2 11.2 5.5 1.2 5.2 16.5

5.4 6.3 5.8

0.5 1.4

6.8 8.3

0.4 2.0 0.6 1.3 2.0 0.6

7.0 13.2 13.3 5.6 2.9

2.5 1.7 1.7 15.8 14.0 3.4

0.8 0.7

3.0 5.2

13.3 6.2

2.7 1.1 0.6 1.5 0.5 0.5 1.1 2.6

0.7 1.1 1.1 3.0 0.5 1.9 1.0 1.8 1.3

3.3 2.5

0.6 2.4 0.7 0.3

10.5 14.1 10.5 2.8

0.6 1.0 0.9 0.4

13.8 17.6 8.8

3.7 0.8

1.7

12.4

1.2 2.5 4.0 8.0 8.4 4.9 2.2 0.5 3.0 2.6

1.6 0.9 1.0 1.0 1.9 1.2 3.5

3.5

5.5

1.2 1.0

16.9 4.8

15.5 7.2

0.9

14.1 14.1

12.1 11.4

6.0 5.7

9.3

2.9 4.8

1.1 2.6

1.5 1.3

5.7 4.2

6.9 3.6

1.3

1.5 4.8

1.0 1.6

0.8

7.1 7.1

7.0 7.1

0.4 0.6

1.4 1.6

Appendix II - p.3

4.1

4.7

0.6 0.6 0.7 0.2

10.5


Pepper Afghanistan Albania Algeria American Samoa Angola Antille Antigua Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia Botswana Bahrain Brazil Brunei Bulgaria Burkina Faso Burundi Burma Cambodia Cameroon Canary Islands Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Colombia Comoros Congo, D Congo, R Cook Islands Costa Rica Côte d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Estonia Ethiopia Falkland Fiji Islands Finland France French Guiana Gabon Gambia Georgia Germany Ghana Greece Grenada Guadeloupe Guatemala Guinea Guinea-Bissau

1.2 1.2 0.6

1.2 0.6 0.6 0.8 0.3

Potato Sorghum Soybean Sugarbeet Sugarcane Sunflower Tobacco 16.8 14.3 19.0 1.4 3.7 1.7 1.9 14.2 0.9 1.7 15.4 2.7 0.5 1.0 1.0 5.5 37.8 0.6 1.0

29.5 12.9 32.1 3.7 9.5

4.4 3.2 0.2

14.0 11.4

1.6

11.2 45.1

9.2

5.5 23.7 13.6 6.5 8.5

0.8

2.1

2.4 2.0 2.0 2.7 1.3

1.2 0.8 0.7 0.8 0.7 1.3 2.2

26.0

68.4 0.5

21.6 7.3

6.7

1.8

95.9 12.0

1.2 2.6

25.0

1.2

39.9 58.1 22.0 22.0 48.2 34.5 31.2 46.4

0.2 3.0 0.4

6.8 0.3 0.6

1.2 1.5 1.2

3.8 0.5 0.5 0.8 0.5 0.5 1.0

0.9 0.4 1.8

1.8 3.4 1.0 0.8

5.2 11.5

1.5 0.9 1.6

12.4 7.8

2.4

28.0

68.1

0.8

1.8

5.4

1.9 6.0 2.4

0.6 1.0 0.8

17.7

73.0 1.0 64.8

1.3

0.8 1.2

1.3 0.5 1.0

15.4 11.3

2.9

1.4

1.8 0.6

27.3 16.7

2.9

2.8

2.6 6.2 16.5 13.9 17.0 14.4 6.3 8.9

1.3 0.7 4.0 3.3 3.2

1.6 1.8 2.5

0.6

0.5

47.2 2.0

1.2 1.2 2.5

85.9 67.9

42.8

1.5

0.3 4.1 1.0

7.3 95.5

1.5 1.6

61.9 29.2 26.0

4.3 12.0 45.6 56.3

6.3 47.4

13.0 7.3

1.3

3.2 3.6

22.0

0.7 0.9 0.5 0.5

8.0 19.8 39.0

3.0 6.2

2.7

75.0 94.8

1.5 2.0

1.7 34.1 73.0 73.0

2.0 43.7 67.7 118.5 68.6

2.2 1.2

1.5 2.3

1.2 115.8

34.5 74.2

1.5 1.2

0.7 1.4

39.5 73.0 36.0 7.7 58.3

2.3

1.0 13.7 38.9 11.5 18.2

9.4

2.8

7.0 1.7 2.0

1.2 0.7 0.9

0.6 2.6 2.0 1.2

0.7 2.5

56.6 56.7

2.8

Appendix II - p.4

25.5 63.0 44.0 39.9 9.8 51.2 27.5

1.5

4.5

11.6 0.8 4.9 8.5 9.1 6.3 7.5

1.1 1.3 2.2 1.2 1.8

1.0 36.4 9.4 36.9 28.8

1.1 0.7

44.2 12.6

6.8 2.7

0.8 0.9 2.8

9.3 28.2 28.2 28.8

8.0 25.3 12.1 15.0 6.6 4.8 16.7 2.0

1.0 0.8 1.4 2.9 1.8 1.8 1.2 0.5 0.4 0.7 1.9 0.5 1.5 0.7 4.2 2.0 1.9

1.2 1.2 2.8 0.6 2.8

2.2 0.9

77.9 20.0 27.0

5.7 12.8 18.7 36.5 26.1 16.6 8.8 3.5 5.4 1.0 14.3 4.0 6.1 6.6 1.5 2.6 5.1 4.6 4.5

25.7 12.5 9.0 8.6 9.5 2.0 17.7 9.0 7.0 5.4 7.0 1.0 13.6 8.2 7.1 5.0 7.0 1.9 23.0 5.8 1.2 12.9 6.7 17.4 13.8

15.6

22.3 11.5 24.7 23.7 19.6 39.5

27.6 1.0

1.8 2.6 2.4

0.6

1.9 1.1

1.6

2.5 1.4 5.8 1.3

0.5 0.7 1.0 1.0

0.9

7.8 3.2 27.7 46.1 43.0 18.1 17.8 28.5 6.9 7.6 14.4 32.9

16.4

1.0 1.9 9.4 21.5 5.7

1.2

1.7 1.0 2.5 2.4 1.2

Tomato Vegetables 9.5 3.2 8.6 17.3 6.6 7.8 3.7 7.3

65.5 23.9 21.7 9.0 7.4 7.5 5.0 25.9 1.0 11.5 4.8 11.1 17.7 43.0

1.5 41.6 1.2 44.3 8.6 16.4 28.3

23.8 1.0 11.5 8.6 4.0 5.1


Pepper Guyana Haiti Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kampuchia Kazakhstan Kenya Kiribati Korea Korea, Republic of Kuwait Kyrgyzstan Laos Latvia Lebanon Lesotho Liberia Libyan Lithuania Macedonia Macao Madagascar Maderia Malawi Malaysia Maldives Mali Malta Martinique Mauritania Mauritius Mexico Micronesia Moldova Mongolia Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norway Oman Pakistan Panama Palestine Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Réunion Romania Russian Federation Rwanda Saint Kitts and Nevis Saint Lucia

Potato

0.7

Sugarbeet Sugarcane Sunflower Tobacco 65.2 0.9 58.8 1.4 2.1 32.0 81.8 0.5 2.4 44.6 1.5 2.0 1.2 1.1 24.0 72.1 0.6 1.5 1.2 68.9 0.6 1.6 29.8 86.2 0.7 1.1 1.3 22.4 21.3 1.3 1.0 5.7 1.2 1.1 1.2 1.2 70.0 1.3 1.0 3.5 46.3 2.7 2.8 63.2 1.5 1.7 54.3 67.5 1.0 2.6 0.2

1.8 3.8

0.6 0.8

1.3 1.2

1.0 1.4

1.1 1.3

0.4

1.2 0.9 0.9

1.2

7.6 24.5 22.3 15.0 6.6 15.9 19.8

0.5 0.7

7.8 14.1 12.4

1.3

0.4

5.9

0.5

1.2

0.8 1.8

11.0 15.0

0.7

0.9 1.0

0.5 1.2 0.5 0.8 1.2 0.5

12.2 1.0 21.3 12.9 18.8 14.7 21.3 14.4 31.9 38.7 24.2 15.8 3.3 25.9

0.8 0.7 0.8

0.5 0.5 0.4 0.7 0.8

Sorghum 0.8 0.9 2.3 0.8 1.2 1.2 0.3 4.2 7.7 6.4

Soybean

1.7 1.1

17.2 13.0

2.3 29.2 47.5

0.8

28.4 39.4

1.3

2.0 1.2 1.0 1.1

1.7

1.2 1.5 2.0 2.0

9.2 44.9 13.7 41.7 13.3 1.0 6.4 23.7 21.9 16.6 16.0 21.9 4.2 6.0 11.3 12.1 15.7 15.7 9.3 18.0 14.4 9.7 6.0 14.7

8.4 29.5

0.8 1.4

2.3 1.1 1.3

1.0 13.2 7.6 18.4

22.0

1.0 68.9

1.6

18.0

75.0 24.0 62.9 63.0 59.7 63.9

0.9

16.6

1.0

62.0

0.4 3.2 2.5 2.6

0.6 0.8 0.9 0.3

8.0 1.4

0.8

0.8 1.8

1.9 0.2 1.1

1.6 2.2 0.8 0.7

3.0 0.6 2.4

1.2 1.2 0.8

4.0 1.4 4.6

8.0

12.0

0.9

9.2

8.3

0.9 0.8

8.8 17.5

0.8

37.3 65.9 16.5

8.3 13.6 15.0 5.3 12.5 12.7 6.4 17.3 7.7 11.7 1.3 8.2 6.5 17.4 6.2 13.4 4.5 7.5 8.9 39.4 1.8 23.6 7.6 8.0 6.9 6.7 22.9 14.4 13.8 12.0 19.7 16.2 6.2 15.0 8.6 19.6 18.8

33.3

1.2

5.0 7.4 3.5 18.3 12.5 1.4

1.3

1.2 36.7 70.0 24.4

2.8 1.4 1.2 1.5 2.5

46.0

1.8 0.8 0.5

14.3 12.4

1.4 2.6 1.5 2.4 5.4

7.4 48.3 64.5 15.8

33.8 6.6

1.0

0.8

0.7 0.8 1.6 1.9

35.0

1.3

1.2

76.5 18.0 44.2

0.7 0.6 0.8 0.8

1.0 1.7 1.7

36.6 42.0

66.2 23.3 28.4 26.0 16.0 26.5

0.5 1.0

53.8 47.0 118.8 63.4 63.0 8.0 3.8

21.7 18.6

0.8

Appendix II - p.5

34.0 47.6 52.8

14.2 6.7 48.7 8.8

0.9 1.0

42.6

0.9

1.5 1.4 0.8 0.4

47.0 1.8 16.3 7.0

1.1 4.5 1.9 1.6

32.3 22.7 2.0 12.3 53.3 4.3 37.5 24.4 8.6 15.4 66.6 12.9 31.9 12.2 16.6 12.3

1.2 20.0 47.8 53.2

9.5 29.3

1.2

1.3 1.0

1.4

1.1 0.7

69.3 1.5 1.0 0.8

1.7 2.2

1.2

1.0

1.2

1.2 2.3

15.9 8.9 14.5 11.9 31.9 56.9 14.7 5.7 22.5 18.4 6.8 5.0 11.6 2.4 15.7

1.2

2.3

0.8 1.7

0.5 0.6

62.0

0.4

5.0 18.5 22.3

86.7

Tomato Vegetables 4.2 6.8 15.5 5.7 1.5 1.0 28.4 14.2 41.7 15.7 1.0 7.4 7.5 27.2 16.7 11.4 6.1 7.0 1.4 17.4 53.2 15.7 17.9 1.7 56.5 26.3 36.5 14.9

1.2 0.8 1.0

1.2 1.6 1.2 1.2 2.9 2.6 1.0 1.0 1.3 0.9 1.3

1.8 7.6 1.8 15.3 7.4 6.7 7.1


Pepper Saint Vincent/Grenadines Samoa Sao Tome and Principe Saudi Arabia Senegal Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States of America Uruguay Uzbekistan Vanuatu Venezuela Viet Nam Virgins Wallis and Futuna Is West Bank Western Sahara Yemen Yugoslavia Zaire Zambia Zimbabwe

Potato

Sorghum

Soybean

0.5 1.7 1.2

25.9 17.1

1.8 2.0

14.0 14.3 19.7

1.2 2.0 0.6

26.6 24.7 12.5 7.3

1.3 0.6 0.8 1.2 2.4

0.3 2.3 5.0 0.8 0.6

0.8 2.0 1.5

2.8

1.0 1.8

2.0 3.2 35.2 23.2 13.2 6.9 7.8

22.0 12.8 25.9 5.6

Sugarbeet Sugarcane Sunflower 25.0 13.0 23.0 19.8

1.2 1.5

1.3 2.2 1.0 1.2 0.9

12.0 4.8 43.2

16.0 6.5

0.6

0.6 1.0 0.9 1.6 0.7

3.3 0.8 1.4 0.4 1.5

46.0 68.0 45.4

16.0

1.1 0.3 1.6

0.7 1.4 1.2 2.0 2.0 1.6 1.2

7.2 8.2 19.5 4.2 4.2 16.4 13.6

1.5 1.2

1.2 1.2

18.4 11.8

2.6

1.2 4.4 3.6 2.6

2.1

1.2 1.2 1.5 2.7 2.5 1.9 1.1

42.6 43.9

7.0 22.0 16.0 35.0 67.2 76.8 55.2 7.1 5.0 18.8

55.0 96.8 56.7 28.0 48.4 40.0 32.0

13.3 15.4 17.4 57.8 49.5

63.0 44.0 79.7 51.6

11.8

0.8 1.1

61.5 5.6

Tobacco 1.2 3.4 1.3 1.1

0.9 1.3 1.7

1.3 0.7

0.7 0.8 1.5 0.9 0.3 2.1 2.6 1.3

0.8 1.2 1.0 2.8 1.8 0.4 1.1

0.6 1.5

0.6 1.0 0.9 1.4 1.2 1.7 1.4

1.6 1.5 2.6 0.8 1.4 0.5 1.2 0.8 0.9 2.5 1.3 0.8 12.6 2.0 2.2 3.3 1.9 1.9 1.9

Tomato Vegetables 7.5 6.7 5.8 24.3 21.8 19.5 18.0 6.1 7.8 9.9 6.3 23.0 17.1 19.9 7.7 27.0 16.4 6.4 31.9 14.2 59.5 21.3 7.5 1.0 12.0 7.8 8.4 15.8 12.5 8.2 32.9 21.0 36.7 23.3 38.7 8.9 19.9 18.0 7.9 6.8 9.1 7.4 4.0 5.0 14.7 1.3 12.6 9.0 37.2 12.5 41.7 15.9 11.6 11.0 7.5 6.5 7.4 13.4 8.3 68.2 25.7 36.6 3.1 48.5 31.8 18.2 8.3 34.0 4.6 15.8 2.6 17.5 12.9

2.0

1.2 1.5

12.7 8.2

1.7 4.7

2.7

0.7 0.9

10.0 15.8

0.7 0.6

2.3 2.1

35.2

Appendix II - p.6

13.1 18.3

2.4

1.5

2.1 1.7

12.7 8.3

8.9 7.6

0.6 0.7

1.9 2.2

1.0 6.7

6.4 6.9


Afghanistan Albania Algeria American Samoa Angola Antille Antigua Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia Botswana Bahrain Brazil Brunei Bulgaria Burkina Faso Burundi Burma Cambodia Cameroon Canary Islands Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Colombia Comoros Congo, D Congo, R Cook Islands Costa Rica Côte d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Estonia Ethiopia Falkland Fiji Islands Finland France French Guiana Gabon Gambia Georgia Germany Ghana Greece Grenada Guadeloupe Guatemala Guinea Guinea-Bissau

W.melon 11.5 15.3

13.9 21.7 15.9 9.2

Wheat Cotton seed 1.2 1.1 2.5 1.0 0.8 0.6 1.3

1.0

2.5 1.9 2.3 5.4 1.9

0.1 1.0 2.0 3.6 2.0 1.3

2.3

1.3

6.5

1.7 8.3

Cabbage

9.0

Carrots

13.7


11.2

Lettuce

12 13.1783

Oats 1.6 0.8

1.0 5.0 15.0 22.6 30.9 46.5 13.1 14.0 20.4 10.3 7.5 17.1 19.1

4.0 26.3 17.1 37.3 31.4 8.3 15.2 6.0 16.5 45.1

50.0 9.2 18.0

10.8 7.6

18.1

7.6923 11 13.3333 15.60005 72.242 8.40215 12 10.0091 4.1111 7.6471 12.9354 65

18.0 22.6 27.9 23.0

2.1 1.4 2.0 4.1 0.8

22.0

1.7 3.4

23.0 9.6

1.2

7.6

1.9

2.2

12.8

2.7 0.8

1.1 1.7 0.9

27.1

1.3

1.4 1.9

18.3 15.0

2.6

1.2

9.6 15.0

16.4 26.3 12.0

22.0 20.0 22.0

19.8 4.9 10.3 64.6

0.9 1.5 1.5 0.9 3.1 1.7

Onion green

22.0

9.2 6.6

7.2

6.9091 30

9.6

0.9 2.4

26.0

15.0

11

19.0

1.1

22.0

12.9

10.8

15.82375

8.1

1.4

9.1 17.0

21.7 24.0

36.6 24.0

16.3 12.0

32.1399 15.3333

27.5 16.0

2.5

22.0

21.0 22.9 12.9

23.1579 15.83625 17.8759

13.8 21.7 13.8

3.3 1.9 1.4

22.0 23.7 22.0

4.07145

31.3

1.8

22.0

7.4

2.6

20.0

10.0 7.6 25.0

1.2 3.1 5.1

6.2 20.0 20.0

0.7 0.7 1.2

5.1 12.0

10.3 7.4

2.5

16.9

12.9

0.5 0.8

2.1 3.5 3.9 2.2

3.8 3.0

27.0 20.3 4.6

27.0 17.8 31.9

1.3

0.4

20.8

2.8

1.0 1.1

7.4

10.0

10.3

4.5

9.0 10.0 38.0 20.6 42.7

16.8

6.6323 2.883 90.78945 11.2232 328.5714

6.0

18.5

3.3

56.3

2.6 4.6 7.2

14.0 15.6 36.7 34.9 27.7

0.7 6.3 2.3

0.7 2.4 1.8

10.0 14.5 9.1 29.2 8.0

10.0 10.0 8.0 26.2

5.7 23.4

1.3 1.1

2.0 1.6

11.8 14.6

12.8

3.5

19.0 22.7 15.1

31.6 42.2

9.2 12.1

7.6 51.6

8.3 44.4

26.7

23.5 10.0 9.0 11.5

31.4 5.3 9.0 10.0

9.8 29.3 18.6

23.4 16.5 9.6

1.0 32.8

2.0 7.2

6.1901 31.25 12.7875 13.81505 10

10.0 14.7 7.1 25.9

32.2937

14.2857 75.2621 116.9438 21.7353

24.0 27.5

1.6 1.0

10.0

2.5 4.4

20.0 19.1

0.3 14.1

42.8

2.3 7.5 1.8 2.3

19.0 25.0

2.0

2.0 0.8 2.8 0.3 2.0 1.3 1.2

Appendix II - p.7

19.4

11.5

8.54165 40.76265 10 82.1579 10 34.49035 4.5

24.0 18.7 7.0 19.9 16.7

0.6 4.8 0.7 1.9

24.2

1.2

22.0

19.1


W.melon Guyana Haiti Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kampuchia Kazakhstan Kenya Kiribati Korea Korea, Republic of Kuwait Kyrgyzstan Laos Latvia Lebanon Lesotho Liberia Libyan Lithuania Macedonia Macao Madagascar Maderia Malawi Malaysia Maldives Mali Malta Martinique Mauritania Mauritius Mexico Micronesia Moldova Mongolia Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norway Oman Pakistan Panama Palestine Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Réunion Romania Russian Federation Rwanda Saint Kitts and Nevis Saint Lucia

Wheat

9.7 17.3

0.7 3.6

13.2

2.6 0.8 1.8 0.5 8.8 1.5 3.2

21.3 1.5 22.6 33.8 19.9 33.6 38.1

Cotton seed 0.4 2.0

0.7 1.3 2.0 1.5 3.6 0.9

3.5 0.2

Cabbage 5.7 6.0 30.8 27.4 34.6 18.3 24.0 18.0 16.2 34.0 29.3 19.4 15.8 39.2 25.7

Carrots

11.7 25.9 32.0 14.5 15.9 10.0 24.4 73.2 48.1 13.9 28.9 36.7


8.0 22.7273 18.2 23.7472 12.0 120.40475 17.3 6.6434 16.0 8.31485 11.0 19.33925 11.3 7.55 9.8 166.6667 26.0 65.625 20.9 27.2059 12.6 14.2105 10.5 49.5831 17.2 81.94265

Lettuce

Oats

2.8 14.4 23.5

2.6

6.6 17.0

1.5 1.2

11.7 24.0 36.6 19.3 12.4 23.5 29.2

0.8 6.1 0.4 2.5 1.8 1.4

Onion green

16.0 15.0 18.0 18.0 12.0 12.1 20.0 31.8 20.0 11.5 21.2 12.3

9.7

1.3 1.4

1.8 0.4

11.5 22.0

11.5 0.0

0.0 0.0

8.0847

0.0 10.0

0.4 1.1

12.0 12.0

19.9 27.2 24.3 15.9

3.0 3.7 1.5 2.4

1.8

0.0 0.0 42.6 0.0 0.0 0.0 23.9

11.9811 53.08535 42.03475 14.97915 0 11.7219 31.3329

21.2 21.2 50.2 0.0 0.0 0.0 123.7

13.1 25.2

2.2

12.0

2.4 2.4 1.2

25.9 25.9 38.4 15.9 0.0 9.1 21.3 0.0 0.0 3.9 14.1 8.6

1.4

26.0

15.6 58.3 44.3 16.1 0.0 14.8 26.0 0.0 0.0 16.9 22.6 18.4

1.7 1.3 1.3

20.0 12.0

9.8 15.4

15.1923 16 20

0.0 2.0 1.3

17.1 20.0 20.0

4.3409

2.6

2.9

24.88095

17.0

12.0

21.1111

31.2

15.33025 26.5625

13.9 19.0

16.2 14.5

21.7

1.2 2.7 3.3 2.5

1.6

18.5

3.7

12.4

0.8 0.8

0.8

2.3 5.0

1.7

2.9

10.0 38.5 0.0 0.0 44.0 15.0 0.0 17.6 34.1

0.0 16.0 0.0 0.0 0.0 5.0 0.0 14.6 23.0

0.0 16.0 0.0 0.0 16.2 0.0 0.0 18.8 12.3

0.9 2.0 0.5 0.7 1.4

13.3 0.0 0.0 20.6 0.0 0.0 0.0

10.9 0.0 6.1 22.4 0.0 0.0 0.0

10.0 0.0 0.0 19.2 0.0 0.0 0.0

0.9

0.0 33.4

0.0 33.4

0.0 10.6

2.8 1.3 0.7

37.5 1.5 0.0 0.0

73.3 0.0 0.0 8.7

45.0 0.0 0.0 0.0

35.4 22.0 15.2 13.6

29.3 20.0 18.4 7.5

14.2 16.0 18.5 0.0

0.0 13.2 11.6 39.6 17.5 5.6 15.5 14.7 22.5 18.6

12.1 16.4 0.0 29.7 32.2 0.0 12.0 20.0 0.0 16.5

0.0 12.0 0.0 20.1 18.7 0.0 12.0 20.0 0.0 6.1

1.0

1.0

22.7

4.9

4.4

2.3 0.6

21.1

1.0

0.8 1.1 1.1 5.6 1.7 8.3 1.8 7.1 2.0 2.1

2.0 22.2 12.8 1.0 5.2 2.3 15.5 6.7 9.5 2.3 4.8

2.5 2.9

4.5 2.3 2.2 1.9 0.7 1.8 1.3 3.5 1.6

1.9

1.2 1.7 0.9 1.8

2.4 2.8 1.6 0.8

0.9

0.4

Appendix II - p.8

1.9

7.833 7.32145 1 28.09645 16

664.2857

127.3375 12 12.5 3.8016

11.27695 4.0357 13.48725 23.3333 15.0265 17.47915 10.0344

0.0 0.0 0.0 0.0 0.0 0.0 0.0 28.7

1.4

20.0 22.9

1.6

20.0

0.7

14.4 12.0

5.0

20.0

36.0 0.0 0.0 0.0

4.0 1.2

45.2 22.0 6.8 20.0

22.8 0.0 1.4 13.2

4.1 0.8

31.3 20.0 12.5

0.0 13.5 0.0 0.0 21.1 0.0 12.9 17.0

1.2 1.0 2.6 0.6

7.2 22.0 5.0 20.0 18.7

1.6 1.2

16.0 20.0 20.0

23.0


W.melon Saint Vincent/Grenadines Samoa Sao Tome and Principe Saudi Arabia Senegal Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States of America Uruguay Uzbekistan Vanuatu Venezuela Viet Nam Virgins Wallis and Futuna Is West Bank Western Sahara Yemen Yugoslavia Zaire Zambia Zimbabwe

11.9 12.9

2.0 22.9 11.9 14.5 37.8 28.6 12.5

22.3 4.8 14.5 8.4 12.5 13.0 29.2 12.8

4.4 2.3 26.3 14.0 11.7 15.4 11.5

Wheat

Cotton seed

4.3

Cabbage

22.0

Carrots Cauliflower Cucumber 10.0

13.8

18.0

Onion green

12.0 23.0

21.0 26.4 32.8

12.0 16.1 21.6

10.0 10.5

33.1 28.8 13.4

24.5 51.3 11.5

24.6 20.9

0.3 2.4 3.0

0.4 1.3 3.8

1.2

0.9

1.5 6.2 5.4 1.7 1.1 1.4 0.6 1.2

0.7

3.8 1.0 0.4 1.5 1.0

22.7 0.0 43.0 22.9 22.1 7.1 0.0 11.9 22.0

4.6 1.5 2.0 3.2

1.5 2.9 1.9

20.5 11.4 22.3 6.2

7.3 21.1 18.3

11.5 13.4 56.1 38.7 11.1 36.3

11.24215

13.18185 63.4612 9.3333

17.0 8.2

2.5 2.6

12.0 20.0

14.2 27.9

1.0 1.8

12.5 21.0 20.0 12.0

61.6 61.6 15.8 12.0 12.5 12.0

10.6771 53.7 40.4 13.8 11.0

21.9 17.0 21.9

54 88.2353 11.41915 3.58335

24.0 24.9 20.9

3.6 5.7 1.2 0.5

12.0

6.9

8.33945

16.7 17.0

1.5

6.6 20.0 18.3

11.579 22 28.67225 4.75

5.2 0.0 17.4

10.3859 55.00105 410 16.4046

13.0 19.7 27.4 33.4

1.8 2.3 0.4 8.6 2.9 1.9 2.7

0.3

2.4

14.4 36.2 28.4 23.0 8.4 53.2

0.4

0.8 1.0

30.0 23.0

28.9 12.0

1.6 3.3

1.4

9.2 13.4

8.7 7.5

7.5 5.6

1.3 1.0

1.7

Oats

22.65995

1.0

1.2 4.1 3.8

Lettuce

18.5 10.0 35.0 13.3 17.5

0.1 1.9

1.4 6.0 2.2 1.0

10 16.4 17.2

14.03265 16

24.3

17.11075 7.36665

4.5

1.2

22.0 21.2 10.5 12.0 12.0 20.0 16.5 13.0 20.0 22.0 20.0 22.0 12.5

22.7 13.5 13.6

1.9

12.0 20.0

1.6

12.0

7.0

Appendix II - p.9


Onion dry Afghanistan Albania Algeria American Samoa Angola Antille Antigua Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia Botswana Bahrain Brazil Brunei Bulgaria Burkina Faso Burundi Burma Cambodia Cameroon Canary Islands Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Colombia Comoros Congo, D Congo, R Cook Islands Costa Rica Côte d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Estonia Ethiopia Falkland Fiji Islands Finland France French Guiana Gabon Gambia Georgia Germany Ghana Greece Grenada Guadeloupe Guatemala Guinea Guinea-Bissau

14.5

Peas Safflower 7.1875 2.5263 6.78635

Spinach

Sweet potato

Artichoke

Citrus

Rice 2.25 2.3 1.5

8.1 8.5

7.9 27.4 18.0 38.9 51.9 10.6 5.5 29.9 4.0 13.3 7.5 26.7 0.0 9.9

24.26765 7.1699 20.4492 41.07015 4.4309

5.8 18.0

0.776 0.77925

15.2 11.6

17.1 11.0

18.7

5.4

14.4

12 5

5.2566 3.2 9.5518 6.2 4.62185

4.2 8 2.125 30.12615 4.5

1.2

4.8

9.4 8.5

19.1

14.0

9.7

15.5

5.0 11.3

7.5 7.5 14.5

5.8239 5.1704

4.3

12.4

8.9022

10.2 5.2

7.8

21.9422

5.7

6

33.5 27.5

11.11915

3 3.076 3.2 2.3 6.2 2.0167 2.08705 3.2 1.6667 1.79305

15.5

5.5 6.2

4 2.7941 1.6519 3.0691 1.9259 3.12495

3.4 7.2

1.86585 3.25195

5.4

7.2

1.5

5.8

5.7 5.3 3.5

20.0 36.5 20.6 16.7

25.81115 7.97325 14

5.9

6

23.5

4.5

7.9 6.6 37.5 15.5 30.4

0.8375

2.16665

14.0 6.8

2.5 7.0 20.3

7.5 4.0 14.0

2.6 3.7 7.5 28.0 11.0 3.0

6.8 12.4 6.8

11.2

5.82635 4.2 6.25 4.523 14.69855

1.2

9.3 11.5 10.0

14 7.7 9.0 23.8 8.2

5.0724 9.3316 14

4.4 10.0

14 5

17.2 40.2

9.5 19.56075

18.0 38.4 7.7 19.1

6 12.074 15.3646 0 7.40965

8.0 6.3

5.2

0.6

18.9

19.0 14.0

9.7

4.0 4.1 3.7 24.0 6.5 2.5

13.5 18.0

5.1 8.8

1.46155 1.48545 4.02985 6.34365 4.89515 1.2143 0.755 0.8571 4.30975 1.57235 5.2 2.77285 5.3 5.2

5.3 17.5

4.43805 3.36425 8.75905 6.07815 5.2

0.52145

8.1 9.5

1.2

0.0 17.2

14.2

6.2

5.4

9.7

5.4

10.0

12 7.8

1.8

15.9 13.4

14.0 1.4 20.0 2.7 10.5

1.2 6.1

Appendix II - p.10

5.1

1.37705 5.2 5.95475 2.6919 2 1.68265 3.2 5.2 1.5491 7.63025 3.2 2.41405 1.50935 1.51415


Onion dry Guyana Haiti Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kampuchia Kazakhstan Kenya Kiribati Korea Korea, Republic of Kuwait Kyrgyzstan Laos Latvia Lebanon Lesotho Liberia Libyan Lithuania Macedonia Macao Madagascar Maderia Malawi Malaysia Maldives Mali Malta Martinique Mauritania Mauritius Mexico Micronesia Moldova Mongolia Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norway Oman Pakistan Panama Palestine Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Réunion Romania Russian Federation Rwanda Saint Kitts and Nevis Saint Lucia

4.7 5.7 24.4

Peas

Safflower

Spinach 13.3

6.5 5.36845

11.4 7.3 25.2 8.8 31.0 30.3 28.2 8.7 50.7 15.1

7.38065 8.5 9.9121 4.91935 9.5 16.6805 20.11925

13.6 5.6

2.5966 5.7143

11.9 58.9 25.9 16.5

9.6154 10.3224

1.2 0.6 0.8

Sweet potato

17.5

3.0 3.3 11.0

2.0

8.5 9.5

Artichoke

Citrus

3.2 9.7 7.8 7.5

16 12.4 8.5

6.1 0.8

22.13145 4.0192

56.3 13.2 7.6 12.5 20.2

33.4 12.6 16.1 23.8

20.0

9.5

14.7 14.7 41.5

12.9 21.2

9.7 10.0 10.0

7.4 11.5 6.8 20

0.4347 11.0

6.1

19.29555

19.0 8.6 8.0

8.10715 6.3 13.2863

8.5

1.83335

7.2 8.2 0.5 29.0 12.6 0.0 0.0 22.1 12.4

5.2 8.5

14.9

4.5

4.00865 6.50645

12

2.4424 2.81975 3.2 2.8

16.0

10.0

6 6.2204

1.2916 5.2 5.03615

5.5

12.0

1.48765

7.9 0.0 3.0 21.5 5.7 8.2

10.6394 6 10

0.8

36.0

22.3658

1.2

20.0 2.2 24.5

42.5

11.7

2.0916 7.3 5.5

11.1 0.2 4.6 8.9 1.0 11.7 19.0 5.9

7.1 9

12.0

7.1

12.0

9

9.7

5.4

1.85515 2.9122 2.24415 3.2 0 4.54265 3.3 4.6435 1.125

4.3423 2.0 15.9 6.9 4.6

20.5 6.7

6 6

14.0 4.3 15.2

5.2108 7.4 12.94155

6.7 22.0 6.8 21.5 25.3

6.93335 6.2485

11.6

4.07495 8.8916

16.5

0.75

10.9

4.7 7.8 14.8 4.2 14.0 7.3 5.9

3.3 5.2059 1.07465 3.1844

2.4335 5.2

3.30695 2.71135 1.549 5.2 3.9 2.9837 2.75565

18.0

3.2

1.7857 3.9396 6.0153 2.8226 5.2 6.0007

4.5 2.6

1.04165

4.4

11.0

9.8 9.58715 2.92515

10 6.8

6.1 5.0 17.9

0.0 15.1 13.2 19.7

25.9 0.0 10.1 10.6

3.0116 2.6238

12.4

5.6 8.6 18.8

Rice 4.1243 1.99 2.4201 3.00945 5.2 2.94915 4.22555 4.25235 1.8642 5.2 5.5 6.214 1.49105 6.31655 2.3

25.0 4.9 4.6 10.9

Appendix II - p.11

2 2.8352 2.88645 1.864


Onion dry Saint Vincent/Grenadines Samoa Sao Tome and Principe Saudi Arabia Senegal Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States of America Uruguay Uzbekistan Vanuatu Venezuela Viet Nam Virgins Wallis and Futuna Is West Bank Western Sahara Yemen Yugoslavia Zaire Zambia Zimbabwe

Peas Safflower 4.0865

12.3 20.3

7.5 5

12.0 11.0 20.2

7.4 16.8 4.9055

Spinach

Sweet potato Artichoke 1.5

12.0

Citrus

Rice

6.2

4.6 2.7

20.6 42.3 7.8 7.1

21.62995 16.1121 8

29.4

12.9167 38.36145 9.0304

19.0 7.7 3.0 14.7

16.1 21.6 6.5

6 12

4.5 4.156 9.828

0.8

12.5 4.5

1.49405

17.1

14.5 10.0 3.9 16.2 5.5 13.4 11.7 1.8

12.0 14.0

5.6 12

11.0

24

1.2

3.3 2.6106 1.15665 3.3 5.2 5.2 3.9679 1.6667 2.26925 7.30225 3.264 0.8598 3.7621 6.83335 5.2

12.0

0.9

12.0

1.0286

15.2 9.6

1.7 15.4 7.2 12.2 6.7 16.0

5.1 3.8 4.5 8.2 8.2 11.5

11.6

4.2

4.28635 2.53045 1.53665 2.372 1.92265 2.9244 2.3 4.75415 0.8975

4.0 7.9 12.0 36.0 44.0 8.1 27.0

5.2 3.2409 8 10.1948 33.6443 9.6311 2.928

25.6 3.0

5.3 8

9.1 6.3

7 15.5

4.8423 4.03165

14.0 5.5

2.7895 5.57055

10.0

3.7 15

2.1 2.3

15.2 14.4

6 1.9986

14.8 2.1

5

0.80885 2

1.676

109.6 12.0 16.3

14.0 16.5 10.0

9.7 12.5

14.2 15.5 24.8 6.8

0.9272

Appendix II - p.12

1.40165 2.96635 3.3 5.2 6.46125 5.80615 2.44815

3

Appendix III. Specific water demands (m /ton) in 1999. Source: calculated on the basis of Appendices I and II. Banana Afghanistan Albania Algeria American Samoa Angola Antille Antigua Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia Botswana Brahain Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi Burma Cambodia Cameroon Canary Islands Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Colombia Comoros Congo, Dem Republic Congo, Republic of Cook Islands Costa Rica Côte d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Estonia Ethiopia Falkland Fiji Islands Finland France French Guiana Gabon Gambia Georgia Germany Ghana Greece Grenada Guadeloupe Guatemala Guinea Guinea-Bissau

2066

Barley Bean dry Bean green 3142 2990 3470 3542 3910 14897 1151

3760 604 887 2538 454

3726 0 492 1665 147 374 3212 440 529

483 0

1599 2789 2069 753 3622 4211

2656 1069 5640 2077 1012

5982

4759

2445 377

3087 796 3588 5251

3490 0 5148 1089

2368 1372

1823

5846

1392

3557

1613 929

8366 3329 1098

679 1640 1437 5367

9480 768 190 258 4929 3102 1207

1377

9912 1646 3145 2417

939 303 520 178

5172

128

660 3043

4346 2306 6458

3533

4730

8400

3350 2072 1205 256 1885 3692 1127 0 1459 803 15400

485

2701

1261

1878

1220

3209 7729 7139

839 4900 4521

4332

923 142 1029

952 861

2998

299 440 275

4382 2619

0 5028 1772 4621

526 1671 336 1066

835 0 1866 533 1007

0 5605 1953 5060

2920 4417

1241 5647

689

1191 2638

2891 4345

264 3015

0

0 773 354 6287 1783 4030 1444 476 2733 232 0

2547

1158

1984 577 2178

4565

8700 2907 2077 1492 0 5797

943 196 308

551 426

598 1308 333 516 268 1034

1134 243 1363

2333

Appendix III - p.1

2891 3320 5495 11357 2891 3370 1327

4372 3136 4109

1678 2734 4520

1286

308

2941 4331

4090 13683

1625

864 951

1680

681 584

773

5303

2415 0

397

612

1627

3075

381 17162

3468 4309 4882 5820 1633 2891 2891

4916

5122

1060

476

365

2607

2763

1287

1700

1257

353 3540

726 418 572

1421 451

7681 3732

10069 1745

362 366

1710

16424

2706 1750

3059 9228 295 708 2088 1840 4431 0

929

0

6110 6353

4645 7405 1870 1676 2769 4690 4923 0

Palm

1211

830

983 12994 3575 2023 2077

1353 1867

2132 2210

314

0

5427 3305 0 347 215 4075 1863

4140 2476 3180 3400

1528 4855 443 1840 1478 478 264 4628 3929 0 2627 506 1893

1352 572

6328

3952

3801 3533

1000 1317 796 1399 1294 623 662

2375 904 680

Millet 5621

3084 1272

2405

5484

530

Mango

3375 627 799 829 330 888 2258

555 543 680 522 1057 2434 0

700

5379 0 8433

1524 804 199

11845

615

4157

465 1839 211 3115 3837 0 0 117 1110

Maize 2227 577 3066

6710

3281

593 6547 7754 1215

Grapes Groundnut 941 908 6317 3717 6611

0 7718 9820

5421 847

724 1432

491 1845 2306 17080 0 8141 2046 2494

4678

0 6965 14230 971 11890

4613 5468 0 4613

621

4137

5849

1255 18310

1841

3790

3814

621 1564 0

0 20326 7504 2504

4092 4527

576 4826 1755

3

Appendix III. Specific water demands (m /ton) in 1999. Source: calculated on the basis of Appendices I and II.


Banana 1177 1277 163

Barley

Bean dry

1140 272 690 500

252 207 1169 1229 404

3051 0 1548

2008 6176 3775 535 5867 1160 924 2796

294

6134 8359 2117

1010 1034 263

13775 2346 2609 8626 588

1899 511 0 1399 2200 0 402

1697 4094 1740

2403 1415

0

1230 0 0 1520

4561 0

622

Bean green

Grapes

Groundnut 5622 5952 1167 4080 686 3533

428 256 376 991

1392 1561

0 6395

2027 2473

331

1685 0

0 557

398 0 0 7303

880 0 0 1428 1555

1284

923 792

873 4882 4747 1977

8210 3450

2807

4684

1270

2179

8087

5056

1630

4895 6067

530

1246

4003 1386

1891 2013

2338 3044

8167 0

563

833

7146 5335

1532

1452 2423 7090 2622 264 722

6225

3304

11550 1525 3121

13431 667 1338 0 1121 1273 3413 9733 1779 2523 8389

3568 4100 4341

4220

1407 5207 678 1544 6262

0 3000

3554

7514 1453

6084

785 451

2099 0

3204

733

1259

3370

6413

216

1134 1410

897 510

2209 6359 4060 6816

2983

5330

3335 566

6826 933

254

595

2891

635

3185 6774 7000

279 1007 483

715

1611

17719 1277

4785

1440 1336

7410 3000 1859

3218 1749 13819 3973

1764 445 0 2861 4176 4170 3845

3792

3355 3911 6034 4245

334 1640 1608 2906

7429 1853 2248

5488 4564 2762 4881 2446 1736

764 1680 1922 2092 551 809 1425 399 1059 874 1608 0

0

4270

1619 857 1983

3560 0

275 2405

2225

573 365

621 1983 854 2910

2200 902 469

6633 3190 2891

4752 7968

Palm

2254

2055 2700 907

1228

7177

2227

5270 2015 1438

297

208 3329

1600 3065 2839

2254 702 11341

0

2455 236 2327

2852 1821 1019 3008 264 502 252 4497 1317 338

8318 2806 2563 1640

Millet

2553

3631 1929 1792 3012

3495

763 239 0

Mango 2171 1995 1641

482 1291 1488 2533 567 811 3447 1241 430 3285

9141

2810 444 3376 3883

Maize 3925 5832 2425 494

2593 4108 4454 17934

1054

3565 4613

621

12393 3024

925 622 1881 7965

0 795 3294 185

1123 5613 3930

5300 9933 14727 7310

409

514

603 470

1172 1374 0

0 490 394 371 4007 309 592 427 1837 310 310

2667 3492 959 2784

3287 3903 4371 1388 6657 12207

1461 1187 1233 1172 1100 351

1340 1448 2492

11802 1646 3240 0

3069 505

627 966 1856 923 971 2760 15980 892 931

12420

8396 2891 0 5704

1402 819 1599 525 1046

2189 841 2272

6826 1921 4428 3941 0 0

Appendix III - p.2

13056

621

3

Appendix III. Specific water demands (m /ton) in 1999. Source: calculated on the basis of Appendices I and II. Banana Saint Vincent/Grenadines Samoa Sao Tome and Principe Saudi Arabia Senegal Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States of America Uruguay Uzbekistan Vanuatu Venezuela Viet Nam Virgins Wallis and Futuna Is West Bank Western Sahara Yemen Yugoslavia Zaire Zambia Zimbabwe

Barley

Bean dry

Bean green

Grapes

Groundnut 0

0 0 0 1212 827 3908 312 345 345 0 1433 581 139 272 222 739 2098 324 2309 2009 969 4840 10406 1839 293

5167

2094 1191 0

4514 1442

926 674 14462 2042 1438 3718

1060 886

1163 2455 0

1598 2383

0 2501 3931

Millet

Palm

0 0 0 1886

4871 3094 4364

2983 531 707 611

7970

0 4511 3753 11011 1887

0 3434 1804 10768 6482 6607 3900 2891

0 1966 401 3500 6325 3775 2760 345 346 1299 1646 2663 776 2266

0 1519

537 189 957 606 3434

3191 1519 2572 3946 2798 15885

252 437 467 400 515

670 1594 1048 733

354 354 1816 13276 1862 439

2388 5673 6302 1884 10548 7750

2019 5667 3383 4100 1909 0

11980 1448 814

1193 638 1118 1670 3266 2836 3978

7315 3340

424 228

1653 1275 1264

6891 1800 6800 2077 1367 6727

400 272 166 391 742

3699 6016 2382 251 466 2621

3985 5413

820 527

556 1240

4418 1361

5751 4609 2891 1064 7080 1388 0 2167 3621

0 5320

7655 1394 7566 12347

1516 805 1198 8736

6964 3284 2544 9037

1025 594 1747

878 1878

1476

848

3222 1693 1030 396 377 664 1089 0 1220 1695

2023 4113 3940 3690 1985 2083 826

4034

1839 4064 658 13205

2018

0 338 476

734

Mango 0

750

3636

402 313

1213 2379 0

1200 314

Maize

1800

12449 11210

2544

1096 0

781 1805

3322

365 450

925 1054

2907 0

1003

6325 3829

3159 1599

3131 3136

589 816

1462 1237

3944

3451 1009

4945 2538

3587

570 503

1450 1548

13214 6517

3338 2238

Appendix III - p.3

3892

3530

6700 6883 5159 14300

1433

3

Appendix III. Specific water demands (m /ton) in 1999. Source: calculated on the basis of Appendices I and II. Pepper Afghanistan Albania Algeria American Samoa Angola Antille Antigua Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia Botswana Brahain Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi Burma Cambodia Cameroon Canary Islands Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Colombia Comoros Congo, Dem Republic Congo, Republic of Cook Islands Costa Rica Côte d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Estonia Ethiopia Falkland Fiji Islands Finland France French Guiana Gabon Gambia Georgia Germany Ghana Greece Grenada Guadeloupe Guatemala Guinea Guinea-Bissau

3375 4825 5217

2850 7800 5183 4213 19760

Potato Sorghum Soybean Sugarbeet Sugarcane Sunflower Tobacco 176 487 671 4528 467 5627 4007 2218 3579 3022 451 2796 18200 5349 0 753 308 5771 3668

179 254 146 879 345

960 1515 15205

0 308

2012

293 58

290

769 184 0 611 309

4706

1488

2458 1700 2933 1261 2720

2875 7075 8206 3213 0 3450 1601

258

58 7586

184 545

1910

2137

193 1069

4531 1133

566

4208

337 222 583 303 363 450 0 260

16209 1470 0

566 14413 5217

3842 2678 3158

619 9840 5180 7500 6260 6260 4470

3489 7645 1833

1617 826 4860 6035

1209 510

0 3830 1746

329 478

2244

220

209

5351

2295

954

1746 1153 2214

6120 5520 5696

226

176 23310 251

2231

9609 3192

2197 10560 3610

243 742

1239

2235

2048 7774

121 360

897

1227

1978 1118 329 311 232 325 605 0

3096 9997 1073 1339 973

3588 3119 1616

4697

9627

284 0

2325 4550 2213

100 223

93

1935

10304 1055 3950

1989 240

2220 2125

107 224 184

1556 608 88 71

788 160

253 745

3756

1063 1750

181

7821 0 6260 6260

411 166 108

0 419

1277

132 134

2887 2385

2866 505 203 176

8770 0 186 181 251

1345 2492

2379 3218

2492 156

116 54

2615 4733

5040 2575

0 176 356 1942 192

1279

6114 240 84 365 364

379

1006

371 2132 2615

2569 4959 4835

5832 1303 3435 3942

4428 1173

71 144

985

Appendix III - p.4

595 185 273 392 875 277 601

4166

1076

273 0 501 498 396 491 382

4810 5632 5241 1279 2585 1779 4083 8150 0 0 1622 8920 3054 6936 1295 1415 1489

80 260 208 662 674 0 0 165 5890 547 1486 660 212 87

4300 0 1550 5092 2722

7080 0 464 159 212

2573 7101

85 580

359 1366

0 3144 1013

0 133 133 164

0 97 202 204 404 757 147 1225

4858 2387 999 7493 2032

1356 4833

48 342 273

805 191 131 123 94 148 430 0 625 4560 171 617 536 542 2073 0 484 526 1151

95 309 524 441 571 1560 210 324 657 610 0 0 193 446 570 926 661 1302 107 757 3756 0 414 297 314

1202

160 478 217 266 168 83

483 12470

2361 2954 2492

3867

1574 2718

2467

0 2355 824 3509

6260 7443 3240 4400

4659

929 1389 135 137 87 206 356 0 698 660 260 114

305

5410 0 433 220 855

3808

2460 2979 2125 1194 2358

Tomato Vegetables 426 2415 568 483 638 0 1257 431

2493 90 4757 174 0 383 151

206 0 348 306 783 668

3

Appendix III. Specific water demands (m /ton) in 1999. Source: calculated on the basis of Appendices I and II. Pepper Guyana Haiti Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kampuchia Kazakhstan Kenya Kiribati Korea Korea, Republic of Kuwait Kyrgyzstan Laos Latvia Lebanon Lesotho Liberia Libyan Lithuania Macedonia Macao Madagascar Maderia Malawi Malaysia Maldives Mali Malta Martinique Mauritania Mauritius Mexico Micronesia Moldova Mongolia Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norway Oman Pakistan Panama Palestine Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Réunion Romania Russian Federation Rwanda Saint Kitts and Nevis Saint Lucia

Potato

6839

Sugarbeet Sugarcane Sunflower Tobacco 211 4533 256 2749 1302 104 105 6048 1395 89 1966 1450 2492 6077 344 281 10449 4535 4031 239 6303 3741 214 287 13484 6146 2795 244 1122 7735 4070 2833 705 2492 2573 4850 353 6931 7000 1586 145 1843 1693 274 2918 2646 97 150 4130 1507 15667

1416 1047

0 4101

2615 5000

3460 0

3768 0

18050

4500 5492 3778

1675

304 0 0 505 585 207 169

6260 4471

663 233 264

2000

13591

985

8340

4958

5788 2411

334 299

3810

4922 5110

6260 5142 5180 5413 2325 4200

398 3580 155 256 262 351 224 389 103 123 227 348 724 156

3250 8145 2950

4760 6260 13371 7894 7713

Sorghum 4238 3466 1134 6087 3358 2875 23086 619 449 677

Soybean

2316 2405

232 544

2899 137 73

4250

140 101

1977

1565 3375 5080 7391

2922

3508 2520 1565 2110

335 73 0 127 368 5750 875 169 311 159 383 0 1051 1121 412 317 209 283 537 402 228 339 0 341

447 138

3738 2136

1685 2573 2211

5100 474 495 204

1069

5351

682

595

4068 5225

500 299

8791

258 0 374

456 255 240 868 0 321 1036 235 456 0 1838 0 820 154 656 230 1179 0 487 62 0 103 654 579 794 0 0 325 503 375 0 182 556 209 435 125 176

585

256

13460 218

434 504

2462 0 4667 2229 716

686 0 0 594

5942 4190

1146 0 1010

2780

261

287 653 255 388 230 192

3977

241

367

2269

2279

6436 3355 3278 10949

3560

58

173 777 301

7225 5096 4265 4507

3240 2491 1988

3592

664 435 1829 224 285 2628

1953

10125 359 183 523

2369 2235

10766

5779

2608

2492 2119

0 439 0 319 0 0 284 531 109 110 617 610 272 1032 156

4767

1792

6750 2550

6115 6224

171

11500

1814 176 191

178

15659

325 0

4517

8394 1889

2694 24989 4991

3763 3008 10763 13990

1997 10216 1989

5250 6593 7631

799 3767 799

438

2664 3238 4342 2267 1836

87

411 305

272 921 803 109 403 386

118 818

5670

4776

6243 9475 2863 1973

551 180 264

254 385 129 230 204 1085 5603

6072 2830

88

6456

2727 3945 8220 18819

96 3769 593 1485

2718 1239 3444 2213

116 349 4748 564 0 1085 163 204 624 242 76 544 196 725 226 304

6063 5620

3591

2718 5841

420 1700 2690 0

1848 4112 0

184 215

0

Appendix III - p.5

377 0 298

1248 100 553

2492

2042 1040 411 313

Tomato Vegetables 1037 416 397 667 2788 2620 132 172 90 476 5460 785 486 320 264 624 470 534 6378 253 118 259 396 2596 93 107 135 161

2405 3586 5810

2875 3555 3691 3652 965 1450 4340 5460 2098 2997 0

2116 573 1336 160 0 529 0

3

Appendix III. Specific water demands (m /ton) in 1999. Source: calculated on the basis of Appendices I and II. Pepper Saint Vincent/Grenadines Samoa Sao Tome and Principe Saudi Arabia Senegal Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States of America Uruguay Uzbekistan Vanuatu Venezuela Viet Nam Virgins Wallis and Futuna Is West Bank Western Sahara Yemen Yugoslavia Zaire Zambia Zimbabwe

Potato

Sorghum

Soybean

0 3188 3417

186 227

2344 2115

320 313 227

4825 1980 10319

176 167 553 538

3565 5085 4271 2983 1461

0 2157 656 7020 6241

7963 1565 3613

1504

4240 817

2296 1030 93 208 426 572 386

243 362 123 781

Sugarbeet Sugarcane Sunflower 0 0 794 676

4258 3308

4337 1927 5228 4083 9056

470 1136 0

361 714

5867

8255 8100 4073 1557 5962

1023 8692 2564 16438 3048

87 59 152

338

4773 11281 2625

7602 4143 4517 1565 1565 2531 3750

551 549 248 792 778 323 422

2286 3185

3158 3227

214 321

1188

2167 595 1178 1776

1055

4902 5415 4607 1259 1380 3225 5845

141 69

1909 683 848 0 276 158 286 1981 5448 837

332 159 200 621 357 384 402

1155 490 397 69 81

290 292 161 249

609

4738 3322

206 2373

Tobacco 0 0 4708 3391

4656 3028 0

4144 5428

5595 5225 2552 0 0 2498 1390 4709

5213 5992 4440 1070 3497 8683 2400

7344 3625

6483 4337 3322 2115 3151 2444 2470

1786 4005 1491 5940 2653 10300 3858 5196 2554 2260 3714 6496 487 1415 1264 1258 2327 1722 1805

Tomato Vegetables 0 0 0 288 212 262 169 1026 540 514 484 228 202 255 426 0 0 0 198 317 76 160 1057 5140 429 444 1236 384 415 412 114 117 102 105 181 519 363 156 795 574 504 410 1488 920 393 2720 570 514 157 314 96 110 515 359 0 962 533 507 504 103 180 102 784 77 77 247 295 148 607 0 1716 167 239

8995

3350 3207

418 621

2711 861

1925

7336 7367

526 251

4630 4817

2124 3057

175

Appendix III - p.6

1312 918

1919

3107

2021 2646

487 697

413 496

7319 4529

2158 2505

5010 966

720 746

3

Appendix III. Specific water demands (m /ton) in 1999. Source: calculated on the basis of Appendices I and II.

Afghanistan Albania Algeria American Samoa Angola Antille Antigua Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia Botswana Brahain Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi Burma Cambodia Cameroon Canary Islands Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Colombia Comoros Congo, Dem Republic Congo, Republic of Cook Islands Costa Rica Côte d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Estonia Ethiopia Falkland Fiji Islands Finland France French Guiana Gabon Gambia Georgia Germany Ghana Greece Grenada Guadeloupe Guatemala Guinea Guinea-Bissau

W.melons 485 408

336 138 362 324

Wheat Cotton seed Cabbage 3528 6736 3210 0 7222 9774 325 2655

5470

1218 1954 2082 688 1928

49347 5292 0 2402 1940 0

1890

4947

0

2159 424

Carrots


0

0

Lettuce

Oats

250 0

Onion green 0 0 0

748 201 0 104 34 138 199 0 0 488 115 94

0 256 0 181 120 0 0 0 228 83

0 661 189

0 0

189

0 396 0 321 42 547 405 0 0 0 232 46

231 184 82 136

2690 0 2902 794 0

242

1297 957

130 310

0 0 0 36

7339 1647 0 3338 1130 2298

5083

596

1706

3095

234

1370 4841

0 3536 8605

140

2640

4005 2919

164 336

1441

4060

394 200

258 189 287

3634 1053 1024 1525 2739

242 149 145

202

311 243

0 0

0

531 100

332

0 1363

235

360

401

203

4592

220

0

0

190

0

1607

328 0

76 170

115 0

290 0

164 0

149 0

2328

202

1769 1893

127 111 644

118 256 146

242 202 381

172 278 190

271 158 207

1632 2887 2686

221 195 164

15637

159

0

0

0

4280 5724

334

341

259

787

1439

118

0 341 0 183 88

203

452 0 62 267 9

313

1244

149

443 301 92

0 1034 630

817 149 149

0 115 282 419 0

0 0 437 162

0 8914 4408

764 464

2351

116

222

13874 14879

0

271

1983

103

3246 805 518

199 222 108 58 62

4465 1005 2135

8075 4811 4514

361 0 343 157 474

0 0 0 147

745 0

2796 4881

1940 5389

0 261

0

0

95 86 263

119 85

493 374

257 38

0 81

170

132 329 423 200

175 0 0 508

374 230 246

0 206 356

0 91

1884 516

93

0 50 32 0

130 113

1953 0

0

1391 798

173 181

30213 211

143

1609 498 2322 3440

254 131

1150

1940 7854 3641 0 2140 5048 6361

Appendix III - p.7

317

474

0 93 0 69 0 0 933

129 190 0 0 230

5565 725 0 3411

3350

181 207

262

3

Appendix III. Specific water demands (m /ton) in 1999. Source: calculated on the basis of Appendices I and II. W.melons Guyana Haiti Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kampuchia Kazakhstan Kenya Kiribati Korea Korea, Republic of Kuwait Kyrgyzstan Laos Latvia Lebanon Lesotho Liberia Libyan Lithuania Macedonia Macao Madagascar Maderia Malawi Malaysia Maldives Mali Malta Martinique Mauritania Mauritius Mexico Micronesia Moldova Mongolia Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norway Oman Pakistan Panama Palestine Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Réunion Romania Russian Federation Rwanda Saint Kitts and Nevis Saint Lucia

Wheat

339 173

3450 1040

486

2675 4938 3330 9560 427 4067 2016

323 3348 304 148 273 124 96 0

1088 15245

Cotton seed Cabbage 647 13626 525 2140 0 72 0 13402 200 5507 170 6540 178 3115 97 58 3332 143 9724 145 228 69 64

Carrots


0 139 0 0 388 0 173 64 133 391 154 159

0 250 0 294 350 569 0 483 0 276 0 464 348

201 160 0 921 528 414 0 23 95 173 386 80 65

5005 3193

2198 17060

157 171

368

221 0 0 433

1273 0 0 2652

3981

235 0 57 112

177 0 0 265

117

1550 1169 2819

117 58

0 287

3748 1386 1143

130 87 107

0 255 0

0 306

0 188 150

290 207

192

0 130 69 201 374 141 152

0 572 0 2019 3178 0

0

0

207

0 0

169 0 0

30

244 277 482 358 173 132 215 493 200 455

13264 0

288 425

3437

351 0

0

288

1856 0 0

173 397

1791 0

387 173 173

1205

1539

2097

3713 5188

6077

389 101

343

328

169

218

372

2998 0

5837 0 229

0

0

0

218 96

0 183

0 423

0 255

175 216

148

0

472

0

0 0

0

383 0 0 155

1597 10540

174

4143 2880 3092 675 3208 449 0 426 3475 3406

628

4380 4092

0

680

508 2807

913 208

289 230

0

824

606

1371

278

173

1187 2049 284

0 247 132

643

353 0 0 307

Onion green

4918

7650

3045 327 310 0

Oats

1796

8080

4670

2485 2148

Lettuce

810 2469 3245 2716 0 2319 2782 1056 3154

2143

9021 2105 10482 8083 4148

6222

2975 7784 12871

5461

8142 4263 8246 3772

1566 1338 2384 0

0

0

1363

4233

160 243

0

173

0

308

252

59

108

321

69 1963

92

136

5

315

80

644

149

115

1428 4267

118 204 1315 257

0 6050

121 328 692

0 55 185 0 318

313 0 49 127 584 201 0 87 105

0 307 0 0

391 341 0

0 517 0 0

101

0 0

0

278

3158 3220

127 146

235 248

0 0 222 164

149

1349 6682

498 200 926 149 206

0 0

0 0

0

0 0 2211 2980

0 149 179

0

19609

Appendix III - p.8

0

172 359

0 0

100

3

Appendix III. Specific water demands (m /ton) in 1999. Source: calculated on the basis of Appendices I and II. W.melons Saint Vincent/Grenadines Samoa Sao Tome and Principe Saudi Arabia Senegal Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States of America Uruguay Uzbekistan Vanuatu Venezuela Viet Nam Virgins Wallis and Futuna Is West Bank Western Sahara Yemen Yugoslavia Zaire Zambia Zimbabwe

Wheat

Cotton seed Cabbage

Carrots


Lettuce

Oats

Onion green

0

544 309

1975 0 0 396 96 145 638

289 1071 271 550 436 342 100 458

1259 2759 114 334 426 222 299

1329

166

391

284

274

6187

3458 941 0 0 1951 1155

0 6875 1342

3295

7962

2553 600 696 3365 4314 3002 4960 4283

10563

192 74 60

443 228 174

485 449

104 137 0

243 86 0

123 197

177

943 2678 1382 1347

381 254

267

0 94 451

201 0

1394 0

271 178

5100 3855

357 302 218 520

48 0 293 248 316 379

0

2325 4399 14800 2933 8158

46 0 165 274

68 0 301 334

215 0 201

56 0 363 0

96 0 193

962 0 0 0

310 151

502

735

613

275 202

3713

169 198 97 335

0 196 0

0 0 0

423 0 191 884

0

4360 933 4639

319 404 65 109 0 101

2351 2067 13697 435 1302 1578 1250

23523

3642

184 101 69 91 483 46

8323

6675 5580

101 139

187 423

2812 1743

4576

355 178

585 0

476 679

5142 5726

3551

350 149

321 0 177 355 308

508 100 7 322

151

50495 3211

0 0 114 123

2482

0 252

0

3667

0 491

866

588 2948 6110

460 0 305

221 247 512 248 445 149 339 265 223 157 149 195 302

0 352 340

2188

451 210

0

333

796

Appendix III - p.9

3

Appendix III. Specific water demands (m /ton) in 1999. Source: calculated on the basis of Appendices I and II. Onion dry Afghanistan Albania Algeria American Samoa Angola Antille Antigua Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia Botswana Brahain Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi Burma Cambodia Cameroon Canary Islands Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Colombia Comoros Congo, Dem Republic Congo, Republic of Cook Islands Costa Rica Côte d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Estonia Ethiopia Falkland Fiji Islands Finland France French Guiana Gabon Gambia Georgia Germany Ghana Greece Grenada Guadeloupe Guatemala Guinea Guinea-Bissau

0

Peas

Safflower

Spinach

Sweet potato

0 0 0

Artichoke

Citrus

Rice 0 2565 0

0 0

0 241 0 170 76 0 0 0 0 0 0 149

185 0 220 66 0

0 356

7693 7661

244 202

375 318

0

1422

753

640 0

459 1285 91 800

2808

0

0 645

122

250

482

317

0 0

0 0 0

0 528

0

528

461

565 0

512

124

0

500

177 0

306

156 523 287

0

572

143

667

317

0 0

0 2720 0 1922 0 0

0 0

4395 0

1741

0

4080

569

1088

2622

278 0

0 0 244

1516 2343 781

0 0 0 0 0 0

1234 594 1210

0

469 0 829 604 257

2808

471 203 233

270 0 584 282 827

830 581 287

0 0

270 804

326 139

398 193

11333

0

0 250

482

1735 0 0 283 0 0

793 767

2255 1107

1703 5374 1135 0

1519 780

0 2042 1096 1349 1135

0

0

3900

210

308

1208

787

772

787

1104

698 1312

0 670 0 246

227

749

353

648

0 0 1821 1072 1471 0 0 0

1113 1135

0

0 0

2233 2666 2797 2565 952 3362 3450 2053 0 3792

0 0

0 183 271 0

0 146 0 428

1636 1938 900 952 1277

0

0

505 0 0 257 131

0

312 3707 0 0 0

4733 0

Appendix III - p.10

1869

0 1135 991 0 3125 5705 1844 1135 5939 1025 2850 3169 0 0

3

Appendix III. Specific water demands (m /ton) in 1999. Source: calculated on the basis of Appendices I and II. Onion dry Guyana Haiti Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kampuchia Kazakhstan Kenya Kiribati Korea Korea, Republic of Kuwait Kyrgyzstan Laos Latvia Lebanon Lesotho Liberia Libyan Lithuania Macedonia Macao Madagascar Maderia Malawi Malaysia Maldives Mali Malta Martinique Mauritania Mauritius Mexico Micronesia Moldova Mongolia Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norway Oman Pakistan Panama Palestine Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Réunion Romania Russian Federation Rwanda Saint Kitts and Nevis Saint Lucia

0 0 229

Peas

Safflower

Spinach

Sweet potato 0

635 704

562 0 0 0 180 171 240 0 113 0

816 433 828 0 398 487 208 161 1349

293 0

0 800

518 0 0 241

401 0

3900 13467 6063

207

0 0 397

1600

838 595

Artichoke

Citrus

2581 0 2360 0

850 1000 1918

0 11500

0 293 563 257 0

168 320 424 212

179

0

239 0 0

0 0

0 0 955

2000 640 1685 353

0 1265

1700

205

0 0 494

496 600 285

824

2275

0 759 0 0 0

879 459

0

1302

1639 0

633

2538 3092 1844 2107

643

0

0

277

670 772

0

873

0 0 0 629

395 945 367

6013

110

122

2808

331 0 365

106

0 249 730 185 216

4168

278

143 0

0

0

0 669

0

930 484

189

0 0 0 0 0

250 0 424

0 1180

892

1441

250

939

482

724 768 1790

0 793 0 1384 250 663 0

0 3056 0 2328 0 1942 1719 0

3636 0 7091 2738

4027 1135

3151 4536 8134 1135 2992 3677 2765

0

3113

0 1990 1368 3089 1135 1133

3556 0

0

3564

0

0 285 1296

1200 1836

0 1270 0

0 393 373

5355

491 0 0

0 0 0 0

917 915 524 816 0

0 1135 1172

0 485

0 0 0

2059 3518

555

0 0 0

Rice 2139 0 3273 2060 0 3350 2201 2401 0 1135 2182 1030 0 1203 3570

135 0 0 0

Appendix III - p.11

0 2081 2044 0

3

Appendix III. Specific water demands (m /ton) in 1999. Source: calculated on the basis of Appendices I and II. Onion dry Saint Vincent/Grenadines Samoa Sao Tome and Principe Saudi Arabia Senegal Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States of America Uruguay Uzbekistan Vanuatu Venezuela Viet Nam Virgins Wallis and Futuna Is West Bank Western Sahara Yemen Yugoslavia Zaire Zambia Zimbabwe

Peas

Safflower

Spinach

Sweet potato Artichoke 0

0

0 0

752 1228

478 361 0

622 226 0

1373

Citrus

2000

0 0

0 155 0 1227

185 331 775

135

293 0 931

374 516 0 487

442 366 0

545 387

856 1155 495

5650

766 0

2811

283

0 0 1448 285 0 0 456 0

922 985

1488 877

1712

600

2808

Rice

3515 4060 0 2642 1135 1135 0 0 3199 1143 2328 14655 2366 0 1135

0

5644

320

6455

0 0

0 381 0 435 0 383

1504 3000 2127 1240 1240 0

742

0

2800 2450 4425 4047 3760 3146 3130 1615 0

0 0 593 105 105 0 147

865 1166 775 372 141 495 0

0 1732

711 459

0 0

1261 598

1586 2163

0 946

0 627

0

2946 701

4129 3896

0 0

728 2247

0 0

2242

11127 5170

4057

0 223 222

0 415 0

0 0

0 317 293 1259

0

Appendix III - p.12

7027 2090 2830 1135 1331 1323 2533

Appendix IV: FAO guidelines on crop water requirements in mm [=10 m3/ha] FAO guidelines

Crop Min

Max

Bananas

700

1700

Beans

250

500

Corn (Maize)

400

750

Cotton

550

950

Dates

900

1300

Grains

300

450

Grapefruit

650

1000

Groundnut

500

700

Onions

350

600

Potato

350

625

Rice

590

950

Sorghum

300

650

Soybean

450

825

Sugar beet

450

850

Sugarcane

1000

1500

Sweet potato

400

675

Tobacco

300

500

Tomato

300

600

Wheat

450

650

Source: Gleick (1993, p.282-283)

Appendix Va: Gross virtual water import per country for the years 1995-1999 (106 m3) Country AFGHANISTAN ALBANIA ALGERIA ANDORRA ANGOLA ANGUILLA ANTIGUA BARB ARGENTINA ARMENIA ARUBA AUSTRALIA AUSTRIA AZERBAIJAN BAHAMAS BAHRAIN BANGLADESH BARBADOS BELARUS BELGIUM-LUX BELIZE BENIN BERMUDA BHUTAN BOLIVIA BOSNIA HERZG BRITISH INDIAN OCEAN TER BR.VIRGIN IS BRAZIL BRUNEI DAR. BULGARIA BURKINA FASO BURUNDI CAMBODIA CAMEROON CANADA CAPE VERDE CAYMAN ISLDS CENT.AF.REP CHAD CHILE CHINA COCOS ISLNDS COLOMBIA COMOROS CONGO CONGO, D.R. COOK ISLANDS COSTA RICA COTE DIVOIRE CROATIA CUBA CYPRUS CZECH REP DENMARK DJIBOUTI DOMINICA DOMINICAN RP ECUADOR EGYPT EL SALVADOR EQ.GUINEA ERITREA ESTONIA ETHIOPIA FAEROE ISLDS FALKLAND ISL FIJI FINLAND FR.GUIANA FR.POLYNESIA FRANCE GABON GAMBIA

1995

1996

1997

1998

1999

Total

59.9 105.7 9523.3 2.8 223.9 2.3 7.0 328.0 307.9 2.8 1433.0 863.1 158.1 51.2 144.2 12427.3 86.1 186.0 13948.1 22.1 717.7 235.2 9.8 346.8 88.4 0.0 24.6 16261.0 177.7 102.7 29.6 1.9 266.3 161.2 3982.1 39.8 48.6 0.1 3.2 2735.6 47926.2 0.0 6352.3 13.0 22.8 645.6 4.1 1510.8 754.3 458.5 1052.0 965.6 1136.4 1181.8 102.2 3.1 642.9 1171.1 15892.2 1006.4 0.0 26.8 233.6 512.7 1.0 0.0 68.4 667.4 1.9 8.7 8721.1 63.7 275.8

76.9 665.9 5683.6 1.9 230.5 3.5 9.1 297.1 220.6 6.5 949.1 1413.6 117.3 16.9 114.6 7349.0 81.6 95.1 14172.0 22.0 304.7 69.6 46.5 575.1 154.3 0.1 80.7 26101.0 217.5 657.0 41.8 1.5 85.9 90.0 4565.6 40.6 137.3 2.1 0.0 3316.0 36450.2 1.8 8178.9 43.7 25.3 628.1 0.0 2956.3 322.7 448.6 1248.9 1208.5 1267.0 1234.5 95.4 1.2 578.6 1176.5 16736.0 942.0 0.5 87.5 146.9 257.4 0.5 0.0 125.4 888.4 0.0 17.2 10242.3 113.6 511.5

107.0 240.2 11328.1 3.0 92.4 0.2 15.4 3598.7 510.8 8.2 905.2 1325.0 132.5 23.8 47.3 4940.4 168.8 244.4 15308.7 21.8 269.6 437.4 43.0 668.1 284.6 0.0 187.4 22776.5 287.6 353.7 81.9 0.0 74.8 241.6 5310.6 34.3 188.3 0.0 2.9 2933.7 21541.0 7.5 7905.1 32.7 95.9 140.8 0.0 1688.5 400.8 886.6 869.8 1360.8 1380.4 1270.9 56.5 3.5 791.6 1524.6 17371.4 1308.7 0.1 69.9 1376.6 160.3 1.3 3.1 270.6 1021.0 0.0 16.1 9369.8 83.9 380.3

5.3 256.3 10687.4 2.0 164.6 0.2 0.5 1735.4 182.2 14.0 777.2 1452.3 2112.6 12.0 294.1 15034.2 126.7 2208.3 14878.1 25.4 562.5 28.7 33.0 738.9 409.6 17.2 58.8 25724.2 569.8 155.6 154.9 5.4 132.1 194.0 4882.8 37.9 1.3 6.6 0.1 2983.3 23307.6 0.0 8485.7 46.7 160.1 110.9 0.0 1707.5 1271.6 623.3 1304.4 1192.5 1372.7 1681.0 111.2 0.8 695.7 2460.3 17799.3 923.5 5.4 177.6 823.0 387.9 1.5 0.1 248.4 1047.6 0.0 13.7 9523.1 93.1 367.7

43.4 120.3 11504.2 1.3 157.9 0.1 11.9 1475.5 359.3 3.0 990.4 1352.4 2501.4 18.1 86.6 1772.2 135.2 3545.7 13755.1 21.2 508.4 24.4 0.0 1045.3 242.0 0.0 1.3 24945.1 366.5 167.0 35.0 9.5 91.4 189.8 5331.0 69.3 106.6 0.0 0.7 4344.2 23529.5 0.9 6755.1 60.5 162.9 73.8 0.0 1875.2 1118.3 415.3 937.2 1164.6 1072.1 1544.9 182.2 5.2 950.1 1638.7 16886.5 1530.9 0.3 12.3 574.9 427.4 1.8 0.0 160.5 970.3 0.0 12.2 9025.3 149.3 61.4

292.4 1388.3 49053.2 11.0 869.4 6.4 43.9 7434.7 1580.8 34.6 5055.0 6404.9 5022.0 122.0 686.9 41523.0 598.4 6279.5 72061.9 112.5 2362.8 795.3 132.3 3374.2 1190.0 17.3 352.9 115807.8 1619.1 1441.8 343.2 18.3 650.4 876.6 24072.1 221.8 482.0 8.9 6.8 16312.8 152752.2 10.3 37677.0 196.6 467.0 1599.3 4.2 9738.3 3867.7 2849.3 5412.3 6318.3 6228.6 6913.1 547.5 13.8 3658.8 7971.2 84685.3 5711.5 6.4 374.1 3154.9 1745.7 6.2 3.3 873.0 4594.7 1.9 68.0 46881.6 503.6 1596.6

Appendix Va - p.1

Appendix Va: Gross virtual water import per country for the years 1995-1999 (106 m3) Country GEORGIA GERMANY GHANA GIBRALTAR GREECE GREENLAND GRENADA GUADELOUPE GUATEMALA GUINEA GUINEABISSAU GUYANA HAITI HONDURAS HONG KONG HUNGARY ICELAND INDIA INDONESIA IRAN (ISLM.R) IRAQ IRELAND ISRAEL ITALY JAMAICA JAPAN JORDAN KAZAKHSTAN KENYA KIRIBATI KOREA D P RP KOREA REP. KUWAIT KYRGYZSTAN LAO P.DEM.R LATVIA LEBANON LIBERIA LIBYA LITHUANIA MACAU MACEDONIA, TFYR MADAGASCAR MALAWI MALAYSIA MALDIVES MALI MALTA MARSHALL IS. MARTINIQUE MAURITANIA MAURITIUS MEXICO MICRON, F.ST MOLDOVA REP. MONGOLIA MONTSERRAT MOROCCO MOZAMBIQUE MYANMAR N.CALEDONIA N.MARIANA NAURU NEPAL NETH.ANTILES NETHERLANDS NEW ZEALAND NICARAGUA NIGER NIGERIA NORFOLK ISLD NORWAY OMAN

1995

1996

1997

1998

1999

Total

206.8 20653.3 320.2 0.5 2740.6 1.0 29.7 49.4 851.9 93.4 14.7 81.2 363.9 528.8 3182.6 452.6 57.0 595.7 25841.8 5904.1 50.8 813.1 2314.5 19087.4 414.6 55326.2 7731.2 16.4 1998.6 0.1 563.0 19013.1 472.4 270.1 89.0 235.8 767.5 66.9 647.1 588.3 122.7 21.3 547.2 42.4 10337.4 25.4 77.3 306.9 2.9 10.3 160.8 489.5 16237.9 8.8 202.8 16.9 0.0 6838.3 445.8 9.2 14.7 16.1 1.0 128.6 48.3 33476.2 931.3 478.7 206.4 1010.8 0.0 2551.2 1263.4

501.8 23907.8 617.4 0.4 3193.6 0.4 49.7 0.0 1209.5 53.5 14.0 59.1 321.6 625.1 3344.2 539.1 59.7 1517.0 24063.7 5393.2 172.7 877.6 5270.7 19275.5 423.9 60193.5 1196.9 74.6 635.5 0.2 440.1 22870.1 356.0 494.6 127.0 470.8 824.2 168.0 669.6 757.7 124.0 149.6 163.5 13.4 11549.1 14.0 29.7 325.6 2.0 0.0 442.6 658.4 25277.9 14.0 86.7 6.1 0.0 6391.8 219.0 4.9 13.5 0.4 0.0 64.1 94.4 35300.6 971.8 684.4 242.4 4410.2 1.8 2085.8 1097.1

259.3 21815.3 657.0 0.8 3093.2 1.3 30.7 0.0 1120.2 33.4 3.1 71.9 305.2 846.7 3119.5 987.2 66.1 4084.9 18687.4 8339.0 1669.0 886.6 5798.8 20528.5 375.3 63662.0 7585.9 40.6 612.4 0.4 1084.1 23638.2 621.4 84.5 169.4 319.4 635.3 60.5 1273.8 578.1 118.1 294.8 228.6 12.3 12024.4 12.0 35.3 306.9 2.4 0.0 517.3 469.1 22053.6 10.7 21.1 27.7 0.0 6433.2 247.5 31.2 42.6 1.9 0.0 26.6 42.7 37646.8 1144.2 446.7 422.7 7711.9 1.7 1872.5 1198.2

392.1 24692.0 727.2 12.1 3584.7 1.7 32.7 0.0 1186.3 63.0 0.4 47.0 376.3 996.6 3046.2 599.2 68.9 4449.0 28330.4 5144.5 1985.9 1090.5 4895.4 20113.9 368.1 60148.1 3695.0 51.2 922.9 0.4 674.0 23933.4 379.6 54.2 61.5 290.1 560.5 20.9 822.5 394.6 4.3 211.3 265.1 2.8 12571.0 2.0 196.3 294.5 1.9 0.0 676.4 566.9 27729.9 8.0 50.3 41.3 0.2 3743.7 499.9 33.4 14.0 2.8 0.0 18.3 32.2 32509.4 770.4 621.6 658.9 9038.5 0.0 1832.7 1138.5

181.9 25206.0 1034.5 40.6 2977.2 1.8 52.4 0.0 1607.5 167.2 9.4 80.4 578.2 998.4 2864.2 599.8 72.3 1418.6 9907.7 7997.4 1618.1 1061.1 7424.9 19123.8 382.3 58830.0 1425.3 29.2 681.5 0.1 453.6 23458.2 570.2 59.0 23.9 191.1 758.0 21.0 532.5 183.8 115.8 67.7 395.6 58.5 11059.7 4.7 60.6 353.2 1.4 0.0 81.6 639.4 30507.5 5.6 53.1 31.2 0.0 3632.1 272.6 26.8 13.9 4.1 0.0 0.5 42.3 35994.6 1188.8 686.9 17.2 6810.9 0.0 2731.3 1407.1

1541.9 116301.9 3356.2 54.4 15607.0 6.2 195.1 49.4 5975.4 410.5 41.5 339.6 1945.2 3995.5 15556.7 3177.9 324.1 12065.2 106831.0 33115.7 5503.3 4728.9 25940.3 98129.1 1964.2 298159.8 22680.1 209.1 4850.8 1.3 3214.9 112913.0 2488.8 962.3 470.9 1507.0 3880.8 337.3 3945.6 2502.1 484.9 744.8 1600.0 129.4 57541.6 58.1 399.2 1587.2 10.6 10.3 1878.6 2823.4 121806.7 47.1 413.9 123.2 0.2 28089.0 1684.8 105.5 98.7 25.3 1.1 238.1 259.8 175011.7 5003.2 2918.2 1547.6 28982.2 3.5 11073.4 6140.4

Appendix Va - p.2

Appendix Va: Gross virtual water import per country for the years 1995-1999 (106 m3) Country PAKISTAN PALAU PANAMA PAPUA N.GUIN PARAGUAY PERU PHILIPPINES PITCAIRN POLAND PORTUGAL QATAR REUNION ROMANIA RUSSIAN FED RWANDA S.AFR.CUS.UN S.VINCENT-GR SAMOA SAO TOME PRN SAUDI ARABIA SENEGAL SEYCHELLES SIERRA LEONE SINGAPORE SLOVAKIA SLOVENIA SOLOMON ISLS SOMALIA SPAIN SRI LANKA ST.HELENA ST.KITTS NEV ST.LUCIA ST.PIER.MIQU SUDAN SURINAME SWEDEN SWITZ.LIECHT SYRIA A. R. TAIWAN (POC) TAJIKISTAN TANZANIA, U.R THAILAND TOGO TOKELAU TONGA TRINIDAD TBG TUNISIA TURKEY TURKMENISTAN TURKS CA.ISL UGANDA UKRAINE UNTD ARAB EM UNTD KINGDOM URUGUAY US MSC.PACIFIC USA UZBEKISTAN VANUATU VENEZUELA VIET NAM WALLIS FUT.IS YEMEN YUGOSLAVIA ZAMBIA ZIMBABWE Grand Total

1995

1996

1997

1998

1999

Total

2517.0 6.4 422.9 47.1 224.9 4912.5 3712.4 0.0 4579.4 6469.1 48.9 380.5 798.4 2422.6 112.4 8609.4 58.4 0.3 2.6 12240.6 1304.3 17.3 325.9 3889.2 245.2 1266.1 0.6 170.0 21848.7 1404.4 0.0 3.2 1.3 0.0 258.6 30.2 514.7 2224.7 1011.0 7331.2 50.8 719.4 2718.4 599.4 0.0 1.0 805.0 6082.4 6494.9 139.0 0.1 171.8 349.8 2658.5 11994.1 618.7 57.4 23579.1 435.7 0.0 5158.6 173.6 0.0 1421.4 43.9 29.0 121.2 558839.1

2335.3 3.4 520.9 65.0 388.5 5723.8 9576.3 0.0 6740.0 6700.8 36.2 0.0 1086.5 15072.4 119.8 7072.8 54.7 0.9 7.9 14016.2 2721.0 11.5 18.7 3910.3 401.3 1132.8 0.3 334.9 18177.9 206708.4 0.0 5.3 0.9 0.3 561.3 32.4 738.1 2088.1 715.9 7553.6 52.6 924.0 3215.1 388.8 0.0 0.2 842.4 2885.5 11644.9 121.3 0.1 134.0 359.6 1380.1 14326.6 809.4 12.7 26513.8 981.3 0.1 5315.8 100.2 0.0 1947.1 647.3 68.8 49.2 812513.7

4091.0 8.0 595.6 68.9 265.4 5788.4 10949.5 0.2 3099.2 6365.3 55.5 0.0 757.1 17122.9 28.3 6058.6 67.6 0.5 4.2 6480.1 2244.3 20.0 29.7 3785.8 709.7 909.4 0.1 285.4 20299.2 173081.4 0.0 4.1 1.5 0.1 752.0 21.5 729.1 2057.0 672.8 7431.2 60.3 1905.0 4232.1 856.8 0.0 0.0 823.5 3725.7 11974.8 20.5 0.1 69.6 375.3 1710.8 15143.9 670.7 5.6 36844.1 586.7 0.0 5786.9 113.1 0.0 1138.5 715.0 5.9 124.7 792869.1

2125.6 1.2 671.3 48.7 284.4 6215.4 12125.7 0.0 3834.9 7373.0 123.6 0.0 966.1 15768.6 116.1 7186.8 57.2 1.1 0.6 14225.7 3327.1 74.6 23.5 3316.5 325.4 956.5 0.6 557.4 24763.4 52171.3 3.5 17.0 1.6 0.2 1014.1 23.3 851.1 2034.5 648.0 7155.4 59.0 1385.2 4564.2 1223.6 0.0 12.2 593.4 3401.1 12108.4 4.8 0.2 325.0 904.5 2184.9 15099.5 1022.0 1.7 27793.3 607.5 0.1 8063.7 176.6 0.0 1374.4 269.9 12.3 52.6 699989.7

1666.5 1.1 488.5 14.9 551.6 5191.5 4562.2 0.0 2797.6 6882.0 32.2 0.0 740.6 22286.2 88.4 5710.5 43.4 0.6 1.9 5063.8 3805.2 15.5 18.3 4294.7 251.6 1049.7 1.3 151.1 25533.8 3269.8 4.0 6.7 0.7 0.0 220.4 30.4 853.4 2090.4 1375.0 6524.2 10.7 1124.2 5760.1 1189.2 0.0 5.3 333.8 2932.1 9264.8 1.5 0.3 343.6 355.1 2241.9 14454.6 988.2 14.5 31591.3 52.7 0.1 6928.9 205.1 0.0 1363.0 87.6 57.9 231.1 600677.0

12735.3 20.1 2699.2 244.5 1714.9 27831.6 41033.6 0.2 21050.6 33790.2 296.4 380.5 4388.4 72672.6 465.0 34638.1 281.3 3.4 17.1 56566.6 13401.9 138.9 416.2 19196.2 1933.2 5314.4 3.0 1498.7 110623.1 436635.3 7.5 36.4 6.1 0.6 2806.3 137.7 3686.4 10494.6 4422.7 35995.5 233.4 6057.7 20489.8 4257.8 0.0 18.7 3398.1 19625.6 51487.8 287.1 0.9 1043.9 2344.3 10546.8 71020.6 4109.0 91.9 146321.5 2663.7 0.3 31253.9 768.8 0.0 7244.5 1763.6 173.9 578.9 3474587.7

Appendix Va - p.3

Appendix Vb. Gross virtual water export per country in the years 1995-1999 (106 m3) Country AFGHANISTAN ALBANIA ALGERIA ANDORRA ANGOLA ANGUILLA ANTIGUA BARB ARGENTINA ARMENIA ARUBA AUSTRALIA AUSTRIA AZERBAIJAN BAHAMAS BAHRAIN BANGLADESH BARBADOS BELARUS BELGIUM-LUX BELIZE BENIN BERMUDA BHUTAN BOLIVIA BOSNIA HERZG BRITISH INDIAN OCEAN TER BR.VIRGIN.IS BRAZIL BRUNEI DAR. BULGARIA BURKINA FASO BURUNDI CAMBODIA CAMEROON CANADA CAP VERDE CAYMAN ISLDS CENT.AF.REP CHAD CHILE CHINA COCOS ISLNDS COLOMBIA COMOROS CONGO CONGO, D.R. COOK ISLANDS COSTA RICA COTE DIVOIRE CROATIA CUBA CYPRUS CZECH REP DENMARK DJIBOUTI DOMINICA DOMINICAN RP ECUADOR EGYPT EL SALVADOR EQ.GUINEA ERITREA ESTONIA ETHIOPIA FAEROE ISLDS FALKLAND ISL FIJI FINLAND FR. GUIANA FR.POLYNESIA FRANCE GABON GAMBIA

1995

1996

1997

1998

1999

Total

31.4 6.2 0.2 0.0 0.0 0.0 0.0 37070.2 0.0 0.0 14702.5 918.1 0.0 38.8 0.0 36.7 8.3 43.6 2218.1 50.4 620.9 36.8 0.0 1755.6 5.8 0.0 0.0 18204.4 0.0 1230.6 39.2 0.4 65.5 176.3 59311.3 0.0 1.2 1.0 0.0 1227.0 5703.9 0.0 752.3 0.1 5.1 9.8 0.0 560.2 173.0 624.7 849.4 262.6 1746.2 2211.0 0.0 662.6 1833.0 1684.2 590.3 88.4 0.0 0.0 39.7 26.0 0.0 0.0 0.0 1098.7 0.0 0.0 27178.3 0.0 125.6

122.1 11.4 25.2 0.0 0.0 0.0 0.0 45187.6 0.1 0.0 43171.3 649.6 34.7 57.8 0.0 5162.7 15.0 6.0 1978.1 55.2 1031.0 4.5 0.0 1977.2 283.9 0.0 0.0 20261.8 0.0 413.2 8.6 0.5 23.7 190.2 58122.6 0.0 0.0 0.4 0.0 1266.4 4456.6 0.0 794.0 0.0 2.7 9.0 0.0 1007.0 47.1 621.0 1322.9 278.3 418.5 1707.3 0.0 801.1 2823.0 2285.9 1457.3 78.4 0.0 0.0 16.1 39.3 0.0 0.0 0.0 972.2 0.0 0.0 24851.6 0.0 165.0

979.7 37.1 0.4 0.0 0.0 0.0 0.0 40267.0 2.1 0.0 35407.8 978.2 21.9 135.9 0.0 1017.0 30.7 6.9 2836.2 107.3 1160.3 0.4 0.0 1781.7 14.0 0.0 0.0 38566.1 0.0 226.1 46.2 0.0 31.2 157.5 71206.1 0.0 18.1 0.5 0.0 1188.1 12851.5 0.0 900.2 0.0 10.5 3.6 0.0 573.2 33.7 115.4 1770.5 131.9 173.0 1923.1 0.9 738.0 4581.4 2872.0 648.8 106.9 0.0 1.2 96.7 24.7 0.0 0.0 0.0 1343.6 0.0 0.0 26509.0 0.3 88.3

154.3 12.6 5.5 0.0 0.0 0.0 0.0 59010.8 23.4 0.0 31600.0 1189.0 26.7 104.6 0.0 6592.4 23.9 64.7 2671.9 60.5 1753.0 55.1 0.0 1481.9 13.7 0.0 0.0 40859.8 0.0 815.9 2628.3 0.3 2.5 163.3 56989.1 0.0 0.0 0.8 0.0 1189.6 15344.9 0.0 883.0 0.0 5.7 1.6 0.0 637.3 112.7 227.6 1170.5 143.0 338.7 1840.1 0.0 334.7 2700.5 1835.9 1099.5 77.0 0.0 0.0 276.5 9.5 0.0 0.0 0.0 1221.9 0.0 0.0 26532.0 3.1 440.6

150.1 4.9 3.3 0.0 27.5 0.0 0.0 52241.1 2.2 0.0 25782.5 1145.4 66.8 36.3 1.2 4.1 9.5 54.0 2780.8 265.4 823.9 25.4 0.0 1664.2 1.4 0.0 0.0 42916.8 0.1 1113.0 2144.5 0.0 15.4 252.5 50913.0 0.0 0.0 11.8 0.0 1184.7 12217.5 0.0 996.4 0.0 10.1 0.7 0.0 672.2 51.3 141.2 1406.8 178.6 1129.7 1536.7 0.0 568.5 1381.5 2244.1 711.9 122.4 0.0 0.0 73.1 14.0 0.0 0.0 0.0 822.5 0.0 0.0 30186.4 0.0 1.6

1437.6 72.2 34.6 0.0 27.5 0.0 0.0 233776.8 27.7 0.0 150651.5 4880.3 150.1 373.3 1.2 12813.0 87.3 175.2 12485.1 538.9 5389.1 122.2 0.0 8660.5 318.8 0.0 0.0 160808.8 0.1 3798.7 4866.8 1.3 138.4 939.7 296542.0 0.0 19.3 14.5 0.0 6055.8 50574.4 0.0 4325.8 0.1 34.1 24.7 0.0 3450.0 417.7 1729.8 6520.0 994.4 3806.1 9218.2 0.9 3104.9 13319.5 10922.1 4507.8 473.1 0.0 1.3 502.1 113.4 0.0 0.0 0.0 5459.0 0.0 0.0 135257.2 3.5 821.1

Appendix Vb - p.1

Appendix Vb. Gross virtual water export per country in the years 1995-1999 (106 m3) Country GEORGIA GERMANY GHANA GIBRALTAR GREECE GREENLAND GRENADA GUADELOUPE GUATEMALA GUINEA GUINEABISSAU GUYANA HAITI HONDURAS HONG KONG HUNGARY ICELAND INDIA INDONESIA IRAN (ISLM.R) IRAQ IRELAND ISRAEL ITALY JAMAICA JAPAN JORDAN KAZAKHSTAN KENYA KIRIBATI KOREA D P RP KOREA REP. KUWAIT KYRGYZSTAN LAO P.DEM.R LATVIA LEBANON LIBERIA LIBYA LITHUANIA MACAU MACEDONIA, TFYR MADAGASCAR MALAWI MALAYSIA MALDIVES MALI MALTA MARSHALL IS. MARTINIQUE MAURITANIA MAURITIUS MEXICO MICRON, F. ST MOLDOVA REP. MONGOLIA MONTSERRAT MOROCCO MOZAMBIQUE MYANMAR N.CALEDONIA N.MARIANA NAURU NEPAL NETH.ANTILES NETHERLANDS NEW ZEALAND NICARAGUA NIGER NIGERIA NORFOLK ISLD NORWAY OMAN

1995

1996

1997

1998

1999

Total

0.0 8425.5 91.4 0.0 5728.8 0.0 32.0 31.3 1735.4 21.5 6.4 95.3 0.0 209.9 241.7 5988.5 0.9 25203.5 730.8 409.7 0.0 137.6 271.5 6380.9 143.3 128.7 102.3 674.0 331.6 0.0 1.7 48.8 0.2 126.7 2.7 11.5 40.5 0.0 36.7 145.7 0.9 53.1 97.7 429.7 350.7 0.0 9.9 20.9 0.0 59.5 0.0 242.1 3804.7 0.0 412.5 44.0 55.8 128.4 69.4 1486.4 0.0 0.0 0.0 0.0 0.0 4164.9 86.1 310.4 100.1 190.6 0.0 13.5 105.0

1.7 10269.3 95.6 0.0 5235.5 0.0 0.5 0.0 1658.0 19.5 6.2 101.2 0.0 343.0 840.8 2501.5 0.2 85625.4 738.8 1221.6 0.1 154.1 644.3 7035.1 163.1 40.2 7.1 8164.1 320.4 0.0 1.7 38.9 0.1 245.2 0.8 18.7 39.2 0.0 25.6 130.7 0.6 146.4 176.2 926.6 2579.4 0.0 7.5 114.5 0.0 0.0 0.2 317.7 11384.8 0.0 205.0 0.0 104.6 88.7 103.0 1141.4 0.0 0.0 0.0 1.7 0.0 4204.1 92.2 330.8 137.8 777.6 0.0 20.6 93.2

82.7 8246.2 172.9 0.0 5486.3 0.0 0.9 0.0 69278.5 27.5 8.8 250.3 0.0 438.4 16.2 3897.5 6.8 28994.7 772.4 504.7 0.1 298.6 651.9 6948.7 143.4 79.7 122.6 10937.1 52.7 0.0 7.3 35.3 0.0 93.9 0.6 101.1 14.2 0.0 31.3 329.5 0.9 122.0 245.4 883.0 1313.1 0.0 16.8 39.0 0.0 0.0 2.9 271.5 32768.8 0.0 213.1 0.0 32.8 95.8 67.6 12430.4 0.0 0.0 0.0 2.7 0.0 5069.3 131.2 462.6 137.4 127.4 0.0 7.9 122.9

239.1 13410.9 364.6 0.0 3571.3 0.0 0.2 0.0 2527.4 52.7 5.5 351.8 0.2 328.7 38.6 6388.1 0.0 29101.4 2538.3 1259.0 16.2 287.3 682.3 6751.3 126.1 498.0 31.3 8188.2 69.0 0.0 0.4 80.5 0.0 133.2 0.9 37.9 44.9 8.7 66.4 513.7 0.0 121.8 72.3 849.2 621.3 0.0 39.2 27.2 0.0 0.0 0.0 291.6 24973.8 0.0 323.6 0.0 7.3 51.1 130.9 2044.7 0.0 0.0 0.0 90.5 0.0 6546.6 153.0 251.0 163.6 194.2 0.0 8.2 141.6

191.3 8004.7 362.8 0.0 5418.0 0.0 2.1 0.0 2483.5 83.1 0.0 334.4 0.0 338.7 77.8 4104.6 0.1 4136.6 915.6 622.1 0.0 131.8 699.3 6694.4 110.5 195.5 11.6 11416.4 74.6 0.0 0.0 141.5 0.0 127.0 3.5 97.5 8.3 0.0 67.0 800.1 0.1 46.1 67.1 844.7 1415.3 0.0 0.9 26.8 0.0 0.0 0.0 251.7 3941.3 0.0 1123.3 9.0 0.0 72.8 54.5 403.7 0.0 0.0 0.0 0.0 0.0 7328.3 103.1 310.8 0.7 3382.4 0.0 5.2 135.2

514.8 48356.6 1087.3 0.0 25439.8 0.0 42.7 31.3 77682.8 204.4 26.9 1132.9 0.2 1658.7 1215.1 22948.1 7.9 173061.6 5695.9 4017.1 16.4 1009.3 2949.3 33810.3 686.5 942.0 275.0 39379.8 848.3 0.0 11.1 345.1 0.3 726.1 8.6 266.8 147.0 8.7 227.0 1919.7 2.5 489.3 658.6 3933.2 6279.7 0.0 74.3 228.5 0.0 59.5 3.1 1374.6 76873.4 0.0 2277.5 50.7 200.5 436.8 425.3 17506.6 0.0 0.0 0.0 95.0 0.0 27313.0 565.6 1665.5 539.7 4672.1 0.0 55.4 597.9

Appendix Vb - p.2

Appendix Vb. Gross virtual water export per country in the years 1995-1999 (106 m3) Country PAKISTAN PALAU PANAMA PAPUA N.GUIN PARAGUAY PERU PHILIPPINES PITCAIRN POLAND PORTUGAL QATAR REUNION ROMANIA RUSSIAN FED RWANDA S.AFR.CUS.UN S.VINCENT-GR SAMOA SAO TOME PRN SAUDI ARABIA SENEGAL SEYCHELLES SIERRA LEONE SINGAPORE SLOVAKIA SLOVENIA SOLOMON ISLS SOMALIA SPAIN SRI LANKA ST.HELENA ST.KITTS NEV ST.LUCIA ST.PIER.MIQU SUDAN SURINAME SWEDEN SWITZ.LIECHT SYRIA A. R. TAIWAN (POC) TAJIKISTAN TANZANIA, U.R THAILAND TOGO TOKELAU TONGA TRINIDAD TBG TUNISIA TURKEY TURKMENISTAN TURKS CA.ISL UGANDA UKRAINE UNTD ARAB EM UNTD KINGDOM URUGUAY US MSC.PACIFIC USA UZBEKISTAN VANUATU VENEZUELA VIET NAM WALLIS FUT.I YEMEN YUGOSLAVIA ZAMBIA ZIMBABWE Grand Total

1995

1996

1997

1998

1999

Total

2945.8 0.0 357.8 17.1 7138.7 123.2 4366.0 0.0 281.6 315.8 0.1 68.7 1538.0 6422.8 0.7 2275.5 0.0 1.7 0.0 1999.5 21.8 0.0 2.1 291.4 1394.0 11.1 1.3 32.3 4459.8 71.3 0.0 5.8 1254.2 0.0 5417.3 60.9 735.7 186.2 9486.6 259.8 1.5 109.9 41728.1 0.9 0.0 0.0 98.3 34.4 5217.1 0.0 0.0 510.0 2128.8 376.8 5614.5 1617.1 0.0 191579.1 1.6 0.0 1125.8 2770.0 1.4 5.5 45.4 67.0 461.6 558839.1

2756.1 0.0 314.3 22.2 8439.5 111.7 9633.0 0.0 218.4 272.3 0.0 0.0 3647.7 16476.2 0.0 3487.6 0.0 1.4 0.0 123.9 65.4 0.0 0.2 301.4 449.3 9.3 0.3 36.0 6038.5 3052.5 0.0 4.8 1335.7 0.0 511.8 130.1 1352.5 124.5 2610.1 208.0 69.0 141.8 90840.7 208.9 0.0 0.0 167.2 61.4 4154.0 0.2 0.0 647.8 6624.3 246.4 6313.7 5672.3 0.0 195159.6 103.7 0.0 2831.3 62568.1 0.0 11.4 1312.3 79.2 708.7 812513.7

1862.2 0.0 392.7 21.2 11766.6 129.9 8821.6 0.0 357.9 718.0 0.0 0.0 1744.5 12951.4 0.0 2537.4 0.0 1.1 0.0 14.8 57.8 0.0 0.2 712.4 515.3 16.4 3.1 31.0 5945.0 2613.8 0.0 9.1 884.0 0.0 381.4 147.5 1922.6 133.4 10175.2 102.1 124.5 358.4 37361.8 1.7 0.0 0.1 116.1 86.8 4430.0 1.5 0.0 35.5 6344.1 409.7 52929.6 3413.9 0.0 172422.8 296.5 0.0 969.2 3048.8 0.0 0.0 223.1 199.0 806.4 792869.1

3585.7 0.0 292.1 21.5 5593.9 263.9 8950.6 0.0 444.7 489.8 0.0 0.0 2620.5 17448.4 0.0 1597.9 0.0 0.6 0.0 32.8 42.2 0.0 0.2 695.8 1160.7 45.7 3.7 14.0 5661.1 2411.4 0.0 6.8 899.5 0.0 946.0 135.5 2091.1 176.2 3949.5 151.5 143.4 391.2 44381.4 428.0 0.0 0.0 44.9 98.4 9058.6 0.8 0.0 190.1 9397.9 507.1 6408.6 2668.7 0.0 165017.7 118.5 0.0 1228.9 21001.7 0.0 40.5 707.5 212.6 663.2 699989.7

1634.3 0.0 298.7 20.0 10901.7 89.0 4438.9 0.0 959.4 853.6 0.0 0.0 3955.3 7104.9 0.0 2893.3 0.0 0.9 0.0 4.8 30.6 0.0 0.5 174.9 1367.6 26.4 1.3 0.0 6000.8 19.5 0.0 5.1 837.5 0.0 1305.2 98.5 1783.9 192.0 94.5 112.3 80.2 415.9 39504.7 434.3 0.0 0.0 26.5 7.8 8717.4 0.4 0.0 90.5 9668.0 550.5 4606.1 2744.5 0.0 180442.5 98.2 0.0 470.7 1539.9 0.0 0.0 153.2 106.9 526.4 600677.0

12784.2 0.0 1655.6 102.1 43840.4 717.7 36210.1 0.0 2262.1 2649.5 0.1 68.7 13506.1 60397.9 0.8 12791.7 0.0 5.7 0.0 2175.9 217.9 0.0 3.2 2175.8 4887.0 109.0 9.7 113.4 28105.2 8168.6 0.0 31.7 5210.9 0.0 8561.7 572.5 7885.8 812.3 26315.8 833.7 418.6 1417.3 253816.7 1073.9 0.0 0.1 453.0 288.8 41222.2 2.8 0.0 1473.8 34163.1 2090.5 75872.5 16116.4 0.0 904621.7 618.5 0.0 6625.9 90928.5 1.4 57.3 2441.5 664.6 3166.4 3474587.7

Appendix Vb - p.3

Appendix Vc. Net virtual water import per country in the years 1995-1999 (106 m3) Country AFGHANISTAN ALBANIA ALGERIA ANDORRA ANGOLA ANGUILLA ANTIGUA BARB ARGENTINA ARMENIA ARUBA AUSTRALIA AUSTRIA AZERBAIJAN BAHAMAS BAHRAIN BANGLADESH BARBADOS BELARUS BELGIUM-LUX BELIZE BENIN BERMUDA BHUTAN BOLIVIA BOSNIA HERZG BRITISH INDIAN OCEAN TER BR.VIRGIN.IS BRAZIL BRUNEI DAR. BULGARIA BURKINA FASO BURUNDI CAMBODIA CAMEROON CANADA CAP VERDE CAYMAN ISLDS CENT.AF.REP CHAD CHILE CHINA COCOS ISLNDS COLOMBIA COMOROS CONGO CONGO, D.R. COOK ISLANDS COSTA RICA COTE DIVOIRE CROATIA CUBA CYPRUS CZECH REP DENMARK DJIBOUTI DOMINICA DOMINICAN RP ECUADOR EGYPT EL SALVADOR EQ.GUINEA ERITREA ESTONIA ETHIOPIA FAEROE ISLDS FALKLAND ISL FIJI FINLAND FR. GUIANA FR.POLYNESIA FRANCE GABON GAMBIA

1995

1996

1997

1998

1999

Total

28.5 99.5 9523.2 2.8 223.9 2.3 7.0 -36742.2 307.9 2.8 -13269.4 -55.0 158.1 12.5 144.2 12390.5 77.9 142.4 11730.1 -28.3 96.8 198.4 9.8 -1408.7 82.5 0.0 24.6 -1943.3 177.7 -1127.9 -9.6 1.5 200.8 -15.1 -55329.2 39.8 47.3 -0.9 3.2 1508.6 42222.3 0.0 5600.0 12.9 17.7 635.9 4.1 950.6 581.3 -166.2 202.6 703.0 -609.8 -1029.3 102.2 -659.5 -1190.1 -513.1 15301.9 918.0 0.0 26.8 193.9 486.7 1.0 0.0 68.4 -431.4 1.9 8.7 -18457.2 63.7 150.2

-45.2 654.4 5658.4 1.9 230.5 3.5 9.1 -44890.5 220.5 6.5 -42222.2 764.0 82.6 -40.9 114.6 2186.3 66.7 89.1 12193.9 -33.2 -726.3 65.1 46.5 -1402.1 -129.6 0.1 80.7 5839.2 217.5 243.8 33.2 1.0 62.1 -100.1 -53557.0 40.6 137.3 1.7 0.0 2049.6 31993.6 1.8 7384.9 43.7 22.6 619.1 0.0 1949.4 275.7 -172.4 -73.9 930.2 848.5 -472.8 95.4 -799.9 -2244.4 -1109.4 15278.7 863.6 0.5 87.5 130.8 218.1 0.5 0.0 125.4 -83.7 0.0 17.2 -14609.3 113.6 346.5

-872.8 203.2 11327.7 3.0 92.4 0.2 15.4 -36668.3 508.8 8.2 -34502.6 346.9 110.6 -112.1 47.3 3923.4 138.0 237.5 12472.4 -85.5 -890.7 437.1 43.0 -1113.6 270.6 0.0 187.4 -15789.6 287.6 127.6 35.7 0.0 43.6 84.2 -65895.5 34.3 170.2 -0.5 2.9 1745.5 8689.5 7.5 7004.9 32.7 85.5 137.2 0.0 1115.3 367.1 771.2 -900.6 1228.9 1207.5 -652.2 55.6 -734.5 -3789.8 -1347.4 16722.6 1201.9 0.1 68.7 1279.9 135.6 1.3 3.1 270.6 -322.6 0.0 16.1 -17139.2 83.6 292.0

-149.0 243.7 10681.9 2.0 164.6 0.2 0.5 -57275.4 158.8 14.0 -30822.8 263.3 2085.9 -92.6 294.1 8441.8 102.8 2143.6 12206.2 -35.1 -1190.6 -26.4 33.0 -742.9 395.9 17.2 58.8 -15135.6 569.8 -660.3 -2473.4 5.1 129.6 30.7 -52106.3 37.9 1.3 5.9 0.1 1793.7 7962.6 0.0 7602.7 46.7 154.4 109.3 0.0 1070.2 1159.0 395.7 133.9 1049.5 1034.0 -159.0 111.2 -333.9 -2004.8 624.4 16699.8 846.5 5.4 177.6 546.5 378.5 1.5 0.1 248.4 -174.3 0.0 13.7 -17008.9 89.9 -72.9

-106.7 115.4 11500.9 1.3 130.3 0.1 11.9 -50765.7 357.0 3.0 -24792.1 206.9 2434.6 -18.2 85.3 1768.0 125.7 3491.7 10974.3 -244.2 -315.5 -1.0 0.0 -618.9 240.6 0.0 1.3 -17971.8 366.3 -946.0 -2109.5 9.5 76.0 -62.7 -45582.0 69.3 106.6 -11.8 0.7 3159.5 11312.0 0.9 5758.7 60.5 152.8 73.1 0.0 1203.0 1067.0 274.2 -469.6 986.0 -57.6 8.2 182.2 -563.3 -431.5 -605.5 16174.5 1408.5 0.3 12.3 501.8 413.3 1.8 0.0 160.5 147.8 0.0 12.2 -21161.0 149.3 59.8

-1145.2 1316.2 49018.6 11.0 841.8 6.4 43.9 -226342.0 1553.0 34.6 -145596.6 1524.6 4871.8 -251.3 685.6 28710.0 511.0 6104.3 59576.8 -426.3 -3026.3 673.1 132.3 -5286.3 871.2 17.3 352.9 -45001.1 1619.0 -2357.0 -4523.6 17.1 512.0 -63.0 -272470.0 221.8 462.7 -5.6 6.8 10256.9 102177.8 10.3 33351.2 196.5 433.0 1574.6 4.2 6288.4 3450.0 1119.5 -1107.6 5323.9 2422.5 -2305.1 546.6 -3091.1 -9660.7 -2950.9 80177.5 5238.4 6.4 372.9 2652.9 1632.2 6.2 3.3 873.0 -864.3 1.9 68.0 -88375.7 500.1 775.5

Appendix Vc - p.1

Appendix Vc. Net virtual water import per country in the years 1995-1999 (106 m3) Country GEORGIA GERMANY GHANA GIBRALTAR GREECE GREENLAND GRENADA GUADELOUPE GUATEMALA GUINEA GUINEABISSAU GUYANA HAITI HONDURAS HONG KONG HUNGARY ICELAND INDIA INDONESIA IRAN (ISLM.R) IRAQ IRELAND ISRAEL ITALY JAMAICA JAPAN JORDAN KAZAKHSTAN KENYA KIRIBATI KOREA D P RP KOREA REP. KUWAIT KYRGYZSTAN LAO P.DEM.R LATVIA LEBANON LIBERIA LIBYA LITHUANIA MACAU MACEDONIA, TFYR MADAGASCAR MALAWI MALAYSIA MALDIVES MALI MALTA MARSHALL IS. MARTINIQUE MAURITANIA MAURITIUS MEXICO MICRON, F. ST MOLDOVA REP. MONGOLIA MONTSERRAT MOROCCO MOZAMBIQUE MYANMAR N.CALEDONIA N.MARIANA NAURU NEPAL NETH.ANTILES NETHERLANDS NEW ZEALAND NICARAGUA NIGER NIGERIA NORFOLK ISLD NORWAY OMAN

1995

1996

1997

1998

1999

Total

206.8 12227.8 228.8 0.5 -2988.3 1.0 -2.4 18.1 -883.5 71.9 8.2 -14.1 363.9 319.0 2940.9 -5535.9 56.0 -24607.8 25111.0 5494.4 50.8 675.5 2043.0 12706.5 271.3 55197.5 7628.9 -657.7 1667.0 0.1 561.3 18964.3 472.2 143.4 86.3 224.3 727.0 66.9 610.3 442.6 121.9 -31.8 449.6 -387.4 9986.7 25.4 67.5 285.9 2.9 -49.3 160.8 247.4 12433.2 8.8 -209.7 -27.1 -55.8 6709.9 376.4 -1477.2 14.7 16.1 1.0 128.5 48.3 29311.3 845.2 168.3 106.3 820.2 0.0 2537.7 1158.5

500.1 13638.5 521.8 0.4 -2041.9 0.4 49.2 0.0 -448.6 34.0 7.8 -42.1 321.6 282.1 2503.4 -1962.4 59.6 -84108.3 23324.8 4171.6 172.6 723.5 4626.4 12240.5 260.7 60153.3 1189.8 -8089.5 315.0 0.2 438.5 22831.2 355.8 249.4 126.2 452.1 785.1 168.0 644.0 627.0 123.4 3.3 -12.7 -913.1 8969.7 14.0 22.3 211.2 2.0 0.0 442.4 340.8 13893.1 14.0 -118.4 6.1 -104.6 6303.1 116.0 -1136.5 13.5 0.4 0.0 62.4 94.4 31096.6 879.6 353.6 104.5 3632.5 1.8 2065.3 1003.9

176.7 13569.1 484.1 0.8 -2393.1 1.3 29.9 0.0 -68158.3 5.9 -5.7 -178.3 305.2 408.2 3103.4 -2910.3 59.3 -24909.9 17915.1 7834.3 1669.0 588.0 5146.9 13579.9 231.9 63582.2 7463.3 -10896.5 559.6 0.4 1076.8 23602.8 621.4 -9.4 168.8 218.3 621.1 60.5 1242.5 248.6 117.2 172.8 -16.8 -870.7 10711.3 12.0 18.4 267.8 2.4 0.0 514.4 197.6 -10715.2 10.7 -192.0 27.7 -32.8 6337.4 179.9 -12399.3 42.6 1.9 0.0 24.0 42.7 32577.5 1013.0 -15.9 285.3 7584.5 1.7 1864.6 1075.4

153.0 11281.1 362.7 12.1 13.4 1.7 32.4 0.0 -1341.1 10.2 -5.1 -304.8 376.1 667.9 3007.6 -5788.9 68.9 -24652.4 25792.0 3885.5 1969.8 803.2 4213.1 13362.6 242.0 59650.2 3663.7 -8137.0 853.9 0.4 673.6 23852.9 379.6 -79.0 60.6 252.2 515.7 12.2 756.2 -119.1 4.3 89.5 192.8 -846.3 11949.8 2.0 157.1 267.3 1.9 0.0 676.4 275.2 2756.1 8.0 -273.3 41.3 -7.1 3692.6 369.1 -2011.3 14.0 2.8 0.0 -72.2 32.2 25962.8 617.4 370.6 495.3 8844.3 0.0 1824.5 996.9

-9.4 17201.3 671.7 40.6 -2440.8 1.8 50.3 0.0 -876.0 84.0 9.4 -254.0 578.2 659.7 2786.4 -3504.8 72.2 -2718.0 8992.2 7375.3 1618.1 929.4 6725.6 12429.4 271.8 58634.5 1413.7 -11387.2 606.9 0.1 453.6 23316.7 570.2 -68.1 20.5 93.6 749.7 21.0 465.5 -616.3 115.7 21.7 328.5 -786.3 9644.4 4.7 59.6 326.4 1.4 0.0 81.6 387.7 26566.2 5.6 -1070.2 22.2 0.0 3559.3 218.1 -376.9 13.9 4.1 0.0 0.5 42.3 28666.4 1085.7 376.1 16.5 3428.5 0.0 2726.1 1271.9

1027.2 67945.3 2268.9 54.4 -9832.8 6.2 152.5 18.1 -71707.4 206.1 14.5 -793.3 1945.1 2336.8 14341.7 -19770.2 316.2 -160996.4 101135.1 29098.6 5487.0 3719.6 22991.0 64318.7 1277.7 297217.8 22405.1 -39170.8 4002.5 1.3 3203.8 112567.9 2488.5 236.3 462.4 1240.2 3733.8 328.6 3718.6 582.4 482.4 255.5 941.4 -3803.8 51261.8 58.1 325.0 1358.6 10.6 -49.3 1875.6 1448.7 44933.3 47.1 -1863.6 72.5 -200.3 27652.1 1259.5 -17401.1 98.7 25.3 1.1 143.1 259.8 147698.7 4437.6 1252.7 1007.9 24310.1 3.5 11018.1 5542.5

Appendix Vc - p.2

Appendix Vc. Net virtual water import per country in the years 1995-1999 (106 m3) Country PAKISTAN PALAU PANAMA PAPUA N.GUIN PARAGUAY PERU PHILIPPINES PITCAIRN POLAND PORTUGAL QATAR REUNION ROMANIA RUSSIAN FED RWANDA S.AFR.CUS.UN S.VINCENT-GR SAMOA SAO TOME PRN SAUDI ARABIA SENEGAL SEYCHELLES SIERRA LEONE SINGAPORE SLOVAKIA SLOVENIA SOLOMON ISLS SOMALIA SPAIN SRI LANKA ST.HELENA ST.KITTS NEV ST.LUCIA ST.PIER.MIQU SUDAN SURINAME SWEDEN SWITZ.LIECHT SYRIA A. R. TAIWAN (POC) TAJIKISTAN TANZANIA, U.R THAILAND TOGO TOKELAU TONGA TRINIDAD TBG TUNISIA TURKEY TURKMENISTAN TURKS CA.ISL UGANDA UKRAINE UNTD ARAB EM UNTD KINGDOM URUGUAY US MSC.PACIFIC USA UZBEKISTAN VANUATU VENEZUELA VIET NAM WALLIS FUT.I YEMEN YUGOSLAVIA ZAMBIA ZIMBABWE Grand Total

1995

1996

1997

1998

1999

Total

-428.9 6.4 65.1 30.0 -6913.9 4789.3 -653.6 0.0 4297.7 6153.3 48.8 311.8 -739.5 -4000.2 111.7 6333.9 58.4 -1.4 2.6 10241.1 1282.5 17.3 323.8 3597.8 -1148.8 1254.9 -0.8 137.7 17388.9 1333.1 0.0 -2.6 -1252.8 0.0 -5158.7 -30.8 -221.1 2038.5 -8475.6 7071.4 49.3 609.5 -39009.7 598.5 0.0 1.0 706.7 6048.0 1277.8 139.0 0.1 -338.3 -1778.9 2281.6 6379.6 -998.4 57.4 -168000.0 434.2 0.0 4032.8 -2596.4 -1.4 1415.9 -1.5 -37.9 -340.4 0.0

-420.9 3.4 206.6 42.8 -8050.9 5612.1 -56.7 0.0 6521.6 6428.5 36.2 0.0 -2561.2 -1403.8 119.8 3585.2 54.7 -0.5 7.9 13892.3 2655.6 11.5 18.6 3608.9 -48.0 1123.5 0.0 298.9 12139.4 203655.9 0.0 0.5 -1334.8 0.3 49.6 -97.7 -614.4 1963.5 -1894.2 7345.6 -16.3 782.2 -87625.6 179.9 0.0 0.2 675.3 2824.1 7491.0 121.1 0.1 -513.8 -6264.7 1133.7 8012.9 -4862.9 12.7 -168645.8 877.6 0.1 2484.4 -62467.8 0.0 1935.7 -665.0 -10.4 -659.5 0.0

2228.8 8.0 202.9 47.6 -11501.1 5658.4 2127.9 0.2 2741.4 5647.3 55.5 0.0 -987.4 4171.4 28.3 3521.2 67.6 -0.6 4.2 6465.3 2186.5 20.0 29.5 3073.4 194.4 892.9 -2.9 254.4 14354.2 170467.6 0.0 -5.0 -882.5 0.1 370.5 -126.0 -1193.5 1923.6 -9502.4 7329.1 -64.3 1546.5 -33129.7 855.1 0.0 0.0 707.4 3639.0 7544.8 18.9 0.1 34.1 -5968.8 1301.1 -37785.8 -2743.2 5.6 -135578.8 290.2 0.0 4817.7 -2935.6 0.0 1138.5 491.9 -193.1 -681.7 0.0

-1460.2 1.2 379.2 27.2 -5309.5 5951.6 3175.1 0.0 3390.2 6883.3 123.6 0.0 -1654.4 -1679.8 116.1 5588.9 57.2 0.4 0.6 14192.9 3285.0 74.6 23.3 2620.7 -835.4 910.8 -3.1 543.3 19102.3 49759.9 3.5 10.2 -897.9 0.2 68.1 -112.2 -1240.0 1858.3 -3301.5 7003.9 -84.5 994.0 -39817.2 795.6 0.0 12.2 548.5 3302.7 3049.8 4.1 0.2 134.9 -8493.4 1677.8 8690.9 -1646.7 1.7 -137224.4 489.0 0.1 6834.8 -20825.0 0.0 1333.9 -437.6 -200.3 -610.6 0.0

32.2 1.1 189.8 -5.2 -10350.0 5102.5 123.3 0.0 1838.2 6028.4 32.2 0.0 -3214.8 15181.3 88.3 2817.2 43.4 -0.3 1.9 5058.9 3774.6 15.5 17.8 4119.8 -1116.0 1023.3 0.0 151.1 19533.0 3250.2 4.0 1.6 -836.8 0.0 -1084.9 -68.1 -930.5 1898.3 1280.5 6411.9 -69.6 708.3 -33744.6 754.9 0.0 5.3 307.2 2924.3 547.4 1.1 0.3 253.1 -9312.9 1691.4 9848.5 -1756.3 14.5 -148851.2 -45.5 0.1 6458.2 -1334.8 0.0 1363.0 -65.6 -49.0 -295.3 0.0

-48.9 20.1 1043.6 142.4 -42125.5 27113.9 4823.5 0.2 18788.6 31140.7 296.3 311.8 -9117.7 12274.7 464.3 21846.4 281.3 -2.3 17.1 54390.7 13184.0 138.9 413.0 17020.4 -2953.8 5205.4 -6.7 1385.4 82517.9 428466.7 7.5 4.7 -5204.8 0.6 -5755.3 -434.7 -4199.5 9682.3 -21893.2 35161.9 -185.3 4640.5 -233326.9 3183.9 0.0 18.6 2945.1 19336.8 10265.6 284.3 0.9 -430.0 -31818.9 8456.2 -4852.0 -12007.5 91.9 -758300.2 2045.2 0.3 24628.0 -90159.7 -1.4 7187.1 -677.8 -490.7 -2587.5 0.0

Appendix Vc - p.3

Appendix VI. Classification of countries into thirteen world regions Central Africa BURUNDI CAMEROON CENT.AF.REP COMOROS CONGO CONGO, D.R. EQ.GUINEA GABON KENYA RWANDA SAO TOME PRN SEYCHELLES TANZANIA, U.R UGANDA Central America ANGUILLA ANTIGUA BARB ARUBA BAHAMAS BARBADOS BELIZE BR.VIRGIN.IS CAYMAN ISLDS COSTA RICA CUBA DOMINICA DOMINICAN RP EL SALVADOR GRENADA GUADELOUPE GUATEMALA HAITI HONDURAS JAMAICA MARTINIQUE MEXICO MONTSERRAT NETH.ANTILES NICARAGUA PANAMA S.VINCENT-GR ST.KITTS NEV ST.LUCIA TRINIDAD TBG TURKS CA.ISL US.MSC.PAC

Central and South Asia AFGHANISTAN BERMUDA BHUTAN CHINA HONG KONG INDIA JAPAN KOREA D P RP KOREA REP. MACAU MALDIVES MONGOLIA NEPAL PAKISTAN SRI LANKA TAIWAN (POC) Eastern Europe ALBANIA BOSNIA HERZG BULGARIA CROATIA CYPRUS CZECH REP ESTONIA GREECE HUNGARY LATVIA LITHUANIA MACEDONIA, TFYR POLAND ROMANIA SLOVAKIA SLOVENIA YUGOSLAVIA Middle East BAHRAIN IRAN (ISLM.R) IRAQ ISRAEL JORDAN KUWAIT LEBANON OMAN QATAR SAUDI ARABIA SYRIA A. R.

TURKEY UNTD ARAB EM YEMEN North Africa ALGERIA BENIN BURKINA FASO CAP VERDE CHAD COTE DIVOIRE DJIBOUTI EGYPT ERITREA ETHIOPIA GAMBIA GHANA GUINEA GUINEABISSAU LIBERIA LIBYA MALI MAURITANIA MOROCCO NIGER NIGERIA SENEGAL SIERRA LEONE SOMALIA SUDAN TOGO TUNISIA North America CANADA ST.PIERRE & MIQUELON USA,PR,USVI Oceania AUSTRALIA BR.IND.OC.TR COCOS ISLNDS COOK ISLANDS FIJI FR.POLYNESIA KIRIBATI MARSHALL IS. MICRON, F. ST N.CALEDONIA N.MARIANA

NAURU NEW ZEALAND NORFOLK ISLD PALAU PAPUA N.GUIN PITCAIRN SAMOA SOLOMON ISLS TOKELAU TONGA VANUATU WALLIS FUT.I FSU ARMENIA AZERBAIJAN BELARUS GEORGIA KAZAKSTAN KYRGYZSTAN MOLDOVA REP. RUSSIAN FED TAJIKISTAN TURKMENISTAN UKRAINE UZBEKISTAN South Africa ANGOLA MADAGASCAR MALAWI MAURITIUS MOZAMBIQUE REUNION S.AFR.CUS.UN ST.HELENA ZAMBIA ZIMBABWE South America ARGENTINA BOLIVIA BRAZIL CHILE COLOMBIA ECUADOR FALKLAND ISL FR. GUIANA GUYANA PARAGUAY

PERU SURINAME URUGUAY VENEZUELA South east Asia BANGLADESH BRUNEI DAR. CAMBODIA INDONESIA LAO P.DEM.R MALAYSIA MYANMAR PHILIPPINES SINGAPORE THAILAND VIET NAM Western Europe ANDORRA AUSTRIA BELGIUM-LUX DENMARK FAEROE ISLDS FINLAND FRANCE GERMANY GIBRALTAR GREENLAND ICELAND IRELAND ITALY MALTA NETHERLANDS NORWAY PORTUGAL SPAIN SWEDEN SWITZ.LIECHT UNTD KINGDOM

Appendix VII. Gross virtual water trade between and within regions (Gm3) Year 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total

Importer Central Africa Exporter Central Africa 0.4976 Central Africa 0.6567 Central Africa 0.0344 Central Africa 0.2755 Central Africa 0.1884 Central Africa 1.6526 Central America 0.0169 Central America 0.0013 Central America 0.0762 Central America 0.1474 Central America 0.0114 Central America 0.2534 Central & South Asia 1.7603 Central & South Asia 0.8645 Central & South Asia 0.1941 Central & South Asia 0.5200 Central & South Asia 0.1939 Central & South Asia 3.5329 Eastern Europe 0.0004 Eastern Europe 0.0071 Eastern Europe 0.0009 Eastern Europe 0.0007 Eastern Europe 0.0079 Eastern Europe 0.0170 Middle East 0.2902 Middle East 0.0604 Middle East 0.1064 Middle East 0.0377 Middle East 0.2996 Middle East 0.7944 North Africa 0.0108 North Africa 0.0066 North Africa 0.0715 North Africa 0.0323 North Africa 0.0083 North Africa 0.1296 North America 0.5246 North America 0.3987 North America 0.6078 North America 0.7213 North America 0.6205 North America 2.8728 Oceania 0.0004 Oceania 0.1183 Oceania 0.1549 Oceania 0.4262 Oceania 0.1086 Oceania 0.8083 FSU 0.0000 FSU 0.0000 FSU 0.0084 FSU 0.0000 FSU 0.0001 FSU 0.0084 Southern Africa 0.0659 Southern Africa 0.0148 Southern Africa 0.0187 Southern Africa 0.1619 Southern Africa 0.4690 Southern Africa 0.7302 South America 0.3406 South America 0.1454 South America 0.2371 South America 0.4825 South America 0.4284 South America 1.6341 South-east Asia 0.2849 South-east Asia 0.3131 South-east Asia 0.3588 South-east Asia 0.4478 South-east Asia 0.4075 South-east Asia 1.8121 Western Europe 0.1380 Western Europe 0.1505 Western Europe 1.3653 Western Europe 0.1932 Western Europe 0.1574 Western Europe 2.0044

Central America 0.0000 0.0002 0.0002 0.0001 0.0000 0.0005 0.7178 1.1950 1.0331 0.5410 1.1329 4.6198 0.0218 0.0327 0.1746 0.1800 0.2623 0.6714 0.0189 0.0995 0.0030 0.0229 0.0064 0.1507 0.0705 0.0119 0.0161 0.0173 0.0189 0.1346 0.0000 0.1341 0.0021 0.0109 0.0000 0.1471 22.4680 31.4015 28.0958 33.2296 38.0402 153.2352 0.0033 0.0684 0.1207 0.1274 0.0829 0.4026 0.0000 0.0605 0.1986 0.0587 0.0081 0.3259 0.0774 0.4304 0.0987 0.0191 0.0507 0.6763 0.5553 1.8655 1.7136 1.9114 1.1105 7.1563 0.4551 0.5623 0.0492 0.9822 0.0892 2.1381 0.4339 0.3463 0.4891 0.4562 0.5373 2.2629

Central & South Asia 0.0147 0.0101 0.0035 0.0559 0.0218 0.1060 0.9242 7.9984 93.3307 21.5666 0.6962 124.5161 3.4605 61.7172 20.8937 8.4108 5.9164 100.3962 0.0750 0.0634 0.4984 1.4679 0.7169 2.8216 0.1083 2.3403 2.8098 4.5177 1.7832 11.5591 0.8781 0.6628 0.2092 0.1301 0.5750 2.4551 97.3939 86.3532 77.3522 65.2549 68.8541 395.2083 7.3305 26.9375 18.0507 15.8428 15.0996 83.2610 0.1686 0.5679 0.5140 4.2047 2.5405 7.9956 1.1837 1.8448 1.2787 0.6378 0.4311 5.3761 4.6692 16.1354 11.0835 16.3524 14.0544 62.2949 19.1315 135.8150 29.3716 32.0168 10.2948 226.6296 3.1199 1.3676 47.1093 6.6849 1.2506 59.5323

Appendix VII - p.1

Eastern Europe 0.0148 0.0175 0.0218 0.0369 0.0274 0.1185 0.1236 0.2523 0.1892 0.1490 0.0615 0.7756 0.2895 1.2141 0.6079 0.5264 0.4313 3.0692 4.7491 3.6900 3.2317 5.0361 3.6283 20.4032 0.4875 0.3581 0.4086 0.3684 0.4666 2.5394 0.1254 0.2632 0.2079 0.2822 0.2611 1.1398 1.7263 2.8439 1.8298 1.4994 1.6147 9.5140 0.0133 0.0344 0.0074 0.0051 0.0141 0.0743 2.2567 3.7433 3.1474 2.8446 1.0728 13.0635 0.0846 0.1097 0.1043 0.0777 0.1200 0.4963 1.0516 1.6227 2.2029 1.5771 1.3799 7.8342 0.4550 0.3038 0.8805 0.4792 0.4391 2.5578 2.6850 5.2138 4.5064 3.3835 3.1859 18.9746

Middle East

North Africa

North America

0.0008 0.0000 0.0315 0.0248 0.0122 0.0694 0.0234 0.0938 0.2159 0.0729 0.0242 0.4304 4.6034 7.1493 3.5783 5.6039 0.7064 21.6413 1.9467 2.4611 1.2022 2.0765 2.6796 10.3661 7.2631 1.0893 5.9675 3.0537 1.2827 25.6536 1.6840 0.3597 0.3088 1.0650 0.3208 3.7382 12.1025 13.4897 15.8272 9.8012 12.5528 63.7734 0.5766 1.6820 1.7365 3.0403 2.4344 9.4698 3.6448 6.4979 4.8750 8.8628 5.3805 29.2610 0.0013 0.0181 0.0237 0.2207 0.1049 0.3687 2.5608 2.0701 6.2214 5.9081 3.5032 20.2636 5.0358 4.7795 4.4413 5.8334 5.6713 25.7613 3.0802 4.4753 3.4981 3.1953 5.9554 20.2044

0.0051 0.0155 0.0051 0.0156 0.0099 0.0512 0.3731 0.1906 0.4837 0.0967 0.3849 1.5290 2.2978 2.3960 2.8211 4.3142 1.9312 13.7603 2.5848 1.5212 0.9695 1.3279 1.1580 7.5614 3.9026 0.5779 2.5509 2.6701 1.5322 13.2090 0.3373 0.7116 0.5386 0.8993 0.2485 2.7353 25.7962 25.4160 24.3203 26.5783 26.4031 128.5138 0.0882 1.7303 4.3002 1.9581 1.2312 9.3081 0.6938 0.5752 0.7675 0.6726 0.3649 3.0740 0.0635 0.0506 0.1691 0.0474 0.0844 0.4150 1.4886 3.0895 6.3842 5.2614 2.4084 18.6321 2.4177 4.8298 7.0455 7.5120 9.7584 31.5634 6.0055 2.9264 4.8419 5.6990 5.9736 25.4465

0.0039 0.0045 0.0112 0.0050 0.0266 0.0512 5.8659 7.7870 13.0115 7.3844 6.3168 40.3656 0.4308 0.9847 1.0028 0.4401 0.4600 3.3184 0.0692 0.0804 0.1540 0.1353 0.1162 0.5551 0.4917 0.5697 0.4971 0.4200 0.3671 2.3456 0.0037 0.1016 0.0035 0.1869 3.8797 4.1754 15.6483 14.5063 19.1281 15.9503 17.5477 82.7806 0.1710 0.4404 0.4029 0.5164 1.1563 2.6870 0.0026 0.4862 0.1581 0.2491 0.0684 0.9644 0.1302 0.5077 0.4175 0.3441 0.3402 1.7397 2.0551 2.6493 3.4523 2.5408 2.6677 13.3652 2.1945 2.5216 2.8520 2.7838 2.6162 12.9680 0.4943 0.4402 1.0670 1.7203 1.3596 5.0814

Appendix VII. Gross virtual water trade between and within regions (Gm3) Year 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total 1995 1996 1997 1998 1999 Total

Importer Exporter Central Africa Central Africa Central Africa Central Africa Central Africa Central Africa Central America Central America Central America Central America Central America Central America Central & South Asia Central & South Asia Central & South Asia Central & South Asia Central & South Asia Central & South Asia Eastern Europe Eastern Europe Eastern Europe Eastern Europe Eastern Europe Eastern Europe Middle East Middle East Middle East Middle East Middle East Middle East North Africa North Africa North Africa North Africa North Africa North Africa North America North America North America North America North America North America Oceania Oceania Oceania Oceania Oceania Oceania FSU FSU FSU FSU FSU FSU Southern Africa Southern Africa Southern Africa Southern Africa Southern Africa Southern Africa South America South America South America South America South America South America South-east Asia South-east Asia South-east Asia South-east Asia South-east Asia South-east Asia Western Europe Western Europe Western Europe Western Europe Western Europe Western Europe

Oceania

FSU

0.0016 0.0032 0.0037 0.0039 0.0028 0.0152 0.0021 0.0012 0.0016 0.0012 0.0008 0.0068 0.0828 0.0968 0.0857 0.0671 0.0705 0.4030 0.0128 0.0243 0.0550 0.0384 0.0830 0.2134 0.1463 0.1621 0.1582 0.1627 0.1916 0.8209 0.0001 0.0000 0.0003 0.0000 0.0000 0.0005 1.1661 0.6804 0.9599 0.5791 0.6364 4.0219 0.5475 0.6456 0.4756 0.3933 0.7380 2.7964 0.0000 0.0000 0.0128 0.0000 0.0000 0.0128 0.0273 0.0152 0.0182 0.0157 0.0213 0.0977 0.0711 0.0738 0.0618 0.0662 0.0681 0.3409 0.3702 0.4566 0.6372 0.5804 0.5823 2.6266 0.1166 0.0082 0.0113 0.0099 0.0064 0.1523

0.0000 0.0000 0.0004 0.0030 0.0095 0.0128 0.0705 0.7957 0.6464 1.4570 1.3183 4.2878 0.6171 2.7256 2.4983 2.7169 1.3252 9.8831 1.2859 1.0543 0.7011 0.7832 1.4087 5.2331 0.0583 0.2322 0.2314 0.3683 0.3229 1.2131 0.0045 0.0852 0.0362 0.0845 0.0078 0.2182 1.1577 1.7920 0.9510 0.5480 5.2041 9.6527 0.0002 0.0233 0.0126 0.0182 0.0025 0.0568 0.5451 9.7397 11.8430 10.6950 15.8625 48.6822 0.0000 0.0297 0.0575 0.0426 0.1274 0.2573 0.2946 0.4403 1.0428 1.2015 1.8700 4.8491 0.1799 0.5666 0.6638 4.0351 0.5305 5.9759 0.5322 0.6934 0.7744 0.4420 1.4464 3.8883

Southern Africa 0.2974 0.2296 0.0689 0.0110 0.0372 0.6440 0.1234 0.0135 0.0038 0.0252 0.0000 0.1660 2.0879 1.7244 1.9615 2.8054 0.8581 9.4374 0.0097 0.0436 0.0047 0.0334 0.0270 0.1184 0.0132 0.0039 0.0028 0.0053 0.0039 0.0291 0.2438 0.0988 0.0079 0.0111 0.0715 0.4330 2.9580 2.2324 1.4799 1.7857 1.3884 9.8444 0.0897 1.1791 0.4342 0.4143 0.7245 2.8418 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.3526 0.4171 0.6457 0.6061 0.7558 2.7772 1.0340 0.3949 0.3870 0.3896 0.5423 2.7477 3.0271 1.7790 1.8766 2.3113 2.8190 11.8129 0.6521 0.3595 0.3661 0.3563 0.2997 2.0337

South America 0.0000 0.0000 0.0000 0.0006 0.0019 0.0024 0.5349 0.4109 0.4511 0.8466 0.2027 2.4461 0.2170 0.4307 0.0725 0.1390 0.0133 0.8725 0.0045 0.0180 0.0184 0.0310 0.0087 0.0807 0.0841 0.0303 0.1068 0.1177 0.1395 0.4784 0.0007 0.0003 0.0013 2.5338 2.0780 4.6141 14.2710 19.8020 19.1690 18.7479 16.6849 88.6748 0.1887 1.3382 1.4269 0.3051 0.4021 3.6611 0.0000 0.0007 0.0592 0.0000 0.0000 0.0599 0.2259 0.4718 0.5718 0.0209 0.0160 1.3063 21.4816 28.2697 29.2891 33.7365 33.9501 146.7270 0.6281 1.0685 0.6033 0.9429 0.2086 3.4514 0.5863 0.1325 0.2419 0.3617 0.2690 1.5914

Appendix VII - p.2

South-east Asia 0.0135 0.0035 0.0052 0.0203 0.0031 0.0456 0.0227 0.0020 0.0662 0.1215 0.1966 0.4090 16.4929 12.9263 8.6227 22.0534 4.7938 64.8891 0.2588 0.0936 0.0984 0.0356 0.0671 0.5536 1.8071 0.1687 0.0653 0.2319 0.3430 2.7234 0.0003 0.0001 0.0794 0.0752 0.0007 0.1556 15.5146 18.5889 19.2247 16.0199 13.4565 82.8046 5.3400 8.1038 7.5285 7.5973 2.9925 31.5618 0.0000 0.0001 0.1445 0.1935 0.0642 0.4024 0.4280 0.3477 0.1307 0.2332 0.0726 1.2123 3.5136 2.1257 4.5405 3.3530 2.9675 16.5003 15.9496 17.5632 14.3879 26.4621 12.8347 87.1976 0.3013 0.2755 0.4019 0.5186 0.2778 1.7752

Western Europe 0.2956 0.3720 0.4330 0.3726 0.5153 1.9884 3.3923 3.2256 3.0797 2.1573 2.4711 14.3330 2.3568 4.8867 5.0332 3.7352 1.7571 17.7689 8.0978 6.5557 6.6503 6.4511 9.6622 37.4171 3.2920 3.5465 3.5249 3.7930 4.0939 18.3654 4.3878 2.4037 1.7243 3.2199 2.0517 13.7874 40.1633 35.7772 34.6834 31.2913 28.3520 170.2672 0.4606 0.9863 0.9140 1.1343 0.9212 4.4077 2.5000 10.2587 9.3471 8.3268 4.5710 35.0022 1.0714 1.5414 1.4755 1.3904 2.1788 7.6575 31.7385 30.1766 35.6362 42.7230 50.9358 191.2102 1.6990 2.4310 2.3415 2.4427 2.1644 11.0786 45.9353 49.6688 51.1823 52.2922 51.3804 250.4589

Value of Water Research Report Series 1. Exploring methods to assess the value of water: A case study on the Zambezi basin. A.K. Chapagain − February 2000 2. Water value flows: A case study on the Zambezi basin. A.Y. Hoekstra, H.H.G. Savenije and A.K. Chapagain − March 2000 3. The water value-flow concept. I.M. Seyam and A.Y. Hoekstra − December 2000 4. The value of irrigation water in Nyanyadzi smallholder irrigation scheme, Zimbabwe. G.T. Pazvakawambwa and P. van der Zaag – January 2001 5. The economic valuation of water: Principles and methods J.I. Agudelo – August 2001 6. The economic valuation of water for agriculture: A simple method applied to the eight Zambezi basin countries J.I. Agudelo and A.Y. Hoekstra – August 2001 7. The value of freshwater wetlands in the Zambezi basin I.M. Seyam, A.Y. Hoekstra, G.S. Ngabirano and H.H.G. Savenije – August 2001 8. ‘Demand management’ and ‘Water as an economic good’: Paradigms with pitfalls H.H.G. Savenije and P. van der Zaag – October 2001 9. Why water is not an ordinary economic good H.H.G. Savenije – October 2001 10. Calculation methods to assess the value of upstream water flows and storage as a function of downstream benefits I.M. Seyam, A.Y. Hoekstra and H.H.G. Savenije – October 2001 11. Virtual water trade: A quantification of virtual water flows between nations in relation to international crop trade A.Y. Hoekstra and P.Q. Hung – September 2002

Acknowledgement The work underlying this report has been sponsored by the National Institute of Public Health and the Environment (RIVM), Bilthoven, the Netherlands. The research is part of the research programme of Delft Cluster. We would like to thank Ton Bresser (RIVM) and Huub Savenije (IHE) for their valuable inputs.

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