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Mesbah J. Motamed, Christine Arriola, 

Jim Hansen, and Stephen MacDonald

Cotton and Hydropower in 

Central Asia

How Resource Competition 

Affects Trade Economic 

Information 

Bulletin

Number 106

January 2013
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Recommended citation format for this publication: 

Motamed, Mesbah J., Christine Arriola, Jim Hansen, and Stephen MacDonald. 

Cotton and Hydropower in Central Asia: How Resource Competition 

Affects Trade, EIB-106, U.S. Department of Agriculture, Economic Research 

Service, January 2013.

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A Report from the Economic Research Service

Abstract

As growing populations demand more electric power and the need for low-emission 

energy sources intensifies, interest has risen in hydroelectric power (hydropower) as an 

alternative to carbon-based sources. However, hydropower’s demand for water resources 

is pushing up against the needs of agricultural irrigation. Hydropower is a feasible energy 

source only in certain regions of the world, and it accounts for only about 2 percent of the 

world’s energy supply. But its interaction with irrigated agriculture—for instance, through 

competition for water or resource-sharing agreements—can generate large effects, from 

local to international. In this report, we approach the topic of water competition between 

the energy and agriculture sectors from a global perspective. To show why this perspective should matter to policy makers, we present a case study of cotton production in 

Central Asia in which we combine high-resolution geographic information system (GIS) 

data covering crops and river basins with a partial equilibrium agricultural trade model to 

simulate the international trade effects of different water policy scenarios.

Keywords: water, agriculture, energy, hydropower, cotton, Central Asia

Acknowledgments

The authors wish to express their thanks for the helpful insight of three anonymous reviewers 

and Alejandro Plastina of the International Cotton Advisory Committee University, as well as 

for assistance from Ralph Seeley of ther United States Department of Agriculture, Economic 

Research Service (ERS). The authors also thank Courtney Knauth for editorial and production services and Wynnice Pointer-Napper for designing the report. 

About the Authors

Mesbah J. Motamed, Jim Hansen, and Stephen MacDonald are economists with USDA’s, 

Economic Research Service. Christine Arriola was formerly with ERS. 

Cotton and Hydropower in 

Central Asia

How Resource Competition Affects Trade

Economic

Information 

Bulletin 

Number 106

January 2013

Mesbah J. Motamed, mmotamed@ers.usda.gov

Christine Arriola, Jim Hansen, and Stephen MacDonald
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Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Hydropower and Agriculture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Central Asia’s Water and Energy Conflict . . . . . . . . . . . . . . . . . . . . . . . . 5

Cotton, Water, and Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Commodity Trade Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Scenario Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Additional Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Implications From This Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
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Summary

What Is the Issue?

As growing populations demand more electric power and the need for lowemission energy sources intensifies, interest has risen in hydroelectric power 

(hydropower) as an alternative to carbon-based sources. However, hydropower’s demand for water resources has pushed up against the needs of irrigated production agriculture. Hydropower is a feasible energy source only 

in certain regions of the world, and it accounts for only about 2 percent of 

the world’s energy supply. But its interaction with irrigated agriculture—for 

instance, through competition for water or resource-sharing agreements—can 

generate large effects, from local to international. In this paper, we introduce 

the topic of water competition between the energy and agriculture sectors 

from a global perspective. To show why this perspective should matter to 

policymakers, we present a case study of cotton production in Central Asia in 

which we combine high-resolution geographic information system (GIS) data 

covering crops and river basins with a partial equilibrium agricultural trade 

model to simulate the international trade effects of different water policy 

scenarios. 

What Did the Study Find?

We identified regions of the world where agriculture and hydropower 

energy sectors compete for the same water resources. Much of the world’s 

hydropower and irrigated agriculture overlap in South and East Asia. As 

an illustrative case study of this competition’s effects on world production 

and trade outcomes, we selected one region—the Syr Darya river basin in 

Uzbekistan—as the basis for three scenarios in which irrigated agricultural 

land is reduced by increasing amounts (10, 25, and 50 percent) due to heightened hydropower demands in the neighboring Kyrgyz Republic. According to 

our model results:

•	For Uzbekistan, all three area-diminishing shocks lead to large reductions in cotton production as well as production of its closest economic 

substitute, wheat. 

•	In the most extreme area-reduction scenario (50 percent), Uzbek cotton

production falls by 17 percent and cotton exports drop by approximately

21 percent relative to baseline projections. 

•	These impacts are felt only modestly in international markets, as major

producers—the United States, Brazil, and Australia—adjust their production and exports slightly upward in response to higher prices resulting

from the reduction in Uzbek cotton exports.

•	The results of the simulation, which confirm that world cotton prices

barely budge in response to the small shock to world supply, nonetheless 

illustrate how high-resolution GIS data sets and partial equilibrium trade 

models can be combined to address new questions concerning natural 

resource management and market behavior. 
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How Was the Study Conducted?

This study used the USDA-ERS Country-Commodity Linked System 

(CCLS), a large-scale dynamic partial equilibrium simulation system 

consisting of 43 country and regional models. For the analysis, the authors

created a country model for Uzbekistan. They confined their analysis to the 

Syr Darya river basin, using GIS to overlay high-resolution production data 

onto a map of the basin to accurately capture the area of production subject 

to the water shock. To simulate the heightened energy demands placed on the 

Kyrgyz Republic’s Toktogul hydropower reservoir, we reduced Uzbekistan’s 

area in cotton by 10, 25, and 50 percent and observed domestic and international outcomes in production, prices, and trade for each level of reduction.
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Introduction

As growing populations demand more electric power and the need for lowemission energy sources intensifies, interest has risen in hydroelectric power 

(hydropower) as an alternative to carbon-based sources (Weisser, 2007; 

Raadal et al., 2011). However, hydropower’s demand for water resources has 

come up against the needs of irrigated production agriculture. Hydropower 

is a feasible energy source only in certain regions of the world, and it 

accounts for only about 2 percent of the world’s energy supply (Moselle et 

al., 2010). But its interaction with irrigated agriculture, for instance, through 

competition for water or resource-sharing agreements, can generate large 

effects, from local to international. In this paper, we introduce the topic of 

water competition between the energy and agriculture sectors from a global 

perspective. To show why this perspective should matter to policymakers, 

we present a case study of cotton production in Central Asia in which we 

combine high-resolution geographic information system (GIS) data covering 

crops and river basins with a partial equilibrium agricultural trade model to 

simulate the international trade effects of different water policy scenarios.
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Hydropower and Agriculture

The competition between agriculture and energy sectors for water is not 

new. A large body of research has focused on the optimal allocation of 

water between competing uses and under varying constraints (Stamford da 

Silva and Campello de Souza, 2008; Bielsa and Duarte, 2003; Chatterjee 

et al., 1998). The problem is twofold. First, power generation typically

occurs upstream, where greater capacities can be summoned behind large 

dams and reservoirs. This may affect water supplies for irrigated agriculture, which takes place in more downstream, riparian settings. Second, the 

seasonalities of water demand for each sector further complicate matters. 

For example, peak energy demand in Kyrgyzstan and Tajikistan occurs in

the winter months, when water needs to be released from the reservoirs to 

generate hydropower for heating. In order to have enough energy for the 

winter months, these upstream countries spend the summer months building 

up their reservoirs, depriving downstream farmers of water when they most 

need irrigation. Taking into account the market value of each of the final 

outputs produced with water, welfare-maximizing solutions can point to 

water’s greatest value use, be it for agriculture, electrical power generation, or perhaps some other use. Often, this value results from some optimal 

combination of uses. These solutions typically assume one optimizing 

decisionmaker, but when the rights to the water are spread across multiple 

owners with differing objectives, globally optimal solutions can be difficult to 

achieve (Abbink et al., 2010).

Outside the United States, much of the competition between energy and 

agriculture sectors appears concentrated in South and East Asia (fig. 1). 

Combining worldwide data on irrigation and hydropower, the map reveals 

that hydropower generation sits atop many of the continent’s most intensively irrigated regions, including the Indus River valley, Central Asia’s 

Amu Darya and Syr Darya valleys, and northeast China’s Yangtze River 

valley. In India, hydropower facilities supply about a quarter of the country’s 

total power needs. But demand there continues to grow, as evidenced by 

frequent power outages, and policymakers are keen to expand beyond the 

23 percent of India’s hydropower potential that is currently exploited (World 

Bank, 2011). About 40 percent of India’s cultivated area was under irrigation 

during the last decade, of which at least 37 percent originated from surface 

water sources. Over half this area was planted in wheat and rice (Food and

Agriculture Organization, 2010b).

Meanwhile, China has the largest hydropower generation capacity in the 

world, yet this resource accounts for only 15 percent of its total power generation (Chang et al., 2010). As in India, growing demand for electricity in 

China, particularly in the context of environmental repercussions of coalburning plants, also portends greater expansion in hydropower capacity in the 

coming years (Chang et al., 2010). Half of China’s cultivated area is irrigated, 

and of this, about 70 percent comes from surface water sources.1 As in India, 

rice and wheat account for the bulk of China’s irrigated production, though 

considerable area is also allocated to maize, vegetables, and soybeans (Food

and Agriculture Organization, 2010a).

1A more complete description of 

China’s water resource challenges appears in Lohmar et al. (2003).
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The growing costs of coal and nuclear power—environmental, economic, 

and social—have raised the profile of alternative sources of clean, sustainable power generation. Hydropower generation offers an option for countries endowed with the right resources. Greenhouse gas emissions from 

hydropower plants are much lower. There are no radioactive byproducts. 

Hydropower’s chief input replenishes itself via evaporation and precipitation, 

making the process generally sustainable. And since water can be stored 

behind dams, power generation can be adjusted in rapid response to fluctuations in demand. On the other hand, hydropower can leave a heavy environmental footprint in its vicinity, disrupting natural water bodies and complex 

ecosystems. Policymakers thus face the challenge of weighing a complex 

balance of costs and benefits in considering the use of hydropower.

Competition for water resources spans geographic regions and relates directly 

to policies concerning the economy, environment, and security. The authors 

address the specific case of Central Asia and explore how the prevalence of 

hydropower may affect international trade in cotton. Our purpose is to offer 

Figure 1

South and East Asia irrigation and hydropower

Note: Circles identify hydropower station locations.

Sources: Siebert et al., 2007; Lehner et al., 2008.

35.01 - 50.00

75.01 - 100.00

50.01 - 75.00

20.01 - 35.00

5.01 - 10.00

10.01 - 20.00

1.01 - 5.00

0.01 - 0.10

0.11 -1.00

0.00

30220.1 - 63000.0

12400.1 - 30220.0

4000.1 - 12400.0

1000.0 - 4000.0

Legend

Maximum Capacity (MW) Fraction of Farmland Under Irrigation
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an overview of the tensions between agriculture and hydropower in this region 

and illustrate the possible impacts of water reduction scenarios on regional and 

global production and trade outcomes. The analysis relies on relatively unrefined data and rests on strong assumptions, but it reveals that the little-studied 

intersection of agriculture and energy deserves more attention.
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Central Asia’s Water and Energy Conflict

Following the dissolution of the Soviet Union in December 1991, the five

Soviet republics of Central Asia suddenly became responsible for decisions 

that were previously made in Moscow. One outcome of this new arrangement 

of power was the absence of a region-wide perspective on natural resource 

management. During the Soviet era, water resources across the republics 

were managed for the purpose of maximizing cotton production, an objective 

that suited the Soviet Union’s larger economic goals. Specifically, rivers that 

originated in the glaciers of the Kyrgyz Republic and Tajikistan flowed into 

the riparian republics of Uzbekistan, Kazakhstan, and Turkmenistan, where 

they were tapped for irrigation. In exchange, the upstream regions received 

gas and coal to cover their energy demand during the winter months. 

Following independence, however, the separate republics have pursued objectives that serve their specific interests. In the case of Uzbekistan, the region’s 

largest cotton producer, cotton production is an important traditional source 

of foreign exchange, and with the development of new trade links, Uzbek 

natural gas, once destined for regional consumption, has become another 

source of foreign exchange. Meanwhile, the neighboring Kyrgyz Republic 

plans to increase hydroelectric production to generate energy for domestic use 

and perhaps even to export to neighboring countries. Kazakhstan also faces 

new challenges, as exporting petroleum to satisfy next-door China’s growing 

demand offers new economic opportunities. However, water consumption is 

also surging in China, potentially limiting Kazakhstan’s access to riparian 

resources now shared by the two countries. 

Recent work by Abdullaev et al. (2009) focused on the effect of different 

trade policy scenarios on Uzbekistan’s scarce water resources. These authors 

show that trade liberalization, elimination of government production quotas, 

and the consequent introduction of international price signals into the Uzbek 

market would raise prices paid to wheat and cotton farmers and drive expansion in area under cultivation. However, given that Uzbek cotton is more 

competitive than Uzbek wheat, the balance of planted area would shift toward 

cotton production, disproportionately raising the demand for water owing to 

cotton’s relative thirstiness.

This study spotlights the effects of competition between Central Asia’s agriculture and energy sectors for the region’s scarce water. It also presents the 

trade and production impacts of plausible water scenarios within the Syr 

Darya river basin. The discussion and simulation results, apart from being 

of interest to commodity market observers, can also inform policymakers 

in agriculture, trade, and security of the circumstances that may drive this 

region’s economic and political outcomes in coming years. We begin with 

some background on Uzbekistan’s cotton sector and regional water management and policy issues, followed by discussion of the water policy scenarios 

and an overview of the economic model used to simulate the scenarios.
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Cotton, Water, and Energy

Cotton provides nearly 40 percent of the world’s textile fiber for clothing, 

home textiles, and other products. The physiology of the cotton plant favors 

irrigated cultivation in arid regions, conditions under which 70 percent of 

the world’s cotton is currently produced. As the Soviet Union industrialized during the 20th century, Uzbekistan and other Central Asian republics 

developed extensive irrigation networks. Soviet planners pursued fiber selfsufficiency for their expanding textile industry, and, partly as a result, Central 

Asia’s irrigation capacity continued to expand through the 1980s. When 

Uzbekistan became independent in December 1991, its exports accounted 

for about 20 percent of world cotton trade and the country was usually the 

world’s second largest exporter, trailing only the United States.

Since then, world fiber demand has continued to expand dramatically. Cotton 

has lost market share to petroleum-derived chemical fibers, but world cotton 

consumption still grew 36 percent between 1990 and 2009. Uzbekistan’s 

cotton area, however, trended downward during this time, and yields there 

have largely stagnated. The country’s isolation from world price signals and 

the limited ability of individual producers in Uzbekistan to freely choose 

alternatives in production and investment have constrained development of 

its cotton sector. Uzbekistan remains a major cotton producer, ranking sixth 

worldwide, but as other major exporters have adopted genetically modified 

varieties and other yield-enhancing technologies, its role has diminished. In 

marketing year 2009-10, Uzbekistan planted approximately 1.3 million hectares in cotton, yielding 893,000 metric tons of fiber. More than 70 percent 

of its output is still exported, representing 10 percent of total traded cotton 

worldwide (FAS, 2010). Although landlocked, Uzbekistan transships its

cotton to markets around the world, with export receipts that totaled nearly 

US$1 billion in 2007 (UNCTAD, 2010; FAOSTAT, 2010).

Abdullaev et al. (2009) present a detailed description of the policy environment surrounding Uzbek cotton. Among the policies that shape the decisions 

and outcomes of cotton producers are land redistribution and farm restructuring, food security initiatives focused on switching from cotton to wheat, 

and, most relevant to this analysis, production quotas. In Uzbekistan, production quotas are partially fulfilled through mandated area plantings, often 

without regard for a particular area’s suitability for cotton. Moreover, the 

Uzbek Government relied increasingly on rent extraction from agriculture for 

a large share of its revenues, and it used State-controlled marketing boards 

to transfer resources out of agriculture (Pomfret, 2007). USDA’s Global 

Agricultural Information Network reports that the Uzbek Government exerts 

complete control over prices, material inputs, purchasing, and, ultimately, the 

domestic and international marketing of all cotton via State-owned trading 

companies (GAIN, 2010). As a result, Uzbek farmers receive about half the 

price offered on the world market (Abdullaev et al., 2009).

Figure 2 depicts the spatial distribution of Uzbekistan’s cotton production, at a

resolution of 5-minute grid cells (You et al., 2010). Most production occurs in 

Uzbekistan’s easternmost provinces along the borders of the Kyrgyz Republic and 

Tajikistan, as well as in its western province of Karakalpakstan, along the border 

of Turkmenistan. Given the country’s arid climate, all cotton in Uzbekistan is 
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cultivated under irrigation. This is supplied from two major river basins, the 

Amu Darya and Syr Darya, with tributaries that originate in the mountains of 

Tajikistan and the Kyrgyz Republic. Figure 3 depicts these two river basins and

the multiple countries through which they flow. The Syr Darya River, the subject 

of this report’s analysis, originates in the Tien Shan Mountains and flows through 

the upstream countries of Kyrgyzstan and Tajikistan, through Uzbekistan and 

Kazakhstan, stretching a total of 2,000 kilometers until it finally flows into 

the Aral Sea.2 From figures 2 and 3, it is clear not only that cotton cultivation

closely depends on water availability, but also that successful water management 

demands significant coordination across countries.

We focus on the Syr Darya river basin due to its vulnerability to major swings 

in management. During the Soviet era, large dams were erected along the 

Naryn River in the Kyrgyz Republic, designed to ensure a consistent yearto-year supply of water for downstream irrigation purposes, as well as to 

supplement the region’s electricity needs. The largest of these dams formed 

a reservoir at Toktogul, about 85 kilometers inside the Kyrgyz border. 

Meanwhile, to meet Kyrgyzstan’s power and heating needs, coal, oil, and 

gas resources were brought in from other republics, namely Uzbekistan and 

Kazakhstan (World Bank, 2004). After independence in 1991, however, the 

water-for-energy exchange that served both republics for over 40 years began 

to deteriorate, accelerated by higher energy prices in the late 1990s and early 

2000s. Economic openness permitted Uzbek natural gas to receive a high 

international price, far above what its eastern neighbor was accustomed to 

paying. Consequently, in order to generate winter heat for its own population, 

2The contamination and near-depletion of the Aral Sea during the Soviet 

era and its aftermath has significantly 

damaged the ecology of the Aral Sea 

region, with impacts on both human 

and wildlife populations (Glantz et al., 

1993). The need for rehabilitation has 

been acknowledged, but damage to the 

Aral Sea continues due to the intensive 

cotton monoculture that the region 

has practiced over the past 50 years. 

While this study does not address the 

environmental and economic impacts 

of water management on the Aral Sea 

specifically, it is clear that this important region cannot be excluded from any 

comprehensive treatment of the topic of 

cotton production in Central Asia.

Figure 2

Grid cell-level cotton production in Uzbekistan

Note: Upper-bound value, represented by the darkest shade of green, is approximately 3,000 metric tons.

Source: You et al., 2010.

Turkmenistan Tajikistan

Kyrgyz Republic

Kazakhstan

Uzbekistan

Afghanistan
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the Kyrgyz Republic increasingly shifted its water releases from the summer 

to the winter. This resulted in less water reaching downstream farmers 

precisely when their crops needed it most. Over the period 1991-2000, the 

fraction of annual water released during the summer fell from three-fourths 

to below one-half (World Bank, 2004).

Recently, rolling blackouts and energy price hikes during the winter of 

2009-10 culminated in the replacement of President Kurmanbek Bakiyev 

with a new interim government, highlighting the importance of a stable 

and affordable electricity supply for the Kyrgyz Republic. Precedents exist 

for such events to add pressure to violate agreements on water and energy 

sharing between Uzbekistan, Kazakhstan, and the Kyrgyz Republic (World 

Bank, 2004).

Eighty percent of Uzbekistan’s water originates from its neighbors (Abdullaev 

et al., 2009). The Toktogul hydropower station, Kyrgyzstan’s largest power 

plant, and four downstream stations along the Naryn River account for 

nearly 80 percent of the country’s electric capacity (The World Bank, 2004). 

About 36 percent of Uzbekistan’s total cotton production occurs within the 

Syr Darya river basin (figures 2 and 3). The Kyrgyz Republic’s decisions 

regarding energy security could affect one of the world’s most important 

cotton exporters. The immediate problems of water management in this 

region are intensified by increasingly scarce water resources that stem from 

a warming climate. For these reasons, this study evaluates a set of water

scenarios designed to simulate Uzbek and worldwide production and trade 

outcomes resulting from a range of management decisions that could be taken 

by the Kyrgyz Republic. 

Figure 3

Amu Darya and Syr Darya Rivers and basins of Central Asia

Source: McKinney, 2005.

Aral Sea

Syr Darya basin

Amu Darya basin
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Commodity Trade Model

This study uses the USDA-ERS Country-Commodity Linked System (CCLS), 

a large-scale dynamic partial equilibrium simulation system consisting of 43 

country and regional models. Each country and region is modeled to reflect 

domestic and trade policies and institutional behavior, such as tariffs, subsidies, and tariff rate quotas. Production, consumption, imports, and exports 

are endogenous and depend on domestic and world prices, which are solved 

within the modeling system. Macroeconomic assumptions and projections are 

exogenous, based on USDA’s 10-year agricultural projections (USDA, 2010). 

The system reaches simultaneous equilibrium in prices and quantities for 24 

world commodity markets for each of the 10 projected years in the analysis. 

The 24 commodity markets include detailed coarse grains, food grains, 

oilseeds, meals, oils, cotton, sugar, and animal products. Primary data sources 

are USDA’s Production, Supply, and Distribution database, USDA’s National 

Agricultural Statistical Service, and the United Nations Food and Agriculture

Organization’s FAOStat.

The USDA-ERS Uzbekistan model is used for analyzing potential changes 

in planting area, the impact of planting changes on Uzbek agriculture, and 

the ultimate effect on international cotton markets and trade. The model 

treats the Uzbek Government as the planting decisionmaker, inasmuch as 

the Government determines how much acreage to plant from year to year. 

Production, consumption, imports, exports, and ending stocks are endogenous 

and depend on prices. World price signals enter the domestic market through 

the border price. However, for Uzbekistan, production decisions are heavily 

influenced by Government intervention. Uzbekistan directly influences the 

international market and world prices through its cotton exports. All commodities are modeled at the national level except for cotton and wheat, which are 

disaggregated at the level of the Syr Darya and Amu Darya river basins.

The Uzbekistan model has a cotton sector, six livestock sectors, four grain 

sectors, and four oilseed sectors. The major commodities include cotton, wheat, 

and beef. The individual commodities of the Uzbekistan model have five major 

components: prices and expected revenue equations, production, consumption, 

ending stocks, and trade equations. As mentioned, cotton and wheat production 

are modeled at the river-basin level. Each basin is represented by individual 

area harvested and yield equations. Country-level production for both cotton 

and wheat is the sum of production in the two basins.

The cotton sector includes cotton, cottonseed, cottonseed oil, and cottonseed 

meal. Cotton production in both river basins is calculated by equations of 

the area harvested times the yield. Area harvested and yields are determined 

by expected returns for cotton and substitute crops, namely wheat. As stated 

above, 36 percent of total cotton area is located in the Syr Darya river basin 

at the beginning of the projection period. Expected returns are determined by 

producer prices times an expected yield. 

The price transmission elasticity from the world cotton price to the producer price 

was estimated to be 0.85.3 Cotton acreage, a Government decision, is a function 

of the international reference price, exchange rates, and the quantity of cotton 

exported. This variable captures the Government incentive to increase foreign 

exchange holdings. 

3This estimate was obtained by a 

double log regression of the Uzbekistan 

cotton lint border price on the real 

international price of cotton, based on 

annual data over the past 16 years.
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The CCLS model relies on a variety of own- and cross-price elasticities, as 

well as substitution elasticities, taken from extensive literature covering cotton 

production, trade, and consumption (Baffes and Ajwad, 1998; Baffes and 

Ajwad, 2001; Poonyth et al., 2004; Meyer, 2002; Shepherd, 2006). Additional 

estimates from the wheat sector are taken from the elasticity database of the 

Food and Agricultural Policy Research Institute (FAPRI, 2012). Based on

a review of this previous research, we set the own-price supply response of 

cotton’s expected revenues to 0.31, the own-price demand elasticity of cotton 

to -0.20, and the income elasticity to 0.40. The cross-price response of cotton 

to wheat is assumed to be -0.15. Area harvested, consumption, and trade adjust 

as the model solves for world prices and reaches a new equilibrium. The cotton 

exports equation is an identity that closes the model. The cottonseed sector 

includes production, crushed cottonseed demand, feed demand, ending stocks, 

and imports and exports. Cottonseed meal and cottonseed oil production 

depend on cottonseed crushed demand. Cottonseed meal and cottonseed oil are 

consumed domestically for feed and food, respectively.

Since the chief substitute for cotton is wheat, we also introduce a river-basinlevel wheat sector. The wheat sector model includes border, producer, and 

consumer prices, expected returns, production, food and feed demand, ending 

stocks, and trade. Domestic prices are determined by the world price, with a 

price transmission from the world price to the producer price assumed to be 

0.50. Expected returns are determined by the producer price times an expected 

yield. Wheat production in both river basins is calculated from the equations 

of area harvested times the yield. At the beginning of the projection period, 

the Syr Darya river basin holds 69 percent of total wheat area in the country. 

Area harvested is a function of expected returns for wheat and its substitutes, 

cotton and cash crops. As with cotton, elasticities are taken from a variety of 

sources in the literature (FAPRI, 2012; Devadoss et al., 1989). The own-price

supply response of wheat’s expected revenues is set at 0.40. Wheat’s cross-price 

elasticity with respect to cotton’s expected return is -0.15. Again, area harvested 

and consumption adjust as the model solves for world wheat prices and reaches 

a new equilibrium. Wheat’s own-price elasticity of demand is set at -0.23, 

and its income elasticity is 0.08. Wheat feed demand elasticities include an 

own-price value of -0.40, and the cross price elasticity of its nearest substitute, 

barley, is 0.25. Wheat import demand is an identity that closes the model.

We stress that our analysis focuses strictly on global production and trade 

effects resulting from reductions in area harvested, a variable that plausibly 

captures the effect of upstream water shocks. Furthermore, we do not address

the complex economic and physical interactions and substitutions between 

electricity demand, water use, and downstream agriculture, nor does our 

model account for prices of inputs. Such tasks are more suited to a resource 

economic model that explicitly incorporates the endogeneity of prices, 

demand, and substitution possibilities. While such features would undoubtedly add greater detail and realism to our results, our primary purpose is to 

examine the macro-level impacts of water shocks, for which the precise and 

subtle details provided in a resource economic model offer little advantage. 

As detailed in the literature review, considerable research has been devoted 

to examining the economic and physical details of electricity and agriculture 

water demand in this region. 
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Scenario Results

Our area-reduction scenarios are based on a hypothetical, sustained reduction 

in water released from the Toktogul reservoir during the summer to meet the 

Kyrgyz Republic’s increasingly strict winter energy security demands, beginning in year 2010. Less water is expected to translate into less area under 

cultivation.4 To capture the effect of a range of possible reductions, we negatively shock Uzbekistan’s area harvested in the Syr Darya basin for cotton 

and its chief substitute, wheat. Over the last 15 years, Uzbekistan has been 

struck by drought twice (Glantz, 2005; Cotton Outlook, 2008). Each time, 

area planted in cotton fell by roughly 10 percent. For the purposes of this

analysis, we determined that 10 percent can therefore be treated as the upper 

end of the shocks typically experienced by Uzbekistan under the current de 

facto regime of regional water management. Systemic changes in climate or 

water management were therefore modeled as shocks approximately twice 

the magnitude of the observed shocks (a 25-percent reduction) and also as an 

extreme case that once more doubles the 25-percent shock to 50 percent.

In each scenario, the reduction in area harvested in the Uzbekistan’s 

Syr Darya basin lowers cotton production and, not surprisingly, reduces 

exports. (See fig. 4 for the projected year-to-year changes to Uzbekistan’s 

area harvested, production, and exports.5 When we take the 25-percent 

area reduction scenario as an example, overall Uzbek production in 2010 

is predicted to fall by about 9 percent relative to the baseline value, a drop 

of 97,000 metric tons. This affects exports nearly one-to-one; they fall by 

nearly 11 percent relative to the baseline, or 91,000 metric tons. The effects 

10 years later are only slightly dampened. (See tables 1 through 3 for a 

summary of level and percentage changes for Uzbekistan’s production, 

imports, exports, and consumption.)

Wheat production in the Syr Darya basin also falls, again due to its dependence on irrigation. Under the 25-percent scenario, wheat production falls 

17 percent relative to the baseline value across the projection period. As 

production falls with the area decrease, imports are needed to meet domestic 

consumption demand. Wheat imports, under the 25-percent scenario, double 

relative to the baseline. U.S. and EU exports of wheat respond slightly, with 

roughly a 1-percent increase above the baseline. As noted, wheat and cotton 

are substitutes governed by a cross-price elasticity. If the price of cotton falls 

relative to the price of wheat, some areas can be expected to be shifted into 

wheat production. Wheat also requires less water per area planted, which is 

not currently reflected in the model. For this reason, the response of wheat

presented in figure 5 is likely to be understated.

In response to the simulated area reduction in Uzbekistan, the international 

price of cotton is projected to rise. Figure 6 shows that, relative to the baseline scenario, prices are only slightly higher. The largest shock drives prices 

upward only about 2 percent relative to the baseline. The baseline projection, moreover, shows a gradual secular decline in prices that dominates 

any effect attributable to Uzbekistan. The increase in cotton prices induces 

a small decrease, relative to the baseline, in the amount of imports from the 

top cotton importing countries China, Pakistan, Bangladesh, and Indonesia 

(tables 1 through 3).

4Rather than assume a quantity of 

electricity or water demand upstream, 

we simply begin with an area-reduction 

scenario. This abstracts away from the 

details associated with energy and water prices and the technical conversion 

factors between water volumes and 

hydropower generation, permitting our 

analysis to focus on the production and 

trade outcomes.

5Note that the model’s baseline 

scenario reflects the shock and subsequent recovery from the year 2008 

world agriculture crisis, which affected 

prices, production, and exports across 

numerous commodities worldwide.
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Figure 4

Syr Darya-specific area-reduction impacts on Uzbek cotton

1,200

0

200

400

600

800

1,000

06/07 08/09 10/11 12/13 14/15 16/17 18/19

Agricultural year

1,000 metric tons

1,200

600

700

800

900

1,000

1,100

1,500

900

1,000

1,100

1,200

1,300

1,400

06/07 08/09 10/11 12/13 14/15 16/17 18/19

Agricultural year

06/07 08/09 10/11 12/13 14/15 16/17 18/19

1,000 hectares Area harvested

Agricultural year

Baseline scenario

25%

50%

Exports

Baseline scenario

10%

25%

50%

Baseline scenario

10%

25%

50%

Production 1,000 metric tons

10%
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Table 1

Results from the scenario of a 10-percent reduction in cotton area 

Production Imports Exports Consumption

2010 2015 2020 2010 2015 2020 2010 2015 2020 2010 2015 2020

1,000 metric tons

Baseline 1,046 1,084 1,054 0 0 0 844 874 844 205 206 208

Uzbekistan scenario 1,009 1,047 1,018 0 0 0 808 838 808 205 206 208

% change -3.53 -3.39 -3.42 - - - -4.29 -4.20 -4.26 0.00 -0.04 -0.04

Top exporters

Baseline 2,991 3,781 4,106 2 2 2 2,324 2,866 3,176 948 900 867

USA scenario 2,991 3,788 4,112 2 2 2 2,331 2,880 3,191 947 893 859

% change 0.00 0.18 0.16 0.00 0.00 0.00 0.30 0.47 0.46 0.00 -0.72 -0.93

Baseline 5,434 6,430 7,254 112 97 85 1,427 1,536 1,639 4,158 4,997 5,725

India scenario 5,438 6,439 7,263 111 96 85 1,436 1,546 1,648 4,158 4,996 5,724

% change 0.00 0 0 0.00 0.00 0.00 0.00 0 0 0.00 - -

Baseline 1,489 1,788 1,930 77 69 66 683 895 973 925 949 998

Brazil scenario 1,489 1,790 1,932 77 69 66 690 897 975 925 949 998

% change 0.00 0.11 0.1 0.00 0.00 0.00 1.09 0.28 0.24 0.00 -0.05 -0.04

Baseline 476 572 621 0 0 0 451 577 626 9 8 8

Australia scenario 476 583 621 0 0 0 451 587 626 9 8 8

% change -0.02 0.04 0.03 - - - 0.03 0.04 0.03 0.00 0.00 0.00

Top importers

Baseline 7,650 8,830 9,560 2,263 2,880 3,241 13 11 11 10,335 12,072 13,188

China scenario 7,650 8,832 9,561 2,259 2,878 3,239 13 11 12 10,333 12,071 13,188

% change 0.00 0.02 0.02 -0.15 -0.09 -0.06 0.31 0.39 0.36 0.00 -0.01 0.00

Baseline 10 11 12 965 1,200 1,419 0 0 0 971 1,209 1,429

Bangladesh scenario 10 11 12 964 1,199 1,419 0 0 0 970 1,209 1,429

% change 0.05 0.09 0.08 -0.05 -0.04 -0.03 - - - 0.00 -0.04 -0.03

Baseline 387 323 268 760 874 967 27 28 29 1,119 1,168 1,206

Turkey scenario 387 323 268 760 874 967 27 28 29 1,119 1,167 1,206

% change 0.00 0.00 0.00 -0.05 -0.03 -0.02 0.00 0.00 0.00 0.00 -0.02 -0.02

Baseline 6 6 6 516 520 527 4 4 4 516 522 529

Indonesia scenario 6 6 6 516 520 527 4 4 4 516 522 528

% change 0.11 0.08 0.07 -0.06 -0.05 -0.04 0.00 0.00 0.00 0.00 -0.05 -0.04

Baseline 2,116 2,525 2,634 678 785 923 50 50 50 2,685 3,224 3,469

Pakistan scenario 2,117 2,526 2,635 676 783 921 50 50 50 2,684 3,223 3,468

% change 0.03 0.04 0.03 -0.29 -0.23 -0.17 0.00 0.00 0.00 0.00 -0.03 -0.02
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Table 2

Results from the scenario of a 25-percent reduction in cotton area

Production Imports Exports Consumption

2010 2015 2020 2010 2015 2020 2010 2015 2020 2010 2015 2020

1,000 metric tons

Baseline 1,046 1,084 1,054 0 0 0 844 874 844 205 206 208

Uzbekistan scenario 953 992 964 0 0 0 753 783 754 205 206 208

% change -8.82 -8.48 -8.55 - - - -10.74 -10.49 -10.65 0.00 -0.11 -0.09

Top exporters

Baseline 2,991 3,781 4,106 2 2 2 2,324 2,866 3,176 948 900 867

USA scenario 2,991 3,799 4,122 2 2 2 2,341 2,900 3,212 945 884 847

% change 0.00 0.46 0.4 0.00 0.00 0.00 0.74 1.16 1.14 0.00 -1.79 -2.32

Baseline 5,434 6,430 7,254 112 97 85 1,427 1,536 1,639 4,158 4,997 5,725

India scenario 5,442 6,454 7,277 110 95 84 1,449 1,561 1,663 4,157 4,995 5,722

% change 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Baseline 1,489 1,788 1,930 77 69 66 683 895 973 925 949 998

Brazil scenario 1,489 1,793 1,935 77 69 66 701 901 979 924 948 997

% change 0.00 0.27 0.24 0.00 0.00 0.00 2.72 0.70 0.61 0.00 -0.11 -0.1

Baseline 476 572 621 0 0 0 451 577 626 9 8 8

Australia scenario 476 584 621 0 0 0 451 587 627 9 8 8

% change -0.05 0.10 0.07 - - - 0.08 0.10 0.07 0.00 0.00 0.00

Top importers

Baseline 7,650 8,830 9,560 2,263 2,880 3,241 13 11 11 10,335 12,072 13,188

China scenario 7,650 8,835 9,564 2,254 2,874 3,236 13 11 12 10,330 12,070 13,187

% change 0.01 0.05 0.05 -0.37 -0.22 -0.16 0.79 0.98 0.91 0.00 -0.01 -0.01

Baseline 10 11 12 965 1,200 1,419 0 0 0 971 1,209 1,429

Bangladesh scenario 10 11 12 964 1,198 1,418 0 0 0 969 1,208 1,428

% change 0.14 0.23 0.19 -0.13 -0.09 -0.08 - - - 0.00 -0.09 -0.08

Baseline 387 323 268 760 874 967 27 28 29 1,119 1,168 1,206

Turkey scenario 387 323 268 759 873 966 27 28 29 1,119 1,167 1,205

% change 0.00 0.00 -0.01 -0.13 -0.07 -0.05 0.00 0.00 0.00 0.00 -0.05 -0.04

Baseline 6 6 6 516 520 527 4 4 4 516 522 529

Indonesia scenario 6 6 6 515 519 526 4 4 4 515 521 528

% change 0.27 0.20 0.17 -0.16 -0.13 -0.11 0.00 0.00 0.00 0.00 -0.12 -0.11

Baseline 2,116 2,525 2,634 678 785 923 50 50 50 2,685 3,224 3,469

Pakistan scenario 2,118 2,527 2,636 673 780 919 50 50 50 2,682 3,222 3,467

% change 0.07 0.1 0.07 -0.74 -0.57 -0.44 0.00 0.00 0.00 0.00 -0.06 -0.06
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Table 3

Results from the scenario of a 50-percent reduction in cotton area

Production Imports Exports Consumption

2010 2015 2020 2010 2015 2020 2010 2015 2020 2010 2015 2020

1,000 metric tons

Baseline 1,046 1,084 1,054 0 0 0 844 874 844 205 206 208

Uzbekistan scenario 861 900 874 0 0 0 663 691 664 204 206 208

% change -17.66 -16.96 -17.08 - - - -21.49 -20.98 -21.29 0.00 -0.22 -0.19

Top exporters

Baseline 2,991 3,781 4,106 2 2 2 2,324 2,866 3,176 948 900 867

USA scenario 2,991 3,816 4,138 2 2 2 2,358 2,933 3,248 941 868 827

% change 0.00 0.92 0.79 0.00 0.00 0.00 1.48 2.32 2.27 -1.00 -3.58 -4.63

Baseline 5,434 6,430 7,254 112 97 85 1,427 1,536 1,639 4,158 4,997 5,725

India scenario 5,451 6,479 7,300 109 94 83 1,470 1,587 1,687 4,155 4,993 5,720

% change 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Baseline 1,489 1,788 1,930 77 69 66 683 895 973 925 949 998

Brazil scenario 1,489 1,798 1,939 77 69 66 720 907 985 923 947 996

% change 0.00 0.55 0.48 0.00 0.00 0.00 5.44 1.4 1.22 0.00 -0.23 -0.2

Baseline 476 572 621 0 0 0 451 577 626 9 8 8

Australia scenario 476 584 621 0 0 0 451 588 627 9 8 8

% change -0.11 0.20 0.14 - - - 0.17 0.19 0.13 0.00 0.00 0.00

Top importers

Baseline 7,650 8,830 9,560 2,263 2,880 3,241 13 11 11 10,335 12,072 13,188

China scenario 7,651 8,839 9,568 2,246 2,867 3,230 13 11 12 10,325 12,068 13,186

% change 0.01 0.10 0.09 -0.74 -0.44 -0.32 1.58 1.95 1.81 0.00 -0.03 -0.02

Baseline 10 11 12 965 1,200 1,419 0 0 0 971 1,209 1,429

Bangladesh scenario 10 11 12 962 1,197 1,417 0 0 0 968 1,207 1,427

% change 0.27 0.46 0.38 -0.27 -0.19 -0.16 - - - 0.00 -0.18 -0.16

Baseline 387 323 268 760 874 967 27 28 29 1,119 1,168 1,206

Turkey scenario 387 323 268 758 873 966 27 28 29 1,118 1,167 1,205

% change 0.00 -0.01 -0.02 -0.26 -0.13 -0.10 0.00 0.00 0.00 0.00 -0.10 -0.09

Baseline 6 6 6 516 520 527 4 4 4 516 522 529

Indonesia scenario 7 6 6 514 519 526 4 4 4 515 521 527

% change 0.54 0.40 0.34 -0.33 -0.25 -0.22 0.00 0.00 0.00 0.00 -0.24 -0.22

Baseline 2,116 2,525 2,634 678 785 923 50 50 50 2,685 3,224 3,469

Pakistan scenario 2,119 2,529 2,638 668 776 915 50 50 50 2,679 3,220 3,465

% change 0.14 0.19 0.15 -1.48 -1.15 -0.87 0.00 0.00 0.00 0.00 -0.13 -0.12
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Meanwhile, production in other countries grows to meet the demand historically satisfied by Uzbekistan. As illustrated in figure 7, no significant difference from the baseline scenario can be detected across the different scenarios, 

as evidenced by the overlapping lines, and any negative effect is more than 

swamped by the overall rise in worldwide cotton production projected by the 

baseline. In short, at the world level, the effects appear small. At the country 

level, as well, the responses are minimal, with major exporters India and the 

United States registering negligible percentage changes in production.

While world cotton markets could easily absorb the shocks to Uzbekistan’s 

cotton output, the impact for Uzbekistan’s economy would be much more 

Figure 5

Syr Darya-specific area-reduction impacts on Uzbek wheat production

7,900

900

1,900

2,900

3,900

4,900

5,900

6,900

06/07 08/09 10/11 12/13 14/15 16/17 18/19

1,000 metric tons

Agricultural year

Baseline scenario

10%

25%

50%

Figure 6

Syr Darya-specific area-reduction impacts on world cotton prices

1,600

1,200

1,250

1,300

1,400

1,350

1,450

1,500

1,550

06/07 08/09 10/11 12/13 14/15 16/17 18/19

US$/metric tons

Agricultural year

Baseline scenario

10%

50%

25%
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significant. With only limited price gains, export revenue from cotton would 

fall by 4 to 20 percent across the three scenarios. Hundreds of millions of 

dollars in hard currency earnings would be forgone. In addition to export 

earnings, cotton production accounts for a significant share of the labor 

opportunities for Uzbekistan’s largely rural population. 

Figure 7

Syr Darya-specific area-reduction impacts on world cotton production

30,000

22,000

23,000

24,000

26,000

25,000

27,000

28,000

29,000

06/07 08/09 10/11 12/13 14/15 16/17 18/19

1,000 metric tons

Agricultural year

Baseline scenario

10%

50%

25%
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Additional Considerations

The model simulations of cotton area reductions lay a foundation for 

capturing the impact of hypothetical water scenarios on Uzbekistan’s most 

important agricultural commodity. Refinements to the model can add realism 

to the predictions. In terms of modeling trade, the effects of Uzbekistan’s 

cotton supply reductions are likely to be felt unevenly across the world 

market. Currently, the model’s one-world price assumption that drives the 

results ignores the reality that Uzbekistan exports its cotton to a handful of 

countries. Imposing Armington assumptions—which assume that internationally traded products are differentiated by country of origin and govern 

the elasticity of substitution between countries—may generate more realistic 

country-specific results.

Furthermore, the model does not explicitly address the question of energy

prices. Under their current, though shaky, arrangement, Uzbekistan and the 

Kyrgyz Republic exchange water for natural gas at predetermined prices 

and quantities. But the agreed-upon quantities are periodically ignored, 

particularly when international prices spike upward or when the winter 

cold is unpredictably extreme. In either instance, endogenizing the demand 

for water to world energy prices can add an extra element of realism to the 

model’s predictions.

Additional extensions can include policy levers over the Amu Darya river 

basin, which falls primarily inside Tajikistan. Unlike the Kyrgyz Republic, 

Tajikistan’s hydropower resources are relatively underdeveloped, and the 

country has struggled to find investors in its electricity sector. The World 

Bank has initiated some financing toward this objective, and at least 

in the medium-term, additional constraints on Uzbekistan’s (as well as 

Turkmenistan’s) cotton production capacity are likely to appear.
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Implications From This Study

This study examines the relatively underexplored issue of competition 

between energy and agriculture for scarce water resources. To illustrate 

the macro-level tradeoffs between these two sectors, the analysis offers a 

first attempt at modeling a small portion of the global cotton sector. The 

scenarios describing the Uzbek-Kyrgyz water conflict are intended to serve 

as a point of departure for policymakers interested in weighing the response 

of the two countries to reduced access to water in the near term. The effects 

of reducing summer water releases from the Toktogul reservoir benefits the 

Kyrgyz Republic, but downstream, the shock to Uzbekistan’s cotton production capacity could be large. Given that one of Uzbekistan’s main sources of 

foreign currency is its cotton sector, such shocks can put vital imports even 

further out of reach of a population that earns about US$2,400 per capita. 

Given that Central Asia holds as much as 20 percent of the world’s petroleum 

and natural gas reserves, the area is a crucial future source for satisfying the 

growing energy needs of China and India. Kazakhstan’s continued willingness to expand its role as gateway and source for Central Asian energy shipments to China could be at risk if the two countries’ conflicting claims to 

shared rivers appear insoluble. 

Investments in irrigation infrastructure and efficiency, yield enhancements, 

domestic water-pricing schemes, and international trade agreements could 

mitigate for Uzbekistan the effects of water scarcity driven by its neighbors’ 

decisions. Indeed, research has shown where improvements in management 

can enable both Uzbekistan and Kyrgyzstan to satisfy their objectives (Cain 

et al., 2003).

Similar situations can be postulated in other river basins where both hydropower generation and irrigation are found. As discussed earlier, India and 

China hold many of these conflicted basins, and in light of each country’s 

growing economy and population, local events can cause dramatic outcomes 

in production and trade worldwide. An understanding of the equilibrating 

response of producers and consumers is critical to developing policies that 

account for the needs of irrigation agriculture. The analysis offers a glimpse 

into the challenges for international cooperation in the face of growing food 

and energy demands, as countries with shared sources of water operate under 

increasing constraints.
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