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ESTIMATING THE OPPORTUNITY COST OF LITHIUM EXTRACTION IN THE SALAR DE UYUNI, BOLIVIA
MASTER PROJECT
by
Rodrigo Aguilar-Fernandez
Dr. Jeffrey R. Vincent, Advisor
December 2009
Masters project submitted in partial fulfillment of the requirements for the Master of Environmental
Management degree in the Nicholas School of the Environment of
Duke University
2009
Estimating the Opportunity Cost of Lithium Extraction in
the Salar de Uyuni, Bolivia
Master Project
by
Rodrigo Aguilar-Fernandez
Nicholas School of the Environment
Source: Ebensperger et.al (2005); SQM (2008)
Batteries, 27%
Lubricant
Greases, 12%
Frits, 9%
Glass, 8%
Air Conditioning,
6%Aluminium, 4%
Polimers, 4%
Continuous
Casting, 3%
Pharmaceuticals,
3%
Chemical
processing, 1%
Main uses of lithium: 2008
ACKNOWLEDGMENTS
To my wife Andrea for her love, trust and patience when I needed it the most.
To my parents, Vicente and Maria del Carmen, who have provided significant support throughout my education.
To Marcelo , Chichi, and my sister Natalia, who have always been by my side.
Thank you all for your continued confidence, guidance and affection.
I ‘m also thankful to my advisor, Dr. Jeffrey Vincent, for his valuable suggestions and counseling during this project.
And lastly, to all my friends who have given me encouragement during this process.
Thank you, your support has been invaluable.
ABSTRACT
If the world plans to be moving away from oil based transport and towards hybrid and electric vehicles,
lithium supply is the key factor. The Salar de Uyuni in Bolivia holds the largest source of lithium in the world;
however, its extraction will bring a trade off with the environment. Due to the arid nature of the climate, the Salar
de Uyuni basin has a sensitive ecosystem heavily dependent on water resources. Consequently, local people’s
subsistence and well-being also depend on water resources on a daily basis. Studies conducted in the Salar de
Uyuni basin concluded that using the same spring as a production input, water consumption for lithium extraction
and crop irrigation cannot simultaneously take place. Thus, the fresh water use from the San Geronimo River
creates two mutually exclusive projects, lithium mining and quinoa crop with irrigation, generating different gains
to the economy of the region. The incremental cash flows model used in this study provides an estimate of the
benefits that each project would provide. The results indicate that even after subtracting the opportunity cost of
not conducting the quinoa irrigation project and reducing the uncertainty of the model parameters, the net
present value (NPV) of the lithium extraction project is still positive and large relative to the economy of the study
area. Nevertheless, the distributional and social differences have to be carefully assessed in the future according to
the ecosystem services and the financial model described in this study. In order to incorporate market distortions
and foreign exchange implications on the financial model, further economic research is required on both projects.
Finally, water resources and its competing uses should be recognized as an economic good, so it could be managed
more efficiently and used more equitably in this ecosystem.
1
TABLE OF CONTENTS
PARTI - INTRODUCTION AND BACKGROUND………………………………………………………………………………………………………….1
I.1 Characteristics of the study area………………………………………………………………………………………………………………………….4
I.2 Population and Economic Activity……………………………………………………………………………………………………………………….7
I.3 Minerals: The enduring treasure….………………………………………………………………………………………………………………………9
I.4 The focus of Master Project ….…………………………………………………………………………………………………………………………..10
PART II – ECOSYSTEM SERVICES IN SALAR DE UYUNI BASIN ………………………………………………………………………………..11
II.1Recreation, Culture and landscape: Ecosystem gift for local development………………………………………………………...11
II.2 Water resources in one of the world’s aridest places………………………………………………………………………………………..12
II.3 Biodiversity: The intangible key to ecosystem services……………………………………………………………………………………..16
II.4 Agriculture and Animal Husbandry…………………………………………………………………………………………………………………..17
PART III – METHODS………………………………………………………………………………………………………………………………………………19
PART IV – RESULTS…………………………………………………………………………………………………………………………………………………26
IV.1 Initial Scenario ………………………………………………………………………………………………………………………………………………..26
a) Lithium Mining Project……………………………………………………………………………………………………………………………………….26
b) Quinoa Irrigation Project……………………………………………………………………………………………………………………………………28
IV.2 Preliminary project selection…………………………………………………………………………………………………………………………..29
IV.3 Sensitivity Analysis………………………………………………………………………………………………………………………………………….30
a) Lithium Mining Project……………………………………………………………………………………………………………………………............30
b) Quinoa Irrigation Project……………………………………………………………………………………………………………………………………32
IV.4 Project selection………………………………………………………………………………………………………………………………………………35
PART V – CONCLUSIONS AND DISCUSSION …………………………………………………………………………………………………………..36
PART VI –REFERENCES ………………………………………………………………………………………………………………………………………….39
PART VII- APPENDIX……………………………………………………………………………………………………………………………………………...43
1
PART I- INTRODUCTION AND BACKGROUND
As the global energy landscape tilts away from fossil fuels towards renewables, the demand for lithium-
ion (Li-ion) battery is growing. Because of its light weight and huge energy storage capabilities, Li-ion batteries are
preferred for electronic devices, such as computers, cameras, and cell phones. Between 2003 and 2007, the world
consumption of lithium for the battery industry increased over 7% per year (Roskill, 2008). Also, Ebensperger et.al
(2005) predicts that because of the many diverse uses for lithium metal1, demand is expected to expand
considerably over the next decade reaching a up to 8.2% increased in 2010. From a global perspective, the most
important application of lithium products in 2008 covered the following applications: battery, glass & ceramics,
lubricating greases, aluminum &casting, air conditioning, pharmaceutical, and others (Ebensperger et al., 2005;
SQM, 2008; USGS, 2008). Figure 1 shows the 2008 main uses of lithium in percentages.
Figure 1: Main uses of lithium
Source: SQM Annual Report, 2008.
In particular, increased worldwide interest in greener transportation has triggered an upswing in the
market for lithium as it is a major component of batteries for electric and hybrid automobiles (Tahil, 2007;
Ebensperger et al., 2005; Nicholson, 1998). In the US, President Obama directed 2 billions of dollars of the
economic stimulus package to fund lithium battery manufacturing (Galbraith, 2009), and GM announced it would
build a plant to manufacture (Li-ion) batteries for the Chevy Volt scheduled to debut in 2011(Warren, 2009;
Lawrence 2009). Likewise, Asia and Europe are making strong commitments to electric, plug-in, and hybrid vehicles
with stated goals of starting production in 2011 (Gartner, 2009). Nissan-Renault which, together with the “Better
Place” project for electric distribution, announced the availability of electric cars in different countries such as
1 Lithium metal and compounds are widely use in lightweight aerospace alloy, ceramics and glass; carbon dioxide
absorption, water disinfection, and pharmaceuticals for treating mood disorders.
Israel and Denmark starting in 2011; and Mitsubishi’s launch of the i-Miev, a compact vehicle operating solely with
an electric motor, which the Company expects to sell outside of Japan starting in 2010 (Abuelsamid, 2009).
Lithium metal is 33rd-most abundant element on the planet and is widely distributed in trace amounts in
most rocks (pegmatite minerals), soils (brine salt flats and clay deposits) and natural waters. Large concentrations
are extracted from pegmatite (lithium-containing minerals spodumene and petalite) and brine salt flats. Lithium is
not found in elemental form due to its high reactivity, so most studies report lithium consumption or deposits in
terms of Lithium Carbonate (Li2CO3) Equivalent (LCE).2
Although the purity of extraction from pegmatites is greater, the extraction from brine salt flats is the
most economic alternative (Evans, 2008; MIR, 2008; Tahil, 2007). Figure 2 shows the world’s total reserves3 of LCE
in million (MM) tonnes from brines and pegmatite. Today the greatest part of the world’s accessible lithium
reserves (over 80%) is in the so-called “Lithium Triangle”, where the borders of Argentina, Bolivia, and Chile meet
(Evans, 2008; MIR 2008). Furthermore, the Lithium Triangle accounts for more than 50% of the world’s total
lithium metal resources (Figure 3). Lithium with extremely high strategic value has led to a race for many lithium
extraction projects on the salt flats of the world during the past two decades (Evans, 2008; Tahil, 2007; MIR, 2008).
Figure 2 : World’s Total LCE Reserves from Brines and Pegmatite by country
in (MM tonnes ; %)
Source: adapted from Evans(2008), USGS (2007), and MIR (2008)
2 Approximately 5.32 units of Lithium Carbonate (Li2CO3) equivalent converts to one unit of Lithium Metal.
3 According to the USGS (2008) “reserves” are that part of the “resources” which could be economically extracted
or produced at the time of determination. The term “reserves” need not signify that extraction facilities are in
place and operative. The term also implies that the material can be extracted with existing technology at a specific
price, usually the prevailing market price.
0.45; 0.55% 0.21; 0.26%
13.83; 16.84%
14.42; 17.56%
29.26; 35.63%
23.94; 29.15%
Chile
Bolivia
Argentina
China & Tibet
Brazil
US
Figure 3: World’s Total Lithium Metal Resources by continent
in (MM tonnes) 4
Source: adapted from Evans(2008)
Not surprisingly, in 2008 more than 55 % (65,000 tonnes) of the global production and consumption of
LCE (118,000 tonnes) came from Chile and Argentina. Because lithium is not traded as a commodity on the open
market, its price is variable depending on the deals directly between producers and manufactures. In the early
2000, the average export value for Chilean and Argentinean lithium carbonate remained around US$2,000 per ton.
That changed in 2005, when the nominal prices for lithium carbonate began to increase sharply (Figure 4). Average
export values for LCE reported by major producing countries in 2008 were more than double those seen in 2004
(Roskill, 2008).
Figure 4: LCE Average annual prices from Chile exports
in (US$ per ton)*
*Chile GDP deflator (2000=100)
Source: adapted from Roskill (2008); International Monetary Fund (2008)
4 To convert 1,000 tonnes of lithium metal to million pounds of lithium carbonate equivalent, multiply by 11.7.
0.24
0.26
2.36
3.60
14.19
6.48
Europe
Australia
Africa
Asia
North America
South America
-
1,000
2,000
3,000
4,000
5,000
6,000
7,000
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
Nominal US$ per ton
Inflation-Adjusted US$ per ton
For all the above mentioned, today the real power player in the lithium market is Bolivia. The Salar de
Uyuni in Potosi Bolivia has close to 42% of the world's lithium reserves from brines only (Evans, 2008; USGS,
2008;MIR, 2008). Although production has not yet commenced, in May 2008, the president of Bolivia signed a
decree investing US$ 8.7 MM to set up a state owned pilot lithium extraction plant in the Salar de Uyuni with the
hopes that future profits will “fund social programs in the country” (COMIBOL, 2008).
In pursuing this, it might open further areas for production, promote further use of lithium or charge
higher taxes and introduce royalties to derive greater benefit from the economic profits of LCE exports. After all,
historically the mining sector has always been an important economic activity for Bolivia. From 1995 to 2005, the
mining sector has contributed in a range from 4.2% to 6.1 % of Bolivia’s gross domestic product (INE, 2009).
Nevertheless, in Potosi concerns remain about the environmental and social impact of the massive lithium mining.
In an impoverished but natural resource-rich Bolivia, the depletion of natural capital is typically not
accounted for. Specifically, the mining industry has traditionally been structured to externalize such environmental
costs so as to maximize profit — the industry appropriates undervalued resources and shifts the environmental
costs to others— rather than improving efficiency and innovating (Escobari, 2003; McMahon et.al., 1999).
Responses are typically short term and no sustainable. Moreover, it is certainty that when comes to evaluating
these costs, the most affected by environmental pollution and biodiversity loss from mining, are generally those
least able to understand and respond to it (e.g. remote miners' families or isolated rural communities and the
tourism business).
I.1 Characteristics of the study area
The Salar de Uyuni basin occupies a surface of 7,185 thousand hectares and is located at extreme
southwest of Bolivia. Figure 5 shows the topography and location of the basin where the gray/white indicates
elevations above 4,000 meters, green below 500 meters and the different shades of brown represent altitudes
between 500 and 4,000 meters. A characteristic of the region is the presence of large salt flats and salt lakes that
are remnants of ancient lakes. The level and area of these lakes has varied greatly over the past 200,000 years,
which is associated primarily with temporary changes in precipitation and temperature (Risache and Fritz, 1991).
Figure 5: Topography of the Salar de Uyuni Basin
Source: Molina Carpio (2007).
The Salar de Uyuni is the largest salt flat in the world and one of the twelve most important watersheds in
South America. It is at an altitude of 11,995 feet and covers an area of 1,062 thousand hectares of salt desert
(World Resources Institute, 2005). The Salar de Uyuni (Figure 5 and 6), located on the Bolivian Altiplano at 20ºS
68ºW, is surrounded by the Andes Mountains. These mountains cause a rain shadow, preventing moisture input
to the Altiplano (Highlands), producing an arid to semi-arid climate, nonetheless, where open water bodies exit
(rivers and lakes) there is a rich avifauna and vegetation cover (Pastures and marshes). Arroyo and et al. (1988)
concluded that plant species have a great dependence on the availability of water due to the arid climate; and,
only minor changes in the water budget can induce gain or decrease in vegetation cover and plant diversity.
As the evaporation rate near the salar (1300-1700 mm/yr) greatly exceeds precipitation (100-200 mm/yr),
salt crust and brines are formed throughout the year in the salar (Molina Carpio, 2007). Although the salar is
normally dry, seasonally flooding changes the volume of outflow water producing a unique pasture and marsh
pattern (Messerli, et.al., 1997).
Figure 6: The Salar de Uyuni Basin in the Bolivian Highlands (Altiplano)
Sources: Risache(1991), RAMSAR (2009) and World Resources Institute (2005).
Furthermore, many animal species live in the basin where vicunas and guanacos are most prominent
amongst the mammals (Liberman, 1995). The salt flats and the Rio Grande of Uyuni house three of the world’s six
flamingo species. During January and March (southern summer), the salar becomes a flamingo breeding ground
after the rains flooded the surface of the salar (Hurlbert, 1979; Messerli, et al., 1997). The discharge of Rio Grande
onto the salar, adjacent to where the lithium concentration is the highest, creates a permanent lagoon area used
by birds and camelids (Figure 6). Consequently, it was included in the list of the 34 biodiversity Hot Spots of the
world in the year 2000(Conservation International, 2007).
Figure 7: Landscape pictures of the Salar de Uyuni
Source: COMIBOL online (2009)
Bolivia
Chile
Salar de Uyuni
8,000)8,000)
.
.
Bolivia
Chile
Salar de Uyuni
8,000)8,000)
.
.
I.2 Population and Economic Activity
The Salar de Uyuni basin occupies approximately 61% of the Department of Potosi in Bolivia. The five
provinces in the southwest of Potosi are: Daniel Campos, Antonio Guijarro, Enrique Baldivieso, Nor Lipez and Sur
Lipez (Figure 8). The 2006 estimated population of the basin was 64,212 people (Molina Carpio, 2007). The most
populated towns in the study area are Uyuni and Kolcha “K”(right panel Figure 6). As can be seen in Table 1 the
population density is less than 1 hab/km2, much lower than the rest of the department of Potosi and the national
average. On the other hand, rates of health and human development are above the average of Potosi and below
the average of Bolivia. Table 1 shows the population, density per square kilometer, and some human development
records of the study area per province.
Figure 8: Administrative distribution of Potosi, Bolivia
Source: Molina Carpio (2007).
Table 1: Population and Human Development information
Source: Adapted from Molina Carpio (2007) based on UNDP and INE (2009)
The activities that employ a larger percentage of the economically active population in the basin are the
quinoa agriculture and camelid livestock. Even though 1% of the total area is suitable for agriculture, Quinoa
ProvincePopulation
(2006)
Density
(people/km2)
Life expectancy
(years)
Adult literacy
rateHDI
Antonio Guijarro 39,126 2.3 59.4 81.8 0.57
Daniel Campos 5,490 0.3 59.0 94.5 0.57
Enrique Baldivieso 1,690 0.8 58.5 87.7 0.53
Nor Lipez 12,171 0.5 57.4 87.7 0.54
Sur Lipez 5,522 0.4 55.4 82.4 0.48
Salar de Uyuni Basin 63,999 0.9 58.0 86.0 0.5
Department of Potosi 772,578 6.5 55.2 64.5 0.5
Bolivia 9,627,269 8.8 63.3 86.7 0.6
harvesting is the main source of income and food security for local people. In the Salar de Uyuni basin only 65% of
the land suitable for agriculture is yearly harvested (25 Th.Ha) and the rest is not cultivated because of lack of
water supply or labor (Molina Carpio, 2007).
Other activities of growing importance are tourism and mining. The study region has many tourist
attractions such as the Salar de Uyuni, the high Andes lakes and wildlife, the spectacular geological formations and
hydrothermal vents. According to Ellingson & Seidl (2006) the most visited ecotourism attraction in Bolivia is the
Salar de Uyuni. In the year 2006, 50,342 people visited Uyuni which 43% were foreigners. Figure 9 shows the influx
of visitors from 2000 -06. Relative to the year 2000, the percentage change of tourists in 2006 was 16%; foreign
visitors were 41% and bolivians were 2.7%. It was estimated that the percentage change of total tourist per year
will be around 5-8% for the following years (INE, VMT& BCB, 2009).
Figure 9: Number of tourists visiting the Salar de Uyuni
Source: adapted from INE, VMT& BCB (2009)
The study area has substantial reserves of minerals, both metallic and nonmetallic (lithium and derivates).
It has the largest antimony deposits in Bolivia, as well as deposits of other metals. The San Cristobal mine started
operations in 2008 and it is considered the largest developed mining project in Bolivia in the last 10 years. The
proven and probable reserves of San Cristobal are estimated at 446 million ounces of silver, 3.45 million tonnes of
zinc and 1.27 million tonnes of lead (Molina Carpio, 2007).
It is self evident the importance of this unique ecosystem to Bolivia and the world, therefore a widespread
extraction of natural resources and excessive pressure on the ecosystem (i.e. tourism, population, mining) could
have irreparable environmental consequences. As a real world example, Figure 10 shows the destroyed landscape
and the loss of natural water courses that the salar in Chile suffered in the last 10 years.
27,935
22,973
52,914
-
10,000
20,000
30,000
40,000
50,000
60,000
2000 2001 2002 2003 2004 2005 2006
BoliviansForeigners
Total Visitors Salar
Figure 10: Loss of landscape reduce the tourist attraction of the basin
Source: Lithium mining at Salar de Atacama, Chile SQM(2008).
I.3 Minerals: enduring treasure
It is the abiotic characteristic of this ecosystem that allows minerals in sufficient quality and quantity to
make mining both desirable and feasible. Nevertheless, unlike other ecosystem services, this service is finite and of
limited capacity. The salt flats have long been a resource for local indigenous peoples. In towns on the banks of the
salar, most houses and buildings are made of bricks of the crystallized mineral. Residents work in salt, drying and
loading it in trucks to be taken to refineries in the lowlands, and carving it into trinkets to sell to tourists who visit
each year.
As mentioned earlier, lithium has become a strategic element that won fame for its use in battery-
powered cars and variety of uses. The Bolivian Pilot Plant is the first phase of the lithium extraction project where
only small amounts of LCE will be produced (400 tonnes/year of LCE). It will be run by the General Directorate of
Evaporative Resources of the Salar de Uyuni under the state owned mining company of Bolivia–COMIBOL. The
second phase called “Lithium Industrialization Plant” is reported to produce 40,000 to 60,000 tonnes per year of
LCE starting in 2014 (COMIBOL, 2008; La Razón, 2009). The positive aspects of the relationship between lithium
extraction and human well-being are mainly due to employment possibilities and the general contribution to
economic activity in the municipality and the country. With regard to the former point, it should be noted that,
the workforce available in the municipalities are mainly unqualified so the potential for participation in mining
activities is uncertain.
In spite of the poverty in Uyuni, attempts in the 1980's and 1990's by foreign companies to extract the
lithium met with resistance from the community. Likewise, many analysts now perceive numerous internal
obstacles to a full lithium industry exist, and it is unclear if Bolivia will be able to participate in the market sooner
than 2018 (Friedman-Rudovsky, 2009). Apart from the political and economical problems that Bolivia might have in
the future, the most notorious difficulty prior undertaking this enormous project is decisive information
(Viñagrande, 2009).
I.4 The focus of Master Project
The ecosystem services and the natural capital stocks that produce them are critical to the functioning of
the Earth's life-support system. They contribute to human welfare, both directly and indirectly, and therefore
represent part of the total economic value of the planet (Daily, 1997; Millennium Ecosystem Assessment, 2005;
Pagiola et al., 2004). Also, it is widely acknowledge that market transactions provide an incomplete picture of the
economic value of ecosystem services.
The acquisition of information about most ecosystems services is especially difficult because of the no
existence of a market (Champ et al., 2003; Bishop et al., 1995). Yet, no government or private agency has
considered the economic desirability and timing of the lithium extraction project which will alter the natural
environment of the salar (Lopez Canelas, 2009). In particular, no study has assessed the existence and magnitude
of differences between benefits and costs of the lithium extraction in the Salar de Uyuni Ecosystem (Zuleta, 2009;
Garzón, 2009; COMIBOL, 2009). Those services (benefits) which are not normally exchanged in markets are
generally ignored in the decision-making process. Consequently, one purpose of this paper is to provide a
preliminary assessment of the ecosystem services provided by the Salar de Uyuni basin. Second, determine the
tradeoffs of the lithium mining project and the ecosystem services by estimating a lower bound opportunity cost.
Perfect information will never be available and uncertainty will be an inherent feature of all important
decisions (Bishop et al., 1995: Pagiola et al., 2004). However, this study optimistically will provide preliminary but
useful information to enhance the ability of decision-makers to evaluate the overall magnitude of differences
between winners and losers resulting from projects that alter the use the Salar de Uyuni unique ecosystem
services.
Ultimately, this study will encourage that further economic valuation research is needed in the Salar de
Uyuni basin. Indeed, ecosystem valuation by itself provides little interest to a country owning the environmental
assets unless they can be turned into revenue flows (Bishop et al., 1995).
PART II- ECOSYSTEM SERVICES IN THE SALAR DE UYUNI BASIN
II.1 Recreation, Culture and landscape: Ecosystem gift for local development
Ecotourism is a potentially important development alternative in undeveloped countries (Ellingson &
Seidl, 2006). Many in the tourist industry classify the Salar de Uyuni as a Natural Wonder of the world because of
its area of outstanding natural beauty (New7Wonders of Nature website, 2009). Since the salt flats have an albedo
similar to that of ice sheets, the Salar de Uyuni is the brightest object on earth’s surface visible from space and
thus the most visited ecotourism attraction in Bolivia (Fricker, et al., 2005; Ellingson & Seidl, 2006).
The major attractions of the community include the mountain landscapes, train cemetery, colonial
churches, rocks formations sculpted by wind, tallest and oldest cactus, dead volcanoes, lakes and lagoons, and the
traditional villages with their historical and cultural heritage. Some of these attractions are located within the
Eduardo Avaroa Reserve located at the Sur Lipez province. Around 50,000 tourists visited the Salar de Uyuni in
2006 and sleep in a hotel made entirely of salt despite the poor infrastructure of the municipalities(towns) close to
the salar, Uyuni and Kolcha “K . Those municipalities have a population of 19,000 and 11,174 inhabitants,
respectively (INE&UDAPE, 2001). Nevertheless, the huge potential to develop with the ecotourism income is intact.
At a national level, Bolivia received 404.7 thousand visitors which generated an estimated income of US$
187.7 million in 2004 (UDAPE, 2005). According to the same report, considering the receptive tourism as a proxy
variable of the sector's contribution to GDP5, the estimated income from receptive tourism amounted in average
more than 2% of GDP in the period 1991-2004. Those figures showed tourism is an activity with a major impact on
the economy of Bolivia and should be considered as part of decision-making.
In the same manner, Uyuni and Kolcha “K” have experienced considerable income inflows and
employment changes, mainly brought about by the explosion of ecotourism over the last twenty years. Although
the majority of the population is considered below the poverty line (INE&UDAPE, 2001), the local employment has
enjoyed a gradual but constant transformation. Both Uyuni and Kolcha “K have shifted from being municipalities
where the only main activities were agriculture and livestock, to one where most of the labor force work in
construction, salt farming, craftwork and tourism. The INE and UDAPE (2001) reported that the main activities
generating employment in the municipalities are directly or indirectly related to the use of ecosystem services:
agriculture and livestock (46%), hotels and restaurants (23%), construction and mining (19%), and salt farming
(12%).
5 Because national accounts do not provide tourism information separately, UDAPE estimated the total income
from receptive tourism (Yx) according to a survey conducted to estimated the average expenditure per tourist
arriving to the country. Afterwards, they used the following expression :
Yx = [average expenditure per day x Number of tourists per year x visiting average days per tourist]
As a result, tourism seems to be a viable development option for both the communities and outsiders,
them being resident in the area or just passing through. This activity experienced sudden and unregulated growth
triggered by the arrival of entrepreneurs who set up the first campsites and tourism agencies, followed by hostels
and restaurants, and finally diverse categories of hotels and internet cafés. A new road, recently constructed
between Uyuni and the regional capital of Potosi, will improve tourists’ access in the following years bringing
employment and income to those communities (Eduardo Avaroa Reserve website, 2009).
The idea would be to balance the economic development to the mining sector and tourism which will be
ideally sustainable.
II.2 Water resources in one of the world’s aridest places
In the Salar de Uyuni water resources are not only vital to flora and fauna, but have also been the basis of
all human activities in the past and present. Messerli and et al. (1997) concluded that water resources in the Salar
de Uyuni Watershed are considered a non-renewable resource (or renewed extremely slow) and specifically
expanding mining industry may lead to ruin this sensitive ecosystem and also provide a threat to the region's water
supply. The same study established that in order to prevent damage even small changes in hydrologic conditions or
salts concentrations of ground and surface waters must be carefully assessed. The study also argues that it is not
appropriate to focus exclusively on areas with high biodiversity (e.g., around rivers or lakes). Instead, the entire
catchments, including the groundwater basins and the source area of the open water, must be assessed. As a
result, the salar has been included in the RAMSAR list of Wetlands of International Importance6.
Despite the economic importance of the mining sector in Bolivia, it has been the main caused for
irreversible environmental damage to land, rivers and communities for the last 80 years. Up until today, mines
continue to discharge hundreds of thousands of tons of toxic sludge into rivers every year (Escobari, 2003). In
Uyuni, past fights against water rights and the voracious water consumption of a mining company have been
detrimental to local farmers who use water supply from ground water reserves (Mc Mahon, 1999). After all, the
most important mining region in Bolivia, the Altiplano, up 13% of the country, has only 0.5% of available fresh
water (Escobari, 2003).
COMIBOL (2008) estimated that the Lithium Industrialization plant will consume approximately 4,000 to
4200 cubic meters per day (m3/day) of freshwater from Rio San Geronimo, and 5,000 to 5,300 m
3/day of brackish
water from Rio Grande (Figure 11).
6
It has come to be known popularly as the “Ramsar Convention”. Ramsar is the first of the modern global
intergovernmental treaties on the conservation and sustainable use of natural resources (RAMSAR, 2009).
Figure 11: Hydrological map and rainfall patterns in the Salar de Uyuni Basin
Source: adapted from Molina Carpio(2007)
Additionally, Table 2 shows the probable water consumption (m3/day) for the future lithium plant from
two different reports. The first row indicates the average water consumption estimated from an operating lithium
plant in Chile (SQM), and the second row indicates the expected future water consumption for the future lithium
plant estimated by COMIBOL (2008).
Table 2: Expected quantity of water consumption for the future lithium plant
Source: Torrez&Ramirez (2006) and COMIBOL (2008).
The same report distributed by COMIBOL (2008) states that salts precipitated in the evaporation ponds
“will be returned via brineduct to the Río Grande” (emphasis added). Hence, the quality of the withdraws to Rio
Grande could raise some concerns because local peasants use the slightly saline water mainly for animal
husbandry. Furthermore, pasture and marsh habitats show less dependence on precipitation and their presence
depends more on the availability of local freshwater and groundwater (Messerli et al., 1997). Many species in this
arid environment are limited to marsh habitats, which are very important grazing resources for the Altiplano
communities.
Rio Grande
(Brackish)
Rio
San Geronimo
SourceFresh water
(m3/day)
Brackish
(m3/day)
Torrez&Ramirez 2006 (*) 8,208 9,504
COMIBOL 2008 4,200 5,300
(*) SQM average consumption 1995-2003
It is important to highlight that the quantity of brackish water is not an immediate issue. Not only a Rio
Grande flow greatly exceeds the estimated lithium plant future daily consumption7, but also the quality of the
water is not suitable for agriculture (Molina Carpio, 2007). While this river should be monitor in the future to
assess the impacts associated with pumping, brackish water from Rio Grande will be not part of this study. On the
contrary, the quantity and consumption privileges of freshwater from Rio San Geronimo will be considered in this
study.
Knigth Piesodl Consulting8 (cited in Molina Carpio, 2007) conducted the only available hydrological study
on the Rio Jaikihua, located 15 kilometers south-east of Rio San Geronimo which is also under the same rainfall
pattern (Right panel Figure 11). The hydrological study concluded that building a dam in Rio Jaikihua, it will take
less than 18 years to consume all the potential available water from the river if pumped water reaches a 40,000
m3/day. Notice in Table 3 that all the recharged water comes directly or indirectly from precipitation representing
only 22% of pumped water and the remaining 78% represents the emptying of the aquifer. Moreover, building a
dam to pump the water will eliminate the natural flow into the Altiplano.
Table 3: Rio Jaikihua Hydrologic balance
Source: adapted from Molina Carpio (2007)
Molina Carpio (2007) also explained that given the characteristics of the region9, when the pumped water
exceeds 15 -25% of total spring recharge, not only the water availability will significantly decline throughout the
years, but also it will take the aquifer long periods of time to return to less than normal levels. For this case, Knigth
7 Rio Grande flows at 32,918 -33,574 (m3/day ) vs. 7,402 (m3/day)equal to the average expected brackish water
consumption. 8 Knight Piésold is a specialised international consulting company offering engineering and environmental services in Mining,
Environment, Hydropower, Water Resources, Roads & Construction Services(www.knightpiesold.com). 9
In terms of rainfall patterns, evapotranspiration, slope, runoff coefficient, and soil chemistry.
Table Rio Jaikihua Hydrologic balance
Daily
m3/day
Recharge
Rio Jaukihua (ephemeral) 200
Rio Toldo Basin and Rio Grande Basin (*) 3,050
Upstream Volcanic-sediments and low yield springs 4,200
Precipitation 1,400
Total recharge 8,850
Pumped water 40,000
Water removed from aquifer 31,150
Discharge into Rio Grande -
Total removable water from the river is 250 Million m3
(*) Because Rio Jaikihua is a tributary of Rio Grande, the flow is
reversed after pumping begins. Also, Rio Toldo is a tributary of Rio
Jaikihua.
Piesodl estimated that Rio Jaikihua volume will return to normal levels approximately 79 years after closing
operations.10
However, Molina Carpio (2007) then argued that because Knigth Piesodl hydrological modeling
assumed rainfall patterns higher (300 mm/year) than the average precipitations in that region (200-250 mm/year;
Figure 11), the recharge and available water of Rio Jaikihua are notably overestimated. As a result, the emptying
period of the river will be shorter and the recovery time will be longer (Molina Carpio , 2007).
Rio Jaukihua and Rio San Geronimo are the result of two hydro-geologic systems characteristic of the
study area. The first type occurs at relatively low elevations (between 3,700 and 3,900 m) and its the result of
erosion of the volcanic sediments located upstream and groundwater regulation (Molina Carpio, 2007). The
second system includes rocksprings located between 3,900 and 4,500 m, where the water precipitated seeps
through fractures and eventually emerges in low yield springs (Molina Carpio, 2007). In other words, both rivers
recharged directly from rainfall and groundwater storage (blue arrows coming from the mountain in Figure 12).
Nevertheless, Chauffaut (1998; cited in Molina Carpio, 2007) concluded that the freshwaters that flow into the
region are mostly from underground sources which were store long time ago. Chauffaut estimated that less than
20% of current runoff water comes from “recent precipitations”. The rest is water from underground sources
between 100 and 20,000 years old, according to radiocarbon-date tests. Furthermore, Molina Carpio (2007)
suggested that water stored underground should be considered a nonrenewable resource because it comes from
rainfall and ground water regulation that occurred between 90 and 19,000 years ago. 11
Figure 12: Rio San Jaikihua hydrological profile
10 250 (Mill m3) / 8850*360*10-6 (Mill m3 / year)
11 In order to support that conclusion, Molina Carpio (2007) also explained and cited other hydrological/paleoclimate studies
conducted in the area (Aravena , 1995; Pourrut etal, 1995) .
Source: adapted from Molina Carpio (2007) obtained from Knigth Piesodl Consulting (2000).
Unfortunately response to such environmental concern over an extensive lithium mining in the salar is
characterized by indifference by the government and local authorities (Lopez Canelas, 2009)
“There’s no information, no water use nor hydrological studies,… So how can they begin to project what
the long-term effects might be? This is supposedly a project to improve the region, but what if it makes
living impossible? How could it be called sustainable development?”
--- Elizabeth Lopez Canelas—Bolivian Environmental Defense League (FOBOMADE)
It must be acknowledged that there is not enough information available to be able to simulate the
complete water cycle of the salar with any precision. However, it is clear that if extensive mining takes place in the
Salar de Uyuni Watershed, water availability might become progressively more critical in the future. According to
Oliveira Costa(1993), if the highest priority is not given to the protection of water resources, especially in the most
arid mountain ecosystems of the Salar de Uyuni Watershed, natural habitats very soon will be destroyed and the
agricultural, tourist, and economic developments will be endangered.
II.3 Biodiversity: The intangible key to ecosystem services
Biodiversity plays a direct role in regulating and supporting some of the ecosystem services fundamental
for human wellbeing, e.g. services obtained directly from the ecosystems, such as food and animal husbandry. It
also plays a fundamental role in the development of other services such as tourism and agriculture since it
provides the elements that support these services (e.g. species, communities, landscapes). Biodiversity also plays
an indirect role in the development of certain activities relevant for the municipality, such co-management of
some areas of the Eduardo Avaroa Reserve. In this case, the wildlife habitat of certain species and natural
landscape means that the reserve can be developed into a tourist attraction (e.g. flamingos at Laguna Colorada or
vicunas).
In arid climates like the Salar de Uyuni basin, it must be highlight that by no means a region without fauna
and flora (Messerli, et al., 1997).Thus, biodiversity is a very important element since it regulates the functioning of
the ecosystem. For example, biodiversity defines the ecosystem biomass (the totality of living organisms in the
ecosystem), which allows the ecosystem to function under a variety of conditions. This determines the conditions
of adaptability and resilience that maintain a diversity of living organisms even in the seemingly inhospitable arid
Altiplano ecosystem. However, because the adaptation to this harsh environment is extremely sensitive, even
minor human interventions can direct to irreversible change or loss (Hurlbert, 1979; Messerli, et al., 1997).
Messerli (1997) also concluded that “some species and associates could form a certain reservoir for migration in
the event of future climatic change…..but most important is that people, plants, and animals depend on the
universal life-limiting factor: water! “.
Unfortunately, it is difficult to define one general condition for biodiversity in the Salar de Uyuni basin
today. While some elements are protected others are under constant pressure due to their traditional uses: for
example, some Altiplano plant species used for fuel (llaretas) or crafts (cardón). However, there have been no
assessments as yet to determine their current situation. There is no systematic information to identify trends and
changes over time or in space in the biodiversity of the municipality. Monitoring has only taken place for some
“charismatic” species (e.g. flamingos) in some sectors of the Eduardo Avaroa Reserve. The same is true for water, a
vital resource for biodiversity in the area due to its scarcity.
According to Lopez Canelas (2009) some local people are concerned about lithium extraction in the
region. They said that if excessive lithium mining occurs that could lead to huge trenches or the destruction of
large areas of the salar. It is almost certain that covering its surface with brine extraction facilities and evaporation
ponds (Figure 9) will irreversibly damage the surface of the salt flat and the Rio Grande delta, used by wild
flamingos as an annual breeding ground. On the other hand, pressure over land use is mainly an issue in areas with
larger settlements, such Uyuni, where marsh lands are being handed over to tourism development projects.
II.4 Agriculture and Animal Husbandry
Agricultural and Animal husbandry practice in the Salar de Uyuni basin occurs at a subsistence level and
contribute to local families’ food and income. About 38.6 thousand hectares of land in the basin is suitable for
agriculture (<1% of the total) and roughly 11% is natural grassland mainly used as pasture for camelids. Table 4
shows the area of agricultural land and the number of heads of cattle by province. Largely due to the limitations of
the ecosystem and lack of capital for implementing large-scale agricultural production (i.e. water irrigation), 40% of
the households have 3 to 4 hectares of yearly cultivated crops. For local peasants, agriculture represents 65 to
85% of their income.
Table 4: Agriculture and Livestock in Salar de Uyuni basin
Source: adapted and translated from Molina Carpio (2007) and Soraide et al (2005).
Table 2. Areas in thousands of Hectares (Th.Ha) and camelids in number of heads(#)
Total area
Th.Ha
Posible crop
Th.Ha
Harvested
Th.Ha
Irrigated
Th.Ha
Non-irrigated
Th.HaCamelids (#)
Antonio Guijarro 1,489.0 14.95 11.52 1.04 10.48 128.3
Daniel Campos 1,210.6 4.84 7.72 0.48 7.24 23.3
Enrique Baldivieso 161.4 4.00 1.05 0.02 1.03 9.1
Nor Lipez 2,089.2 14.47 4.74 0.36 4.38 68.4
Sur Lipez 2,235.5 0.29 0.27 0.20 0.07 42.3
Total basin 7,185.7 38.6 25.3 2.1 23.2 271.4
There are various factors that constrain the ability of agriculture land pastoral activities to grow as
productive activities in the region. One of these factors is the ecosystem and its limitations: water supply is a factor
limiting activities, as already mentioned in this study; this limitation is compounded by soil conditions that do not
allow for intensive activity. Soil is predominately alkaline in the basin with high salt content, making it unsuitable
for intensive agricultural practices (such as fruit cultivation), except for the cereal quinoa and potatoes (Jacobsen
and Mujica, 2001).
Apart from these environmental factors, social, economic and cultural conditions have tended to favor
other economic and productive activities, particularly tourism and mining. The migration of the younger
population from the countryside to towns and cities further limits the possibilities for growth in agricultural and
pastoral sectors.
In spite of the above, 20.7 thousand hectares of quinoa are cultivated each year (Crespo et al., 2001),
approximately 82% of the total harvested crops (Columnn 3 of Table 4). The Salar de Uyuni basin is the principal
organic quinoa producer region in Bolivia, with an annual contribution of 60 % of the national production
corresponding to 13,485 tonnes (Crespo et al., 2001; Soraide et al, 2005). According to the last census, 66% of the
population (42,255) is involved in quinoa agriculture.
Quinoa cereal is highly appreciated for its nutritional value, as its protein content is very high. Unlike
wheat or rice (which are low in lysine), quinoa contains a balanced set of essential amino acids, making it an
unusually complete meal. Quinoa has more iron, phosphorus, and calcium than wheat, corn or white rice. It is also
a good source of dietary fiber and magnesium. Quinoa is gluten free and considered easy to digest. Its seed can
also be used to make a high protein drink (Quinua Real, 2009).
Currently, Bolivia is the second largest producer of quinoa in the world and the only one that can produce
organic quinoa. Bolivia constitutes the principal world exporter of quinoa, contributing 43% of the total quinoa
production in the world and 0.14% of the Bolivian GDP (FAO, 2009; CAMEX, 2009). There are approximately more
than 35,000 hectares harvested crops producing 23,000 tonnes of quinoa each year. As an average, the official
exports reached 4,000 tonnes a year during 1998 and 2002. This product involves about 70 thousand small
producers and exports of U.S. $ 5.1 million per year (U.S. $ 2.7 official $ 2.4 and unofficial). Due to the international
market demand for organic food, quinoa crop harvesting has become an important commercial product.
According to CAMEX, during the year 2003 the exports have registered increases of more than US$ 700,000, this
increase is accompanied with an increase of the number of national enterprises that export this grain.
The importance of quinoa agriculture is important for the Salar de Uyuni basin and Bolivia because of the
following:
Food safety: The quinoa is native to the Salar de Uyuni Basin and used in stews, salads or croquettes, the breakfast
cereal and soup. Quinoa is essentially used as food and to a lesser extent for medicinal purposes; there are
different ways to eat: grains and flakes. Out of the 70 thousand agro units of production, 55 thousand occur
irregularly and subsistence, 13 thousand produced continuously for sale and consumption, and only 2 thousand to
sell produce to market (Soraide et.al, 2005). Quinoa is considered a substitute product of any kind of meat and
resembles the qualities of milk. It is a very important source of income for local peasants. Also, there is a fledgling
industry in Bolivia of products such as quinoa or pasta, cereal preparations and bars quinoa with chocolate.
Agriculture sector: it is the main product of the Bolivian highlands producing an average of 25.6 thousand tons of
cereal per year with an average yield of 660 kg / ha. Quinoa provides 2.35% of agricultural domestic product of
peasant origin. As a byproduct, quinoa is used to feed animals and firewood (Molina Carpio, 2007).
Foreign trade: legal exports account for 4.5% of the Bolivian exports clearly peasants. Export $ 2.7 million are
legally registered and approximately $ 2.4 million smuggled out of Peru (Crespo et al., 2001).
PART III- METHODS
The initial phase of this study was devoted to gathering information and analyzing the current state of the
Salar de Uyuni basin. This was accomplish by analyzing past and present research on every relevant aspect in the
study area. That is, main economic activities, ecological and environmental characteristics, social and demographic
indicators (COMIBOL, 2009; INE, VMT& BCB, 2009; CAMEX, 2009; Messerli et al., 1997; Molina Carpio, 2007, etc).
The focus of this phase led to gather information particularly on the lithium mining project and the water
consumption in the Salar de Uyuni basin. Despite the significance of both topics in that region for Bolivia,
quantitative and qualitative information was very limited in detail and disaggregation. In particular, there are no
studies conducted recently on the economic profile of the region and the ecological interactions. Additionally, this
phase also included identifying and contacting experts in that region which provided the author with valuable
information, opinion and guidance (personal communication with, Curi, Lopez Canelas, Zuleta). Part of the results
of this phase was summarized in the previous sections.
Once the information was gathered and analyzed, the author was only able to focus its study in water
resources competing use, mainly because the lithium mining activities are not yet systematically perceived as a
threat for other ecosystem services (i.e. recreation and biodiversity). Moreover, information on that subject is still
deficient. For instance, research on the interactions between tourism development and water consumption is null
in the Salar de Uyuni basin. Thus, the only two important elements that have a clear competing use and relatively
consistent information were the water consumption of the mining sector and crop irrigation.
As mentioned earlier, not only lithium has become a strategic element with promising source of income
for the Salar de Uyuni basin, but also the fresh water resources converted into crop irrigation. The future lithium
mining plant will use freshwater from the San Geronimo river which could alternatively be utilized for quinoa
irrigation (Figure 11). In fact, the Water Ministry of Bolivia has a quinoa irrigation project for the same municipally
of Kolcha “K12
. Although the name of the river is not mentioned in the report, the approximate area that the
project might benefit is 612 Hectares of quinoa crops.13
Because there is no hydrologic study conducted on Rio San Geronimo, the hydrologic balance provided by
Knigth Piesodl will represent a rough and overestimated approximation. Given that Rio Jaukihua and Rio San
Geronimo have the same precipitation patterns and hydro-geologic formation system; it will be assumed in this
study that both rivers have similar hydrologic balance14
.
Table 5: An approximation of Rio San Geronimo Hydrologic balance with and without lithium plant
Source: adapted from Molina Carpio (2007)
Notice in Table 5 that the expected water consumption of the future Lithium Plant from Rio San Geronimo
is more than the overestimated recharge. Moreover, if a 20-year lithium mining operation takes place, Rio San
Geronimo would require at least 21 years to be suitable for water extraction again15
. Therefore, following Molina
Carpio (2007), Chauffaut(1998) and Messerli et al., (1997) conclusions, water consumption for lithium mining and
12
Irrigation Project “COLLCHA K”; featured by Water Ministry of Bolivia (Ministerio de Agua de Bolivia) and PROAGRO/GTZ at
http://www.riegobolivia.org/proyectos.html?accion=edit&id=11
13 According to Table 7 and irrigation Project “COLLCHA K”
14 With the exception of Rio Toldo recharge which In fact is not part of Rio San Geronimo basin (Figure 11).
15 6204*360*20(m3) / 5800*360 (m3 / year)
Table Aproximation of Rio San Geronimo Hydrologic balance
Without
Project Daily
m3/day
With Project
Daily
m3/day
Recharge
Rio San Geronimo (ephemeral) 200 200
NA
Upstream Volcanic-sediments and low yield springs 4,200 4,200
Precipitation 1,400 1,400
Total recharge 5,800 5,800
Pumped water(*) - 6,204
Discharge into the Altiplano 6,048 -
(*) average of the expected freshwater consumption from table 2
crop irrigation cannot take place at the same time and long recovery period should take place after extensive
extraction periods.16
The competing project of providing irrigation for quinoa harvesting could increase the income of local
peasants and the exporting sales of quinoa. Like private investment in extensive lithium mining can generate
important tax revenues flows for the local government and many other multiplicative economic effects in the
whole economy (COMIBOL, 2009; Ebensperger et al., 2005; Ellingson et al. 2006; Evans, 2008; Tahil, 2007; MIR,
2008); a medium scale water irrigation project for quinoa crops can also generate positive effects for the local and
national economy (CAMEX, 2009; Crespo, et.al, 2004; JICA, 2002; Molina Carpio, 2007; Soraide et al., 2005;
Victorio, 1999). A study conducted by ESMAP, UNDP, and the World Bank in the study region (explicated in Crespo,
et.al, 2004) concluded that water irrigation increased the quinoa yield by almost 180% in average. Therefore, if
quinoa production is expanded it could be assumed: 1) local quinoa Farmer gross income will grow, 2) domestic
supply, exporting and national sales will increase, and 3) domestic and exporting sales taxes will rise (Soraide et al.,
2005; JICA, 2002).
In the arid Salar de Uyuni basin, the fresh water use from the San Geronimo River creates two mutually
exclusive projects (i.e. lithium mining and quinoa crop with irrigation) generating different net gains to the
economy of the region and the country. In order to estimate the gains and losses from the economy as a whole,
cash flows for each project were constructed for alternatives economic actors. The lithium mining project
considered two economic actors: a private mining company and the government. Likewise, the quinoa irrigation
project considered a) the quinoa famers, b) private quinoa industry accounting for domestic and export sales, and
c) the government. The estimation of the cash flows for those economic actors facilitated to approximate the
benefits and costs of each project (Harbeger & Jenkins, 2003). This study assumed that if any of the projects is
conducted, the net benefits of the not-chosen project will constitute a lower-bound opportunity cost of
undertaking the chosen project.
The cash flow model for the lithium mining considered a 23-year timeframe, from 2009 to 2031, two
years of construction followed by 20 years of operations and 1 year of closure. Because the lithium extraction,
where the concentration is the highest, will last approximately 20 years, and much of the information was
estimated in early 2008, that time frame was chosen. Due to the intense competitive rivalry, much of the technical
information regarding operating costs and investments is considered proprietary by the current industry players,
and thus they are unwilling to share such information. However, COMIBOL provide the author fairly disaggregate
estimates of operating costs, construction investments and technical background for the future lithium mining
plant. For instance, Table 6 shows that the total investment in the project will be around US$ 273.5 million. The
16
For this ecosystem, extensive extraction means when the water extraction reaches 15-25% of the total spring
recharge.
production recognized three main exporting products: Lithium Carbonate, Potassium Chloride, and Boric Acid.
Additionally, the cash flow model for the private company considered 100% equity for the initial investments,
annual real prices growth of outputs and inputs, and most of the initial investment period. On the other hand, the
cash flow model for the government considered 35% of income taxes and 4.5 % of exporting fees plus royalties
estimated by COMIBOL schemes and current regulations.
Table 6. Estimated investment Costs of the Lithium mining Project
Source: adapted from COMIBOL (2009).
Unlike the lithium mining project, the irrigation project has an initial investment incurred by government
to build the irrigation infrastructure. That is, the government would have to incur in a long term loan of US$ 1.54
million at 11% of annual interest rate for 10 years. Yet, this project will benefit 161 households and 612 hectares of
quinoa crops (Water Ministry of Bolivia, 2008). The irrigation land size was computed based on distribution of
cultivable land and resting/rotation crops summarized in Table7 which is a common sustainable harvesting practice
in the Salar de Uyuni basin. The timeframe for this project is certainty larger, but for comparison reasons, the
author 23-year time frame was assumed. However, it could be argued that the irrigation project could be
replicated infinite number of times in the future. Thus, the net present value of the entire project was computed
using an infinite formula to account for this assumption. 17
17
NPV∞23=NPV23* (1+r)23 / [(1+r)23 - 1] from Harbeger & Jenkins, 2003. Ch.5 Pag.8.
Table Estimated Investment Capital for the Lithium mining Project (*)
Investment Closure
Th.US$ Th.US$
A. Materials & Equipment
Lithium Carbonate Plant and Equipment 58,240
Potassium Chloride Plant and Equipment 45,873
Boric Acid Plant and Equipment 41,481
Solar Evaporation Ponds 16,330
Service Facilities(water supply systems, Electrical power (10 MW) plant ,etc) 11,648
Buildings (Office, maintenance, water-house, laboratory, medical, wharehouse, communications, etc) 6,765
Storage Facilities (input materials, by-products, inventories and finished outputs) 9,710
B. Machinery
Production wells and brine delivery System 10,537
Trucks and loading machines 5,080
Harvesting and pumping Equipment(pipelines, pumps, etc) 9,167
C. Labor Construction
Wages for construction, engineering and consultants 13,361
D. Working Capital Construction
Importing and freight Tariffs 25,580
Working Capital 10,185
E. Other expenses Construction
Start up expenses (hiring, marketing, legal representation, multiple stakeholders meetings) 3,654
Land and transaction costs (municipal, government, etc) 1,797
Other infraestructure costs and contingencies 4,111
F. Mining closure and other expenses 2,800
Total project Investment 273,519 2,800
(*) Calculations based in 2009 nominal prices and bi-annual labor wages
Table 7. Cultivable and resting land
Source: Crespo, et.al (2001).
Because of the unavailability of detailed information, costs were taken from the previously mentioned
quinoa harvesting studies. The total cash flows model of the irrigation project for each economic actor was
estimated by the incremental change of the net benefits/costs with and without the irrigation project. In other
words, the quinoa famers, the private quinoa industry and the government cash flows with the irrigation project
were subtracted from the cash flows without the project. Subsequently, adding the three net benefits represented
an overall estimate of the possible benefits of the San Geronimo River irrigation project on quinoa harvesting and
production. Table 8. Estimated investment Costs for Quinoa Farmers per cycle
Source: adapted from Crespo, et.al (2001) and JICA (2002).
Table Land size per Household in Nor Lipez province
Available land per household (Ha) % Families
Total property
01 to 10 39.5%
11 to 20 46.5%
21 to 30 11.5%
31 to 40 2.5%
Cultivable Land
2.1 to 3 12.0%
3.1 to 4 40.5%
4.1 to 5 37.5%
5.1 to 6 7.5%
6.1 to 7 2.5%
Resting Land
05 to 10 87.8%
15 to 20 9.7%
25 to 30 2.5%
Table Estimated Investment Costs for Quinoa Farmers2002 2009
US$ per Ha US$ per Ha
A. Labor
Soil preparation and Seeding 54 78
Harvest 41 59
Cultural ritual work 17 24 -
B. Input materials -
Tools and materials 16 24
Seeds 5 7
Guano - 5 tons (transportation included) 38 55
No Irrigation assuming 100 m3 per cycle (*) 85 123
Other services inputs 11 16
Irrigation assuming 100 m3 per cycle 13 19 -
C. Machinery and transportation -
Crawler service fee 18 26
Harvest transportation 8 11
Threshing 13 18 -
D. Other expenses -
Organic Certification 16 22
Total Investment Costs 333 483 (*) water is get from water trucks 3 times per cycle, thus it including trasportation and fuel costs
Both project cash flow models were used to evaluate how the benefits/costs and the net present values
(NPV) may evolve under various circumstances, by developing possible scenarios of how each of the main
parameters may change over time. Hence, sensitivity analysis was conducted on input and output prices each of
the projects. This analysis was done using both discounted benefits and costs for each model. Analysis with the
model was restricted to fluctuations of real prices of the inputs and outputs (e.g. Lithium Carbonate prices,
farmers’ quinoa price, yield efficiency), and the discount factor to compute the NPV. With @RISK and Excel any
uncertain parameter situation was modeled using Monte Carlo Simulation with 10,000 iterations. The output of
the model was interpreted in the RESULTS section of this document.
In the lithium mining project, the following rates of growth were considered variable each year: Price of
Lithium Carbonate, Potassium Chloride and Boric Acid; Wage of Production Workers, Costs of Operating Materials,
Supplies and Fuels, Administrative and General Expenses, and Working capital . Because information is not
available to fit an appropriate distribution for any of those variables, a Pert Distribution will be assumed to
simulate the rates of growth per year. 18
For example, the real rate of growth for miners wages was expected to
follow a Pert Distribution with a minimum and maximum of -1% and 9%, respectively. The mean was set equal
5.9% which is the average percentage change of real wages in the bolivian mining sector from 1996-2008(INE,
2009 and UDAPE, 2009). 19
Figure 13. Cumulative ascending probabilities: a) % Rate of growth of miners’ wages (left plot) b) expected
Discount Rates (right plot)
Source: own plots based on UDAPE (2009) and Lopez, H. (2008).
18 The PERT distribution uses the most likely value (mode) and it is designed to generate a distribution that more closely
resembles realistic probability distribution by providing minimum and maximum values. Depending on the values provided, the
PERT distribution can provide a close fit to the normal or lognormal distributions. The shape parameter is calculated from the
defined most likely value. 19 Assuming that the mean % change of wages is a good estimator of the real level of miners wages from 1996-2008. Appendix
G shows the cumulative ascending probabilities for the rest of variables used in the cash flow model for the lithium mining
project.
-0.0
2
0.0
0
0.0
2
0.0
4
0.0
6
0.0
8
0.1
0
0.0
6
0.0
7
0.0
8
0.0
9
0.1
0
0.1
1
0.1
2
0.1
3
0.1
4
0.1
5
Figure 13 displays the cumulative ascending probabilities where y-axis shows the probability of a value
less than any x-axis value. Thus, the probability that the discount rate will be less than 9.5% is 0.4.
The annual percentage rates of growth of inputs and output prices, quinoa yield efficiency, and the
discount rate were considered variables in the quinoa irrigation project for every year. All of these parameters
were assumed to follow a Pert Distribution according to the reports provided on quinoa agriculture and production
(CAMEX, 2009; Crespo, et.al, 2004; JICA, 2002; Soraide et al., 2005; Victorio, 1999). For instance, the annual rate of
change of the price paid to the farmer was assumed to have a mean of 4%, a maximum of 15% and minimum of -
5% (Crespo, et.al, 2004; Soraide et al., 2005). Figure 14 illustrates the expected annual rates of change of the price
paid to the farmer per ton of quinoa, and Figure 15 the annual quinoa yield with and without the irrigation
project.20
Figure 14. Cumulative ascending probabilities of %Rate of growth Farmers and export prices to USA market
Source: own plots based on CAMEX (2009), Crespo, et.al(2004) and Soraide et al.(2005).
Figure 15. Cumulative ascending probabilities of Quinoa yield efficiency with/without irrigation (Tons/Ha)
Source: own plots based on Crespo, et.al (2004) and Soraide et al.(2005).
20
Appendix H shows the cumulative ascending probabilities for the rest of variables used in the cash flow model
for irrigation project.
-0.0
2
0.0
0
0.0
2
0.0
4
0.0
6
0.0
8
0.1
0
0.1
2
0.1
4
5.0% 90.0% 5.0%
0.4471 0.7001
0.0
0.2
0.4
0.6
0.8
1.0
Quinoa yield with no irrigation project (ton/Ha)
Pert(0.35,0.59,0.76)
Minimum 0.3500
Maximum 0.7600
Mean 0.5783
Std Dev 0.0770
5.0% 90.0% 5.0%
1.081 1.907
0.0
0.2
0.4
0.6
0.8
1.0
Quinoa yield with irrigation project (ton/ Ha)
Pert(0.76,1.55,2.1)
Minimum 0.7600
Maximum 2.1000
Mean 1.5100
Std Dev 0.2514
In the absence of any inductive technique research to value irrigation water, the output of the quinoa
irrigation model is a computation of NPV of a simple farm crop cost and return budget of net return for every
household crop per year, which assumes a specific input mix and product yield (Young, 2005).
PART IV- RESULTS
IV.1 Initial Scenario
a ) Lithium Mining Project
Based on technical information about lithium mining operations, costs and timing, the author was able to
construct cash flows for the 23-year project from a private company perspective. The economic, financial, and
technical parameters were the starting point to obtain the annual operating plan and the cash flows of the mining
plant. The initial parameters used for the model are shown in the Appendix A and detailed information on the
annual operating plan is presented in the Appendix B. The initial results of the cash flow model demonstrated that
lithium mining is a profitable activity. The cash flow profile of the project with the initial conditions can be seen in
Figure 16 where the NPV of the Net Cash Flows (NCF) was 421,585 (Th. US$), consistent to the cash flow model
profile of the mining project. Similarly, the discounted cash in and cash out flows to equity for the 23-year Lithium
Mining Project are presented in Figure 16.
Figure 16. Estimated Cash Flows to Equity (Th. US$)
The declining shape of the discounted cash flows was expected due the effects of the chosen at 10%
discount rate per year for this phase. Because the NCF were positive from first year of operations (year 2) until the
mine closure, the initial investment payback period approximately occurred in the fifth year.
-
50,000
100,000
150,000
200,000
250,000
300,000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Discounted CASH IN at 10% Discounted CASH OUT at 10% Total CASH IN Total CASH OUT
The cash model used here assumes 1% increase in LCE real price per year, while wages and general
expenses were assumed to rise 5.9% and 3.3%, respectively (INE, 2009 and UDAPE, 2009)21
. As it can be seen in
Figure 17, the majority of the operating costs derived from operating materials, supplies, fuels22
and labor. In
average, 63% of the operating expenses were materials, supplies including fuels and transportation, while labor
represented 10%.
Figure 17. Estimated Operating Costs Break Down(Th. US$)
In terms of average sales, LCE represented nearly 60% and Potassium Chloride 38% of total sales during
the 23-year project. The obvious result at sales is the direct dependence on market prices. Though the private
lithium mining is going to assume the capital cost and risk of the project, it will still generate positive NPV.
Additionally, the cash model calculated the income and exporting taxes incurred by the private company.
The cash flow model for the government perspective was based on 35% of income taxes and 4.5% of exporting
taxes and royalties (COMIBOL, 2009). Figure 18 provides initial estimates of income and exporting taxes that the
government could receive during mining operations.23
21
Labor from the mineral and private sector. Assuming that the mean % change of wages from 1996-2008 is good
estimators for both sectors. 22
Maintenance materials, chemicals, flotation reagents, mobile equipment supplies, etc; transportation expenses
(fuels, loading, rail, trucks). 23 Refer to Appendix C and D for detailed information on how income and exporting taxes were calculated.
$-
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
A. Operating Materials,
Supplies and Fuels
B. Labor C. Working Capital
Operating (Cash)
D. Other expenses
Operating
Exporting Expenses
Transaction costs and Insurance
Operations (Freight, transportation and
packing) and Wages
General Administrative Expenses
Operating and Maintenance Labor
Other Production costs and contingencies
Transportation Costs (loading, rail, trucks, etc)
Maintenance Materials
Fuels (plants, power generation and utilities)
Operating supplies and Materials
Figure 18. Estimated Taxes revenues flows for the government (Th. US$)
It is clear that the lithium mining project could provide an important inflow of tax-income to Bolivia,
ultimately representing the potential benefits that the Salar de Uyuni basin might provide if lithium (and
derivatives) is extract. According to the initial results of this model, the NPV of the tax-revenues was 394,662 (Th.
US$) at 10% discount rate.
b ) Quinoa Irrigation Project
Today 161 households, located 20 Km west of the Rio San Geronimo (Figure 11), harvest 612 hectares of
quinoa crops each year without irrigation (Project “COLLCHA K”; Water Ministry of Bolivia, 2008; PROAGRO/GTZ). In
order to evaluate the irrigation project net present benefits, the total cash flows model of the irrigation project
was estimated by the incremental change of the cash flows with and without the project for: a) quinoa famers, b)
private quinoa industry, and c) government. Those cash flows were added to obtain the possible estimated
benefits that the Salar de Uyuni basin provided.
First, a set of parameters were extracted based on the literature found on quinoa harvesting and past
irrigation projects conducted in the same region. The initial set of parameters for the cash flow models are
described in Appendix E. Second, a detail spreadsheet of annual quinoa harvesting, production, costs, consumption
and sales were developed to imitate an operating plan for the next 23-years (Appendix F). Third, quinoa famers
and private quinoa industry cash flows were estimated based on historic patterns on domestic and exporting sales,
real prices, taxes, and cost structures. The results from each economic actor cash flows were computed separately.
-
9,000
18,000
27,000
36,000
45,000
54,000
63,000
72,000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
Discounted Taxes-Income at 10% Total Taxes
Since the quinoa harvesting in the Salar de Uyuni basin is mainly focused on the domestic and
international markets24
, both quinoa farmers and exporting industry directly benefit from this project. Focusing
only inside the quinoa irrigation project scope, Figure 19 illustrates that the project incremental NCF is positive for
farmers and quinoa production industry perspective, but negative for the government during the loan payment
period, followed by positive NCF afterwards.
Figure 19. Estimated Incremental Net Cash flows from each perspective (Th. US$)
Farmers direct input costs decreased (i.e water) nearly 40% because they would not need to buy water
from water tankers anymore. Also, quinoa farmers and industry estimated sales increased by 120%. This was
expected, and is consistent with assumption of increasing crop yield as a result of the irrigation project. On the
other hand, the government domestic sales/income taxes (14%) and exporting fees (1.5%) were offset by the loan
amortization payments during the first 10 years.
The initial model estimated that the 23-year quinoa irrigation project would have a NPV of 9,459(Th. US$).
Yet, assuming that the project can repeated many times in the future, the estimated NPV is now 10,784 (Th. US$)
at 10% discount rate. The Salar de Uyuni basin economy development would definitely increase with the project,
specially the quinoa farmers.
IV.2 Preliminary project selection
Based on the preliminary results discuss above, the lithium mining project had greater benefits in present
US$ values for the Salar de Uyuni basin. Even after subtracting the opportunity cost of not conducting the quinoa
irrigation project, NPV is still enormous (383,878 Th. US$) relative the economy of the study area. From the
24
Crespo et.al (2001) and Soraide et.al (2005) reported that approximately 63% (average) of the quinoa harvested in the Salar
de Uyuni Basin is exported to USA , Europe, Peru and Ecuador each year. While domestic and farmer consumption represent
27% and 18%, respectively.
(600)
(300)
-
300
600
900
1,200
1,500
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Net Cash Flow to Quinoa Farmers Net Cash Flow to Private Quinoa Production Sector
Net Cash Flow to Government
perspective of the Salar de Uyuni basin economy, the Lithium mining would have to be developed despite
foregoing benefits of the quinoa irrigation project.
It is not difficult to choose the lithium mining project based on the economic concepts presented here,
however, the preliminary model has a set of fixed assumptions that certainty will change over time and the value
of both projects. Thus, sensitivity analysis was conducted on the parameters of the model to compare the results
for both projects. Those results are described in the next section.
IV.3 Sensitivity Analysis
a ) Lithium Mining Project
The sensitivity analysis performed on the cash flow model tried to reduce the uncertainty of the real
prices growth rates in the future, as well as the gross benefits estimation of the project from both perspectives.
The mean expected NPV from the private equity perspective was 691,180 (Th.US$) and St. Dev of 180,553(Th.US$),
whereas the government’s expected NPV mean was 560,344(Th.US$) and St. Dev of 109,315 (Th.US$). Only the
latter perspective constituted the gross benefits provided by the Salar de Uyuni basin after 23-year of lithium
mining. Figure 20 and 21 show the frequency distribution of the NPV from both perspectives after the model was
run 10,000 times.
Figure 20. NPV frequency distribution- Lithium Mining Company (MM US$)
20
0
40
0
60
0
80
0
10
00
12
00
14
00
16
00
Figure 21. NPV tax-revenues of Government frequency distribution- (MM US$)
Obviously, the expected NPV from both perspectives are linked because of the income and sales taxes
scheme. The government tax revenues will increase if the lithium mining sales are not offset by the operating costs
or debt, which according to the model, it will never happen because the expected NPV is never negative.
Not all the parameters equally impacted the cash flow model and it is important to determine which
parameters had greater impact on NPV estimates. The regression of the NPV estimates against the parameters of
the model provides that information. Figure 22 illustrates the parameters that had impacted the NPV estimates of
the lithium mining benefits using either a multivariate stepwise regression analysis, or a rank order correlation
analysis.25
Figure 22. Coefficient estimates of NPV vs. model parameters
25
The excel Add-in @Risk automatically performed both analysis on the output variables and their associated
inputs in the model. The coefficients are ranked by their impact on the output using Multivariate Stepwise
Regression and Rank Order Correlation. Refer to Appendix G for more detail on the coefficient ranking procedure.
20
0
40
0
60
0
80
0
10
00
12
00
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
Although the Price of Lithium Carbonate (annual rate of growth) positively impacted the NPV estimates in
every year, the highest impacts occurred during the first year. After that, the impact gradually declined over the
years. This tendency was determined by the discount factor which has an obvious impact on the expected NPV
estimates. Figure 22 and 23 confirmed that well known fact. Approximately, real discount rates between 7 to 8%
tend to place weight in both first and second halves of the project’s cash flows declining over time estimating
higher NPV. On the contrary, real discount rates greater than 8% estimated lower NPV because more weight was
placed on the first half of the project life.
Figure 23. Discount rate vs. Expected NPV
b ) Quinoa Irrigation Project
In examining the sensitivity analysis performed on the incremental cash flow model for the 23-year
quinoa irrigation project, it is important to understand the estimated NPV for each perspective. The expected NPV
summary statistics of each perspective are presented in Table 9.
Table 9. Estimated Incremental NPV- Quinoa Irrigation Project
The estimated expected NPV for the 23-year quinoa irrigation project is 8,824 (Th.US$) and standart
deviation of 5,943 (Th.US$). However, assuming that the project can repeated many times in the future, the
infinite estimated NPV represents the estimated benefits that the Salar de Uyuni would provide if the quinoa
irrigation project is chosen.
Figure 24. Frequency distribution NPV (Th. US$) of Infinite 23-year Quinoa Irrigation Projects
Given the cash flow and the sensitivity analysis provided good estimates for future costs and benefits of
the quinoa irrigation project, there is 90% probability that the expected NPV of the benefits of quinoa irrigation
Project over the long run will be between -1,105 and 21,971 thousands US$.
As can be seen in Table 9, the expected NPV of the irrigation project which directly benefits the Salar de
Uyuni basin is similar as the previously computed. Yet, the variability of this figure is outstanding and resulted from
the nature of agriculture. Negative NPV may occur primarily caused to the intrinsic dependent on natural process
and random weather patterns. For this model, that uncertainty was introduced by the yearly quinoa yield
Expected Incremental NPV (Th. US$)
Mean Median Std Dev Min Max
Farmer NPV 6,109 6,002 1,706 1,523 13,300
Industry NPV 3,528 3,552 4,223 (9,460) 19,503
Government NPV (644) (644) 222 (1,324) 208
NPV Salar de Uyuni 23-year -Irrigation project 8,824 8,799 5,943 (8,593) 29,502
NPV Salar de Uyuni -Infinite Irrigation projects 10,183 9,989 6,989 (10,114) 36,564
-15
00
0
-40
00
70
00
18
00
0
29
00
0
40
00
0
efficiency. Furthermore, the farmer’s NPV was never negative probably reflecting that no matter how low the yield
return was, farmers will always sell quinoa to supply some part of the local market and own consumption(food
security). However, low yield efficiencies directly impact the well being of 161 families. That is, if the “median”
and “min” scenario (Table 9) were assumed to happen, the average daily income would drop from $ 1.0 to $ 0.2
per person.26
The quinoa industry not only relies on the yield return, but also on the domestic and exporting prices.
Industry negative expected NPV revealed that if low yield efficiency occurred, not enough supply would negatively
affect the industry performance, established by 2 to 4 operating companies in the region (Soraide et.al., 2005).
Based on the previously discussed, the most significant drivers of quinoa irrigation project are captured
on the regression coefficients of the NPV vs. model parameters.
Figure 25. Coefficient estimates of expected NPV vs. model parameters
Figure 25 confirms that the most important drivers are quinoa yield efficiency, discount rate, inputs
materials, farmer and exporting prices rates of change. Thus, if any substantial cost reduction in the quinoa
production chain is to be achieved, it must be accomplished by a decrease in tools, materials, guano and seeds at
the farmer’s level (input materials). Also, the quinoa agriculture performance is very important for the whole
economy in every year.
26
An average of 201 households and 3.5.people per household were assumed in the Salar de Uyuni Basin in 23-year project
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
IV.4 Project selection
The cash flows model used in this study provided an approximation of the benefits that each project
would provide after reducing the uncertainty. The project with the higher NPV (after the sensitivity analysis) was
the lithium mining; however, the estimated foregone benefits of the irrigation project should be considered.
Figure 26. Density distribution of NPV for both projects (MM US$) –
Figure 26 displays that the distribution of the estimated NPV for both projects in thousands of US$ will
never overlap. In other words, the minimum possible NPV of the lithium mining will never exceed the NPV of the
quinoa irrigation project.
-20
0 0
20
0
40
0
60
0
80
0
10
00
12
00
Summary Statistics of the NPV for both projects in (Th. US$)
Mean Median Std Dev Min Max
Lithium Mining 560,344 550,471 109,316 232,021 1,149,360
Quinoa Irrigation project 10,183 9,989 6,989 (10,114) 36,564
PART V- CONCLUSIONS AND DISCUSSION
Since humans are an integral part of ecosystems, the ecosystem services are the benefits people obtain
from ecosystems (Daily, 1997). This study identified and described five ecosystems services in the Salar de Uyuni
basin: 1) Recreation, Culture and landscape; 2) Water resources, 3) Biodiversity, and 4) Agriculture and Animal
Husbandry. Today, all of them are very important for local people subsistence and well-being on a daily basis,
whereas minerals could be the key for regional economic development in the future (e.g. Improvements on
education and health systems, road infrastructure). Primarily, the Salar de Uyuni basin is an exceptional yet hostile
environment due to the extreme shortage of water resources.
The constituents of well-being are situation-dependent reflected on local geography, culture and
ecological circumstances (Daily, 1997; Pagiola et.al, 2004). Also, well-being is at the opposite end of a continuum
from poverty, which has been defined as “a pronounced deprivation of well-being” (Millennium Ecosystem
Assessment, 2005). Human well-being in the Salar de Uyuni has multiple components including basic material for
living, health, social relations and food security; however, their well-being is also affected by poverty, globalization,
trade, market, legal and policy framework. In any case, people in the study area are heavily dependent on the
services provided by this ecosystem, especially, water resources.
Other characteristics identified in this study are the high degree of social complexity (high levels of
poverty and legitimate demands from the indigenous peoples, low concentration of government development
initiatives, possible large-scale mining, rapid growing tourism industry); and lack of scientific information for better
understanding of the relationships with regard to the services this ecosystem provides. Unfortunately, the author
faced a constant information challenge throughout this research.
Moreover, there are a perceptible conflicts between users of the ecosystem —particularly the local
people, tourism operators and mining companies— firstly over water availability and secondly over equality of
access to the emerging economic opportunities. Again, without scientific knowledge it is impossible to identify the
magnitude of the threads and determine the right courses of actions to minimize those conflicts. Water and its
competing uses should be recognized and estimated as an economic good, so it could be managed more efficiently
and use more equitable(Young, 2005). Therefore, if we allow capacity of ecosystems to provide these services to
deteriorate without knowing it, sooner or later the Salar de Uyuni’s people may suffer.
From this primary assessment, apparently, the only feature in the Salar de Uyuni basin that has the world
talking today is the lithium resources.
Lithium carbonate is the key property in today’s battery technology of choice – lithium-ion. If the
transportation sector plans to be moving away from oil based transport and towards hybrid and electric vehicles,
lithium supply is the key factor (Evans, 2008; Tahil, 2007; Ebensperger et al., 2005; Zuleta, 2009). While the Salar
de Uyuni in Bolivia holds the largest single source of lithium in the world, the accuracy of the statement has been
challenged, nonetheless, the point remains that the resource is huge (Evans, 2008 ; Ebensperger et al.,2005; Tahil,
2007; Zuleta, 2009). Risacher (1991) revised the reserve estimate for the upper salt for the whole salar, covering
nearly 7,000 thousand hectares, and calculated 8.9 millions tones of Li in a brine with an average grade of 542
mg/lt (0.045% Li) 27
. Also, Risacher made a separate reserve calculation for the delta of the Rio Grande (240 km2)
with grades that excesses 1,000 mg/lt.
Bolivia’s national mining department, (COMIBOL) has been given the responsibility to bring Uyuni to
production of LCE plant within the next three years (private or state own capital investment). As with many highly
valued resources, lithium mining will bring a trade off with the environment in Salar de Uyuni which has a highly
sensitive biodiversity due to the arid nature of the climate (Hurlbert, 1979; Messerli, et al., 1997; Molina Carpio,
2007). Yet, no government or private agency has considered the economic trade-offs of altering the natural
environment of the salar (Curi, 2009; Lopez Canelas, 2009; Zuleta, 2009). In particular, no study has assessed the
magnitude of differences between probable benefits of the lithium extraction and the foregone benefits of the
next best use of freshwater resources from Rio San Geronimo (i.e. quinoa agriculture).
Not only mining lithium has become a strategic element with promising source of income for the Salar de
Uyuni basin, but also the fresh water resources converted into quinoa crop irrigation. This study built a cash flow
model for both competing uses of fresh water from the Rio San Geronimo, to estimate the gains and losses from
the economy as a whole derived from implementing one of the projects. Based on the relatively simple cash flow
model presented here, the lithium mining project had greater benefits in present US$ values for the Salar de Uyuni
Basin. Even after subtracting the opportunity cost of not conducting the quinoa irrigation project and reducing the
uncertainty of the model parameters, NPV is still positive and large relative the economy of the study area.
Nevertheless, the cash flow model and parameters presented in this study are just a crude illustration of
the extremely complex social and economic interactions of an economic system. In one hand, this study brings
together known figures reported in most cases during the early 2000 and in other cases up to 2007. On the other
hand, the estimates of this simple model may be bias associated with tax distortions, cost of capital, exchange
rates, social discount rates or other market distortions. Also, the model relies heavily on prices changes from year
to year which are even more uncertain if projected more than 15 years from now.
Until recently, lithium went primarily into ceramics and glass. Now batteries make up one-fifth of the
world’s end-use market for the mineral. But shortages could stop an emerging industry or dramatically reshape it
27
Tahil(2007), Ebensperger et al.(2005) and Evans(2008) and others commentators on lithium reserves and resources including
the United States Geological Survey (USGS) have quoted the reserves estimates in the Salar de Uyuni as 5.5 million tonnes of
lithium (Li) in a brine grading 423 mg/lt (0.035%).
within a decade. Evans (2008) and US Geological Survey offer a more conservative estimate, forecasting that
demand will begin to drive lithium prices up in the next 10 to 15 years.
Higher lithium prices could also give the nascent U.S. battery industry a steeper climb to the top. The U.S.
consumes more lithium than any other country, despite having only 760,000 tons of the world’s 13.8 million tons
of identified lithium resources (those of known quantity, quality and grade), according to the U.S. Geological
Survey. While most U.S. lithium imports now come from Chile and Argentina, China has brought new supply online
in the last few years. Additionally, it could be argue that the larger the production Bolivia throws into the market,
the larger the reduction in lithium prices that Lithium mining company will have to face as a result.
Finally, it is important to highlight that the reserves of lithium alone will not define the future of industrial
projects, nor the economic development in the region and the country, what matters is a timely and fine plan to
participate in foreign markets as soon as possible. However, the distributional effects of both projects have to be
carefully assessed according to the ecosystem services profile and the cash flow model presented in this master
project.
PART VI- REFERENCES
Abuelsamid S. (July 6, 2009). Renault-Nissan and EDF to start 100 EV field test in Paris in 2010. Better Place.
Retrieved July 18, 2009, from www.betterplace.com/news/
Arroyo, Kalin M. T., Squeo, E A., Armesto,J. J., and Villagran, C.1988.Effects of aridity on plant diversity in the
northern Chilean Andes: Results of a natural experiment. Ann. Missouri Botanical Gardens, 75: 55-78.
Bishop, R. Bingham, G. & et al. 1995. Issues in ecosystem valuation: improving information for decision making.
Ecological Economics Journal. 14, 73-90.
Cámara de Exportadores de Bolivia-CAMEX(Bolivian Exporting Bureau). 2009. Cuadernos Sectoriales: La Quinua.
La Paz- Bolivia. Retrieved July 29, 2009 from www.camexbolivia.com
Champ, Patricia A. Boyle, Kevin J. Brown, Thomas C.(2003). A primer on Nonmarket Valuation. The economics of
Non-Market Goods and Resources. Kluwer Academic Publishers. 2003 Edition. Chapter 12, pp 445-500.
COMIBOL-Corporación Minera de Bolivia (National Mining Department) 2008. COMIBOL Annual Report. Retrieved
July 6, 2009, from www.comibol.gov.bo/comibol.html
COMIBOL-Corporación Minera de Bolivia (National Mining Department) and Dirección Nacional de Recursos
Evaporíticos. 2009. Press Releases
:www.evaporiticosbolivia.org/indexi.php?Modulo=NotasPrensa&Opcion=LstGeneral and personal
communication and e-mail exchanges May-July, 2009.
Conservation International.2007.Tropical Andes. Biodiversity Hotspots. Retrieved July 25, 2009, from
http://www.biodiversityhotspots.org/xp/hotspots/andes/Pages/default.aspx
Crespo, F., E. Brenes, and K. Madrigal. 2001. El cluster de la quinua en Bolivia: Diagnóstico competitivo y
recomendaciones estratégicas (The Quinoa Cluster in Bolivia: A Competitive Diagnostic and Strategic
recommendations). Proyecto Andino de Competitividad-CAF. INCAE. La Paz, Bolivia.
Curi, Marianela, head director of Sustainable Forest Management Project in Bolivia, BOLFOR. The Nature
Conservancy. Personal communication and various e-mail exchanges July-August, 2009.
Daily, Gretchen C. 1997. Nature's services : societal dependence on natural ecosystems. Washington, DC: Island
Press. ISBN: 1559634758.
Ebensperger, Arlene. Maxwell, Philip. Moscoso, Christian. 2005. The lithium industry: Its recent evolution and
future prospects. Australian School of Mines, Curtin University of Technology. Perth, WA 6845, Australia.
Eduardo Avaroa Reserve, Bolivia (REA). Characteristics of REA National Park .Accessed Jun 29, 2009, from
www.boliviarea.com/index.php?option=com_content&task=view&id=5&Itemid=6
Ellingson, Lindsey. Seidl, Andrew. 2006. Comparative analysis of non market valuation techniques for the Eduardo
Avaroa Reserve, Bolivia. Ecological Economics.
Escobari, Jorge. 2003. Problemática Ambiental en Bolivia (Environmental Challenges in Bolivia). Unidad de Análisis
de Políticas Sociales y Económicas-UDAPE (Unit of Policy, Social and Economic Analysis) La Paz Bolivia.
Retrieved May 8, 2009, from www.udape.gov.bo
Evans, R. Keith.2008. An Abundance of Lithium. Retrieved May 10, 2009, from
www.worldlithium.com/An_Abundance_of_Lithium_1_files/An%20Abundance%20of%20Lithium.pdf
Fricker, H. Borsa, A. Minster, B. Carabajal, C. Quinn, K. Bills, B. 2005. Assessment of ICES at performance at the
Salar de Uyuni, Bolivia. Geophysical Journal International. VOL. 32, L21S06.
Friedman-Rudovsky, Jean. 2009. For Lithium Car Batteries, Bolivia Is in the Driver's Seat. Time Magazine.
Galbraith K. February 17, 2009. Obama Signs Stimulus Packed With Clean Energy Provisions. The New York Times.
Garrett and Martin Laborde. 1983. Salting Out Process for Lithium Recovery. Sixth International Symposium on
Salt, Salt Institute. Vol. II.
Gartner J. July 7, 2009. Lithium China Gearing Up for EV Dominance. Reuters.
Garzón, Dionisio J. April 23, 2009. Minería, sueños y realidades (Mining, dreams and realities). La Razon Editorial
(independent Bolivian Information Agency).
Harbeger, Arnold. Jenkins, Glenn. Cost-Benefit Analysis of Investment Decisions. University of Chicago. 2003
Edition. Chapters 3-10.
Hurlbert, Stuart. Keith, James O. 1979. Distribution and Spatial Patterning of Flamingos in the Andean Altiplano.
University of California Press on behalf of the American Ornithologists' Union. Vol. 96, No. 2, pp. 328-342.
Instituto Nacional de Estadística- Bolivia (INE- National Statistic Institute)-Unidad de Análisis De Políticas Sociales y
Económicas (UDAPE-Unit of Policy, Social and Economic Analysis). Potosí: Estadísticas e Indicadores de
Pobreza según Sección Municipal, 200(Poverty indices). Cuadro Nº 3.06.02.08.
Instituto Nacional de Estadística- Bolivia- INE (National Statistic Institute). 2009. GDP per economic sector annual
report.
Instituto Nacional de Estadística- Bolivia-INE, Banco Central de Bolivia-BCB y Viceministerio de Turismo de Bolivia
-VMT. (2009). Encuesta Gasto del Turismo Receptor y Emisor: 2007(Survey on the Receptor and Emisor
Tourism Spending in Bolivia: 2007)
Jacobsen, S.-E. and A. Mujica. 2001. Avances en el conocimiento de resistencia a factores abióticos adversos en la
quinua (Chenopodium quinoa Willd).. Memorias, Primer Taller Internacional sobre Quinua –Recursos
Geneticos y Sistemas de Producción, 10–14 May 1999.Universidad Nacional Agraria La Molina, Lima, Peru.
Jacobsen, S.-E. and Z. Portillo Edition
Jennifer Rietbergen-McCracken and Hussein Abaza.2000. Environmental valuation: a worldwide compendium of
case studies. London : Earthscan. # ISBN: 1853836958
Japan International Cooperation Agency (JICA-Agencia de Cooperación Internacional del Japón).2002.Proyecto de
mejoramiento del proceso industrial y comercialización de Quinua (Improvements on the Industrial and
Commercialization processes of Quinoa). MAGER Publishers.
La Razón (Jun 14, 2009). Economic Supplement. Retrieved Aug 9, 2009, from
http://www.la-razon.com/versiones/20090808_006813/nota_257_858225.htm
Lawrence U. January 17, 2009. Lithium-Ion Batteries Could Become Cheaper. The New York Times.
Liberman, M. 1995. Los Bosques de Polylepis Tarapacana en el Parque Nacional del Nevado Sajama(Polylepis
Tarapacana Forests in the Sajama National Park). II International Symposium on Sustainable Mountain
Development. Field guide, Bolivia, pp.61-69.
Lopez Canelas, Elizabeth.2009. Bolivian Environmental Defense League (FOBOMADE). Personal communication
via E-mail.
Lopez, Humberto.2008. The Social Discount Rate: Estimates for Nine Latin American Countries. The World Bank
Latin America and the Caribbean Region Offce of the Chief Economist.
Mc Mahon, G., Evia, J.L., et. al. 1999. An Environmental Study of Artisanal, Small, and Medium Mining in Bolivia,
Chile, and Peru. Technical Paper N° 429, World Bank. Retrieved from www.naturalresources.org.
Millennium Ecosystem Assessment. 2005. Concepts of Ecosystem Value and Valuation Approaches. Island Press.
Chapter 6.
Meridian International Research (MIR). 2008. The Trouble with Lithium 2: Under the Microscope.
www.meridian-intres.com/Projects/Lithium_Microscope.pdf
Messerli, Bruno. Grosjean, Martin, Vuille, Mathias. 1997. Water Availability, Protected Areas, and Natural
Resources in the Andean Desert Altiplano. Mountain Research and Development. Vol. 17, No. 3, The
United Nations University. Managing Fragile Ecosystems in the Andes, pp. 229-238. Published by:
International Mountain Society Stable.
Molina Carpio, Jorge. 2007. Agua recurso hídrico en el Sudoeste de Potosí (Water hydric resource in the south-
east of Potosi). Comité para la Gestión Integral del Agua en Bolivia Coordinación General: Centro de
Estudios Superiores Universitarios Universidad Mayor de San Simón - CESU UMSS. Publish by Foro
Boliviano sobre Medio Ambiente y Desarrollo-FOBOMADE.
New7Wonders of Nature. 2009. Nominees: South America. Accessed Jul 12, 2009, from
www.new7wonders.com/nature/en/nominees/southamerica/c/SalardeUyuniLake/
Oliveira Costa, J .P. 1993. Programa Integrado de Conservación Ambiental y Desarrollo Sustentable de la
Cordillera de los Andes(Environmental Conservation and Sustainable Development in the Andes
Mountains). Anexo 2. Instituto Universitario de Conservación Nacional -IUCN, La Paz, Bolivia, pp. 44-48.
Pagiola, S., Konrad Von Ritter, Bishop, J. 2004. Assessing the Economic Value of Ecosystem Conservation. The
World Bank Environment Department. Environment Department Paper No.101.
Pavlovic-Zuvic, P. & et al. 1983. Recovery of Potassium Chloride, Potassium Sulfate and
Boric Acid from the Salar de Atacama Brines. Sixth International Symposium on Salt, Salt Institute. Vol. II.
Piers Nicholson and Keith Evans. 1998. Evaluating New Directions for the Lithium Market. Journal of Minerals
Economics.
Quinoa Real is a producers / retailers / distributors. Information extracted from webpage at
www.quinoareal.com.br.
RAMSAR , The Convention on Wetlands.(2009). http://www.ramsar.org/index_about_ramsar.htm
Risache, François and Fritz, Bertrand. 1991. Quaternary geochemical evolution of the salars of Uyuni and Coipasa,
Central Altiplano, Bolivia. Chemical Geology. Vol 90, 211-231.
Roskill Information Services. 2007. Retrieved Jun 8, 2009, from http://www.roskill.com/reports/lithium
Selvaradjou, S-K., L. Montanarella, O. Spaargaren and D. Dent. 2005. European Digital Archive of Soil Maps
(EuDASM) - Soil Maps of Latin America and Caribbean Islands. EUR 21822 EN. Office of the Official
Publications of the European Comunities. Luxembourg.
Soquimich-SQM. 2008. Annual report. Retrieved May 28 , 2009, from
http://www.sqm.com/aspx/Lithium/Default.aspx
Soraide, David. Carvajal, Mirko.Claver Mamani, Pedro. Choque Marca, Willy. 2005. Estudio Linea Base 2001-2004,
Programa Quinua Altiplano Sur( Base Research 2001-2004, Southern Highlands Quinoa Program)
Fundacion Autapo-Educacion para el Desarrollo.
Tahil, William. 2007. The Trouble with Lithium Implications of Future PHEV Production for Lithium Demand.
Meridian International Research.
Torres Henriquez, Jorge, Ramirez, Maria Virginia. 2006. Gestión del Conocimiento en SQM Salar(Information
Management in SQM Salar). Tesis para optar al Grado de Magister en Gestion y Direccion de Empresas
(Thesis for Master's Degree in Business Management and Administration).Universidad de Chile. Facultad
de Ciencias Fisicas Y Matematicas. Departamento de Ingenieria Industrial.
Unidad de Análisis de Políticas Sociales y Económicas-UDAPE (Unit of Policy, Social and Economic Analysis). 2005.
Estructura del Sector Turismo En Bolivia.La Paz- Bolivia.
US Geological Survey.2008. Mineral Commodities Summary, Accessed May 23, 2009 from Duke University
Libraries.
http://minerals.usgs.gov/minerals/pubs/commodity/lithium/mcs-2008-lithi.pdf
Victorio, Giusti. 1999. Mejoramiento de las Tecnologías Tradicionales de Poscosecha de Quinua en el Altiplano
Boliviano (Improvements on the Traditional technology of Quinoa harvesting in the Bolivian Highlands).
Proyecto FAO-Poscosecha. La Paz- Bolivia.
Viñagrande, Honorato. 2001. La Maldición De La Riqueza y Las Ansias Desatadas por El Litio Boliviano( The
Resources Curse and the anxiety caused by the Bolivian Lithium). García y Morales, Enzarzados a Causa De
La Revuelta Indígena Capital Madrid. Monitor de Latinoamérica.
Warren, M. 2009. AUTOS: Volt Electric Cars Start Pre-Production. SPEEDtv. Retrieved Jul 1, 2009, from
www.automotive.speedtv.com/article/autos-volt-electric-cars-start-pre-production/
Water Ministry of Bolivia (Ministerio de Agua de Bolivia). 2008. Proyecto: Riego Colcha K( Colcha K Irrigation
Project). PROAGRO/GTZ. Retrieved Sept 1, 2009, from www.riegobolivia.org/proyectos
World Resources Institute. 2005. Watersheds of the World 2005. Earth Trends Data Tables: Water Resources and
freshwater Ecosystems.
Young, Robert A. Determining the economic value of water: concepts and methods. Washington, DC : Resources
for the Future, c2005. ISBN: 189185397X (hardcover : alk. paper).
Zuleta Calderon Juan Carlos. Ph.D. Independent lithium economics analyst based in Bolivia. Various e-mail
exchanges June-July, 2009.
PART VII- APPENDIX
APPENDIX A: Economic, technical, financial and tax parameters during annual operations
Source: COMIBOL, 2009; Ebensperger et al., 2005; Tahil, 2007; MIR, 2008; Mc Mahon et al., 1999; UDAPE, 2009;
INE 2009; Zuleta, 2009.
Table of parameters assumed during annual operations
A. Economic B. Technical
Prices (*) Output (***)
Initial Price of Lithium Carbonate (US$ per ton) 5,302 Initial Lithium Carbonate Production (tons) 23,000
Initial Price of Potassium Chloride (US$ per ton) 754 Initial Potassium Chloride Production (tons) 102,000
Initial Price of Boric Acid (US$ per ton) 576 Initial Boric Acid Production (tons) 6,500
Annual growth rates (**) Growth Rate of Lithium Carbonate Production (%) 1.0%
Growth Rate of Potassium Chloride Production (%) 1.0%
Price of Lithium Carbonate (%) 1.0% Growth Rate of Boric Acid Production (%) 1.0%
Price of Potassium Chloride (%) 1.0%
Price of Boric Acid (%) 1.0% End of the year Inventories and materials in process (% output)
Lithium Carbonate 15%
Wage of Production Workers( %) 5.9% Potassium Chloride 17%
Price of Operating Materials, Supplies and Fuels (%) 4.7% Boric Acid 10%
Administrative and General Expenses (%) 3.3%
Working capital (%) 3.7%
C. Financial D. Taxes
Accounts Receivable (% of Sales) Income Tax Rate (%) 35.0%
Lithium Carbonate 20% Exporting fees and other taxes (%) 4.5%
Potassium Chloride 8%
Boric Acid 5% (***) Following 2000-2008 SQM production trend and Technical information from (COMIBOL, 2009)
Accounts Payable (% of Total Direct Labor and Operating Materials&Fuels ) 10%
Depreciation Rates (%)
Plants, Ponds , Buildings and Storage 10%
Machinery, trucks, pumping equipment and other infraestructure 15%
Discount rate private investment 10.0%
Discount rate Government 10.0%
(*) Average nominal Prices (FOB) from SQM (2008) and Roskill (2008)
(**) Expected prices growth ; Average annual change in Consumer Price Index of Bolivia and growth of real wages from 1997-2008 INE&UDAPE (2009)
1
AP
PE
ND
IX B
: Li
thiu
m M
inin
g E
stim
ate
d O
pe
rati
ng
Pla
n
So
urc
e:
CO
MIB
OL,
20
09
; E
be
nsp
erg
er
et
al.
, 2
00
5;
Ha
rbe
ge
r &
Je
nki
ns,
20
03
; Z
ule
ta,
20
09
.
Ta
ble
Est
ima
ted
An
nu
al P
rod
uct
ion
01
23
45
67
89
10
111
21
314
151
61
718
19
2021
22
2009
201
020
112
012
2013
201
420
152
016
2017
201
820
192
020
2021
2022
202
320
24
2025
2026
202
720
2820
29
203
02
031
A.
Ou
tpu
t Q
uan
titi
es
and
inve
nto
rie
s (t
on
s)
Pro
du
ctio
n o
f Li
thiu
m C
arb
on
ate
23
,000
23,2
30
23
,462
23,6
97
23
,93
4
2
4,1
73
24
,41
5
2
4,6
59
24
,90
6
2
5,1
55
25
,40
6
25
,66
0
2
5,9
17
26,1
76
26
,43
8
26
,702
2
6,9
69
27
,23
9
27
,511
27,
787
Pro
du
ctio
n o
f P
ota
ssiu
m C
hlo
rid
e
102
,000
10
3,0
20
104
,050
10
5,0
91
106
,14
2
107,
203
1
08,2
75
10
9,3
58
110
,45
1
111,
556
1
12,6
71
11
3,7
98
114,
936
116
,08
6
11
7,2
46
118
,419
119,
603
1
20,7
99
1
22,0
07
123,
227
Pro
du
ctio
n o
f B
ori
c A
cid
6,
500
6,5
65
6,
631
6,6
97
6,
764
6,8
32
6,
900
6,9
69
7,
039
7,1
09
7,
180
7,2
52
7,
324
7,39
8
7,4
72
7,
546
7,6
22
7,6
98
7,
775
7,
853
Inv
en
tori
es
of
Lith
ium
Ca
rbo
nat
e
3,45
0
3
,485
3,51
9
3
,555
3,59
0
3,
626
3,66
2
3,
699
3,73
6
3,
773
3,81
1
3
,84
9
3,8
88
3,
926
3
,96
6
4,00
5
4,
045
4
,08
6
4,12
7
4,16
8
Inv
en
tori
es
of
Po
tass
ium
Ch
lori
de
17
,340
17,5
13
17
,689
17,8
65
18
,04
4
1
8,2
25
18
,40
7
1
8,5
91
18
,77
7
1
8,9
65
19
,15
4
19
,34
6
1
9,5
39
19,7
35
19
,93
2
20
,131
2
0,3
33
20
,53
6
20
,741
20,
949
Inv
en
tori
es
of
Bo
ric
Aci
d
650
657
663
670
676
683
690
697
704
711
718
725
732
74
0
747
755
7
62
77
0
77
7
78
5
Vo
lum
en
of
Sale
s Li
thiu
m C
arb
on
ate
19
,550
19,7
46
19
,943
20,1
42
20
,34
4
2
0,5
47
20
,75
3
2
0,9
60
21
,17
0
2
1,3
82
21
,59
5
21
,81
1
2
2,0
29
22,2
50
22
,47
2
22
,697
2
2,9
24
23
,15
3
23
,385
23,
619
Vo
lum
en
of
Sale
s P
ota
ssiu
m C
hlo
rid
e
84,6
60
85
,507
86,3
62
87
,225
88,0
98
88,
979
89,8
68
90,
767
91,6
75
92,
591
93,5
17
94,4
52
95,
397
96
,35
1
97,3
14
98,2
88
99,
271
100
,26
3
101
,266
10
2,27
9
Vo
lum
en
of
Sale
s B
ori
c A
cid
5,
850
5,9
09
5,
968
6,0
27
6,
088
6,1
48
6,
210
6,2
72
6,
335
6,3
98
6,
462
6,5
27
6,
592
6,65
8
6,7
24
6,
792
6,8
60
6,9
28
6,
997
7,
067
B.
Ou
tpu
t P
rice
s
Pri
ce in
de
x f
or
Lith
ium
Ca
rbo
nat
e1
1.0
11.
021.
03
1.04
1.0
51.
061.
07
1.08
1.0
91.
101.
12
1.1
31
.14
1.1
51
.16
1.1
71.
181.
20
1.2
11.
221.
231.
24
Pri
ce in
de
x f
orP
ota
ssiu
m C
hlo
rid
e
11.
01
1.02
1.0
31.
041.
05
1.06
1.0
71.
081.
09
1.10
1.1
21
.13
1.1
41.
15
1.1
61
.17
1.18
1.2
01
.21
1.22
1.23
1.2
4
Pri
ce in
de
x f
orB
ori
c A
cid
1
1.0
11.
021.
03
1.04
1.0
51.
061.
07
1.08
1.0
91.
101.
12
1.1
31
.14
1.1
51
.16
1.1
71.
181.
20
1.2
11.
221.
231.
24
Pri
ce f
or
Lith
ium
Ca
rbo
nat
e (
US
$ p
er
ton
)5
,355
5,46
3
5
,517
5,57
2
5
,628
5,68
4
5,
741
5,79
9
5,
857
5,91
5
5,
974
6,03
4
6
,09
5
6,1
55
6,
217
6
,27
9
6,34
2
6,
405
6
,46
9
6,53
4
6,59
9
6
,665
Pri
ce f
or
Po
tass
ium
Ch
lori
de
(U
S$
pe
r to
n)
762
77
7
7
85
79
3
8
01
80
9
8
17
82
5
8
33
84
2
8
50
85
8
86
7
8
76
884
89
3
90
2
911
920
930
939
9
48
Pri
ce f
or
Bo
ric
Aci
d (
US
$ p
er
ton
)5
82
593
599
605
611
618
624
630
636
643
649
656
662
669
67
5
682
689
6
96
70
3
71
0
71
7
724
C.
Inp
ut
Ind
ex
Pri
ces
Wag
e o
f P
rod
uct
ion
Wo
rke
rs1
1.0
61.
121.
19
1.26
1.3
31.
411.
49
1.58
1.6
81.
771.
88
1.9
92
.11
2.2
32
.36
2.5
02.
652.
81
2.9
73.
153.
333.
53
Pri
ce o
f O
pe
rati
ng
Ma
teri
als,
Su
pp
lie
s a
nd
Fu
els
11.
05
1.10
1.1
51.
201.
26
1.32
1.3
81.
441.
51
1.58
1.6
61
.74
1.8
21.
90
1.9
92
.09
2.18
2.2
92
.39
2.51
2.62
2.7
5
Ad
min
istr
ativ
e a
nd
Ge
ne
ral
Exp
en
ses
11.
03
1.07
1.1
01.
141.
18
1.22
1.2
61.
301.
34
1.38
1.4
31
.48
1.5
31.
58
1.6
31
.68
1.74
1.7
91
.85
1.91
1.98
2.0
4
Wo
rkin
g c
apit
al1
1.0
41.
081.
12
1.16
1.2
01.
241.
29
1.34
1.3
91.
441.
49
1.5
51
.60
1.6
61
.72
1.7
91.
851.
92
1.9
92.
072.
142.
22
D.
De
pre
ciat
ion
in U
S$ p
er
year
Pla
nts
, Po
nd
s ,
Bu
ild
ings
an
d S
tora
ge
19,0
05
17
,104
15,3
94
13
,854
12,4
69
11,
222
10,1
00
9,0
90
8,
181
7,3
63
6,
627
5,9
64
5,
367
4,83
1
4,3
48
3,
913
3,5
22
3,1
69
2,
853
2,
567
Bo
ok
val
ue
of
Pla
nts
, Po
nd
s , B
uil
din
gs
and
Sto
rag
e
171
,043
15
3,9
38
138
,544
12
4,6
90
112
,22
1
100,
999
90
,89
9
8
1,8
09
73
,62
8
6
6,2
65
59
,63
9
53
,67
5
4
8,3
07
43,4
77
39
,12
9
35
,216
3
1,6
95
28
,52
5
25
,673
23,
105
Ma
chin
ery
, tr
uck
s, p
um
pin
g e
qu
ipm
en
t an
d o
the
r in
frae
stru
ctu
re3,
718
7,6
91
6,
537
5,5
57
4,
723
4,0
15
3,
412
2,9
01
2,
466
2,0
96
1,
781
1,5
14
1,
287
1,09
4
930
790
6
72
57
1
48
5
41
3
Bo
ok
val
ue
of
Mac
hin
ery
, tru
cks,
pu
mp
ing
eq
uip
me
nt
51,2
72
43
,582
37,0
44
31
,488
26,7
64
22,
750
19,3
37
16,
437
13,9
71
11,
876
10,0
94
8,5
80
7,
293
6,19
9
5,2
69
4,
479
3,8
07
3,2
36
2,
751
2,
338
Tab
le E
stim
ate
d A
nn
ual
Op
era
tio
n C
ost
s in
Th
. U
S$
01
23
45
67
89
10
111
21
314
151
61
718
19
2021
A. O
pe
rati
ng
Mat
eri
als,
Su
pp
lie
s an
d F
ue
ls
Op
era
tin
g su
pp
lie
s an
d M
ate
rial
s (c
he
mic
als
, fl
ota
tio
n r
ea
ge
nts
, m
ob
ile
eq
uip
me
nt
su
pp
lie
s, e
tc)
4,8
53
5,32
0
5
,570
5,83
2
6
,106
6,39
3
6,
694
7,00
8
7,
338
7,68
3
8,
044
8,42
2
8
,81
7
9,2
32
9,
666
10
,12
0
10
,596
1
1,0
94
11
,61
5
12
,161
12,
733
Fue
ls (
pla
nts
, po
we
r ge
ne
rati
on
an
d u
tili
tie
s)1
0,9
63
12
,017
12,5
82
13
,174
13,7
93
14
,44
1
1
5,1
20
15
,83
0
1
6,5
75
17
,35
4
1
8,1
69
19
,02
3
19
,91
7
2
0,8
53
21,8
33
22
,86
0
23
,934
2
5,0
59
26
,23
7
27
,470
28,
761
Ma
inte
nan
ce M
ate
rial
s3,
197
3,
505
3,6
70
3,
842
4,0
23
4,
212
4,4
10
4,
617
4,8
34
5,
061
5,2
99
5,
548
5,8
09
6,
082
6,36
8
6,6
67
6,
981
7,3
09
7,6
52
8,
012
8,
389
Tra
nsp
ort
ati
on
Co
sts
(lo
adin
g, r
ail,
tru
cks,
etc
)1
1,1
91
12
,268
12,8
44
13
,448
14,0
80
14
,74
2
1
5,4
35
16
,16
0
1
6,9
20
17
,71
5
1
8,5
48
19
,41
9
20
,33
2
2
1,2
88
22,2
88
23
,33
6
24
,433
2
5,5
81
26
,78
3
28
,042
29,
360
Oth
er
Pro
du
ctio
n c
ost
s an
d c
on
tin
gen
cie
s1,
564
1,
715
1,7
96
1,
880
1,9
68
2,
061
2,1
58
2,
259
2,3
65
2,
476
2,5
93
2,
715
2,8
42
2,
976
3,11
6
3,2
62
3,
416
3,5
76
3,7
44
3,
920
4,
104
B. L
ab
or
Op
era
tin
g a
nd
Mai
nte
nan
ce L
abo
r2,
855
3,
202
3,3
91
3,
591
3,8
02
4,
027
4,2
64
4,
516
4,7
82
5,
065
5,3
63
5,
680
6,0
15
6,
370
6,74
6
7,1
44
7,
565
8,0
12
8,4
84
8,
985
9,
515
Ge
ne
ral
Ad
min
istr
ati
ve E
xp
en
ses
874
933
964
996
1,0
29
1,
062
1,0
98
1,
134
1,1
71
1,
210
1,2
50
1,
291
1,3
34
1,
378
1,42
3
1,4
70
1,
519
1,5
69
1,6
20
1,
674
1,
729
C. W
ork
ing
Cap
ital
Op
era
tin
g (
Ca
sh)
Op
era
tio
ns
(Fre
igh
t, t
ran
spo
rtat
ion
an
d p
ack
ing
) a
nd
Wag
es
7,4
23
7,98
2
8
,277
8,58
4
8
,901
9,23
1
9,
572
9,92
6
1
0,2
94
10
,67
5
1
1,0
70
11
,47
9
11
,90
4
1
2,3
44
12,8
01
13
,27
5
13
,766
1
4,2
75
14
,80
3
15
,351
15,
919
Tra
nsa
ctio
n c
ost
s an
d In
sura
nce
4,6
82
5,03
5
5
,221
5,41
4
5
,615
5,82
2
6,
038
6,26
1
6,
493
6,73
3
6,
982
7,24
1
7
,50
9
7,7
86
8,
074
8
,37
3
8,68
3
9,
004
9
,33
7
9,68
3
10,
041
D. O
the
r e
xp
en
ses
Op
era
tin
g
Exp
ort
ing
Exp
en
ses
994
1,06
8
1
,108
1,14
9
1
,191
1,23
5
1,
281
1,32
9
1,
378
1,42
9
1,
482
1,53
6
1
,59
3
1,6
52
1,
713
1
,77
7
1,84
2
1,
911
1
,98
1
2,05
5
2,13
1
Esti
mat
ed
An
nu
al O
pe
rati
on
Co
sts
-
5
3,0
46
55
,42
4
5
7,9
10
60
,50
9
6
3,2
27
66
,06
9
6
9,0
42
72
,15
0
7
5,4
00
78
,79
9
8
2,3
54
86
,07
3
89
,96
1
94
,02
9
98
,28
3
10
2,7
34
10
7,3
89
11
2,2
58
11
7,3
52
12
2,6
81
Initial Costs measured in nominal prices
550 work force both employees and eventual workers. Based on Comibol bi-annual projections.
Ponds function 365 days but plant operates 300 days
AP
PE
ND
IX C
: In
com
e S
tate
me
nt
Lith
ium
Min
ing
Pro
ject
So
urc
e:
CO
MIB
OL,
20
09
; E
be
nsp
erg
er
et
al.
, 2
00
5;
Ha
rbe
ge
r &
Je
nki
ns,
20
03
.
T
able
Co
st o
f G
oo
ds
Sold
of
Lit
hiu
m C
arb
on
ate
(Th
. US$
)
01
23
45
67
89
10
11
12
13
14
15
16
17
18
19
20
21
22
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
20
31
Ave
rage
Dir
ect
Co
st o
f P
rod
uct
ion
(U
S$
per
to
n)
1,0
05
1
,13
1
1,1
38
1
,15
1
1,1
70
1,1
92
1
,21
9
1,2
50
1
,28
5
1,3
22
1,3
63
1,4
07
1,4
53
1,5
02
1,5
54
1,6
09
1,6
66
1,7
26
1,7
89
1,8
54
Co
mp
uta
tio
n o
f C
os
t o
f Li
thiu
m C
arb
on
ate
So
ld
Bo
ok
Va
lue
of
Inv
ento
ries
at
Beg
inn
ing
of
Yea
r-
3,4
68
3
,94
1
4,0
06
4
,09
2
4
,19
9
4,3
23
4
,46
6
4,6
24
4
,79
9
4
,98
9
5
,19
4
5
,41
4
5
,64
9
5
,89
9
6
,16
4
6
,44
4
6
,74
0
7
,05
3
7
,38
2
Dir
ect
Lab
or
Co
st1
,72
9
1,8
31
1
,93
9
2,0
53
2
,17
4
2
,30
3
2,4
39
2
,58
3
2,7
35
2
,89
6
3
,06
7
3
,24
8
3
,44
0
3
,64
3
3
,85
8
4
,08
5
4
,32
6
4
,58
1
4
,85
2
5
,13
8
Dir
ect
Ma
teri
als
Exp
ens
e2
1,1
12
22
,11
9
2
3,1
75
2
4,2
81
25
,44
0
26
,65
5
2
7,9
29
29
,26
4
3
0,6
63
32
,12
9
33
,66
6
35
,27
6
36
,96
5
38
,73
4
40
,58
9
42
,53
4
44
,57
2
4
6,7
08
4
8,9
48
51
,29
6
Dir
ect
Ma
chin
ery
De
pre
cia
tio
n2
,00
7
4,1
53
3
,53
0
3,0
01
2
,55
0
2
,16
8
1,8
43
1
,56
6
1,3
31
1
,13
2
9
62
8
18
6
95
5
91
5
02
4
27
3
63
30
8
2
62
22
3
Les
s B
oo
k V
alu
e o
f In
ven
tori
es
at
En
d o
f Y
ear
(3,4
68
)
(3
,94
1)
(4,0
06
)
(4
,09
2)
(4,1
99
)
(4
,32
3)
(4,4
66
)
(4,6
24
)
(4,7
99
)
(4,9
89
)
(5
,19
4)
(5,4
14
)
(5
,64
9)
(5,8
99
)
(6
,16
4)
(6
,44
4)
(6
,74
0)
(7,0
53
)
(7
,38
2)
(7,7
28
)
Co
st o
f L
ith
ium
Ca
rbo
na
te S
old
21
,38
0
2
7,6
30
28
,57
9
29
,24
8
3
0,0
59
3
1,0
01
32
,06
8
3
3,2
54
34
,55
4
3
5,9
67
3
7,4
90
3
9,1
22
4
0,8
65
4
2,7
18
4
4,6
84
4
6,7
65
4
8,9
65
51
,28
6
53
,73
3
5
6,3
10
-
Tab
le C
om
pu
tati
on
of
Sa
les
and
ex
po
rtin
g ta
xe
s fo
r So
ld o
f Li
thiu
m C
arb
on
ate
(Th
. US
$)
01
23
45
67
89
10
11
12
13
14
15
16
17
18
19
20
21
22
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
20
31
Gro
ss
Sa
les
bef
ore
ta
xes
10
6,7
95
1
08
,94
2
11
1,1
31
1
13
,36
5
11
5,6
44
1
17
,96
8
12
0,3
39
12
2,7
58
12
5,2
25
12
7,7
42
1
30
,31
0
13
2,9
29
13
5,6
01
13
8,3
27
14
1,1
07
14
3,9
43
14
6,8
37
1
49
,78
8
15
2,7
99
1
55
,87
0
Exp
ort
ing
fee
s a
nd
oth
er
tax
es
4,8
06
4
,90
2
5,0
01
5
,10
1
5,2
04
5,3
09
5
,41
5
5,5
24
5
,63
5
5,7
48
5,8
64
5,9
82
6,1
02
6,2
25
6,3
50
6,4
77
6,6
08
6,7
40
6,8
76
7,0
14
Ne
t Sa
les
10
1,9
89
10
4,0
39
10
6,1
30
10
8,2
64
11
0,4
40
11
2,6
59
11
4,9
24
11
7,2
34
11
9,5
90
12
1,9
94
12
4,4
46
12
6,9
48
12
9,4
99
13
2,1
02
13
4,7
57
13
7,4
66
14
0,2
29
14
3,0
48
14
5,9
23
14
8,8
56
0
Tab
le T
ota
l Op
era
tin
g C
ost
s o
f Li
thiu
m C
arb
on
ate
(T
h. U
S$)
01
23
45
67
89
10
11
12
13
14
15
16
17
18
19
20
21
22
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
20
31
Co
sts C
ost
of
Go
od
s So
ld2
1,3
80
27
,63
0
2
8,5
79
2
9,2
48
30
,05
9
31
,00
1
3
2,0
68
33
,25
4
3
4,5
54
35
,96
7
37
,49
0
39
,12
2
40
,86
5
42
,71
8
44
,68
4
46
,76
5
48
,96
5
5
1,2
86
5
3,7
33
56
,31
0
Inte
rest
Exp
ens
e
Ad
min
istr
ati
on
an
d G
ener
al
Exp
en
ses
50
4
5
20
53
8
5
55
57
4
5
93
61
2
63
2
65
3
67
5
6
97
7
20
7
44
7
68
7
94
8
20
8
47
87
5
9
04
93
4
De
pre
cia
tio
n o
n P
lan
ts,
Po
nd
s , B
uil
din
gs a
nd
Sto
rage
1
0,2
63
9,2
36
8
,31
3
7,4
81
6
,73
3
6
,06
0
5,4
54
4
,90
9
4,4
18
3
,97
6
3
,57
8
3
,22
0
2
,89
8
2
,60
9
2
,34
8
2
,11
3
1
,90
2
1
,71
2
1
,54
0
1
,38
6
Tota
l C
ost
32
,14
73
7,3
87
37
,42
93
7,2
85
37
,36
63
7,6
54
38
,13
43
8,7
95
39
,62
54
0,6
17
41
,76
54
3,0
63
44
,50
74
6,0
95
47
,82
64
9,6
98
51
,71
45
3,8
72
56
,17
75
8,6
30
0
Tab
le N
et
Inco
me
of
Lit
hiu
m C
arb
on
ate
(Th
. US$
)
01
23
45
67
89
10
11
12
13
14
15
16
17
18
19
20
21
22
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
20
31
Gro
ss
Inco
me
be
fore
ta
xes
69
,84
3
6
6,6
52
68
,70
1
70
,97
9
7
3,0
74
7
5,0
06
76
,79
0
7
8,4
39
79
,96
5
8
1,3
77
8
2,6
81
8
3,8
85
8
4,9
92
8
6,0
07
8
6,9
32
8
7,7
68
8
8,5
15
89
,17
5
89
,74
6
9
0,2
26
Inco
me
ta
xes
24
,44
5
2
3,3
28
24
,04
5
24
,84
2
2
5,5
76
2
6,2
52
26
,87
6
2
7,4
54
27
,98
8
2
8,4
82
2
8,9
38
2
9,3
60
2
9,7
47
3
0,1
02
3
0,4
26
3
0,7
19
3
0,9
80
31
,21
1
31
,41
1
3
1,5
79
Ne
t In
com
e a
fte
r ta
xes
45
,39
84
3,3
24
44
,65
64
6,1
36
47
,49
84
8,7
54
49
,91
35
0,9
86
51
,97
75
2,8
95
53
,74
35
4,5
25
55
,24
55
5,9
05
56
,50
65
7,0
49
57
,53
55
7,9
64
58
,33
55
8,6
47
0
AP
PE
ND
IX D
: E
stim
ate
d T
ota
l C
ash
Flo
ws
to E
qu
ity
–Li
thiu
m M
inin
g P
roje
ct
So
urc
e:
Eb
en
spe
rge
r e
t a
l.,
20
05
; H
arb
eg
er
& J
en
kin
s, 2
00
3.
T
ab
le T
ota
l C
ash
Flo
w t
o E
qu
ity
(T
h. U
S$)
01
23
45
67
89
10
11
12
13
14
15
16
17
18
19
20
21
22
20
09
20
10
20
11
20
12
20
13
20
14
20
15
20
16
20
17
20
18
20
19
20
20
20
21
20
22
20
23
20
24
20
25
20
26
20
27
20
28
20
29
20
30
20
31
A.
CA
SH
IN
Sa
les
-
-
17
6,0
62
1
79
,60
0
18
3,2
10
1
86
,89
3
19
0,6
49
1
94
,48
1
19
8,3
91
20
2,3
78
20
6,4
46
2
10
,59
6
21
4,8
29
2
19
,14
7
22
3,5
51
2
28
,04
5
23
2,6
29
2
37
,30
4
24
2,0
74
2
46
,94
0
25
1,9
03
25
6,9
67
-
Less
Ex
po
rtin
g f
ee
s a
nd
oth
er
tax
es
-
-
(7,9
23
)
(8,0
82
)
(8
,24
4)
(8,4
10
)
(8,5
79
)
(8
,75
2)
(8
,92
8)
(9,1
07
)
(9,2
90
)
(9,4
77
)
(9
,66
7)
(9,8
62
)
(1
0,0
60
)
(10
,26
2)
(1
0,4
68
)
(10
,67
9)
(1
0,8
93
)
(11
,11
2)
(1
1,3
36
)
(1
1,5
63
)
-
Ch
an
ge i
n A
cco
un
ts R
ec
eiv
ab
le-
-
(2
7,3
17
)
(5
49
)
(5
60
)
(5
71
)
(5
83
)
(5
95
)
(6
07
)
(61
9)
(6
31
)
(6
44
)
(65
7)
(6
70
)
(68
3)
(6
97
)
(71
1)
(7
25
)
(74
0)
(7
55
)
(77
0)
(7
86
)
-
Go
ve
rnm
en
t In
tere
st
Sub
sid
y-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Loa
ns
Pro
cee
ds
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
As
set
Liq
uid
ati
on
s-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
La
nd
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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APPENDIX E: –Initial paramentes for Quinoa Irrigation Project
Source: CAMEX, 2009; Crespo et al (2001); Soraide et.al, (2005); Water Ministry of Bolivia, 2008.
Table Amounts and Consumption of Conventional Quinoa in 2002 ( No irrigation)
Th.Tons Th. US $
Domestic Consumption 17.5 9400
Peasants Families 14.2 7,400
Urban market 3.3 2000
Exports
USA and Europe 1.8 2,700
Peru and Ecuador(unofficial) 2.8 2,400
Gross production of Quinua Bolivia 22.1 14,500
Gross production of Quinua Salar de Uyuni Basin 13.49 8,848
Crespo et al., 2001 & Soraide et.al, 2005.
Table Real prices and annual growth of Quinua (Average 2003 prices)
Lower Upper
Nominal prices (US$ per Ton)
Agricultor 441 559
Domestic market 650 750
Peru and Ecuador(FOB -official) 800 1200
USA market (FOB) 1200 1600
Average Annual Growth (%)
Agricultor 3.5%
Domestic market 2.5%
USA market 3.5%
Peru and Ecuador(official) 2.8%
Crespo et al., 2001 & Soraide et.al, 2005.
Table Distribution of the Harvested Quinoa at the Salar de Uyuni Basin
Lower Upper
Domestic Consumption
Peasants Families 16% 17%
Urban market 21% 28%
Exports
USA and Europe 29% 29%
Peru and Ecuador(official) 34% 35%
Crespo et al., 2001 & Soraide et.al, 2005.
Table Average Quinoa yield (Tons per hectare)
Lower Upper
Quinoa yield with no irrigation project 0.5 0.58
Quinoa yield with irrigation project 1.4 1.625
Total crops near San Geronimo( Ha) 0.3 0.612
APPENDIX F: Estimated Operating Plan–Quinoa Irrigation Project
Source: CAMEX, 2009; Crespo et al ( 2001); Soraide et.al, (2005); Water Ministry of Bolivia, 2008.
Table Quinoa Estimated Annual Production, Consumption and prices
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031
A. Quinoa Harvesting and Consumption (Th. tons)
I. Total Quinua Harvesting without irrigation 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355 0.355
Domestic Consumption
Peasants Families 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06Urban market 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07
Exports
USA and Europe 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10
Peru and Ecuador 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12
II. Total Quinua Harvesting with irrigation 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99
Domestic Consumption
Peasants Families 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08Urban market 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28 0.28
Exports
USA and Europe 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29 0.29
Peru and Ecuador 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.35
B. Quinoa Prices and indexes
I. Quinoa Price indexes
Domestic Consumption
Peasants Families 1 1.04 1.09 1.13 1.18 1.23 1.28 1.33 1.39 1.45 1.51 1.57 1.64 1.71 1.78 1.85 1.93 2.01 2.10 2.19 2.28 2.37 2.47
Urban market 1 1.06 1.11 1.17 1.24 1.31 1.38 1.45 1.53 1.62 1.71 1.80 1.90 2.01 2.12 2.23 2.36 2.48 2.62 2.77 2.92 3.08 3.25
Exporting Quinoa Price indexes
USA and Europe 1 1.07 1.13 1.21 1.29 1.37 1.46 1.55 1.65 1.76 1.88 2.00 2.13 2.27 2.41 2.57 2.74 2.92 3.11 3.31 3.52 3.75 4.00 Peru and Ecuador 1 1.06 1.13 1.20 1.27 1.35 1.43 1.52 1.62 1.72 1.82 1.94 2.06 2.19 2.32 2.47 2.62 2.78 2.95 3.14 3.33 3.54 3.76
II. Domestic Consumption Quinoa Prices (US$ per Ton)Peasants Families 850 885 923 961 1,002 1,044 1,088 1,133 1,181 1,230 1,282 1,336 1,392 1,451 1,511 1,575 1,641 1,710 1,782 1,857 1,935 2,016 2,101 Urban market 1,190 1,255 1,324 1,397 1,474 1,555 1,641 1,731 1,826 1,927 2,033 2,144 2,262 2,387 2,518 2,657 2,803 2,957 3,120 3,291 3,472 3,663 3,865
Exporting Quinoa Prices (US$ per Ton)USA and Europe 2,380 2,535 2,699 2,875 3,062 3,261 3,473 3,698 3,939 4,195 4,468 4,758 5,067 5,397 5,747 6,121 6,519 6,943 7,394 7,874 8,386 8,931 9,512 Peru and Ecuador 1,700 1,805 1,917 2,036 2,162 2,297 2,439 2,590 2,751 2,921 3,102 3,295 3,499 3,716 3,946 4,191 4,451 4,727 5,020 5,331 5,662 6,013 6,385
C. Input Index Prices
Labor 1 1.05 1.11 1.17 1.23 1.30 1.37 1.45 1.52 1.61 1.69 1.78 1.88 1.98 2.09 2.20 2.32 2.45 2.58 2.72 2.86 3.02 3.18
Input materials 1 1.03 1.06 1.10 1.13 1.16 1.20 1.24 1.28 1.32 1.36 1.40 1.44 1.49 1.53 1.58 1.63 1.68 1.73 1.79 1.84 1.90 1.96
Machinery and transportation 1 1.04 1.08 1.12 1.16 1.20 1.25 1.30 1.35 1.40 1.45 1.51 1.56 1.62 1.69 1.75 1.82 1.89 1.96 2.03 2.11 2.19 2.27
Other expenses 1 1.03 1.05 1.08 1.10 1.13 1.16 1.19 1.22 1.25 1.28 1.31 1.34 1.38 1.41 1.45 1.48 1.52 1.56 1.60 1.64 1.68 1.72
Table Quinoa Agriculture Estimated Annual Costs in Th. US$
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Labor 99 105 110 116 123 129 136 143 151 159 168 177 187 197 207 218 230 243 256 270 284 300 316
Input materials (No irrigation) 137 142 146 151 155 160 165 170 175 181 187 192 198 204 211 217 224 231 238 245 253 261 269
Machinery and transportation 34 36 37 38 40 41 43 44 46 48 50 52 54 56 58 60 62 65 67 70 72 75 78
Other expenses 14 15 15 15 16 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23 23 24 24
Input materials (Irrigation) 74 76 78 81 83 86 88 91 94 97 100 103 106 109 113 116 120 124 127 131 135 140 144
Table Quinoa Estimated Annual Sales in Th.US$
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
A. Crops without irrigation
Peasant Family Sales 253 264 275 287 299 311 324 338 352 367 382 398 415 433 451 470 489 510 531 554 577 601 626
Domestic Value added Sales 89 94 99 104 110 116 122 129 136 144 152 160 169 178 188 198 209 220 233 245 259 273 288
Exporting Sales
USA and Europe 245 261 278 296 315 336 357 381 405 432 460 490 522 556 592 630 671 715 761 811 863 919 979
Peru and Ecuador 205 218 231 246 261 277 294 313 332 353 374 398 422 448 476 506 537 570 606 643 683 726 771
B. Crops with Irrigation
Peasant Family Sales 777 810 844 880 916 955 995 1,037 1,080 1,126 1,173 1,222 1,274 1,327 1,383 1,441 1,502 1,565 1,630 1,699 1,770 1,844 1,922
Domestic Value added Sales 331 350 369 389 411 433 457 482 509 537 566 597 630 665 701 740 780 823 869 916 967 1,020 1,076
Exporting Sales
USA and Europe 686 731 779 829 883 940 1,002 1,067 1,136 1,210 1,288 1,372 1,461 1,556 1,658 1,765 1,880 2,002 2,132 2,271 2,419 2,576 2,743
Peru and Ecuador 592 628 667 709 753 799 849 902 957 1,017 1,080 1,147 1,218 1,293 1,374 1,459 1,549 1,645 1,747 1,856 1,971 2,093 2,223
Table Quinoa Production Estimated Annual Costs in Th. US$
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Direct Labor 164 173 182 192 202 213 225 237 249 263 277 292 308 324 342 360 380 400 422 445 469 494 521
Materials and Equipment 245 252 260 268 276 285 294 303 312 322 332 342 353 364 375 387 399 411 424 437 451 464 479
Machinery and transportation 53 55 57 59 62 64 66 69 72 74 77 80 83 86 89 93 96 100 104 108 112 116 121
Administrative and General Expenses 27 28 28 29 30 31 31 32 33 34 35 35 36 37 38 39 40 41 42 43 44 45 46
Table Loan amorization Irrigation Project in Th. US$
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Interest Payments 169 159 148 136 122 106 89 70 49 26
Principal Payments 92 102 113 126 140 155 172 191 212 236
Loan Balance 1,448 1,345 1,232 1,106 966 811 639 448 236 0
APPENDIX G: Lithium Mining Project- cumulative ascending probabilities of prices in annual rates of growth (%)
Source: COMIBOL, 2009; Ebensperger et al., 200; UDAPE, 2009; INE 2009; Zuleta, 2009.
Output Prices in annual growth rates
Operation Costs in annual growth rates
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0.0
5
0.1
0
0.1
5
5.0% 90.0% 5.0%
-0.0584 0.0784
0.0
0.2
0.4
0.6
0.8
1.0
Price of Boric Acid
Pert(-0.1,0.01,0.12)
Minimum -0.1000
Maximum 0.1200
Mean 0.0100
Std Dev 0.0416
5.0% 90.0% 5.0%
0.0318 0.0872
0.0
0
0.0
1
0.0
2
0.0
3
0.0
4
0.0
5
0.0
6
0.0
7
0.0
8
0.0
9
0.1
0
0.1
1
0.0
0.2
0.4
0.6
0.8
1.0
Wage of Production Workers
Pert(0.01,0.0635,0.1)
Minimum 0.0100
Maximum 0.1000
Mean 0.0607
Std Dev 0.0169
5.0% 90.0% 5.0%
0.0212 0.0761
0.0
0
0.0
1
0.0
2
0.0
3
0.0
4
0.0
5
0.0
6
0.0
7
0.0
8
0.0
9
0.1
0
0.1
1
0.0
0.2
0.4
0.6
0.8
1.0
Price of Operating Materials, Supplies and Fuels
Pert(0.01,0.043,0.1)
Minimum 0.0100
Maximum 0.1000
Mean 0.0470
Std Dev 0.0167
APPENDIX H: Quinoa irrigation Project- cumulative ascending probabilities of prices in annual rates of growth (%)
Source: CAMEX, 2009; Crespo et al ( 2001); Soraide et.al, (2005); Water Ministry of Bolivia, 2008 Output Prices annual growth rates
Inputs annual growth rates
0.0
0
0.0
1
0.0
2
0.0
3
0.0
4
0.0
5
0.0
6
0.0
7
0.0
8
0.0
9
0.1
0
0.1
1
-0.0
4
-0.0
2
0.0
0
0.0
2
0.0
4
0.0
6
0.0
8
0.1
0
0.1
2
0.0
1
0.0
2
0.0
3
0.0
4
0.0
5
0.0
6
0.0
7
0.0
8
0.0
9
0.0
0
0.0
1
0.0
2
0.0
3
0.0
4
0.0
5
0.0
6
0.0
7
0.0
8
APPENDIX H: Multivariate Stepwise Regression and Rank Order Correlation Source: @Risk Manual
Even though @Risk uses two methods for calculating and rank coefficients, the author could not find any option to display the
coefficients or regression data base, nor the Stepwise regression equation or p-values of the coefficients.
@Risk uses Multiple regression to fit multiple input data sets to a planar equation that could produce the output data set. The
values then are returned by @RISK, are normalized variations of the regression coefficients.
According to @Risk Manual, the Stepwise regression is a technique for calculating regression values with multiple input values
and it’s the technique preferred for large numbers of inputs because it removes all variables that provide an insignificant contribution
from the model. The coefficients listed in the tornado report (Figures 22 and 25) are normalized regression coefficients associated with
each input. A regression value of 0 indicates that there is no significant relationship between the input and the output, while a
regression value of 1 or -1 indicates a 1 or -1 standard deviation change in the output for a 1 standard deviation change in the input.
@Risk computes a Rank order correlation which calculates the relationship between two data sets by comparing the rank of
each value in a data set. To calculate rank, the data is ordered from lowest to highest and assigned numbers (the ranks) that
correspond to their position in the order. This method is preferable to linear correlation when we do not necessarily know the
probability distribution functions from which the data were drawn. For example, if data set A was normally distributed and data set B
was log normally distributed, rank order correlation would produce a better representation of the relationship between the two data
sets.
0.0
0
0.0
1
0.0
2
0.0
3
0.0
4
0.0
5
0.0
6
0.0
7
0.0
8 5
10
15
20
25
30
35
40
45
50
55