Supplementary files are available for this work. For more information about accessing
these files, follow the link from the Table of Contents to "Reading the Supplementary Files".
Table of Contents
Go Back
SYSTEM DYNAMICS MODELLING IN RESOURCE MANAGEMENT: A
SUSTAINABLE DEVELOPMENT APPROACH TO RESOURCE EXTRACTION IN
SIERRA LEONE.
JONATHAN BONOPHA TENGBE
SKILLSHARE INTERNATIONAL, P.O.BOX 269, MASERU 100, LESOTHO
Email: lcumh@ lesoff.co.za
Tel: +266 780390
Fax: +266 780390
ABSTRACT: This paper is a contribution to the urgent need for effective sustainable
development strategies within the framework of Agenda 21 put forward at the 1992 World
Conference on Environment and Development. It describes the development of a natural
resource management model, which is able to capture the economic, social and ecological
variables that influence resource management. The model is validated and used to analyse
policies available to government to regulate national development. Three policy approaches
are analysed: the conservative policy approach, which allows mining activity to continue in
its current state, and which is shown to lead to near catastrophic environmental results; the
radical policy approach, which would terminate all mining activities immediately, and which
is shown not to be economically viable for an economy dependant on natural resources; and
the harmonious policy approach, which is based upon the first two analyses and advocates a
gradual reduction of mining activities whilst at the same time investing in intensive
agricultural development. This third policy is then used as an approach to control
environmental degradation through the gradual reduction of mining activities and the
improvement of agriculture, with the objective of achieving sustainable development. The
implementation strategies of this policy are also discussed.
INTRODUCTION
This paper applies Simulation Modeling techniques to the problems of the sustainable
management of natural resources. It specifically describes the development and use of a
System Dynamics Model of the mining systems of Sierra Leone for policy analysis in order
to achieve the goals of sustainable development.
The main goal of development in general is to satisfy human needs and wishes, a goal which,
does not seem to be achieved by a large proportion of people in developing countries. There
are many possible contributing factors to this underdevelopment, which are still under
discussion. What is however evident is that achieving an acceptable form of development in
developing countries is often associated with irreversible ecological and social problems,
which are effectively contributing to the present global environmental problems.
Consequently, discussions around this problem, were a major element of the World
Conference on Environment and Development at Rio de Janeiro in 1992, where the inter-
relationship between environment and development was strongly emphasised. On the basis of
this discussion, Agenda 21 for Sustainable Development was recommended, in which
recommendations were made for the development of methodologies and strategies to achieve
this goal (United Nations Conference on Environment and Development (UNICED), 1992).
This paper presents a contribution to this aspect of development through applying System
Dynamics Modeling technique to recommend sustainable resource extraction policies. It first
describes of the study area - the mining regions of Sierra Leone, followed by model
development and validation processes. The validated model is then used to simulate the
existing environmental problems of resource extraction. The empirical results and
recommendations for policy implementation are also discussed.
THE STUDY AREA
Sierra Leone is a small West A frican state located along the Atlantic Ocean with a population
of about 4 million, and a land area of about 72,000 square km (Sierra Leone Government,
1974). The country was initially established as two separate entities - the crown colony
(1808) and the Protectorate (1896), which merged after independence (27.04.1961) to form
the present Sierra Leone (William, 1975).
Before the outbreak of the present war, the economy of Sierra Leone was characterised by
two major components: (i) the modern high productivity sectors like mining, foreign trade
and related financial services, concerned primarily with the export of minerals and the import
and distribution of manufactured goods. And (ii) agriculture, on which more than 75% of
the population depended, which was characterised mainly by subsistence farming but with
some production both for domestic and export markets.
Almost 28,000 square kilometres (approximately 40%) of Sierra Leone is covered by mining
leases or prospecting licenses (Clarke, 1975), and the mining industry is one of the most
important in terms of employment, exports and contribution to the national economy.
According to the Government of Sierra Leone (1974), export of minerals contributed around
80% of the total value of domestic export during the last decade. It contributes to revenues of
the government through taxes on the mining companies, export duties on diamonds, royalties
and license fees and profits of the joint enterprise of National Diamond Mining Company
(NDMC).
Diamond mining, which started in 1930, forms the oldest of the mining systems. The two
methods of mining used in diamond mining are as follows: (i) Industrial mining; and (ii)
alluvial mining. The industrial mining by the National Diamond Mining Company (NDMC)
is done by removing gravel from river valleys and streambeds with the use of draglines and
dredges. The gravel is then loaded into trucks and taken to the washing plants. Eight such
plants were in operation in Y engema and two at Tongo Field in Eastern Sierra Leone. Each
area has a separation house for the final treatment of the gravel concentrates. The alluvial
diamond-mining scheme was set up by government in 1956 to allow the native operators to
take out mining licenses in certain declared chiefdoms. These licensees' claims are worked on
the “tributor” system, that is, the licensee employs several labourers who work for a
proportion of the diamonds won. The miners use hand digging and hand panning along the
marginal river swamps. This type of mining is mainly done on a seasonal basis, and due to
the primitive methods used, the recovery rate is low. It also encourages smuggling and has
many social and environmental problems at the local level.
Until 1975, iron ore was mined at Marampa in northern Sierra Leone. This mining activity
started in 1933, and was at that time the country’s second largest foreign exchange eamer.
But due to technical problems it was forced to close down in 1975. Open cast methods were
employed in the mining operations.
Bauxite was mined at Mokanji in southern Sierra Leone. Opencast methods were used in the
mining operation. This was done, by cutting ‘benches’ along the hillside in order to reduce
erosion of the ore during the rainy season. Prior to the excavation, prospecting and surveying
of the mining area was done in order to classify the ore bodies, which exist in different
quantities and qualities. The resulting map was then used during the excavation to blend the
different ores together in order to obtain a homogenous quality. Bulldozers were used to clear
the vegetation, stockpile the topsoil and remove the overburden until the ore was exposed.
The mine face was then opened up at different levels and excavators were used to scoop out
the ore and load it directly into trucks for transportation to the washing plants. This mine was
forced to close down in 1996 due the civil war in Sierra Leone.
Rutile was mined in Southem Sierra Leone using two methods: (i) Bucket and Ladder
dredging, and (ii) open cast method. The dredging operation is done after the ore deposit has
been flooded (mostly through damming of nearby watercourses). The floating dredge which
needs a minimum of about 6 meters depth of water, is capable of digging up to 15 meters
below the water level. The extracted ore is scrubbed and screened by the primary processing
plant integrated in the dredge, and the waste is discarded behind the dredge. The screened ore
is then pumped through floating pipes to the wet plant located behind the dredge that further
processes the ore to the grade of 50% recoverable rutile. The concentrate is then stockpiled
and transported to the table plant where it is upgraded to about 70% recoverable rutile and
95% total heavy mineral sand content.
Gold mining is also in operation in Sierra Leone but on a very insignificant scale.
MODEL DEVELOPMENT
This section discusses the structure of the Natural Resource Management Model (NRMM).
Its description involves a general introduction to the model structure that comprises the
variables of the economic, ecological and social subsystems. Causal loops of each subsystem
are formulated based on its respective function. Figure 1 below shows the basic building
block of the modelling exercise. It depicts the flow of material and information in the
feedback systems of the three subsystems.
Figure1: Structural components of the Natural Resource Management Model
(NRMM)
ECONOMIC
MODEL
ECOLOGICAL SOCIAL
MODEL MODEL
a)
LEGEND
System
mame =Boundary
Sub-system
— boundary
Information
p> and material
Development of the economic model
The structure of the economic subsystem is shown in the causal loops of figure 2. It shows
the interaction between two negative loops and one positive loop. The positive loop depicts
the interaction between mineral production and the Gross Domestic Product (GDP) of the
country. It shows a systematic increase in the GDP. One negative causal loop depicts the
effect of rehabilitation cost incurred through mining activities on the GDP as a systematic
decline. The second negative causal loop depicts the effect of mining on agricultural
production, which also causes a systematic decline in the GDP. The actual effects of this
interaction are determined through the development of model equations. The model
developed using the DY NAMO programming language is detailed in Tenghe (1994, 2000b).
Figure 2:The causal loop network of the economic system
-_—— Destroyed through
mineral production
+
mining
At
CG:
Agricultural
Production
A
4d
Agricultural
- activities
World market Price
of minerals
+ 4
——p produced
+
ie
&
Investments in
Gross Domestic +
roduct (GDP)
+
Rehabilitation
ig
Vegetated land +——————> Quantity of minerals -————> Total amount <+
<< financial benefit ‘
+
4
Government share
of the financial
benefit
Tax
Costs ¢
Modelling the economic system
The aim of this sub-model is to determine the relationship between the mineral produced
through open cast mining and the vegetated land destroyed to achieve this. It is shown in the
causal loop of Figure 2 that the vegetated land destroyed through mineral production, leads to
the production of a certain quantity of mineral. The government’s share of the financial
benefit is obtained through taxing the total financial benefit obtained from this mineral
production. This accrues to the Gross Domestic Product (GDP) of the country. The level of
the GDP is also a decisive factor for further investment in mining. The cost of rehabilitation
measures take up part of the financial benefit of government on the assumption that the
rehabilitation will not be undertaken by the mining company after it terminates its mining
operation. This assumption is made because the current government policy on rehabilitation
is relatively ineffective and not properly monitored (Sierra Leone Govemment, 1989).
Agricultural production, which contributes to the GDP is also affected by the mining
activities as put forward in the causal loop. The model attributes the reduction of agricultural
production to the depletion of cultivated areas.
The following dynamo equations were developed:
L PRO.K=PROJ+DT*RPROJK
R RPRO.KL=(DVEGM.K*ARCON)/PAT
C PAT=5
C ARCON=400
A DVEGM.K=VRTL.K+DMINE.K
Where :
PRO - Produced mineral (tons)
RPRO_ - Rate of mineral production (tons/yr)
PAT - Production adjustment time associated with vegetation destruction (yrs)
DVEGM - Destroyed vegetation through mining (ha)
VRTL_ - available artificial lakes (ha)
DMINE - dry mining areas (ha)
The rate of production therefore depends on the destroyed vegetation, which is caused by
artificial lakes formed through mining and excavation. The average mineral content and the
production adjustment time associated with destroyed vegetation are assumed to be a constant
during the simulation process, although the rutile content of land varies from one place to
another. This is shown in the Table 1.
Table 1: Showing the production records of some of the already mined out deposits of the
Sierra Rutile mining activities.
Name of mined out | Size of deposit | Total Time taken to | Rutile content
deposit (ha) production mine out (yrs) | (tons/ha)
(tons)
Mogbwemo 303.00 196619.00 5 648.90
Bamba/Belebu 540.80 173056.00 4 320.00
Pejebu North 411.02 127526.00 0.58 310.26
Pejebu South 882.00 291100.00 1.08 330.06
Information source: Sierra Rutile Mine Planning Department.
From this table, the average rutile content of land is calculated as 400 tons/ha with a
production adjustment time of 5 years, determined after a series of simulation runs.
Modelling the aspects of overheads and profits
This model is trying to determine the amount of money obtained from the sale of the mineral
produced. This amount could be obtained by multiplying the world market price for mineral
with those of the produced mineral. The government share is obtained through taxing the
amount obtained. The net amount after taxes is considered the overheads and profits of the
mining company. The cost of rehabilitation measures is also taken into account assuming that
itis to be undertaken by the mining company.
The following dynamo equations could be written:
RECOV.K=RECOV J+DT*RCOV JK
AMOUNT.KL=PRO.KL*PRICE
PRICE= 400
GOVT.K=AMOUNT.K*TAX
TAX=0.07
RCOV.KL=A MOUNT.KL-RGOVT.KL-RCOST.KL
RECOST.K=RELAND.K*COST
COST=1000
EREAX.K=1
rFOrPDWArADmr
AMOUNT is the mount obtained from the sale of mineral (US$/yr); PRICE - world market
price for mineral (US$/ton); TAX - taxes and royalties (fraction); RECOV - overheads and
profits(US$); RECOST - rehabilitation costs (US$); COST - cost of rehabilitation per ha
(us$/ha); EREAX - effects of overheads and profits on mining. The 1992 price level for
tutile is given as US$400.00 per ton (according to Sierra Rutile mining company), the
government tax of about 7%, and the rehabilitation cost was approximated at US$1000.00 per
hectare
Modelling the aspects of government taxes and royalties
Government taxes and royalties (GOVT) are determined as the percentage of the amount of
money obtained from the rutile sale (AMOUNT). This is a contribution to the Gross
Domestic Product of the country (LGDP). The effect of this local contribution to the gross
domestic product as a decision making factor to the mining operation is presently very
insignificant as the mining operation is carried out by foreign companies whose profits are
not contributing to the GDP of Sierra Leone. This effect (ELGDP) will be given a multiplier
of 1 as the mining will continue at the present rate. The following dynamo equations will
represent these aspects.
GOVT.K=GOVT J+DT*RGOVT JK
RGOVT.KL=TAX*AMOUNT.KL
TAX =0.07
LGDP.K=LGDP.J+DT*RLGDP JK
RLGDP.KL=RGOVT.KL+RAGPRO.KL-ERCOST.KL
ELGDP.K=1-MULT1.K
raronr
GOVT - taxes and royalties paid to govemment (US$); RGOVT - rate of taxing (US$/yn);
TAX - percentage tax (fraction); LGDP - local contribution to the Gross Domestic Product
(US$); ELGDP - effect of GDP contribution on mining; AGPRO - agricultural production
(US$); ERCOST - rehabilitation cost incurred by government (US$)
Modelling the aspects of rehabilitation costs
The rehabilitation cost (RCOST) is the expenditure incurred by the company to re-vegetate
the mined out areas. The model assumes that this cost will only be undertaken during the
mining operation, and the government would have to bear the rest of the ecological cost
(ERCOST). The following equation depicts the situation.
L_ RECOST.K=RECOST J+DT*RCOST JK
R_ RCOST.KL=REHAB.KL*COST
C COST=1000
R_ ERCOST.KL=CLIP(RCOST.KL,0,TIME.K,2020)
RECOST - rehabilitation cost bome by the company (US$); RCOST - rate of cost incurment
(US/yr); COST - unit cost of rehabilitation (US$/ha); ERCOST - rate of cost incurment by
government (US$/yr); REHAB - rehabilitation rate (ha/yr)
Modelling the aspects of agricultural production
Agricultural production, which also contributed to the Gross Domestic Product (GDP) is also
affected by mining activities. The model attributes the reduction of agricultural production, to
the depletion of cultivated areas. The following equations will models the situation.
L AGPRO.K=AGPROJ+DT*RAGPRO JK
10
R RAGPRO.KL=AGRU.KL*AGCOST
C AGCOST=300
AGPRO - Income abtained from agricultural production (US$); RAGPRO - Rate of
agricultural production (US$/yr); AGCOST - Unit cost of agricultural production (US$/ha);
AGPRO - Agricultural production (US$)
Figure 3: Flow diagram of the economic model
‘ppat
odov"
BB
&
{seu} —-
ecological model
Development of the ecological model
The causal loop network of the ecological sub-system is shown in figure 4. Unlike the
economic model where all the causal loops are connected to the GDP, the causal loops of the
ecological model are all connected to the vegetation cover. The structure of this model takes
two physicalfactors into consideration: (i) the reduction of agricultural land, and (ii) the
reduction of vegetation cover (aggregated variable for all flora and fauna). Three main causal
11
loops are identified in Figure 4. One of the causal loops shows how mining activities cause a
systematic decline to the vegetation cover and potential agricultural land. The second loop
shows how increase in erosion rate due to mining causes constraint to the re-vegetation of
mined-out areas. The third loop shows the impact of shifting cultivation methods on
vegetation cover.
Figure 4: The causal loop network of the ecological system
Gross Domestic Erosion rate ____ Success of
product (GDP) - rehabilitated areas
+
2)
+
Matured rehabilitated areas
Mining ue
Financial 4 Vegetation agricultural
benefits _ ,Cover + activities
¥ Ay
Investments in vy
Mining +
Cultivated
areas
> =
+¥
Fallow
ty Areas
Artificial lakes —___» _ Destroyed
+ vegetation
Mature fallowg
+
12
Modelling the effects of mining activities
The type of mining activities modelled is mainly the open cast or excavation method and to a
lesser extent the dredging method used in the mining of rutile. As the latter creates artificial
lakes in some areas due to dredging, these two mining techniques are modelled separately.
Artificial lakes, which are created through dredging soil material, reduce vegetation in river
valleys of high agricultural potential. The rate and level of artificial lake formation is
determined from the total lease area of the mining operation; the total area of the existing
artificial lakes; and the total number of years to form these lakes. This information whose
values are given in Table 1 are used to develop the ecological sub-model using the
DY NAMO programming language.
Excavation is an aggregated variable that represents all activities associated with excavation
in the mining area. These include road and canal construction, earth borrowing, and the
excavation of the mineral. The inputs needed for formulating this model includes, the total
lease area, the existing excavated area as already defined, and the total number of years to
achieve the existing state of excavation.
Modelling the effects of soil erosion on rehabilitation measures
The rehabilitation of mined out areas was not an established policy of government at the time
of this modelling exercise. However, since it was at that time practised by the mining
companies, and it is also a foreseeable future policy of government, the model has assumed a
rehabilitation rate, which was used for the initial runs. This rate of rehabilitation was based
on data obtained from the experimental plots of land around the Sierra Rutile mining area.
The effectiveness of the re-vegetation depends on the rate of soil erosion determined from the
universal soil loss model as put forward by Wischmeier (1978). Its main use in this modelling
exercise was to determine the average rate of soil erosion that is accelerated by the mining
activities in the region and the constraint it puts on the re-vegetation efforts. This constraint
increases as the predicted losses exceed the soil loss tolerances of the region. The soil loss
equation is as follows:
Soil loss (A) =RKLSC
The variables are defined as: rainfall erosion index (R), Soil Erodibility factor (K), Slope
Length factor (LS), Vegetation Cover factor (C). Details of this model is described in Tengbe
(1994, 2000b), Lal (1979), Roose (1977), Wischmeier (1978).
Modelling the vegetation and agricultural land use systems
This model determines the impact of shifting cultivation methods on vegetation cover.
Since agriculture is the main occupation of the people in this region it is estimated in Sierra
Leone (1980) that 75% of the people residing in the region receive a major portion of their
income from agricultural activities. Shifting cultivation, which is practised by approximately
65% of the farming population is the preferred method of farming in the region. In this
method, plots of land are cultivated for 1 to 2 years and left for a fallow period. The fallow
13
period is normally 15 years. During this fallow period, the cultivated land is allowed to
regenerate itself, but due to pressure from other land use activities such as mining, this fallow
period has been shortened to 7 years. According to Sierra Leone Government (1980), the
shortened fallow period has reduced soil productivity, and farmers complain of declining
crop yield. This is modelled in DY NAMO programming language as depicted in the causal
loop network of the ecological system in Figure 3
Figure 5: Flow diagram of the ecological model
Development of the social model
Figure 4 shows the causal loop network of the social model. In this model, the displacement
of population is the most important variable around which causal loops are developed. Two
main causal loops could be identified: (i) the positive loop of the effect of resettlement of
displaced population on vegetation cover, and (ii) the positive loop of the effects of
displacement of population on agricultural production and ultimately on the Gross Domestic
Product. Another effect depicted is the migration of population to other areas with
agricultural potential or to urban areas.
14
Figure 6: The causal loop network of the social system
(connects to causal loop of the ecological model)
Displacement + Mining +
+ — of population activities
= :
v
Resettlement
of population
% employed in
mining sector
Destroyed Pe)
vegetation + +
‘4
Need of land
for resettlement
v4
Migration gt Unemployment
to urban in the agricultural sector
areas
Gross Domestic Product
(connects to causal loop of the economic model)
Modelling the social system
This assumes the displacement and resettlement of population due to mining activities to be
the most important sociological problem. The rate of displacement of the population depends
on the destroyed vegetated area and the density of population. According to Sierra Rutile
Limited, (1988), a certain percentage of the displaced population is employed by the mining
industry as labourers. The rest of the population is unemployed due to the lack of agricultural
land. Some of these people migrate into urban areas in search of employment. Others migrate
to areas outside the mining regions, where land is available. Acquisition of land outside their
15
home areas is often difficult due to the nature of the land tenure system. In the land tenure
system, land ownership is at two levels. At the first level the village chief is the keeper of the
land belonging to that village. At the lower level, each family in the village has its own share,
which is passed on from one generation to the other. If a family loses land due to mining
activities, it is virtually impossible to claim ownership elsewhere. However, it is sometimes
possible to lease land somewhere else, but the payment involved is beyond the reach of most
of them, as they are not properly compensated. This social system is modelled in DY NAMO
programming language as depicted in the causal loop network of the social model in figure 4.
Figure 7: Flow diagram of the social model
FRA
tT
DATA ACQUISITION AND MODEL VALIDATION
In building this model, hypotheses of the system were formulated and transformed into model
equations using the DY NAMO programming language with data and information from the
mining systems of Sierra Leone. One of the major constraints encountered during the process
was the availability of data. This model is based on both primary data from the assessor and
secondary existing data. The main sources of secondary data and information are the Sierra
Rutile (1988), SIEROMCO (1988), Sierra Leone Government (1974a, 1974b), Sierra Leone
Government (1980), Sierra Leone Government (1989), Clarke (1975). The sources of primary
data are measurements, observations, interviews and through visits to all the mining areas
16
over a certain period of time. The structure of this model was first developed for the Sierra
Rutile mining system, and was later adapted to the other three mining systems. Structures that
were not relevant to other mining systems were discarded and new ones developed based on
the type of mining in use. However, the main difference was in the values of the model
parameters and the artificial lake structure, which exists for the Rutile Mining System only.
All the other mining systems used open cast methods of mining.
Table 2: Main parameters used to design the structure of the model of the mining system of
Sierra Leone.
Parameter Type Rutile Bauxite Native Diamond | National Diamond
Mining | mining system | miningsystem | Mining Company
system
Average mineral 400 3950 279 279
content (tons/ha) (tons/ha) (carats/ha) (carats/ha
Market price 400 25 52 52
(US$/ton (US$/ton) (US$/carat) (US$/Carat)
)
Government tax 7% 7% 0% 51%
Rehabilitation costs 1000 2,465 NA NA
(US$/ha) (US$/ha)
Agricultural 300 300 300 300
production (US$/ha) US$/ha)
Success rate of re- 65% 75% NA NA
vegetation
Artificial lake 0.002 NA NA NA
formation (ha/year)
Vegetated area 134,700 32,370 3,221,504 77,440
(lease area) (ha)
Settlement areas (ha) 539 126 12,652 252
Cultivated area (ha) | 13,228 3,179 74,095 14,823
Population density 0.3 0 NA NA
(persons/ha)
The information in the table was obtained from sources already referenced and was used in
the model in (Tengbe, 1994). NA means that model structure is not available
After developing the model, simulation runs were undertaken over the historical periods of
the mining operations. The production outputs predicted by the model were compared to the
actual production of the company over the same period. Although there were missing data,
which is a typical problem in developing countries, a very high correlation was achieved
between the simulated data and the actual data. This validation process in Tengbe (1994,
2000b) was undertaken for other variables such as “artificial lake formation, “excavated
areas” and “water storage”, and the results had good correlation with the actual system. The
table below shows a typical result for rutile production.
17
Table 3: Production of Sierra Rutile from 1979 to 1991
Year Actual Simulated production
Production
1979 11,565 11,560
1980 47,499 34,070
1981 57,668 57,070
1982 80,887 81,980
1983 Not available 103,000
1984 Not available 123,200
1985 Not available 144,100
1986 160,269 165,700
1987 Not available 187,900
1988 Not available 210,600
4989 Not available 233,900
1990 236,156 256,700
1991 215,171 282,000
Source: (Tengbe, 1994)
POLICY FORMULATION AND ANALYSIS
Policy formulation is a process through which adequate alternative policies for a system are
developed based on the failures and problems of existing policies.
The developed model is to be used to simulate these alternative policies, through parameter
and structural changes. The evaluation of each altemative policy will be based on the
following criteria:
* Its capability to optimise the agricultural land preservation programs;
* Its capability to optimise national-park protection and wildlife conservation programs.
* Its capability to optimise rehabilitation programs.
* Its capability to optimise soil conservation programs.
* Its effects on the national economy.
Three alternative policy approaches are evaluated here using the model within the framework
of the above criteria. These are the "Conservative” Policy Approach, the “Radical” Policy
Approach and the “Harmonious” Policy Approach.
18
The “conservative” policy approach
This type of policy approach approximates to the way the Government in Sierra Leone is
currently practising its environmental programs. Although environmental institutions place
much emphasis on land rehabilitation, the schemes are inadequately monitored and are
consequently only partly effective. Furthermore the rate of mineral extraction is not
considered in planning procedures, and mining companies are allowed continuous operation.
The “conservative” policy approach assumes mining would continue as at present but allows
for optimum land rehabilitation and soil conservation programs. The following changes are
made to the model:
« A parameter for erosion control practice is added as a structure of the original model.
¢ Rehabilitation rates are increased to three times the current actual value for optimum land
rehabilitation
These policies were imposed on the model through DYNAMO programming, details
available in Tengbe (1994, 2000b).
The results of the simulation predict short-term economic benefits but with adverse long-term
ecological, social and economic consequences. A typical result is shown in the time series for
agricultural land protection programmes in the Rutile mining system. The graph in figure 6
below shows a steady decline in both the potential agricultural land and the cultivated areas
after reaching an early peak.
The outcomes of this policy model indicates that immediate govemment policy changes are
needed to avert a serious decline in the agricultural sector of the economy, especially where
mining is taking place in the inland valley swamps which are the most fertile areas for
cultivating the staple food crop, rice.
19
Figure 8: Effects of the “conservative” policy approach on agricultural land protection (Rutile
mining)
(Rutile mining)
oe Potential A gricultural land (ha) (0.,100.e3)
___ Cultivated land (ha) (.,20.e3)
188.e3,
28.e:
25.e \
5688. a Pe
8. :
5. i978. 1988. 1998, 2888 2818. 2828.
The “radical” policy approach
This approach would terminate all mining activities immediately. The approach represents
the thinking of the inhabitants of Sierra Rutile mining region, especially the farmers, as
exhibited during interviews in the mining region. Environmental groups in Sierra Leone,
especially NGOs, also support this approach. This is also supported to a large extent by the
Department of Wildlife Conservation and the Rural Development section of the Ministry of
Social Welfare (In contrast, most of the farmers of the diamond mining area welcome the
continuation of mining because of the financial benefits of illicit diamond working).
The policy approach was simulated in the model by making the following adjustments:
« All mining variables were set to zero to stop mining activities with immediate effect.
« Parameters for erosion control practices were added as a structure of the original model
* Rehabilitation rates were increased to three times the value used in the original model for
20
optimum land rehabilitation.
Although the option showed ecological benefits it indicated both short- and long-term
adverse economic and social consequences. The model indicates, as would be expected, that
there would be no contribution to the Gross Domestic Product, which would be catastrophic
for a country with such a fragile economy. Further, it can be seen from figure 7 that
compared to the “conservative policy approach” there is only a slight change in the projected
quantity of potential agricultural land available especially during the first 20 years, even
though mining activities are terminated. This unexpected result, which does not become
apparent using other techniques of projection especially where there is acute lack of data, is
possibly due to the continuation of shifting cultivation method. This method currently has
relatively short fallow periods, which result in a high rate of conversion of potential
agricultural land to land under cultivation.
Figure 9: The effects of the “radical approach” on agricultural land protection (Rutile mining)
(Rutile mining)
Potential A gricultural land (ha)(50.e3,90.e3)
Cultivated land (ha)(0.,20.e3)
1978. 1988. 1998. 2088. 2618. 2828.
The “harmonious policy approach”
This approach strikes a balance between the “radical and the “conservative” approaches in an
attempt to realise sustainable development - the main goal of environmental planning, and
draws on insights arising from those approaches. The original model is adjusted as follows:
* Agricultural land use rate is decreased through intensive sustainable agricultural
practices. This is to reduce the adverse consequences of shifting cultivation practices.
21
* The Fallow period is increased from the current 7 years to 12 years.
* Cultivation period is increased from the current 2 years to 10 years through intensive
sustainable agricultural practices.
¢ Agricultural production is increased from the current US$300.00 per ha to
US$600.00 per ha due to intensive sustainable agricultural practices.
* Native illicit diamond mining rate is set to zero thus terminating it with immediate effect.
¢ Rutile mining terminates at the year 2005
« SIEROMCO (bauxite) mining terminates at the year 2010
¢ National Diamond Mining Company (NDMC) stops practising open cast method of
mining and changes to underground deep mining (“kymberlite” mining).
As already discussed, the "conservative" policy approach, realises short-term maximum
economic benefits, which results in adverse ecological consequences, that ultimately reflect
in long term economic problems due to the depletion of valuable agricultural land areas. The
“radical” policy approach tries to curb this problem, without due consideration to short and
medium term economic and social consequences. The "harmonious" approach aims to
formulate policies that could serve as a base for sustainable development. It is hoped that the
implications of the modelled outcomes of this policy approach will contribute to the
preparation of programs to meet the objectives of Agenda 21. Below is a discussion of a
typical result of simulating this policy on agricultural land protection programmes.
The effectiveness of the "harmonious" approach for agricultural land protection
programs
In Sierra Leone Agricultural protection programs, which intends to save useful agricultural
land areas from being encroached on by other land use activities have not been very effective,
as shown above in discussing the “conservative” policy approach, where cultivated areas and
potential agricultural land are lost to mining land use. If Figure 7 below is compared with
Figures 5 and 6, it can be seen that without the introduction of the "harmonious " approach,
potential agricultural land and cultivated areas are in danger. By introducing the
“hamonious” policy approach, there is a complete change in the trends of the time series,
which is not realised in either the "conservation" or the "radical" policy approaches. An
increase is exhibited in the amount of land under cultivation, and the decline in potential
agricultural land areas occurs much later. The early decline of potential agricultural land in
the first two models is explained by the high land demands of shifting cultivation. The later
decline of such land with application the “harmonious “policy model is due to the use of
potential agricultural land for intensive cultivation, and the longer fallow cultivation periods
allowed for under this policy. The model thus shows that "harmonious" policy approach is
very effective in the protection of valuable agricultural land. It is also effective in the
protection of flora and fauna, through the gradual reduction of mining activities and the
intensification of agricultural land use.
22
Figure 10: Effect of "harmonious" approach on agricultural land protection programs
Potential A gricultural land area (ha)(60.e3,100.e3)
a Cultivated land area (ha)(0.,20.e3)
Rutile Mining
ed _
a
15.03
66.c¢2___ ee — ee Oe
* 1978. 1988, 1998. 2088. 2618. 2828,
DISCUSSION
The previous section was basically concerned with the formulation of new policies for the
sustainable management of natural resources. During this process, the model developed was
experimented with in the "laboratory" (in this case the computer) using different policy
alternatives. Sensitive parameters were identified and manipulated, to improve the behaviour
of the model. This is a major advantage of simulation modelling methodology. However,
after the development of policy altematives, strategies should be put in place to translate
these parameter changes in the simulation exercise into policy actions for application in the
“real world” system. This is because improving the behaviour of the model with a particular
policy is not enough for positive changes to be realised in the real system. What is important,
is to know why a particular policy improves model behaviour. This understanding can then
be added to what is known or believed about the system so that correct decisions can be made
within the limits of existing opportunities and constraints.
This discussion focuses on two important questions:
* Can those responsible for policy implementation in the “real world” system be convinced
of the value of a model-based policy recommendation?
¢ What are the opportunities and constraints of this policy implementation?
23
In order to address the first question, one has to look at the current political debate
conceming this issue. Since the development of this model, the National Report of Sierra
Leone to United Nations Conference on Environment and Development (UNCED) in Sierra
Leone (1992, 1989) discussed sector reform measures in the form of new agreements.
Although, these agreements may not be entirely adequate, they are however in accord with
the policies of the model-based “harmonious approach”, which recommends how mining
could be done in the most sustainable way so that ecological and social impacts can be
minimised and at the same time realise economic benefits. The model outcome of such
policies will give them greater credibility and assist in their being implemented. The second
question addresses issues of opportunities and constraints. Opportunities for implementation
can be defined as the different possibilities available within the present planning system that
could ease the problems of implementation. The following possibilities are available:
« Leases and agreements between the government and the mining companies.
* Rehabilitation laws.
¢ Environmental Impact Assessment law
¢ Agricultural policies
The formulation of such leases, laws and policies would be helped by an understanding of the
Natural Resource Management model outputs, especially the ‘harmonious” policy model.
Constraints to implementation can be defined as problems existing within the system that
could hinder the realisation of these alternative policies.
The basic types of problems identified are as follows: (i) availability of data and information,
(ii) financial resources, (iii) technical know-how, (iv) lack of administrative and political will.
The implementation of the “harmonius” policy approach will be greatly enhanced if some of
the above problems are addressed.
Another aspect that needs consideration is the effect of external trade. Because the demand
for these resources is mostly from industrial countries, the realisation of these proposals could
not be achieved without their support. The development strategies of industrial countries
should therefore take cognisance of the problems of developing countries, as was highlighted
during the United Nations Conference on Environment and Development at Rio de Janeiro.
The unfavourable condition of international trade, under which developing countries operate,
is also one of the main reasons for the unsustainable exploitation of natural resources.
CONCLUSION
The main concer of this paper has been to contribute to the management of natural resources
for sustainable development. It shows how the development and use of computer modelling
using the Natural Resource Management Model (NRMM) can give insights into the mineral
extraction system of Sierra Leone. During the construction phase of the model, data and
information were obtained from the Sierra Rutile mining company, which stopped operations
in 1996 due to civil war in Sierra Leone. The structure of the model was then adapted to data
and mining techniques of the Bauxite mining, National Diamond Mining and native/illicit
diamond mining enterprises in Sierra Leone. The ‘Harmonious Policy’ approach derived
24
from the modelling process was shown to be the option most appropriate to the social,
economic and ecological problems arising from the activities of the mineral extraction
industry. Although the model has been developed for the Sierra Leone mining system it
could, with appropriate changes in the values of the parameters and constants, equally well be
applied to similar systems in other countries. It could also be reduced or increased in size
according to the purpose of the study.
It is hoped that the results of this study will contribute to the development and
implementation of Sustainable Development policies.
25
REFERENCES
Clarke J. I. (1975) Sierra Leone in Maps, University of London Press (Ltd), London, UK.
Forrester J. W. (1968) Industrial dynamics, Ed. Cambridge Massachusets, USA, MIT Press
(students edition)
Joe Ravetz (2000) City Region 2020 Integrated Planning for a Sustainable Environment,
Earthscan.
Lal R. (1977) Soil Erosion Problems on an Alfisol in Wester Nigeria and their control. In
IITA Monograph No. 7.
Nancy Robert, David F Anderson, Ralf M. Dean (1983) Introduction to Computer
Simulation: The System Dynamics approach, Reading, Mass, Addison-Wesley.
Pugh- Roberts Associates, Inc. (1989) Professional DY NANO Plus Reference Manual, U.S.A.
Roose E. (1977) Use of the Universal Soil Loss Equation to predict erosion in West A frica.
In: Soil erosion,: prediction and control, Proceeding of the nation conference on erosion, Soil
Conservation Society of America, Ankeney, Iowa, pp 60-74, 1977.
SIEROMCO (1988) 25 years of Bauxite mining in Sierra Leone (Unpublished)
Sierra Leone Government (1974) National Population Census, Central Statistics Office,
Freetown, Sierra Leone.
Sierra Leone Government (1974) Elements for the formulation of the National Development
Plan 1974/79 - 1978/79, Freetown, Sierra Leone.
Sierra Leone Government (1980). V egetation and Land Use Maps of Sierra Leone, Land
Resources Survey, Project, SIL/73/FAO/UNDP-MANR, Freetown Sierra Leone.
Sierra Leone Government (1989) A greement between the Government of Sierra Leone and
Sierra Rutile Limited (Unpublished).
Sierra Leone Government (1992) National Report for the United Nations Conference on
Environment and Development, Ministry of Lands, Housing and the Environment,
Freetown, Sierra Leone.
Sierra Rutile Limited (1988) History and review of all long range mine planning work for the
Gbangbama deposits (Unpublished).
Sterman John D. and Ford David N. (1998). Dynamic Modelling of Product Development
Process, System Dynamic Review Vol. 14, No. 1 pp. 31-68.
26
Sterman John D. (1992). System Dynamic Modelling for Project Management, System
Dynamics Group, Sloan School of Management, Massachusetts Institute of Technology,
Cambridge MA 02139.
Tengbe J. B. (2000a) The Application of Multi-Criteria Analysis to Environmental
Assessment: a case of Environmental Policy Analysis of Mining activities in Sierra
Leone. In: African Journal of Environmental Assessment and Management 2(1/2), 19-33.
Tengbe J. B. (1994) Zur Umweltvertraglichkeitsprifung in Sierra Leone: Eine Fallstudie zur
Bewertung der Umweltfolgen des Bergbaus - Umweltpolitische Analyse mit Hilfe von
Simulationsmodellierung, Institute fiir Landschaftplanung und Okologie,Universitat
Stuttgart, ARBETSBERICHTE, N.F. 1.
Tengbe J. B. (2000b) Towards Environmental Impact Assessment in Sierra Leone: a case
Study of the assessment and evaluation of the environmental impacts of mining
Environmental Policy Analysis using Simulation Modelling (Unpublished English version of
Ph.D Thesis of 1994. Available at the Institute fur Landschaftsplanung und Okologie,
Univesitat Stuttgart, Germany.
United Nations Conference on Environment and Development (UNCED) (1992) Earth
Summit ‘92 ,London, Regency Press.
World Commission on Environment and Development (1987) Our Common Future Oxford,
Oxford University Press.
William A. Hance (1975) the Geography of Modem Africa, second edition, Columbia
University Press, New Y ork and London.
Wischmeier W. A. (1978): Use and misuse of the universal soil loss equation, Journal of Soil
and Water Conservation, (31), 5-9
APPENDIX
LIST OF VARIABLES
SYMBOL
AGPRO
(US$)
AGCOST
AGRU
AGRU1
AGRU2
AMOUNT
ALUP
ARCON
ARTLA
AVEG
AVGR
AVGRN
BR
C
COST
CP
DAT
DP
DEPTH
DPOP
DSTORE
DVEG
DMINE
ERCOST
ERVEG
EHAB
EPVAR
EREAX
ERAEX
EREAT
EBOR
EAGRV
EHAB
EPRU
EPVAR
EBVEG
ERO
TYPE
L
Pd rrrr.,r wot DBDrAAQANQAANQAAMFKDWaS Qn rraa
rr Ferro
DEFINITION
INCOME ABTAINED FROM AGRICULTURAL PRODUCTION
UNIT COST OF AGRICULTURAL PRODUCTION (US$/ha)
EFFECTIVE AGRUCULTURAL LAND USE RATE (US$/yr)
NORMAL AGRUCULTURAL LAND USE RATE (US$/yr)
AGRUCULTURAL LAND USE DUE TO DISPLACED
POPULATION (US$/yr)
AMOUNT OBTAINED FROM THE SALE OF MINERAL (US$/yr)
AVERAGE AGRICULTURAL LAND USE PER PERSON PER
YEAR (ha/person/ yr)
AVERAGE MINERAL CONTENT OF LAND (tons/yr)
ARTIFICIAL LAKE FORMATION (ha/yr)
AVAILABLE VEGETATION (ha)
CULTIVATED LAND AREA (ha)
INITIAL CULTIVATED LAND (ha)
BREATH OF SLOPE (m)
VEGETATION COVER FACTOR
UNIT COST OF REHABILITATION (US$/ha)
AVERAGE CULTIVATION PERIOD (yrs)
POPULATION ADJUSTMENT TIME (yrs)
AVERAGE DEPTH OF BORROWED PITS (m)
AVERAGE DEPTH OF DAM (m)
DISPLACED POPULATION (persons)
RATE OF DESTRUCTION OF LAND THROUGH WATER
STORAGE (ha/yr)
DESTROYED VEGETATION (ha)
EXCAVATED AREAS THROUGH DRY MINING (ha)
RATE OF REHABILITATION COST INCURRED BY
GOVERNMENT (US$/yr)
EFFECT OF ROAD CONSTRUCTION ON VEGETATION
EFFECT OF MINED OUT AREAS ON REHABILITATION
Effect Of Potential V egetated Land On Artificial Lakes.
EFFECTS OF OVERHEADS AND PROFITS ON MINING
EFFECT OF OVERHEADS AND PROFITS ON ROAD
CONSTRUCTION RATE
EFFECT OF OVERHEADS AND PROFITS ON THE ARTIFICIAL
LAKE FORMATION.
BORROWED AREAS (HA)
EFFECT OF POTENTIAL VEGETATED AREA ON AGRIC.
EFFECT OF MINED OUT AREAS ON REHABILITATION
NUMBER OF DISPLACED PEOPLE EMPLOYED IN MINING
EFFECT OF POTENTIAL VEGETATED AREAS ON ARTIFICIAL
LAKE FORMATION
EFFECT OF EARTH BORROWING ON VEGETATION
EFFECT OF SOIL EROSION ON REHABILITATION
ESTOV A EFFECT OF WATER STORAGE ON VEGETATION
ELGDP A EFFECT OF LOCAL GDP ON MINING
EXCV R EXCAVATION RATE (ha/yr).
EXCVL L EXCAVATED LAND (ha)
EXVEG A EFFECT OF POTENTIAL VEGETATION ON EXCAVATION
EMVEG A EFFECT OF DRY MINING ON VEGETATION
FAGRV A FRACTION OF POTENTIAL AGRIC. LAND THAT IS
CULTIVATED
FALLOW R RATE AT WHICH CULTIVATED LAND IS PLACED UNDER
FALLOW (ha/yr)
FAMAT R RATE AT WHICH FALLOW LAND MATURES (ha/yr)
FEXCV A FRACTION OF POTENTIAL VEGETATED LAND EXCAVATED
FLAND L LAND UNDER FALLOW (ha)
FLANDN Cc INITIAL FALLOW LAND (ha)
FROVEG A FRACTION OF VEGETATED LAND THAT IS USED FOR
ROADS CONSTRUCTION
FARTLA A FRACTION OF LAND OCCUPIED BY ARTIFICIAL LAKES
FMHAB A FRACTION OF MINED OUT AREA REHABILITATED
FBOVEG A FRACTION OF VEGETATED LAND BORROWED
FMINV A FRACTION OF VEGETATED AREAS THAT ARE MINED OUT
FP Cc FALLOW PERIOD (yr)
FRRU ¢€ NORMAL PERCENTAGE OF DISPLACED PEOPLE EMPLOY ED
IN MINING
FRTLA A FRACTION OF POTENTIAL VEGETATED LAND OCCUPIED
BY ARTIFICIAL LAKES
FSTOVO A FRACTION OF TOTAL PRECIPITATION THAT IS STORED
GOVT L TAXES AND ROYALTIES TO GOVERNMENT (US$)
K € SOIL ERODIBILITY FACTOR
LOSSR A EFFECTIVE SOIL LOSS RATE (tons/ha/yr)
I, Cc LENGTH OF THE SLOPE (m)
LSTORE L LAND DESTROYED DUE TO WATER STORAGE IN DAMS (ha)
MATUR L MATURED FALLOW LAND - SECONDARY FOREST (ha)
MATURN C INITIAL MATURED FALLOW LAND (ha)
MNOUT A MINED OUT AREA NOT FLOODED (ha)
MINE R RATE OF DRY MINING (ha/yr)
NMINEF Cc NORMAL DRY MINING FRACTION (frac./yr)
NAGRF Cc NORMAL AGRIC. LAND USE FRACTION
NREHAB Cc REHABILITATION RATE IMPOSED BY GOVERNMENT
(fraction/yr)
NARTLF Cc NORMAL ARTIFICIAL LAKE FORMATION FRACTION.
(fraction/yr)
NEXCVF Cc NORMAN EXCAVATION FRACTION (fraction/yr)
NSTORF Cc NORMAL FRACTION OF LAND DESTROY ED THROUGH
WATER STORAGE (fraction/yr)
NROADF € NORMAL FRACTION OF ROADS CONSTRUCTED PER
YEAR (fraction/yr)
NBORF 163 NORMAL EARTH BORROWING RATE (frac./yr)
PAT Cc PRODUCTION ADJUSTMENT TIME (hrs)
PVEG ¢€ POTENTIAL VEGETATED AREA (ha)
PRICE & World Market Price For Mineral (US$/unit)
POAGR A POTENTIAL AGRIC. LAND (ha)
POPD Cc POPULATION DENSITY (persons/ha)
PRO L PRODUCED MINERAL (unit)
RECOV A OVERHEADS AND PROFITS (US$)
RECOST A REHABILITATION COSTS (US$)
R Cc RAINFALL EROSION INDEX
RCOST R RATE OF REHABILITATION COST INCURMENT (US/yr)
REHAB R REHABILITATION RATE (ha/yr)
RAGPRO R RATE OF AGRICULTURAL PRODUCTION (US$/yr)
RELAND R REHABILITATED LAND (ha)
RECOV A MINING COMPANY’S OVERHEADS AND PROFITS (US$)
REMAT R RATE OF MATURITY OF THE REHABILITATED AREA (ha/yr)
ROADL L SURFACE AREA OF ROADS CONSTRUCTED (ha)
RSUCC ¢ SUCCESS RATE (FRACTION)
RSLOSS R TOTAL RATE OF SOIL LOSS (tons/yr)
RPOP R RATE OF POPULATION DISPLACEMENT (persons/yr)
RPRO R RATE OF MINERAL PRODUCTION ((tons/yr)
RSTORE R RATE OF WATER STORAGE m3/yr)
SL A SLOPE-LENGTH FACTOR
SAND A AREA OF SAND TAILINGS (ha)
SF A FRACTION OF ARTIFICIAL LAKE THAT IS SAND TAILINGS
SLOSS L TOTAL SOIL LOSS (TONS)
ST Cc STORAGE ADJUSTMENT TIME (yrs)
STORE L STORAGE VOLUME (m3)
TSS Cc TIME TAKEN FOR THE REHABILITATED AREAS TO MATURE
TO A FULLY VEGETATED AREA (yrs)
TAX Cc TAX AND ROYALTIES (fraction)
TEAGRV- T TABLE FOR THE EFFECT OF POTENTIAL VEGETATED
AREA ON AGRICULTURE
TEPVAR T TABLE FOR THE EFFECT OF VEGETATED LAND ON
ARTIFICIAL LAKES
TERVEG T TABLE FOR THE EFFECT OF ROAD CONSTRUCTION ON
VEGETATION
TEHAB T TABLE FOR THE EFFECT OF MINE OUT AREA ON
REHABILITATION
TERO T TABLE FOR THE EFFECT OF EROSION ON REHABILITATION
TESTOV T TABLE FOR THE EFFECT OF WATER STORAGE ON
VEGETATION
TEXVEG T TABLE FOR THE EFFECT OF POTENTIAL VEGETATION
ON EXCAVATION
TLOSSR R TOLERABLE SOIL LOSS RATE (tons/yr)
TIME N BEGINNING OF SIMULATION
TOVOL Cc TOTAL MAXIMUM VOLUME OF WATER IN THE MINING
AREA (m3/yr)
TSL T TABLE VARIABLE FOR SLOPE-LENGTH FACTOR
UEMP A UNEMPLOYED POPULATION (persons)
VEGET L RE-VEGETATED AREAS THAT ACTUALLY MATURES (ha)
VEARTH A VOLUME OF EARTH BORROWED (cubic meter)
VOLS A VOLUME TO STORED (m3)
VRTL L AVAILABLE ARTIFICIAL LAKES (ha)
Back to the Top
°K ENVIRONMENTAL IMPACT ASSESSMENT MODEL FOR THE SIERRA LEONE***
"MINING SYSTEM BY JONATHAN BONOPHA TENGBE***
** VERSION 10- APRIL 1993***
SUB-SYSTEM 1 - SIERRA RUTILE MINING SYSTEM
ECONOMIC MODEL
PRODUCTION
PRO.K=PROJ+DT*RPROJK
PRO=0
PRODUCED RUTILE (tons)
RPRO.KL=(DVEG.K*ARCON)/PAT
RATE OF RUTILE PRODUCTION (tons/yr)
PAT=5
PRODUCTION ADJUSTMENT TIME
DVEGM.K=VRTL.K+DMINE.K
AREAS WHERE MINERAL WAS EXTRACTED (ha)
ARCON=400
AVERAGE RUTILE CONTENT OF LAND (tons/ha)
OVERHEADS AND PROFITS
RECOV.K=RECOV J+DT*RCOV JK
RECOV =0
OVERHEADS AND PROFITS OF THE COMPANY (US$)
AMOUNT.KL=RPRO.KL*PRICE
AMOUNT FROM THE SALE OF RUTILE (US$/yr)
PRICE=400
WORLD MARKET PRICE FOR RUTILE (US$/ton)
RCOV.KL=A MOUNT.KL-RGOVT.KL-RCOST.KL
RECOVERY RATE FROM SALES (US$/yr)
TAXES AND ROYALTIES
GOVT.K=GOVT J+DT*RGOVT JK
GOVT=0
GOVERNMENT PROCEEDS FROM MINING (US$)
RGOVT.KL=TAX*AMOUNT.KL
TAXES AND ROYALTIES TO GOVERNMENT (USS$/yr)
TAX =0.07
PERCENTAGE TAX (dimensionless)
LOCAL GROSS DOMESTIC PRODUCT
LGDP.K=LGDP.J+DT*RLGDPJK
LGDP=0
LOCAL GROSS DOMESTIC PRODUCT (US$)
RLGDP.KL=RGOVT.KL+RAGRPO.KL-ERCOST.KL
RATE OF CHANGE OF GROSS DOMESTIC PRODUCT (US$/yr)
ELGDP.K=1-MULT1.K
EFFECT OF LOCAL GROSS DOMESTIC PRODUCT ON MINING
MULT1.K=0
MULTIPLIER THAT DETERMINES GOVERNMENT CONTROL OVER MINING
REHABILITATION COSTS
RECOST.K=RECOST J +DT*RCOST JK
RECOST=0
REHABILITATION COST (US$/yr)
RCOST.KL=TREHAB.KL*COST
RATE OF REHABILITATION COSTS (US$/yr)
COST=1000
UNIT COST OF REHABILITATION (US$/ha)
ERCOST.KL=CLIP(RCOST.KL,0,TIME.K,2020)
COST UNDERTAKEN BY GOVERNMENT (US$/yr)
AGRICULTURAL PRODUCTION
AGPRO.K=AGPROJ+DT*RAGPRO JK
AGPRO=0
INCOME OBTAINED FROM AGRICULTURAL PRODUCTION (US$)
RAGPRO.KL=AGRU.KL*AGCOST
RATE OF AGRICULTURAL PRODUCTION (US$/yr)
AGCOST=300
INCOME FROM AGRICULTURAL PRODUCTION PER HECTARE (US$/ha)
EFFECTS OF OVERHEADS AND PROFITS ON MINING ACTIVITIES
EREAT.K=1
EFFECTS OF OVERHEADS AND PROFITS
ECOLOGICAL MODEL
REHABILITATION MEASURES
RELAND.K=RELAND J+DT*TREHAB JK
NEWLY REHABILITATED LAND
RELAND=0
INITIAL REHABILITATED LAND (ha)
REHAB.K=NREHAB*EHAB.K*MNOUT.K
RATE OF REHABILITATION (ha/yr)
TREHAB.KL=CLIP(REHAB.K,0,TIME.K,1986)
START OF REHABILITATION
VEGET.K=VEGET J+DT*REMAT JK
REHABILITATED AREAS THAT ACTUALLY MATURES (ha)
VEGET=0
INITIAL REHABILITATED AREAS THAT ACTUALLY MATURES (ha)
REMAT.KL=RSUCC*ERO.K*DELAY 1(TREHAB.KL,5)
MATURITY RATE OF THE NEWLY REHABILITATED AREA (ha/yr)
NREHAB=0.032
NORMAL REHABILITATED RATE PRACTICED BY SRL (frac./yr)
RSUCC=0.65
SUCCESS RATE OF THE TREES (fraction)
EHAB.K=TABHL(TEHAB,FNHAB.K,0,1,0,1)
EFFECT OF MINOUT AREA ON THE RATE OF REHABILITATION (dimensionless)
TEHA B=3.83/2.22/2.1/1.8/1.67/1.53/1.25/1.8/2.5/1.67/0
TABLE VARIABLE NAME FOR EHAB
FMHAB.K=RELAND.K/MAX(MNOUT.K,0.01)
FRACTION OF MINED OUT AREA REHABILITATED (dimensionless)
MNOUT.K=DMINE.K+EBORL.K+SAND.K
MINED OUT AREAS CAPABLE OF BEING REHABILITATED
ARTIFICIAL LAKES
VRTL.K=VRTLJ+TARTLA JK
AVAILABLE ARTIFICIAL LAKES (ha)
VRTL=0
INITIAL ARTIFICIAL LAKES (ha)
ARTLA.K=NARTLF*AVEG.K*EPVAR.K*EREAT.K*ELGDP.K
TARTLA.KL=CLIP(ARTLA.K,0,TIME.K,1978)
ARTIFICIAL LAKE FORMATION (ha)
NARTLF=0.002
NORMAL ARTIFICIAL LAKE FORMATION (fraction/yr)
EPVAR.K=TABHL(TEPVAR,FRTLA.K,0,0.3,0.03)
EFFECT OF POTENTIAL VEGETATED LAND ON ARTIFICIAL LAKES
TEPVAR=.6/1.2/3.6/4.8/6/7.2/6/7.2/7.2/7.2/0
TABLE FOR THE EFFECT OF VEGETATED LAND ON ART. LAKES
FRTLA.K=VRTL.K/PVEG.K
FRACTION OF POTENTIAL VEGETATED LAND OCCUPIED BY ART. LAKES
SAND.K=SF*VRTL.K
AREA OF SAND TAILINGS (ha)
SF=.05
FRACTION OF ARTIFICIAL LAKES THAT IS SAND TAILINGS
EXCAVATION
EXCVL.K=EXCVLJ+DT*(ROADC.JK+EBOR JK +A MINE JK)
TOTAL EXCAVATED AREA
EXCVL=47
INITIAL EACAVATED AREA (ha)
ROAD AND CANAL CONSTRUCTION
ROADL.K=ROADLJ+DT*ROADCJK
SURFACE AREA OF ROADS CONSTRUCTED (ha)
ROADL=A7
SURFACE AREA OF ROADS INITIALLY IN THE REGION
ROADC.KL=NROADF*AVEG.K*ERVEG.K*EREAT.K*ELGDP.K
NROADF=0.00019
NORMAL FRACTION OF ROAD CONSTRUCTED PER YEAR (frac./yr)
ERVEG.K=TABHL(TERVEG,FROVEG.K,0,0.1,0.01)
EFFECT OF ROAD CONSTRUCTION ON VEGETATION
TERVEG=1,1,1,1,1,1,1,1,1,1,0
FROVEG.K=ROADL.K/PVEG.K
FRACTION OF VEGETATED AREAS THAT ARE USED FOR ROADS
EARTH BORROWING
EBORL.K=EBORLJ+DT*EBORJK
BORROWED AREAS (ha)
EBORL=0
INITIAL BORROWED AREAS (ha)
EBOR.KL=NEBORF*AVEG.K*EBVEG.K*ELGDP.K*EREAT.K
EFECT OF EARTH BORROWING ON VEGETATION
NEBORF=0.00017
NORMAL EARTH BORROWING RATE (fraction/yr)
EBVEG.K=TABHL(TEBVEG FBOVEG.K,0,0.1,0.01)
EFFECT OF EARTH BORROWING ON VEGETATION
TEBVEG=1,1,1,1,1,1,1,1,1,1,0
TABLE VARIABLE NAME FOR EBVEG
FBOVEG.K=EBORL.K/PVEG.K
FRACTION OF VEGETATED LAND USED FOR EARTH BORROWING
VEARTH.K=EBORL.K*10000.DP
VOLUME OF EARTH BORROWED (cubic meter)
DP=2
AVERAGE DEPTH OF BORROWED PITS
DRY MINING OPERATION
DMINE.K=DMINEJ+AMINEJK
EXCAVATED AREAS THROUGH DRY MINING (ha)
DMINE=0
INITIAL DRY MINED AREAS (ha)
MINE.K=NMINEF*AVEG.K*EMVEG.K*EREAT.K*ELGDP.K
RATE OF DRY MINING (ha/yr)
NMINEF=0.00014
NORMAL DRY MINING FRACTION (fraction/yr)
AMINE.KL=CLIP(MINE.K,0,TIME.K,1989)
ACTUAL STARTING DATE OF THE DRY MINING OPERATION
EMVEG.K=TABHL(TEMVEG,FMINV.K,0,0.1,0.01)
EFFECT OF DRY MINING ON VEGETATION
TEMVEG=1,1,1,1,1,1,1,1,1,1,0
TABLE VARIABLE NAME FOR EMVEG
FMINV.K=DMINE.K/PVEG.K
FRACTION OF VEGETATED AREAS THAT ARE MINED OUT
SOIL EROSION
SLOSS.K=SLOSS.J*RLOSS JK
TOTAL SOIL LOSS (tons)
SLOSS=0
INITIAL SOIL LOSS (tons)
RLOSS.KL=R*K*C*SL.K*EROBLE.k*10
TOTAL SOIL LOSS RATE (tons/yr)
LOSSR.K=R*K*C*P*SL.K
SOIL LOSS RATE
R=1400
RAINFALL EROSION INDEX
K=0.0251
SOIL ERODIBILITY
P=l
EROSION CONTROL PRACTICE
SL.K=TABHL(TSL,L.K,0,50000,5000)
SLOPE LENGTH FACTOR
TSL=0/42.5/60.1/73.6/85/95/104.1/112.5/120.3/127.5/134.5
TABLE VARIABLE FOR SLOPE LENGTH FACTOR
L.K=EROBLE.K*10000/BR
EROBLE.K=EX CVL.K+SAND.K
AREAS IN THE MINED OUT AREAS THAT ARE SUBJECT TO EROSION
BR=22.13
C=1
COVER FACTOR
ERO.K=TABHL(TERO,LOSSR.K,20,1520,300)
EFFECT OF SOIL EROSION ON REHABILITATION
TERO=1.0/.95/.92/.9/.85/.8
TABLE VARIABLE FOR ERO
TLOSSR=20
TOLERABLE SOIL LOSS RATE (kg/m2/yr)
VEGETATION AND AGRICULTURAL LAND-USE SY STEMS
VEGETATION
AVEG.K=AVEG J+DT*(REMAT JK+TARTLA JK +ROADC..JK-DSTORE JK*
-EBOR.JK-AMINE.JK-RSETTLJK)
AVAILABLE VEGETATION (ha)
AVEG=PVEG
INITIAL VEGETATED AREA (ha)
DVEG.K=PVEG.K-AVEG
DESTROY ED VEGETATION (ha)
LEASE=134700
TOTAL LEASE AREA (ha)
HOUSE=539
TOTAL SETTLEMENT AREAS (ha)
UPLAND CULTIVATION
AVGR.K=AVGRJ+DT*(0.7*AGRU JK-0.3*ROADC JK-0.3*EBORJK*
-AMINE JK-FALLOW JK)
AVAILABLE CULTIVATED AREAS (ha)
AVGR=13227.5
INITIAL CULTIVATED AREA (ha)
AGRU.KL=AGRUI.K-AGRU2.K
EFFECTIVE AGRICULTURAL LAND USE RATE (ha/yr)
AGRU1.K=NAGRF*POAGR.K*EAGRV.K
AGRICULTURAL LAND USE RATE (ha/yr)
NAGRF=0.02
NORMAL AGRICULTURAL LAND USE FRACTION (fraction/yr)
EAGRV.K=TABHL(TEAGRV,FAGRV,0,1,0.1)
TEAGRV =1/1/.95/.9/.85/.8/.75/.7/.5/.3/0
TABLE FOR EAGRV
FAGRV.K=AVGR.K/POAGR.K
FRACTION OF POTENTIAL AGRICULTURAL LAND THAT IS CULTIVATED
AGRU2.K=MGRATE.K*ALUP
AGRICULTURAL LAND USE DUE TO DISPLACED POPULATION (ha/yr)
ALUP=0.13
AVERAGE AGRICULTURAL LAND USE PER PERSON PER YEAR (ha/person/yr)
SWAMP CULTIVATION
SCULT.K=SCULT J +DT*(0.3*AGRU JK-0.3*TARTLA JK)
CULTIVATED VALLEY SWAMPS (ha)
SCULT=135
INITIAL CULTIVATED SWAMPS (ha)
VALLEY SWAMPS
SWAMPS.K=SWAMPS,J +DT*(-0.6*TARTLA JK-DSTORE JK-0.3*AGRU JK*
-0.1*ROADC JK)
NOTE
VALLEY SWAMPS INCLUDING MANGROVE
SWAMPS=43346
EXISTING SWAMPS (ha)
RSWAMPS.KL=0
USWAMP.K=SWAMPS.K-SCULT.K
UNCULTIVATED SWAMPS (ha)
TCULT.K=SCULT.K+AVGR.K
TOTAL CULTIVATED AREAS (ha)
FARM BUSH
FLAND.K=FLAND J+DT*(FALLOW JK-FAMAT JK-RSETTLE JK-0.2*TARTLA JK-
0.5*ROADC JK-0.6*EBOR)
FARM BUSH
FLAND =58594.5
INITIAL FARM BUSH (ha)
FALLOW.KL=(0.65*AVGR.K)/CP2
RATE AT WHICH THE CULTIVATED LAND IS PUT UNDER FALLOW (ha/yr)
CP2=2
AVERAGE CULTIVATION PERIOD (yrs)
SECONDARY FOREST (MATURED FALLOW LAND)
MATUR.K=MATURJ+DT*(FAMAT JK-0.7*AGRU JK-0.2*TARTLA JK*
-0.1*ROADC JK-0.5*RSETTLJK)
NOTE
N
NOTE
R
NOTE
Cc
NOTE
SECONDARY FOREST
MATUR=18993
SECONDARY FOREST (ha)
FAMAT.KL=FLAND.K/FP
RATE AT WHICH THE FALLOW LAND MATURES (ha/yr)
FP=10
THE FALLOW PERIOD (yrs)
POAGR.K=MATUR.K+USWAMP.K
POTENTIAL AGRICULTURAL LAND (ha)
TIME=1978
BEGINNING OF SIMULATION
WATER MANAGEMENT IMPACT MODEL
WATER STORAGE IN DAMS
STORE.K=STOREJ+DT*RSTORE JK
STORE=0
STORAGE VOLUME (cubic meter)
RSTORE.KL=(V OLS.K-STORE.K)/ST
RATE OF STORAGE (cubic meter/yr)
VOLS.K=VRTL.K*DEPTH
VOLUME TO BE STORED (cubic meter)
DEPTH=6
AVERAGE DEPTH OF DAMS (m)
ST=3
STORAGE ADJUSTMENT TIME (yrs)
PRECIPITATION
Cc TOVOL=3.4E9
NOTE TOTAL VOLUME OF WATER INTHE MINING AREA (cubic meter/yr)
NOTE
NOTE WATER STORAGE EFFECT ON VEGETATION
NOTE
L LSTORE.K=LSTORE.J+DT*DSTORE JK
N LSTORE=0
NOTE LAND DESTROYED DUE TO WATER STORAGE IN DAMS (ha)
R DSTORE.KL=NSTORF*ESTOV.K*AVEG.K
NOTE RATE OF DESTRUCTION OF LAND THROUGH STORAGE (ha/yr)
Cc NSTORF=0.00001
NOTE NORMAL FRACTION OF LAND DESTROYED THROUGH STORAGE OF WATER
(fraction/yr)
A ESTOV.K=TABHL(TESTOV,FSTOVO.K,0.003,0.027,0.002)
NOTE EFFECT OF WATER STORAGE ON VEGETATION
A FSTOVO.K=STORE.K/TOVOL
NOTE FRACTION OF THE TOTAL PRECIPITATION THAT IS STORED
T TESTOV =.15/.06/.2/.23/1.0/.05/.18/.2/.03/.04/.13/.33/.4
NOTE TABLE VARIABLE NAME FOR ESTOV
NOTE
NOTE SOCIAL MODEL
NOTE
NOTE POPULATION DISPLACEMENT
NOTE
L DPOP.K=DPOP.J+DT*RPOP JK
N DPOP=0
NOTE DISPLACED POPULATION (persons)
R RPOP.KL=(DVEG.K*POPD)/DAT
NOTE RATE OF POPULATION DISPLACEMENT
Cc POPD=0.3
NOTE POPULATION DENSITY (persons/ha)
Cc DAT=1
NOTE POPULATION ADJUSTMENT TIME (yrs)
NOTE
NOTE EMPLOYMENT AND UNEMPLOYMENT SITUATION
NOTE
A EPRU.K=FRRU*DPOP.K
NOTE NUMDER OF DISPLACED PEOPLE EMPLOYED BY SIERRA RUTILE
Cc FRRU=.05
NOTE NORMAL PERCENTAGE EMPLOY ED
A UEMP.K=DPOP.K-EPRU.K
NOTE UNEMPLOYED POPULATION
NOTE MIGRATION INTO OTHER AREAS
A MGRATE.K=FRMG*DPOP.K
NOTE OUT-MIGRATION INTO OTHER AREAS (persons)
Cc FRMG=0.001
NOTE FRACTION OF DISPLACED PEOPLE NOT RESETTLED IN MINING ZONE
NOTE RESETTLEMENT AND ITS EFFECTS
L SETTL.K=SETTLJ+DT*RSETTLJK
NOTE LAND NEED FOR RESETTLEMENT (ha)
N SETTL=0
NOTE INITIAL LAND NEED FOR RESETTLEMENT (ha)
SPEC
RSETTL.KL=DPOP.K*LAND/DAT
RATE OF LAND NEED FOR RESETTLEMENT (ha/yr)
LAND=0.002
LAND NEEDED PER PERSON FOR RESETTLEMENT (ha/person)
CONTROL STATEMENTS
AVEG,DVEG,VRTL,ARTLA,EXCVL,LSTORE,AVGR,*
LGDP,DSTORE,RELAND,REHAB,SLOSS,RLOSS,LOSSR,AGRU,FLAND,*
AGPRO,MATUR,POAGR,PRO,RPRO,AMOUNT,GOVT,RECOV,RECOST,DPOP,*
SAND,RPOP,UEMP,LSTORE,ROADL,EBORL,DMINE,SCULT,SAND,VEARTH,SWAMPS,*
TCULT,SAND,VEARTH,SWAMPS,SCULT,FLAND,MATUR,MGRATE,SETTL,MNOUT,*~
DVEGM,USWAMP,STORE,VEGET,REMAT,RGOVT,RCOST,RAGPRO,RLGDP,RCOV,*
TREHAB
DT=0.25/LENGTH=2040/SA V PER=1/PRT PER=1/PLOTPER=1
SUB-SYSTEM 2- SIEROMCO MINING SYSTEM
ECONOMIC MODEL
PRODUCTION
PRO.K=PROJ+DT*RPROJK
PRO=0
PRODUCED BAUXITE (tons)
RPRO.KL=(DVEG.K*ARCON)/PAT
RATE OF BAUXITE PRODUCTION (tons/yr)
PAT=4
PRODUCTION ADJUSTMENT TIME
DVEGM.K=VRTL.K+DMINE.K
AREAS WHERE MINERAL WAS EXTRACTED (ha)
ARCON=3950
AVERAGE BAUXITE CONTENT OF LAND (tons/ha)
OVERHEADS AND PROFITS
RECOV.K=RECOV J+DT*RCOV JK
RECOV =0
OVERHEADS AND PROFITS OF THE COMPANY (US$)
AMOUNT.KL=RPRO.KL*PRICE
AMOUNT FROM THE SALE OF BAUXITE (US$/yr)
PRICE=23
WORLD MARKET PRICE FOR BAUXITE (US$/ton)
RCOV.KL=A MOUNT.KL-RGOVT.KL-RCOST.KL
RECOVERY RATE FROM SALES (US$/yr)
TAXES AND ROYALTIES
GOVT.K=GOVT J+DT*RGOVT JK
GOVT=0
GOVERNMENT PROCEEDS FROM MINING (US$)
RGOVT.KL=TAX*AMOUNT.KL
TAXES AND ROYALTIES TO GOVERNMENT (USS$/yr)
TAX =.0.375
PERCENTAGE TAX (dimensionless)
LOCAL GROSS DOMESTIC PRODUCT
LGDP.K=LGDP.J+DT*RLGDPJK
LGDP=0
LOCAL GROSS DOMESTIC PRODUCT (US$)
RLGDP.KL=RGOVT.KL+RAGRPO.KL-ERCOST.KL
RATE OF CHANGE OF GROSS DOMESTIC PRODUCT (US$/yr)
ELGDP.K=1-MULT1.K
EFFECT OF LOCAL GROSS DOMESTIC PRODUCT ON MINING
MULT1.K=0
MULTIPLIER THAT DETERMINES GOVERNMENT CONTROL OVER MINING
REHABILITATION COSTS
RECOST.K=RECOST J +DT*RCOST JK
RECOST=0
REHABILITATION COST (US$/yr)
RCOST.KL=TREHAB.KL*COST
RATE OF REHABILITATION COSTS (US$/yr)
COST =2469
UNIT COST OF REHABILITATION (US$/ha)
ERCOST.KL=CLIP(RCOST.KL,0,TIME.K,2020)
COST UNDERTAKEN BY GOVERNMENT (US$/yr)
AGRICULTURAL PRODUCTION
AGPRO.K=AGPRO.J+DT*RAGPRO JK
AGPRO=0
INCOME OBTAINED FROM AGRICULTURAL PRODUCTION (US$)
RAGPRO.KL=AGRU.KL*AGCOST
RATE OF AGRICULTURAL PRODUCTION (US$/yr)
AGCOST=300
INCOME FROM AGRICULTURAL PRODUCTION PER HECTARE (US$/ha)
EFFECTS OF OVERHEADS AND PROFITS ON MINING ACTIVITIES
EREAT.K=1
EFFECTS OF OVERHEADS AND PROFITS
ECOLOGICAL MODEL
REHABILITATION MEASURES
RELAND.K=RELAND J+DT*TREHAB JK
NEWLY REHABILITATED LAND
RELAND=0
INITIAL REHABILITATED LAND (ha)
REHAB.K=NREHAB*EHAB.K*MNOUT.K
RATE OF REHABILITATION (ha/yr)
TREHAB.KL=CLIP(REHAB.K,0,TIME.K,1986)
VEGET.K=VEGET J+DT*REMAT JK
REHABILITATED AREAS THAT ACTUALLY MATURES (ha)
VEGET=0
INITIAL REHABILITATED AREAS THAT ACTUALLY MATURES (ha)
REMAT.KL=RSUCC*ERO.K*DELAY 1(TREHAB.KL,5)
MATURITY RATE OF THE NEWLY REHABILITATED AREA (ha/yr)
NREHAB=0.032
NORMAL REHABILITATED RATE PRACTICED BY SRL (frac./yr)
RSUCC=0.75
SUCCESS RATE OF THE TREES (fraction)
EHAB.K=TABHL(TEHAB,FNHAB.K,0,1,0,1)
EFFECT OF MINOUT AREA ON THE RATE OF REHABILITATION (dimensionless)
TEHA B=3.83/2.22/2.1/1.8/1.67/1.53/1.25/1.8/2.5/1.67/0
TABLE VARIABLE NAME FOR EHAB
FMHAB.K=RELAND.K/MAX(MNOUT.K,0.01)
FRACTION OF MINED OUT AREA REHABILITATED (dimensionless)
MNOUT.K=EXCVL.K
MINED OUT AREAS CAPABLE OF BEING REHABILITATED
EXCAVATION
EXCVL.K=EXCVLJ+DT*AMINE JK)
TOTAL EXCAVATED AREA
EXCVL=0
INITIAL EACAVATED AREA (ha)
AMINE.K=NMINEF*AVEG.K*EMVEG.K*EREAT.K*ELGDP.K
RATE OF EXCAVATION (ha/yr)
NMINEF=0.0017
NORMAL EXCAVATION FRACTION (fraction/yr)
EMVEG.K=TABHL(TEMVEG,FMINV.K,0,0.5,0.05)
EFFECT OF DRY MINING ON VEGETATION
TEMVEG=1,1,1,1,1,1,1,1,1,1,0
TABLE VARIABLE NAME FOR EMVEG
FMINV.K=EXCVL.K/PVEG.K
FRACTION OF VEGETATED AREAS THAT ARE MINED OUT
SOIL EROSION
SLOSS.K=SLOSS.J*RLOSS JK
TOTAL SOIL LOSS (tons)
SLOSS=0
INITIAL SOIL LOSS (tons)
RLOSS.KL=R*K*C*SL.K*EROBLE.k*10
TOTAL SOIL LOSS RATE (tons/yr)
LOSSR.K=R*K*C*P*SL.K
SOIL LOSS RATE
R=1400
RAINFALL EROSION INDEX
K=0.0251
SOIL ERODIBILITY
P=l
EROSION CONTROL PRACTICE
SL.K=TABHL(TSL,L.K,0,50000,5000)
SLOPE LENGTH FACTOR
TSL=0/42.5/60.1/73.6/85/95/104.1/112.5/120.3/127.5/134.5
TABLE VARIABLE FOR SLOPE LENGTH FACTOR
L.K=EROBLE.K*10000/BR
EROBLE.K=EX CVL.K+SAND.K
AREAS IN THE MINED OUT AREAS THAT ARE SUBJECT TO EROSION
BR=22.13
C=1
COVER FACTOR
ERO.K=TABHL(TERO,LOSSR.K,20,1520,300)
EFFECT OF SOIL EROSION ON REHABILITATION
TERO=1.0/.95/.92/.9/.85/.8
TABLE VARIABLE FOR ERO
TLOSSR=20
TOLERABLE SOIL LOSS RATE (kg/m2/yr)
VEGETATION AND AGRICULTURAL LAND-USE SY STEMS
VEGETATION
AVEG.K=AVEG J+DT*(REMAT JK-AMINEJK)
AVAILABLE VEGETATION (ha)
AVEG=PVEG
INITIAL VEGETATED AREA (ha)
DVEG.K=PVEG.K-AVEG
DESTROY ED VEGETATION (ha)
LEA SE=32370
TOTAL LEASE AREA (ha)
HOUSE=126
TOTAL SETTLEMENT AREAS (ha)
PVEG.K=LEASE-HOUSE
UPLAND CULTIVATION
AVGR.K=AVGRJ+DT*(0.7*AGRU JK-0.2*A MINE JK-FALLOW JK)
AVAILABLE CULTIVATED AREAS (ha)
AVGR=3179
INITIAL CULTIVATED AREA (ha)
AGRU.KL=AGRUI.K-AGRU2.K
EFFECTIVE AGRICULTURAL LAND USE RATE (ha/yr)
AGRU1.K=NAGRF*POAGR.K*EAGRV.K
AGRICULTURAL LAND USE RATE (ha/yr)
NAGRF=0.02
NORMAL AGRICULTURAL LAND USE FRACTION (fraction/yr)
EAGRV.K=TABHL(TEAGRV,FAGRV,0,1,0.1)
TEAGRV =1/1/.95/.9/.85/.8/.75/.7/.5/.3/0
TABLE FOR EAGRV
FAGRV.K=AVGR.K/POAGR.K
FRACTION OF POTENTIAL AGRICULTURAL LAND THAT IS CULTIVATED
AGRU2.K=MGRATE.K*ALUP
AGRICULTURAL LAND USE DUE TO DISPLACED POPULATION (ha/yr)
ALUP=0.13
AVERAGE AGRICULTURAL LAND USE PER PERSON PER YEAR (ha/person/yr)
SWAMP CULTIVATION
SCULT.K=SCULT J +DT*0.3*AGRU JK
CULTIVATED VALLEY SWAMPS (ha)
SCULT=135
INITIAL CULTIVATED SWAMPS (ha)
VALLEY SWAMPS
SWAMPS.K=SWAMPS,J +DT*(-0.3*AGRU JK)
VALLEY SWAMPS INCLUDING MANGROVE
SWAMPS=291
EXISTING SWAMPS (ha)
RSWAMPS.KL=0
USWAMP.K=SWAMPS.K-SCULT.K
UNCULTIVATED SWAMPS (ha)
TCULT.K=SCULT.K+AVGR.K
TOTAL CULTIVATED AREAS (ha)
FARM BUSH
FLAND.K=FLAND J+DT*(FALLOW JK-FAMAT JK-0.3*A MINE JK)
FARM BUSH
FLAND=19122
INITIAL FARM BUSH (ha)
FALLOW.KL=(0.65*AVGR.K)/CP2
RATE AT WHICH THE CULTIVATED LAND IS PUT UNDER FALLOW (ha/yr)
CP2=2
AVERAGE CULTIVATION PERIOD (yrs)
SECONDARY FOREST (MATURED FALLOW LAND)
MATUR.K=MATURJ+DT*(FAMAT JK-0.7*AGRU JK-0.3*A MINE JK)
SECONDARY FOREST
MATUR=9518
SECONDARY FOREST (ha)
FAMAT.KL=FLAND.K/FP
RATE AT WHICH THE FALLOW LAND MATURES (ha/yr)
FP=10
THE FALLOW PERIOD (yrs)
POAGR.K=MATUR.K+USWAMP.K
POTENTIAL AGRICULTURAL LAND (ha)
TIME=1962
BEGINNING OF SIMULATION
SOCIAL MODEL
POPULATION DISPLACEMENT
DPOP.K=DPOP.J+DT*RPOPJK
DPOP=0
DISPLACED POPULATION (persons)
RPOP.KL=(DVEG.K*POPD)/DAT
RATE OF POPULATION DISPLACEMENT
POPD=0
POPULATION DENSITY (persons/ha)
DAT=1
POPULATION ADJUSTMENT TIME (yrs)
EMPLOY MENT AND UNEMPLOY MENT SITUATION
EPRU.K=FRRU*DPOP.K
NUMDER OF DISPLACED PEOPLE EMPLOYED BY SIERRA RUTILE
FRRU=0.05
NORMAL PERCENTAGE EMPLOYED
UEMP.K=DPOP.K-EPRU.K
UNEMPLOYED POPULATION
MIGRATION INTO OTHER AREAS
MGRATE.K=FRMG*DPOP.K
OUT-MIGRATION INTO OTHER AREAS (persons)
FRMG=0.001
FRACTION OF DISPLACED PEOPLE NOT RESETTLED IN MINING ZONE
RESETTLEMENT AND ITS EFFECTS
SETTL.K=SETTLJ+DT*RSETTLJK
LAND NEED FOR RESETTLEMENT (ha)
SETTL=0
INITIAL LAND NEED FOR RESETTLEMENT (ha)
SPEC
RSETTL.KL=DPOP.K*LAND/DAT
RATE OF LAND NEED FOR RESETTLEMENT (ha/yr)
LAND=0.002
LAND NEEDED PER PERSON FOR RESETTLEMENT (ha/person)
CONTROL STATEMENTS
AVEG,DVEG,VRTL,EXCVL,,AVGR,LGDP,RELAND,REHAB,SLOSS,*
RLOSS,LOSSR,AGRU,FLAND,AGPRO,MATUR,POAGR,PRO,RPRO,*
AMOUNT,GOVT,RECOV,RECOST,DPOP,RPOP,UEMP,SCULT,SWAMPS,*
TCULT,FLAND,MATUR,MGRATE,SETTL,MNOUT,DVEGM,SCULT,*
USWAMP,VEGET,REMAT,RGOVT,RCOST,RAGPRO,RLGDP,RCOV,TREHAB
DT=0.25/LENGTH=2040/SA V PER=1/PRT PER=1/PLOTPER=1
SUB-SYSTEM 3 - NATIVE MINING SYSTEM
ECONOMIC MODEL
PRODUCTION
PRO.K=PROJ+DT*RPROJK
PRO=0
PRODUCED DAIMONDS (carats)
RPRO.KL=(DVEG.K*ARCON)/PAT
RATE OF DIAMOND PRODUCTION (carats/yr)
PAT=5
PRODUCTION ADJUSTMENT TIME
DVEGM.K=VRTL.K+DMINE.K
AREAS WHERE MINERAL WAS EXTRACTED (ha)
ARCON=279
AVERAGE DAIMOND CONTENT OF LAND (carats/ha)
OVERHEADS AND PROFITS
RECOV.K=RECOV J+DT*RCOV JK
RECOV =0
OVERHEADS AND PROFITS OF THE COMPANY (US$)
AMOUNT.KL=RPRO.KL*PRICE
AMOUNT FROM THE SALE OF DIAMONDS (US$/yr)
PRICE=52
WORLD MARKET PRICE FOR DAIMONDS (US$/carat)
RCOV.KL=A MOUNT.KL-RGOVT.KL-RCOST.KL
RECOVERY RATE FROM SALES (US$/yr)
TAXES AND ROYALTIES
GOVT.K=GOVT J+DT*RGOVT JK
GOVT=0
GOVERNMENT PROCEEDS FROM MINING (US$)
RGOVT.KL=TAX*AMOUNT.KL
TAXES AND ROYALTIES TO GOVERNMENT (USS$/yr)
TAX=0
PERCENTAGE TAX (dimensionless)
LOCAL GROSS DOMESTIC PRODUCT
LGDP.K=LGDP.J+DT*RLGDPJK
LGDP=0
LOCAL GROSS DOMESTIC PRODUCT (US$)
RLGDP.KL=RGOVT.KL+RAGRPO.KL-ERCOST.KL
RATE OF CHANGE OF GROSS DOMESTIC PRODUCT (US$/yr)
ELGDP.K=1-MULT1.K
EFFECT OF LOCAL GROSS DOMESTIC PRODUCT ON MINING
MULT1.K=0
MULTIPLIER THAT DETERMINES GOVERNMENT CONTROL OVER MINING
REHABILITATION COSTS
RECOST.K=RECOST J +DT*ERCOST JK
RECOST=0
REHABILITATION COST (US$/yr)
RCOST.KL=TREHAB.KL*COST
RATE OF REHABILITATION COSTS (US$/yr)
COST=1000
UNIT COST OF REHABILITATION (US$/ha)
ERCOST.KL=CLIP(RCOST.KL,0,TIME.K,2020)
COST UNDERTAKEN BY GOVERNMENT (US$/yr)
AGRICULTURAL PRODUCTION
AGPRO.K=AGPRO.J+DT*RAGPRO JK
AGPRO=0
INCOME OBTAINED FROM AGRICULTURAL PRODUCTION (US$)
RAGPRO.KL=AGRU.KL*AGCOST
RATE OF AGRICULTURAL PRODUCTION (US$/yr)
AGCOST=300
INCOME FROM AGRICULTURAL PRODUCTION PER HECTARE (US$/ha)
EFFECTS OF OVERHEADS AND PROFITS ON MINING ACTIVITIES
EREAT.K=1
EFFECTS OF OVERHEADS AND PROFITS
ECOLOGICAL MODEL
REHABILITATION MEASURES
RELAND.K=RELAND J+DT*TREHAB JK
NEWLY REHABILITATED LAND
RELAND=0
INITIAL REHABILITATED LAND (ha)
REHAB.K=NREHAB*EHAB.K*MNOUT.K
RATE OF REHABILITATION (ha/yr)
TREHAB.KL=CLIP(REHAB.K,0,TIME.K,1986)
VEGET.K=VEGET J+DT*REMAT JK
REHABILITATED AREAS THAT ACTUALLY MATURES (ha)
VEGET=0
INITIAL REHABILITATED AREAS THAT ACTUALLY MATURES (ha)
REMAT.KL=RSUCC*ERO.K*DELAY 1(TREHAB.KL,5)
MATURITY RATE OF THE NEWLY REHABILITATED AREA (ha/yr)
NREHAB=0
NORMAL REHABILITATED RATE PRACTICED BY SRL (frac./yr)
RSUCC=0.65
SUCCESS RATE OF THE TREES (fraction)
EHAB.K=TABHL(TEHAB,FNHAB.K,0,1,0,1)
EFFECT OF MINOUT AREA ON THE RATE OF REHABILITATION (dimensionless)
TEHA B=3.83/2.22/2.1/1.8/1.67/1.53/1.25/1.8/2.5/1.67/0
TABLE VARIABLE NAME FOR EHAB
FMHAB.K=RELAND.K/MAX(MNOUT.K,0.01)
FRACTION OF MINED OUT AREA REHABILITATED (dimensionless)
MNOUT.K=DMINE.K+EBORL.K+SAND.K
MINED OUT AREAS CAPABLE OF BEING REHABILITATED
ARTIFICIAL LAKES
VRTL.K=VRTLJ+TARTLA JK
AVAILABLE ARTIFICIAL LAKES (ha)
VRTL=0
INITIAL ARTIFICIAL LAKES (ha)
ARTLA.K=NARTLF*AVEG.K*EPVAR.K*EREAT.K*ELGDP.K
TARTLA.KL=CLIP(ARTLA.K,0,TIME.K,1978)
ARTIFICIAL LAKE FORMATION (ha)
NARTLF=0.000322
NORMAL ARTIFICIAL LAKE FORMATION (fraction/yr)
EPVAR.K=TABHL(TEPVAR,FRTLA.K,0,0.3,0.03)
EFFECT OF POTENTIAL VEGETATED LAND ON ARTIFICIAL LAKES
TEPVAR=.6/1.2/3.6/4.8/6/7.2/6/7.2/7.2/7.2/0
TABLE FOR THE EFFECT OF VEGETATED LAND ON ART. LAKES
FRTLA.K=VRTL.K/PVEG.K
FRACTION OF POTENTIAL VEGETATED LAND OCCUPIED BY ART. LAKES
SAND.K=SF*VRTL.K
AREA OF SAND TAILINGS (ha)
SF=.05
FRACTION OF ARTIFICIAL LAKES THAT IS SAND TAILINGS
EXCAVATION
EXCVL.K=EXCVLJ+DT*AMINE JK
TOTAL EXCAVATED AREA
EXCVL=0
INITIAL EACAVATED AREA (ha)
AMINE.KL=NMINEF*AVEG.K*EMVEG.K*EREAT.K*ELGDP.K
RATE OF EXCAVATION (ha/yr)
NMINEF=0.000322
NORMAL EXCAVATION FRACTION (fraction/yr)
EMVEG.K=TABHL(TEMVEG,FMINV.K,0,0.1,0.01)
EFFECT OF EXCAVATION ON VEGETATION
TEMVEG=1,1,1,1,1,1,1,1,1,1,0
TABLE VARIABLE NAME FOR EMVEG
FMINV.K=DMINE.K/PVEG.K
FRACTION OF VEGETATED AREAS THAT ARE MINED OUT
SOIL EROSION
SLOSS.K=SLOSS J*RLOSS JK
TOTAL SOIL LOSS (tons)
SLOSS=0
INITIAL SOIL LOSS (tons)
RLOSS.KL=R*K*C*SL.K*EROBLE.k*10
TOTAL SOIL LOSS RATE (tons/yr)
LOSSR.K=R*K*C*P*SL.K
SOIL LOSS RATE
R=1400
RAINFALL EROSION INDEX
K=0.0251
SOIL ERODIBILITY
P=l
EROSION CONTROL PRACTICE
SL.K=TABHL(TSL,L.K,0,50000,5000)
SLOPE LENGTH FACTOR
TSL=0/42.5/60.1/73.6/85/95/104.1/112.5/120.3/127.5/134.5
TABLE VARIABLE FOR SLOPE LENGTH FACTOR
L.K=EROBLE.K*10000/BR
LENGTH OF ERODABLE AREAS (m)
EROBLE.K=EX CVL.K+SAND.K
AREAS IN THE MINED OUT AREAS THAT ARE SUBJECT TO EROSION
BR=22.13
C=1
COVER FACTOR
ERO.K=TABHL(TERO,LOSSR.K,20,1520,300)
EFFECT OF SOIL EROSION ON REHABILITATION
TERO=1.0/.95/.92/.9/.85/.8
TABLE VARIABLE FOR ERO
TLOSSR=20
TOLERABLE SOIL LOSS RATE (kg/m2/yr)
VEGETATION AND AGRICULTURAL LAND-USE SYSTEMS
VEGETATION
AVEG.K=AVEG J+DT*(REMAT JK-ARTLA JK-AMINEJK)
AVAILABLE VEGETATION (ha)
AVEG=PVEG
INITIAL VEGETATED AREA (ha)
DVEG.K=PVEG.K-AVEG
DESTROYED VEGETATION (ha)
LEASE=3221504
TOTAL LEASE AREA (ha)
HOUSE=12652
TOTAL SETTLEMENT AREAS (ha)
UPLAND CULTIVATION
AVGR.K=AVGRJ+DT*(0.7*AGRU JK-0.3*A MINE JK-FALLOW JK)
AVAILABLE CULTIVATED AREAS (ha)
AVGR=74095
INITIAL CULTIVATED AREA (ha)
AGRU.KL=AGRUI.K-AGRU2.K
EFFECTIVE AGRICULTURAL LAND USE RATE (ha/yr)
AGRU1.K=NAGRF*POAGR.K*EAGRV.K
AGRICULTURAL LAND USE RATE (ha/yr)
NAGRF=0.02
NORMAL AGRICULTURAL LAND USE FRACTION (fraction/yr)
EAGRV.K=TABHL(TEAGRV,FAGRV,0,1,0.1)
TEAGRV =1/1/.95/.9/.85/.8/.75/.7/.5/.3/0
TABLE FOR EAGRV
FAGRV.K=AVGR.K/POAGR.K
FRACTION OF POTENTIAL AGRICULTURAL LAND THAT IS CULTIVATED
AGRU2.K=MGRATE.K*ALUP
AGRICULTURAL LAND USE DUE TO DISPLACED POPULATION (ha/yr)
ALUP=0.13
AVERAGE AGRICULTURAL LAND USE PER PERSON PER YEAR (ha/person/yr)
SWAMP CULTIVATION
SCULT.K=SCULT J +DT*(0.3*AGRU JK-0.3*ARTLA JK)
CULTIVATED VALLEY SWAMPS (ha)
SCULT=90202
INITIAL CULTIVATED SWAMPS (ha)
VALLEY SWAMPS
SWAMPS.K=SWAMPS J +DT*(0.3*AGRU JK-ARTLA.JK)
VALLEY SWAMPS INCLUDING MANGROVE
SWAMPS=90202
EXISTING SWAMPS (ha)
USWAMP.K=SWAMPS.K-SCULT.K
UNCULTIVATED SWAMPS (ha)
TCULT.K=SCULT.K+AVGR.K
TOTAL CULTIVATED AREAS (ha)
FARM BUSH
FLAND.K=FLAND J+DT*(FALLOW JK-FAMAT JK-0.5*A MINE. JK)
FARM BUSH
FLAND =2805930
INITIAL FARM BUSH (ha)
FALLOW.KL=(0.65*AVGR.K)/CP2
RATE AT WHICH THE CULTIVATED LAND IS PUT UNDER FALLOW (ha/yr)
CP2=2
AVERAGE CULTIVATION PERIOD (yrs)
SECONDARY FOREST (MATURED FALLOW LAND)
MATUR.K=MATURJ+DT*(FAMAT JK-0.7*AGRU JK-0.5*AMINEJK)
SECONDARY FOREST
MATUR=161075
SECONDARY FOREST (ha)
FAMAT.KL=FLAND.K/FP
RATE AT WHICH THE FALLOW LAND MATURES (ha/yr)
FP=10
THE FALLOW PERIOD (yrs)
POAGR.K=MATUR.K+USWAMP.K
POTENTIAL AGRICULTURAL LAND (ha)
TIME=1956
BEGINNING OF SIMULATION
SOCIAL MODEL
IN-MIGRATION
MIG.K=MIG J+DT*RMIG JK
MIG=0
MIGRANTS
RMIG.KL=FRMG*(DVEG.K*LABN)/DAT
RATE OF IN-MIGRATION
DAT=1
POPULATION ADJUSTMENT TIME
FRMG=2
FACTOR TO TAKE CARE OF FAMILIES
CONTROL STATEMENTS
AVEG,DVEG,VRTL,ARTLA,EXCVL,AVGR,*
LGDP,RELAND,REHAB,SLOSS,RLOSS,LOSSR,AGRU,FLAND,*
AGPRO,MATUR,POAGR,PRO,RPRO,AMOUNT,GOVT,RECOV,RECOST,DPOP,*
SAND,DMINE,SWAMPS,TCULT,FLAND,MATUR,DVEGM,*
USWAMP,VEGET,REMAT,RGOVT,RCOST,RAGPRO,RLGDP,RCOV,TREHAB,MIG,RMIG
SPEC DT=0.25/LENGTH=2040/SA V PER=1/PRTPER=1/PLOTPER=1
SUB-SYSTEM 4.- NATIONAL DIAMOND MINING COMPANY
ECONOMIC MODEL
PRODUCTION
PRO.K=PROJ+DT*RPROJK
PRO=0
PRODUCED DAIMONDS (carats)
RPRO.KL=(DVEG.K*ARCON)/PAT
RATE OF DIAMOND PRODUCTION (carats/yr)
PAT=4
PRODUCTION ADJUSTMENT TIME
DVEGM.K=VRTL.K+DMINE.K
AREAS WHERE MINERAL WAS EXTRACTED (ha)
ARCON=279
AVERAGE DAIMOND CONTENT OF LAND (carats/ha)
OVERHEADS AND PROFITS
RECOV.K=RECOV J+DT*RCOV JK
RECOV =0
OVERHEADS AND PROFITS OF THE COMPANY (US$)
AMOUNT.KL=RPRO.KL*PRICE
AMOUNT FROM THE SALE OF DIAMONDS (US$/yr)
PRICE=52
WORLD MARKET PRICE FOR DAIMONDS (US$/carat)
RCOV.KL=A MOUNT.KL-RGOVT.KL-RCOST.KL
RECOVERY RATE FROM SALES (US$/yr)
TAXES AND ROYALTIES
GOVT.K=GOVT J+DT*RGOVT JK
GOVT=0
GOVERNMENT PROCEEDS FROM MINING (US$)
RGOVT.KL=TAX*AMOUNT.KL
TAXES AND ROYALTIES TO GOVERNMENT (USS$/yr)
TAX=.0.51
PERCENTAGE TAX (dimensionless)
LOCAL GROSS DOMESTIC PRODUCT
LGDP.K=LGDP.J+DT*RLGDPJK
LGDP=0
LOCAL GROSS DOMESTIC PRODUCT (US$)
RLGDP.KL=RGOVT.KL+RAGRPO.KL-ERCOST.KL
RATE OF CHANGE OF GROSS DOMESTIC PRODUCT (US$/yr)
ELGDP.K=1-MULT1.K
EFFECT OF LOCAL GROSS DOMESTIC PRODUCT ON MINING
MULT1.K=0
MULTIPLIER THAT DETERMINES GOVERNMENT CONTROL OVER MINING
REHABILITATION COSTS
RECOST.K=RECOST J+DT*RCOST JK
RECOST=0
REHABILITATION COST (US$/yr)
RCOST.KL=TREHAB.KL*COST
RATE OF REHABILITATION COSTS (US$/yr)
COST =2469
UNIT COST OF REHABILITATION (US$/ha)
ERCOST.KL=CLIP(RCOST.KL,0,TIME.K,2020)
COST UNDERTAKEN BY GOVERNMENT (US$/yr)
AGRICULTURAL PRODUCTION
AGPRO.K=AGPRO.J+DT*RAGPRO JK
AGPRO=0
INCOME OBTAINED FROM AGRICULTURAL PRODUCTION (US$)
RAGPRO.KL=AGRU.KL*AGCOST
RATE OF AGRICULTURAL PRODUCTION (US$/yr)
AGCOST=300
INCOME FROM AGRICULTURAL PRODUCTION PER HECTARE (US$/ha)
EFFECTS OF OVERHEADS AND PROFITS ON MINING ACTIVITIES
EREAT.K=1
EFFECTS OF OVERHEADS AND PROFITS
ECOLOGICAL MODEL
REHABILITATION MEASURES
RELAND.K=RELAND J+DT*TREHAB JK
NEWLY REHABILITATED LAND
RELAND=0
INITIAL REHABILITATED LAND (ha)
REHAB.K=NREHAB*EHAB.K*MNOUT.K
RATE OF REHABILITATION (ha/yr)
TREHAB.KL=CLIP(REHAB.K,0,TIME.K,1986)
VEGET.K=VEGET J+DT*REMAT JK
REHABILITATED AREAS THAT ACTUALLY MATURES (ha)
VEGET=0
INITIAL REHABILITATED AREAS THAT ACTUALLY MATURES (ha)
REMAT.KL=RSUCC*ERO.K*DELAY 1(TREHAB.KL,5)
MATURITY RATE OF THE NEWLY REHABILITATED AREA (ha/yr)
NREHAB=0
NORMAL REHABILITATED RATE PRACTICED BY SRL (frac./yr)
RSUCC=0.75
SUCCESS RATE OF THE TREES (fraction)
EHAB.K=TABHL(TEHAB,FNHAB.K,0,1,0,1)
EFFECT OF MINOUT AREA ON THE RATE OF REHABILITATION (dimensionless)
TEHA B=3.83/2.22/2.1/1.8/1.67/1.53/1.25/1.8/2.5/1.67/0
TABLE VARIABLE NAME FOR EHAB
FMHAB.K=RELAND.K/MAX(MNOUT.K,0.01)
FRACTION OF MINED OUT AREA REHABILITATED (dimensionless)
MNOUT.K=EXCVL.K
MINED OUT AREAS CAPABLE OF BEING REHABILITATED
EXCAVATION
EXCVL.K=EXCVLJ+DT*AMINE JK
TOTAL EXCAVATED AREA
EXCVL=0
INITIAL EACAVATED AREA (ha)
VEARTH.K=EXCVL.K*10000*DP
VOLUME OF EARTH BORROWED (m3)
DP=3
AVERAGE DEPTH OF MINING (m)
DRY MINING OPERATION
DMINE.K=DMINE.J+DT*AMINE JK
EXCAVATED AREAS THROUGH DRY MINING (ha)
DMINE=0
INITIAL DRY MINED AREAS (ha)
MINE.KL=NMINEF*AVEG.K*EMVEG.K*EREAT.K*ELGDP.K
RATE OF DRY MINING (ha/yr)
NMINEF=0.0014
NORMAL DRY MINING FRACTION (fraction/yr)
ACTUAL STARTING DATE OF THE DRY MINING OPERATION
EMVEG.K=TABHL(TEMVEG,FMINV.K,0,0.1,0.01)
EFFECT OF DRY MINING ON VEGETATION
TEMVEG=1,1,1,1,1,1,1,1,1,1,0
TABLE VARIABLE NAME FOR EMVEG
FMINV.K=DMINE.K/PVEG.K
FRACTION OF VEGETATED AREAS THAT ARE MINED OUT
SOIL EROSION
SLOSS.K=SLOSS.J*RLOSS JK
TOTAL SOIL LOSS (tons)
SLOSS=0
INITIAL SOIL LOSS (tons)
RLOSS.KL=R*K*C*SL.K*EROBLE.k*10
TOTAL SOIL LOSS RATE (tons/yr)
LOSSR.K=R*K*C*P*SL.K
SOIL LOSS RATE
R=1400
RAINFALL EROSION INDEX
K=0.0251
SOIL ERODIBILITY
P=l
EROSION CONTROL PRACTICE
SL.K=TABHL(TSL,L.K,0,50000,5000)
SLOPE LENGTH FACTOR
TSL=0/42.5/60.1/73.6/85/95/104.1/112.5/120.3/127.5/134.5
TABLE VARIABLE FOR SLOPE LENGTH FACTOR
L.K=EROBLE.K*10000/BR
EROBLE.K=EX CVL.K+SAND.K
AREAS IN THE MINED OUT AREAS THAT ARE SUBJECT TO EROSION
BR=22.13
C=1
COVER FACTOR
ERO.K=TABHL(TERO,LOSSR.K,20,1520,300)
EFFECT OF SOIL EROSION ON REHABILITATION
TERO=1.0/.95/.92/.9/.85/.8
TABLE VARIABLE FOR ERO
TLOSSR=20
TOLERABLE SOIL LOSS RATE (kg/m2/yr)
VEGETATION AND AGRICULTURAL LAND-USE SY STEMS
VEGETATION
AVEG.K=AVEG J+DT*(REMAT JK-AMINE JK)
AVAILABLE VEGETATION (ha)
AVEG=PVEG
INITIAL VEGETATED AREA (ha)
DVEG.K=PVEG.K-AVEG
DESTROY ED VEGETATION (ha)
LEASE=77440
TOTAL LEASE AREA (ha)
HOUSE=252
TOTAL SETTLEMENT AREAS (ha)
PVEG.K=LEASE-HOUSE
UPLAND CULTIVATION
AVGR.K=AVGRJ+DT*(0.7*AGRU JK-0.2*A MINE JK-FALLOW JK)
AVAILABLE CULTIVATED AREAS (ha)
AVGR=14823
INITIAL CULTIVATED AREA (ha)
AGRU.KL=AGRUI.K-AGRU2.K
EFFECTIVE AGRICULTURAL LAND USE RATE (ha/yr)
AGRU1.K=NAGRF*POAGR.K*EAGRV.K
AGRICULTURAL LAND USE RATE (ha/yr)
NAGRF=0.02
NORMAL AGRICULTURAL LAND USE FRACTION (fraction/yr)
EAGRV.K=TABHL(TEAGRV,FAGRV,0,1,0.1)
TEAGRV =1/1/.95/.9/.85/.8/.75/.7/.5/.3/0
TABLE FOR EAGRV
FAGRV.K=AVGR.K/POAGR.K
FRACTION OF POTENTIAL AGRICULTURAL LAND THAT IS CULTIVATED
AGRU2.K=MGRATE.K*ALUP
AGRICULTURAL LAND USE DUE TO DISPLACED POPULATION (ha/yr)
ALUP=0.13
AVERAGE AGRICULTURAL LAND USE PER PERSON PER YEAR (ha/person/yr)
SWAMP CULTIVATION
SCULT.K=SCULT J +DT*0.3*AGRU JK
CULTIVATED VALLEY SWAMPS (ha)
SCULT=270
INITIAL CULTIVATED SWAMPS (ha)
VALLEY SWAMPS
SWAMPS.K=SWAMPSJ+DT*(0.3*AGRU JK)
VALLEY SWAMPS INCLUDING MANGROVE
SWAMPS=282
EXISTING SWAMPS (ha)
RSWAMPS.KL=0
USWAMP.K=SWAMPS.K-SCULT.K
UNCULTIVATED SWAMPS (ha)
TCULT.K=SCULT.K+AVGR.K
TOTAL CULTIVATED AREAS (ha)
FARM BUSH
FLAND.K=FLAND J+DT*(FALLOW JK-FAMAT JK-0.3*A MINE. JK)
FARM BUSH
FLAND =42476
INITIAL FARM BUSH (ha)
FALLOW.KL=(0.65*AVGR.K)/CP2
RATE AT WHICH THE CULTIVATED LAND IS PUT UNDER FALLOW (ha/yr)
CP2=2
AVERAGE CULTIVATION PERIOD (yrs)
SECONDARY FOREST (MATURED FALLOW LAND)
MATUR.K=MATURJ+DT*(FAMAT JK-0.7*AGRU JK-0.5*AMINEJK)
SECONDARY FOREST
MATUR=23268
SECONDARY FOREST (ha)
FAMAT.KL=FLAND.K/FP
RATE AT WHICH THE FALLOW LAND MATURES (ha/yr)
FP=10
THE FALLOW PERIOD (yrs)
POAGR.K=MATUR.K+USWAMP.K
POTENTIAL AGRICULTURAL LAND (ha)
TIME=1930
BEGINNING OF SIMULATION
SOCIAL MODEL
POPULATION DISPLACEMENT
DPOP.K=DPOP.J+DT*RPOPJK
DPOP=0
DISPLACED POPULATION (persons)
RPOP.KL=(DVEG.K*POPD)/DAT
RATE OF POPULATION DISPLACEMENT
POPD=0
POPULATION DENSITY (persons/ha)
DAT=1
POPULATION ADJUSTMENT TIME (yrs)
EMPLOY MENT AND UNEMPLOY MENT SITUATION
EPRU.K=FRRU*DPOP.K
NUMDER OF DISPLACED PEOPLE EMPLOYED BY SIERRA RUTILE
FRRU=.05
NORMAL PERCENTAGE EMPLOYED
UEMP.K=DPOP.K-EPRU.K
UNEMPLOYED POPULATION
MIGRATION INTO OTHER AREAS
MGRATE.K=FRMG*DPOP.K
SPEC
OUT-MIGRATION INTO OTHER AREAS (persons)
FRMG=0.001
FRACTION OF DISPLACED PEOPLE NOT RESETTLED IN MINING ZONE
RESETTLEMENT AND ITS EFFECTS
SETTL.K=SETTLJ+DT*RSETTLJK
LAND NEED FOR RESETTLEMENT (ha)
SETTL=0
INITIAL LAND NEED FOR RESETTLEMENT (ha)
RSETTL.KL=DPOP.K*LAND/DAT
RATE OF LAND NEED FOR RESETTLEMENT (ha/yr)
LAND=0.002
LAND NEEDED PER PERSON FOR RESETTLEMENT (ha/person)
CONTROL STATEMENTS
AVEG,DVEG,VRTL,ARTLA,EXCVL,AVGR,*
LGDP,RELAND,REHAB,SLOSS,LOSSR,AGRU,FLAND,*
AGPRO,MATUR,POAGR,PRO,RPRO,AMOUNT,GOVT,RECOV,RECOST,DPOP,*
RPOP,UEMP,DMINE,SWAMPS,TCULT,TCULT,FLAND,MATUR,DVEGM,*
USWAMP,VEGET,REMAT,RGOVT,RCOST,RAGPRO,RLGDP,RCOV,TREHAB,MIG,RMIG
DT=0.25/LENGTH=2040/SA V PER=1/PRT PER=1/PLOTPER=1
LIST OF VARIABLES
SYMBOL
AGPRO
AGCOST
AGRU
AGRU1
AGRU2
AMOUNT
ALUP
ARCON
ARTLA
AVEG
DPOP
DSTORE
DVEG
DMINE
ERCOST
TYPE
Pr PErPr BE BHAQDAAQAQAQNQAMFH DAO OD YSERA
rrrr Frrrr
DEFINITION
INCOME ABTAINED FROM AGRICULTURAL PRODUCTION (US$)
UNIT COST OF AGRICULTURAL PRODUCTION (US$/ha)
EFFECTIVE AGRUCULTURAL LAND USE RATE (US$/yr)
NORMAL AGRUCULTURAL LAND USE RATE (US$/yr)
AGRUCULTURAL LAND USE DUE TO DISPLACED
POPULATION (US$/yr)
AMOUNT OBTAINED FROM THE SALE OF MINERAL (US$/yr)
AVERAGE AGRICULTURAL LAND USE PER PERSON PER
YEAR (ha/person/ yr)
AVERAGE MINERAL CONTENT OF LAND (tons/yr)
ARTIFICIAL LAKE FORMATION (ha/yr)
AVAILABLE VEGETATION (ha)
CULTIVATED LAND AREA (ha)
INITIAL CULTIVATED LAND (ha)
BREATH OF SLOPE (m)
VEGETATION COVER FACTOR
UNIT COST OF REHABILITATION (US$/ha)
AVERAGE CULTIVATION PERIOD (yrs)
POPULATION ADJUSTMENT TIME (yrs)
AVERAGE DEPTH OF BORROWED PITS (m)
AVERAGE DEPTH OF DAM (m)
DISPLACED POPULATION (persons)
RATE OF DESTRUCTION OF LAND THROUGH WATER
STORAGE (ha/yr)
DESTROYED VEGETATION (ha)
EXCAVATED AREAS THROUGH DRY MINING (ha)
RATE OF REHABILITATION COST INCURRED BY
GOVERNMENT (US$/yr)
EFFECT OF ROAD CONSTRUCTION ON VEGETATION
EFFECT OF MINED OUT AREAS ON REHABILITATION
Effect Of Potential V egetated Land On Artificial Lakes.
EFFECTS OF OVERHEADS AND PROFITS ON MINING
EFFECT OF OVERHEADS AND PROFITS ON ROAD
CONSTRUCTION RATE
EFFECT OF OVERHEADS AND PROFITS ON THE ARTIFICIAL
LAKE FORMATION.
BORROWED AREAS (HA)
EFFECT OF POTENTIAL VEGETATED AREA ON AGRICULTURE
EFFECT OF MINED OUT AREAS ON REHABILITATION
NUMBER OF DISPLACED PEOPLE EMPLOYED IN MINING (persons)
EFFECT OF POTENTIAL VEGETATED AREAS ON ARTIFICIAL
LAKE FORMATION
EFFECT OF EARTH BORROWING ON VEGETATION
EFFECT OF SOIL EROSION ON REHABILITATION
EFFECT OF WATER STORAGE ON VEGETATION
EFFECT OF LOCAL GDP ON MINING
EXCV
EXCVL
EXVEG
EMVEG
FAGRV
FALLOW
FAMAT
FEXCV
FLAND
FLANDN
FROVEG
FARTLA
FMHAB
FBOVEG
FSTOVO
GOVT
K
LOSSR
L
LSTORE
MATUR
MATURN
MNOUT
MINE
NMINEF
NAGRF
NREHAB
NARTLF
NEXCVF
NSTORF
NROADF
QoFrre SFPOMPFTRD DEerrraD
PRODADAPEHFAPANQAAQ A ANAAAADRFAHPKAFAMySE Bb
EXCAVATION RATE (ha/yr).
EXCAVATED LAND (ha)
EFFECT OF POTENTIAL VEGETATION ON EXCAVATION
EFFECT OF DRY MINING ON VEGETATION
FRACTION OF POTENTIAL AGRIC. LAND THAT IS CULTIVATED
RATE AT WHICH CULTIVATED LAND IS PLACED UNDER
FALLOW (ha/yr)
RATE AT WHICH FALLOW LAND MATURES (ha/yr)
FRACTION OF POTENTIAL VEGETATED LAND EXCAVATED
LAND UNDER FALLOW (ha)
INITIAL FALLOW LAND (ha)
FRACTION OF VEGETATED LAND THAT IS USED FOR
ROADS CONSTRUCTION
FRACTION OF LAND OCCUPIED BY ARTIFICIAL LAKES
FRACTION OF MINED OUT AREA REHABILITATED
FRACTION OF VEGETATED LAND BORROWED
FRACTION OF VEGETATED AREAS THAT ARE MINED OUT
FALLOW PERIOD (yr)
NORMAL PERCENTAGE OF DISPLACED PEOPLE EMPLOY ED
IN MINING
FRACTION OF POTENTIAL VEGETATED LAND OCCUPIED
BY ARTIFICIAL LAKES
FRACTION OF TOTAL PRECIPITATION THAT IS STORED
TAXES AND ROYALTIES TO GOVERNMENT (US$)
SOIL ERODIBILITY FACTOR
EFFECTIVE SOIL LOSS RATE (tons/ha/yr)
LENGTH OF THE SLOPE (m)
LAND DESTROYED DUE TO WATER STORAGE IN DAMS (ha)
MATURED FALLOW LAND - SECONDARY FOREST (ha)
INITIAL MATURED FALLOW LAND (ha)
MINED OUT AREA NOT FLOODED (ha)
RATE OF DRY MINING (ha/yr)
NORMAL DRY MINING FRACTION (frac./yr)
NORMAL AGRIC. LAND USE FRACTION
REHABILITATION RATE IMPOSED BY GOVERNMENT (fraction/yr)
NORMAL ARTIFICIAL LAKE FORMATION FRACTION. (fraction/yr)
NORMAN EXCAVATION FRACTION (fraction/yr)
NORMAL FRACTION OF LAND DESTROYED THROUGH
WATER STORAGE (fraction/yr)
NORMAL FRACTION OF ROADS CONSTRUCTED PER
YEAR (fraction/yr)
NORMAL EARTH BORROWING RATE (frac./yr)
PRODUCTION ADJUSTMENT TIME (hrs)
POTENTIAL VEGETATED AREA (ha)
World Market Price For Mineral (US$/unit)
POTENTIAL AGRIC. LAND (ha)
POPULATION DENSITY (persons/ha)
PRODUCED MINERAL (unit)
OVERHEADS AND PROFITS (US$)
REHABILITATION COSTS (US$)
RAINFALL EROSION INDEX
RATE OF REHABILITATION COST INCURMENT (US/yr)
REHABILITATION RATE (ha/yr)
RATE OF AGRICULTURAL PRODUCTION (US$/yr)
REHABILITATED LAND (ha)
MINING COMPANY'S OVERHEADS AND PROFITS (US$)
TAX
TEAGRV
TEPVAR
TERVEG
TEHAB
TERO
TESTOV
TEXVEG
TLOSSR
TIME
TOVOL
TSL
UEMP
VEGET
VEARTH
VOLS
VRTL
HO AQMrarSsemrazWAMD
a
Hagan
reer eyozw
RATE OF MATURITY OF THE REHABILITATED AREA (ha/yr)
SURFACE AREA OF ROADS CONSTRUCTED (ha)
SUCCESS RATE (FRACTION)
TOTAL RATE OF SOIL LOSS (tons/yr)
RATE OF POPULATION DISPLACEMENT (persons/yr)
RATE OF MINERAL PRODUCTION ((tons/yr)
RATE OF WATER STORAGE m3/yr)
SLOPE-LENGTH FACTOR
AREA OF SAND TAILINGS (ha)
FRACTION OF ARTIFICIAL LAKE THAT IS SAND TAILINGS
TOTAL SOIL LOSS (TONS)
STORAGE ADJUSTMENT TIME (yrs)
STORAGE VOLUME (m3)
TIME TAKEN FOR THE REHABILITATED AREAS TO MATURE TO
A FULLY VEGETATED AREA (yrs)
TAX AND ROYALTIES (fraction)
TABLE FOR THE EFFECT OF POTENTIAL VEGETATED
AREA ON AGRICULTURE
TABLE FOR THE EFFECT OF VEGETATED LAND ON
ARTIFICIAL LAKES
TABLE FOR THE EFFECT OF ROAD CONSTRUCTION ON VEGETATION
TABLE FOR THE EFFECT OF MINE OUT AREA ON REHABILITATION
TABLE FOR THE EFFECT OF EROSION ON REHABILITATION
TABLE FOR THE EFFECT OF WATER STORAGE ON VEGETATION
TABLE FOR THE EFFECT OF POTENTIAL VEGETATION
ON EXCAVATION
TOLERABLE SOIL LOSS RATE (tons/yr)
BEGINNING OF SIMULATION
TOTAL MAXIMUM VOLUME OF WATER IN THE MINING AREA (m3/yr)
TABLE VARIABLE FOR SLOPE-LENGTH FACTOR
UNEMPLOYED POPULATION (persons)
RE-VEGETATED AREAS THAT ACTUALLY MATURES (ha)
VOLUME OF EARTH BORROWED (cubic meter)
VOLUME TO STORED (m3)
AVAILABLE ARTIFICIAL LAKES (ha)
