Geospatial Analysis
of Cotton Production Potential in Sub-Saharan Africa
Arthur Green1 and Kai Bucher2
1McGill
University
Department
of Geography
805 Sherbrooke St. W.
– Montreal, Quebec
Canada
H3A 2K6
Email: arthur.green@mcgill.ca
2International Food &
Agricultural
Trade Policy Council (IPC)
1616 P Street, NW
Washington DC - 20036
USA
Email: bucher@agritrade.org
Abstract. This study applies a
geospatial analytic
approach to assess human, economic and physical constraints to cotton
production potential in Mali,
Burkina Faso and Benin.
A
Geographic Information System (GIS) is used to create a cost grid
reflecting an
integrated, geographic model of cotton production potential including a
theoretically diverse set of parameters. Parameters include
infrastructure,
socio-political, demographic, agronomic, and novel geographic
parameters among
others. The GIS model uses geostatistics and cost grid functions to
explore and
expand upon existing datasets. The study results in a visual
representation of
cotton production potential.
Keywords. Sub-Saharan Africa;
geospatial analysis; GIS; cotton; agriculture; production potential
1
Introduction and
Problem Statement
This
study applies a geospatial analytic approach to assess human, economic
and
physical constraints to cotton production potential in Mali, Burkina
Faso
and Benin.
A Geographic Information System (GIS) is used to create a cost grid
reflecting
a theoretically diverse set of parameters including infrastructure,
socio-political, demographic, agronomic, and geographic parameters
among
others. Although still in early stages, the goal of this model is to
allow policy
makers, investors, or academic researchers to evaluate hypothesized
scenarios
for the cotton sub-sector.
Cotton
plays an important role in the economies and societies of Sub-Saharan
Africa
(SSA). It is estimated that up to 16 million people directly or
indirectly
benefit from the cotton production economy generated by between two and
three
million of the region’s small farmers [13]. In fact, the combined
region of
West and Central Africa is set to become the world’s second largest
exporting
region of cotton after the United States. Cotton
production in West Africa has rapidly
increased over recent decades, rising from some 150,000 tons of cotton
lint in
the 1970s to over one million tons in 2003-2004 [13]. Despite this increase, variability in parameters
influencing cotton production creates a spatially heterogeneous
landscape for
cotton production in SSA [8, 10]. The purpose of this paper is to apply
a
transparent, integrative, and parsimonious model to geographically
understand
what factors limit cotton production potential in the region. The
three
countries targeted in this study - Mali, Burkina Faso,
and Benin
-were chosen because of
their economies’ high dependency on cotton and because they are among
the largest
cotton producers in SSA [4]. Moreover, given the significance of cotton
for the
livelihood of millions people in the region, the potential benefits of
a
successful cotton industry could have a wide-spread impact on society
as a
whole.
Cotton
production potential (CPP) in West and
Central African countries is a dynamic system conditional, like many
human-environment
systems, on complex trends (ecologic, demographic, political, economic,
etc.)
that interact on multiple scales (global, regional, national,
provincial,
household, and individual) [2]. There are many approaches to measuring
commodity production potential that use political, agronomic, or other
limited
sets of variables [5, 13, 21]. However, these approaches are often not
integrated or comprehensive in scope, focusing only on one
theoretically
isolated set of variables as opposed to examining how parameters
interact in
combination with each other. Moreover, these approaches do not usually
provide
models with explicit manipulable parameters allowing comparative
regional
studies.
One possible
way of combining, visualizing, and
evaluating these parameters is by operationalization in a GIS. The
advantage of
using GIS stems from the fact that they require specific parameters,
provide
manipulable conditions, and are geographically referenced. By requiring
that
information be spatially explicit, a GIS can identify areas of
potential growth
or pattern anomaly which can then be investigated to understand how
policy may
play a role in promoting development.
With cotton
being an input-intensive crop, its
production inevitably raises questions as to its environmental impact
and
long-term sustainability. The central elements in expanding cotton
yields over
the last decades have generally been input-related, focusing on
enhancing
irrigation as well as fertilizer and pesticide use. However, it is
precisely this
strategy that is also a source of environmental degradation [1], often
leading
to soil degradation and decline in soil fertility [3]. The following
sustainability problems have been reported in relation to cotton
cultivation [14]:
degraded land as a result of salinization and erosion; water depletion
by
excessive use of soil and surface water; natural habitat conversion due
to
cutting of forests and dam constructions; eutrophication of surface
water;
wildlife contamination by pesticides (insects, fish, mammals, birds);
human
health due to direct pesticide intake primarily by farm workers but
also by
regional inhabitants through contamination of drinking water and food
contamination. Furthermore, the monoculture characteristic of cotton
often adds
to the destructive effect on natural habitats as farmers expand their
production areas to increase yields. Cotton fields often lack
vegetative cover
and organic matter to limit the impact of soil erosion [3, 26]. All
these
factors have very serious implications for intensive and wide-spread
cotton
production for long-term sustainability.
This model
provides a tool to identify areas for
future production and the constraints that limit those areas. The
ongoing
development of the CPP model recognizes that cotton growth areas in Africa are ever changing [13], but sees benefit
in
attempting to understand the complex systems of agency and structural
constraints to sustainable production.
2
Methods
This
section describes procedural steps in data selection, parameter scaling
and
weighing, and construction of a GIS reflecting geographic patterns of
cotton
production potential. The ultimate goal of these processes is to
construct a
cost grid [29] that reflects the most likely areas for cotton expansion
in Mali, Burkina Faso,
and Benin.
In the CPP MODEL cost grid, low value cells are considered to be more
likely to
be available for cotton expansion and higher value cells are less
likely to
have cotton. The possible min-max range for pixel values in the cost
grid is
0-485, though the true range of values (185.24-325.07) is much smaller.
2.1
Parameter Selection
Preliminary
parameter selection was modified by data availability and reliability,
which
are problems for human-environment models in general [22] and for the
cotton sub-sector
of SSA in particular. The 33 parameters selected for this model are
presented
in Table 1. Reducing the parameters,
specifically those based on coarse or unreliable data is seen as a next
step
for this model. The current parameters were divided into three
heuristic
categories: economic, human, and physical assets or capitals. The
division of
the parameters into these three groups is a heuristic device that does
not
necessarily reflect any version of capital theories [23-25]. The
divisions are
manipulable and do not ultimately change the model outcome because
every
parameter is independently scaled and weighed.
2.2
Parameter
Processing: Scaling and Weighing
Parameter
data must be scaled and given a specific weight relative to all other
parameters. Scaling of the data reflects levels of heterogeneity,
statistical
patterns, and social categories in data. Weighing the data gives each
scale a
relative value which defines relationships between parameters. The
scaling and
weighing of parameters were undertaken in static equilibrium. Complex
system
interactions [12] and emergent properties were not given dynamic,
longitudinal
equations. Although, modeling of the complex, dynamic relationships
that exist
between parameters is ultimately necessary for scenario development.
Modeling
these relationships is also viewed as a next step for the CPP model.
National
Scale
Data
came in scales at and below the national scale. Different methods were
applied to
datasets due to the way that GIS recognize raster and vector data at
different
scales and due to data quality (preclassified or raw). Most
of the parameters (23/33) lacked
disaggregated data below the national scale. These parameters are
presented in Table
2. Since these were all processed in a similar fashion, the scaling
and
weighing of one of these parameters (Arable Land)
is presented
here in detail as an example of the basic procedures.
Arable Land
This
parameter is judged to be an indicator of land that is most likely for
the
extension of commercial crops and by inference cotton expansion in SSA
where
the large majority of cotton is grown on 1-2 ha. plots by small
farmers.
Reliability of the data suffers in the data acquisition stage. Data was
reported individually by different countries that may have different
data
standards or definitions. The average hectares for the years 1997-2002
was
classified against all SSA countries on a relative scale of 0-4 using
Jenks’
Natural Breaks formula [17-18]. A weight of “*2” was applied to the
scale
making a maximum score of eight for the parameter. The lower the value,
the
better performing a parameter. The final scores (Table 3)
were converted into raster grids representing the three
countries.
Subnational
Scale
Ten
of CPP model’s 33 variables represented phenomena at the subnational
scale.
Various methods were needed to operationalize, scale, and weigh this
data. Two
of the parameters (Distance to Ginneries and
Distance to Cities) were originally
vector files that were converted to raster and then processed using the
Straight-line
Distance function in Spatial Analyst (ArcGIS). The results of this
process were
raster grids that expressed distance to center points (cities and
ginneries) in
kilometers. Classification of these grids supposed that distances of 70
km
intervals served as effective class levels for farmers trying to reach
markets
and transport to ginneries.
The
reclassification of the grids for many
parameters took place on data that had already been classified. For
example, Problem Soils, Terrestrial Ecoregions,
Historic Cotton Areas, and Road
Conditions parameters came as preclassified datasets that had to be
reclassified in relation to the growth and transport of cotton. These
processes
are detailed in Table 4.
Unfortunately, estimates of Producer and User Accuracy were not
available for
all the preclassified data received. Technical sheets from the FAO Land
and Water Development division
determined parameters of cotton growth and scales. Some of the
subnational
parameters (Precipitation and Population
Density) were subjected to
Jenks or manual data reclassifications, but unlike the national level
data,
this was done in relation to the data values in the grid itself and not
to
other SSA countries or administrative districts. All grids were clipped
to
represent Mali, Burkina Faso, and Benin.
2.3
Stepwise Procedures:
Cost Grid and Statistical Validation
Once
all data was in raster format it was subjected to the basic, cell-based
analysis and geoprocessing procedures following [28] to form a cost
grid.
The
resulting datasets express the value of all land in the region for
cotton
production potential at a 1 km resolution (pixel).
As mentioned
above, validation of the model is
limited by data constraints. Ground truth data acquisition will take
place in
the next stage of the model. Despite this, there is a need to compare
our data
results against some common standards. We cross-validate the model
against
datasets representing historic provincial cotton production data for
2002 from
the Sahel West African Club (SWAC) [13] and an agroecological model
developed
by the FAO and IIASA [5].
We
compare these datasets at the lowest administrative level, though in
full
realization that the scale of SWAC data biases results and limits
inferences
below the provincial level.
In order to
compare the models some data
standardization was necessary. Data values for all three models were
summarized
at the lowest administrative level in Mali,
Benin, and Burkina Faso.
Data
thus summarized was then reclassified according to a ranking system of
eight
hierarchical steps based on IIASA data. Table
5 shows that SWAC and CPP administrative units are classified by
range (for
SWAC production in metric tons and for CPP mean
values) whereas the IIASA values were
reclassified based on median values due to the more conservative
estimate of Very High values that median provided
over mean for IIASA.
The IIASA
pixel resolution of 9 km was based on
a multitude of ecological parameters that they detail in their
methodology.
We
collapsed the original 0-NoData and
9-Water values with Not Suitable to
form the category Very Marginal. The SWAC data was
problematic in that we were not able to get data at the lowest
administrative
levels by the time this paper was being written, therefore provincial
level
production data was disaggregated to lower administrative levels. The
raw SWAC
provincial scale production numbers were grouped into an eight step
hierarchy
similar to the IIASA model. After rank ordering the historic production
capability per province, we assumed that all lower administrative
levels would
maintain an equal ranking relative to each other. Finally, since it was
difficult to assign a contextualized understanding of the data produced
in our
model, we used Jenks to classify our model into eight divisions like
SWAC and
IIASA schemes. Figure 1 shows the
general distribution of the administrative units by rank for each
model. The
reclassification results (Table 6) indicate
that the classification structure was sufficient in producing similar
kurtosis
and standard errors in the model datasets.
Exploratory
statistical analyses were performed
on the data sets at regional and national scales to observe how the
models
correlated regionally and when national units were considered.
Administrative
units were subjected to ANOVA tests at both the regional and national
scales. Correlation
matrices allowed observation of directionality between the models.
Overall
accuracy was calculated by smallest administrative unit for the entire
region.
Kappa Index of Agreement (KIA) was performed on raster versions of the
smallest
administration units. All subsequent
maps were produced in ESRI ArcGIS 9.1. Results are presented as both
statistics
and maps to help visualize data relationships.
3
Results
Parameter
values at the national scale are reported in
Table 7. Visual comparison of the economic, human, and physical
maps (Figures 2-4) reveals spatial
heterogeneity at the pixel level and how coarse national scale
parameters can
heavily influence cost grid scores. The outlines of the Economic Assets
map (Figure 2) reflect parameters at the
national scale; whereas the Physical and Human Assets maps (Figures
3 & 4) better represent the
results of incorporating localized data. Differences in the capital
assets maps
scores reflect how much weight was given to parameters for the cost
grid
calculation. The physical infrastructure dimension was nearly
equivalent to the
combined values of economic and human dimensions. Table 8
summarizes the physical, economic, and human descriptive
statistics over the entire region. These three grids combine to form
the
overall cost grid in Figure 5. The
final cost grid shows cotton production potential to be highest in
sections of
western Mali, near
Mopti in Mali,
close to bodies of water, in a belt
through the mid and southern reaches of Burkina
Faso, and in the northern areas of Benin.
The SWAC and
IIASA models were introduced in
order to compare and validate the results of the CPP model. Neither
SWAC nor
IIASA represent true cotton production potential; they are useful
comparative
models for drawing inferences about the accuracy and relevancy of the
CPP map.
Maps of the administrative level groupings are shown in Figures
6-8. Descriptive statistics for the administrative divisions
of model data at the regional level are presented in Table
9.
Pixels
classified according to the eight step
hierarchy were examined through the use of a Kappa Index of Agreement
test
(KIA) (see Table 10) [19]. The
results indicate poor agreement of the CPP model with the IIASA and
SWAC models
at the pixel level. IIASA and CPP show better agreement than the SWAC
and CPP.
Overall accuracy assessments of the models’ pixels were high (49%)
between CPP
and the SWAC and IIASA models. However, due to the numerical bias of
using
pixels from the agreeably unsuitable land in northern Mali,
we prefer to use the
administrative levels as a better measure for overall accuracy.
Aggregating
pixel values to local administrative levels, the models were
statistically
compared. Out of the 650 administrative units, 106 were similarly
classified by
CPP and IIASA, 41 were similarly classified by CPP and SWAC, and 34
were
classified as the same value by all three models (see Table
11). It must be recalled that we are evaluating CPP against
non-ground truth models. Given that the models do not represent
reality, we
chose to represent the data from CPP and focus on Producer’s
Accuracy here. Figure 9
shows the agreement between administrative units at each rank of the
classification hierarchy. The figure closely resembles Producer’s
Accuracy. The highest amount of agreement occurs in Unsuitable
land.
ANOVA F-value
(83.52) on the models (Table 12)
shows that there is a
significant difference between the model means. The correlation matrix (Table 13) illustrates that CPP model mildly
agrees with the IIASA model (r= 0.52, r2= 0.27). Whereas
there is no
explanatory value between SWAC and CPP (r= -0.10, r2= 0.01).
The
SWAC and IIASA model also had a low correlation (r= 0.27, r2=
0.07).
None of the models showed a high level of agreement in either their raw
data
(Kappa) or at the lowest administrative levels (ANOVA and Correlation
Matrix),
SWAC diverging more than the other two models.
Statistics
comparing model performance at the
national levels were undertaken to inspect how models differed in the
relative
understanding of national capacities. Descriptive statistics for models
in each
country are presented in Table 14.
ANOVA tests performed on models for every country (see Table
15) found that there was no significant difference in the way
that models judged Benin,
though Mali and
particularly
Burkina Faso
showed high levels of disagreement of cotton production potential.
Correlation
matrices inspect the correlation of trends in administrative values
between
each model. CPP found that the best country was Burkina Faso while Mali
and Benin
ranked equally. IIASA also found that Burkina
Faso outranked Benin
and Mali
(see Table 14). In contrast, SWAC
found that Mali’s
potential
for cotton growth was much higher than Benin
and that Burkina
Faso
was a marginal area. While a relative ranking of the countries is
interesting,
more detailed analysis of model performance in each country will allow
us to
see how the models performed differently.
Mali
There
was significant performance difference between models in Mali
(ANOVA: p
< 0.001) (Table 15). There is much
disagreement in western Mali
where IIASA rates the region poorly, CPP rates it as marginal, and SWAC
shows
significant levels of production. SWAC with a mean score of 2.7 rates Mali
generally higher than IIASA (4.5) and CPP (4.5) mean scores.
Correlations were
all strong and significant. Model
correlations were strongest for CPP and IIASA (r=0.66, r2=0.44)
(Table 16). When inspecting spatial
heterogeneity by administrative level, one can see that the large
amount of
unusable land in the north, the usable land near Mopti and near water
bodies,
and the usable land in the southern parts of Mali correlate well in all
models.
Benin
ANOVA
tests found no significant difference between the models in Benin.
Thus the
models judged the country at about the same value. However, visual
inspection
of the models shows that there is much disagreement in the southern
provinces
and in the relative scaling of the districts. In fact, the correlation
matrices
indicate that there was a weak, non-significant correlation between
SWAC and CPP.
IIASA and CPP show a medium correlation based on their relative ranking
of
administrative levels and the higher spatial diversity they show than
the SWAC
model.
Burkina Faso
Burkina Faso revealed the
highest
significant differences in the model (ANOVA: p<0.001) due to the
divergent
predictions made by SWAC. CPP and IIASA generally agreed on Burkina Faso
as the best country
(mean values: CPP 1.72, IIASA 2.78) at the national level while SWAC
found it
to be the worst producing country (mean value: SWAC 6.06).
Administrative
levels showed interesting correlation patterns: CPP and SWAC showing a
medium
negative correlation, SWAC and IIASA showing a medium positive
correlation, and
CPP and IIASA showing only a weak positive correlation. CPP generally
agrees
with IIASA in the western and eastern areas of the country. SWAC and
CPP
predict opposing trends through the middle, north, and eastern regions
of the
country.
4
Discussion
Constraints
to data and to how parameters are constructed limit inferences.
Selection of
model parameters is constrained by availability and reliability of data
[17, 22].
Human factors and physical factors limit acquisition and
operationalization of
reliable data for models of human-environment interfaces [22]. A
discussion of
general limitations to data acquisition for human-environment interface
models
can be found in Rindfuss et al
(2003), Lambin et al (2003), and
Meyer & Turner (1994) [17-18, 22]. There is a paucity of data in
SSA due to
human and physical constraints to data acquisition. Particular
constraints to
data reliability and acquisition for the cotton sub-sector in SSA are
numerous.
The main practical hurdle we faced was that data on local and
provincial
institutions or the localized actions of national institutions involved
in the
cotton sub-sector were not available. Problems of data reliability and
operationalization related to each selected parameter of this model are
summarized in the tables presenting national and subnational scale
parameters
in Tables 2 & 4. There is much
room for improvement in the CPP model. Lack of rigorous data ultimately
led us
to generate new datasets, attempt to rescale data, or to omit and
modify the
selection of parameters used in the model.
Some problems
with data reliability can be
overcome by ground-truthing a model or comparing a model to other
datasets. However,
reliable and appropriate data must be available or acquired for
validation [16,
22]. Similar constraints to raw data acquisition in SSA limit
validation
methods and possibilities to draw conclusions from comparison with
other
datasets [17, 22]. Other problems that were common to both human and
ecological
parameters included data anachronism. In dynamic systems, remote
sensing and
other methods may reflect onetime events rather than trends. Also,
while
certain practices of land management or institutional settings may be
in place,
there is often a time lag before clear causal results of these
practices or
relations can be empirically measured. Even when results are measured
and
accrued to specific management practices or institutional settings
there are
problems of equifinality and multifinality [7]. When possible, efforts
were
made to incorporate the amplitude of change, slope of change,
consideration of
possible time lag, consideration of equifinality and multifinality, and
other
longitudinal data characteristics into the model. The limits of
reliable data
pose a great hurdle to our understanding of aspects of
human-environment
interface and to our ability to create appropriate policy to adapt to
these
conditions.
CPP
Model and
Comparisons
The
CPP model tells us that large swaths of northern Mali are unsuitable for
cotton production.
Other models agree on this point. However, there are many areas of
disagreement. The CPP model projects large areas in Burkina Faso
and the country itself
as the best places for cotton production.
The most
significant difference between the models
was in the case of Burkina
Faso, which scored particularly well
in
terms of its economic and physical parameters. Producer price related
variables, indicating a stable institutional setting, were better for Burkina Faso than for either Mali or Benin (see Table
7). Burkina Faso’s
Textile Fibres Company (SOFITEX) is
the largest operator in the country’s cotton sector and although it
holds a
monopolistic position for the sale of production inputs, producers are
very
much involved in the management of the company [10, 27]. The strong
involvement
of producers in the management of the sector may explain Burkina Faso’s
economic
parameter performance. High producer participation would in this case
translate
into the ability of institutions to lower costs and maintain consistent
producer costs and would also indicate the efficiency and adaptability
(reforms) of the institutional setting.
While the CPP
and IIASA models generally agreed Burkina Faso
had the best cotton production potential at national level, SWAC found
it to be
the worst. This discrepancy could have several explanations. First, the
models
themselves have fundamental differences in measurements. SWAC is based
on
historic production data while the CPP and IIASA models measure
multiple
parameters affecting production. Models that focus on the interplay of
a
variety of factors may be better for measuring potential. Second,
cotton
plantation areas shift regularly in West Africa
in response to climatic factors (availability of water and fertility of
soils) [13].
The SWAC model at a given point in time and does not take into account
longitudinal
trends. While all three countries’ overall production dipped in 2002,
production levels increased again in the period between 2003 and 2005
(see Figure 10). The IIASA and CPP models
paint a less stark picture than SWAC because they look at multiple,
long term
parameters. Moreover, there are other than agroecological factors at
play that
explain increased production in 2003 and 2005. This is where the more
multifaceted CPP model provides a more nuanced insight than IIASA,
accounting
for economic and human factors in addition to agroecological trends.
Third,
CPP’s physical parameters play a large role in why Burkina Faso
did so well compared
to other regions. Perhaps most influential of all the physical
parameters are
cattle distribution and the lack of an independent parameter reflecting
landscape slope. The CPP model includes a variable accounting for
problem soils
(which indicates sloped lands as a problem), but the CPP model maps do
not
account for the poor growing regions around the Mossi Plateau in
central
Burkina Faso. These regions are included in the IIASA maps, and
significantly
downgrade the areas for cotton production. Lastly, the way in which the
models’
raw data is classified (in the eight step hierarchy) may be the
ultimate source
of disagreement. Without a way to calibrate the CPP pixel values to
actual
points on the land, the classification hierarchy is ultimately flawed.
In the CPP
model, Burkina Faso
did well due to the
density of quality roads, better cattle distribution, and high amount
of
potential irrigation land. These factors raised the quality of Burkina Faso while at the same time
downgrading
places like western Mali
where SWAC showed higher production levels than either of the other
models. Figure 11 shows how the relationships of
these three physical parameters are manifested in the final CPP
administrative
map. The disagreement in western Mali
and some divergent results in southern Mali appear to be caused by
the
same phenomena. The CPP model also classifies more land near Mopti and
in the
dryer regions as potentially suitable for cotton. Again these
differences are
most likely due to the emphasis put on potential irrigation areas in
the north.
Despite the disagreements, the models found significant agreement in
the
unsuitable lands of northern Mali
and in the Niger River areas.
Remarkable
about the case of Benin
is that the
country scored quite badly in economic parameters and, conversely,
quite well
for human parameter scores. This result gives reason to examine not
only how
parameters were heuristically organized and operationalized, but also
why
economic conditions in this country are not commensurate with human
parameters.
Perhaps one reason is that the economic parameters contain variables
more
specific to cotton production. In contrast to Burkina Faso, Benin
scored particularly low in the producer price related variables,
indicating an
unstable institutional setting. Like other countries in the region, Benin
has been going through a liberalization process of its cotton sector.
But this
process has been less than smooth following the beginning of
privatization of
certain activities of SONAPRA, the public entity in Benin’s
cotton sector. While the
vertically integrated SONAPRA entity had previously ensured the
distribution of
inputs, access to credit, and provision of public services, the
structure which
is evolving as a result of the reforms of the cotton sector is much
more
splintered. Unlike Burkina Faso,
where producer groups are actively engaged in the management of the
cotton
sector, splits have emerged among the producers’ groups in Benin
following the reforms. There
has been a steady rise in non-compliance or delays in meeting
obligations in
payments, loan servicing and delivery of services, resulting in a
dangerous
erosion of confidence in the system [9]. In a production system that
relies on
a basic level of trust in the functioning of institutions, such an
erosion of
confidence can have detrimental effects on the overall cotton
production
potential [9]. While the CPP model indicates that Benin
has sound physical and human
capital, the unstable variables of its economic parameters caused by
the
institutional malfunction suggest that cotton production potential
could be
much higher. The results of Benin
underscore the importance of an interdisciplinary approach to the
analysis of
cotton production.
Sustainability
and
Policy
The
questions that these findings raise in regard to cotton policy are
closely
linked to sustainability. Cotton
production is intimately linked with questions of sustainable land use
and
human-environment systems in SSA. For example, much of the increased
production
of cotton in recent years in Africa
comes from
land expansion [11], often disregarding ecological considerations [1].
There
are other options besides land expansion, but these all ultimately
implicate
sustainability as well: increased use of fertilizers and pesticides; GM
cotton
experimentation in Burkina Faso;
and new seeds from China.
However, it is sure that land expansion according to rainfall will
remain the
main vehicles of increased production for the regions’ small farmers in
the
near if not long term future.
The CPP model
has at least two functions in
informing sustainable policy decisions. The first is to examine ongoing
trends
in production compared to potential trends in production in order to
understand
constraints to ecologically and socially sustainable production
relations. In
this case, administrative districts in south-central Burkina Faso and southern Mali
that express high potential
but low actual production could be investigated. Although the model
cannot
predict particulars on how sustainable policies will unfold in certain
administrative districts, it can identify districts where production
potential
is limited by constraints. The second way in which the CPP model can be
used to
inform and shape sustainable policy is by using it to run scenarios.
For
example, one could project cotton production potential under varying
conditions
of precipitation (e.g. simulating drought). Perhaps most intriguingly,
one
could simulate the effect that a higher world price of cotton would
have on
production levels over the entire region or in particular areas [8]. At
this
point, scenario simulations are limited to leveraging one variable.
However,
establishing more dynamic relationships between the parameters would
allow
complex, georeferenced simulations of cotton growth.
If areas with high production potential are
identified early, appropriate policy measures can be taken to ensure
that the
particular environmental situation in these areas is taken into
consideration.
5
Conclusions
By
applying the CPP model to Mali,
Benin, and Burkina Faso
we find spatially-referenced
trends that indicate where cotton can be grown and what might be
constraining
cotton production in the region. Although the model does offer
interesting
discussion points and potentially serves as a tool to support
sustainable
practices in cotton production in the region, it will require more
work. One of
the basic advantages of this model is that it is georeferenced.
However, the
model needs validation. Ground truth data acquisition in the region
would
accomplish the goal of validation as well as offering the model a way
of using
historical data from known points to build dynamic relationships
between
parameters. Postulated dynamic relationships (whether causal or
correlation)
are necessary for scenario modeling. As well, understanding the
relationships
between parameters is key to creating a parsimonious model that can
eliminate
collinearity. Ground truth points also offer more reliable and
localized data,
overcoming many problems with unreliable data, anachronism, and scale.
Specifically, this need is manifest for economic parameters. Most
economic
parameters are available only at a national level.
The policy
relevance of this model is obvious, offering
a transparent method of valuating policy for meeting sustainable goals,
such as
those of the Millennium Ecosystem
Assessment [19], in the particular context of cotton production in
the
poorest continent in the world. Moreover, this model is an attempt to
improve
the understanding of the spatial components of cotton – which affects
millions
of livelihoods in the region – so as to provide a tool to make policy
decisions
effective and more relevant to people on the ground. We hope to advance
this
model to its next stages through ground truth acquisition and
reconfiguration
of parameters in order to offer a viable way to improve cotton
production.
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