Although, there is a great potential in the field of agriculture, developing and reaping maximum benefits from this sector need more effort in order to move the wheel of production towards improvement and progress. In 2003, the Ministry of Investment in Sudan commenced an investment map for Sudan, which is still in progress. The data related to the investment in GIS software are being gathered, but no analysis has yet been carried out on them to define which areas have greater potential for investment with minimum risk. Nevertheless, one of the ways to encourage investment is to provide the investors with good and high quality information because wrong information can be costly in investment activity. Hence, this study provides the potential areas for agricultural investment by evaluating them based on several types of criteria using Geographic Information System (GIS).
There are many factors contributing to the success of decision making in yielding
the right investment, such as information and technology (Trull,
1966). In the past, potential investors wasted a lot of time and resources
to search for answers to many questions, such as: Who is the owner of the land?
Is the land suitable for agriculture? Besides, they were also faced with many
problems, such as the lack of correct data when needed, as well as outdated
maps and data. Needless to say, combining the data and maps for a study is time-consuming
because different sets of data and maps have incompatible formats, definitions
and scale (M-NCPPC, 1999).
The second issue deals with compatible technology and the right people who
can use it to solve problems. GIS offers the potential to minimize the above-mentioned
problems and to generate many benefits through its flexibility, speed, availability
and processing power (M-NCPPC, 1999). Following this,
GIS and Multi Criteria Evaluation are useful tools to support the decision makers
in achieving greater effectiveness through the aggregation of geographical data
and the decision-makers preferences into one-dimensional values of alternative
decisions (Malczewski, 1999). One approach of Multi Criteria
Method is the pair-wise comparison method-a method developed by Satty
(1980) in the context of the Analytical Hierarchy Process (AHP). This method
involves pair-wise comparisons to create ratio matrix. It takes the input of
pair-wise comparisons and produces the relative weights as output. There are
GIS applications used in the construction industry, however, its usefulness
in other areas like business and economics is still being explored (Cheng
et al., 2007). In one of the earlier studies, the developed system
was based on GIS and Multi criteria method. The system contained criterion definition,
management, evaluation scenario and user interface. The purpose of the system
was to develop and analyse the environment to support various investment researchers
and investors, as well as to assist users to find the information about particular
projects (Lin et al., 2008). Moreover, in a study
done by Cheng et al. (2007) the researchers presented
the utility for shopping mall location selection, which was one of the core
business activities, or specifically, the improvement of investment in Hong
Kong. In the study, the project was demonstrated to create the features associated
with household incomes, demand points, etc. Nevertheless, the limitation of
this study was that the results depended on one factor only without considering
the selection of the best mall through the analysis of all factors which had
different weights. Similarly, another study was done on the selection of shopping
centre location, but it was based on combined MCDM methodology. In addition,
a study was also conducted by Moldovanyi (2003), which
performed a spatial multi-criteria analysis in order to rank and display the
marketability of 32 pay pond businesses in West Virginia. The results of this
study, as compared with the first one, were more reasonable because they depended
on the criteria evaluation of spatial data and considered the factors that influenced
marketability. On the other hand, by using the ranking method, it was difficult
to get the accurate expression of relative preferences on the criteria due to
the limitations of the 9-value scale of Saaty (Lin et
al., 2008). To overcome this problem, Saaty proposed the Analytical
Hierarchy Process (AHP). Fuzzy AHP (i.e., Analytical Hierarchy Process) was
utilized for assigning weights of the criteria for site selection and fuzzy
TOPSIS (i.e., a technique for ordering preference by similarity to ideal solution),
and it was used to determine the most suitable alternative using these criteria
weights (Onut et al., 2009). Apparently, the
study overcame the limitation in the previous one.
The use of GIS, following the Multi Criteria Analysis, has become popular in
site selection (Hossain and Das, 2009; Zucca
et al., 2008). The application of GIS technique in combination with
a multi-criteria approach is not restricted only to finding potential places
for agricultural investment or development (Hossian et
al., 2007), but it goes beyond to analyze land suitability for different
crops. There are many studies covering this area such as the ones done by Chuong
(2008) and Liu et al. (2006).
In this study, the suitable area for agriculture was determined by the Ministry of Agriculture, and the analysis provided the areas that have high potential of investment in agriculture.
MATERIALS AND METHODS
The selection of criteria was restricted by the available data. The principle supporting the data for this study was provided in 2003. The criteria that had spatial reference were land use, road, railways and water resources. The stability of results was tested using the sensitivity analysis and trade-off method, which assisted in producing significant results. The flow chart of the methodology is shown in Fig. 1.
GIS model: Buffer Wizard was created for road, railway and water resources.
On the other hand, land use was classified to replace the values based on new
Analytical Hierarchy Process (AHP): In AHP method, every criterion under
consideration was ranked in the order of the decision makers preference.
The square pair-wise comparison matrix is presented in Table 1.
To generate the criterion values for each evaluation unit, each factor was weighted
according to the estimated significance for agricultural investment project.
The normalized matrix is shown in Table 2.
|| Flow chart of methodology
|| Specify square pairwise comparison matrix (A)
|C1: Landuse, C2: Water, C3: Road and C4: Railway
||Normalize matrix A by dividing each column entry by the sum
in the column
|C1: Landuse, C2: Water, C3: Road and C4: Railway
|| Random indices for matrices of various sizes
Meanwhile, the individual judgment, which never agreed perfectly with the degree
of consistency achieved in the ratings, was measured by using Consistency Ratio
(CR), indicating the probability that the matrix ratings were randomly generated.
The Random Indices for matrices are listed in Table 3. The
rule of thumb is that a CR less than or equal to 0.1 indicates an acceptable
reciprocal matrix, while a ratio over 0.1 indicates that the matrix should be
revised. Revising the matrix entails finding inconsistent judgments regarding
the importance of criteria, and revising these judgments by re-comparing the
pairs of criteria which are judged inconsistently.
Using the above method, the weight of the following criteria can be calculated:
Calculating Consistency Ratio (CR):
|| Model builder
||Random consistency index
||No. of criteria
||The priority vector multiplied by each column total
The significant findings from the study showed the Consistency Ratio (CR) value of 0.0500992, which fell much below the threshold value of 0.1 and it indicated a high level of consistency, as shown below. Hence, the weights can be accepted.
Map calculator: The data was converted to raster format, then the linear
weightage combination using the map calculator and the final weights were used
to provide the final results.
Model builder: A model was created for retrieving significant results
in order to summarize all the important steps that were carried out in designing
the model builder, as shown in Fig. 2.
Classification map: Finally, the classification map, which shows the
potential investment in agricultural field, was produced. This map will help
the investors to choose between the alternatives and reduce the doubts around
Sensitivity analysis: Subsequent to obtaining ranking of alternatives,
the sensitivity analysis was performed to determine the robustness. This was
to identify the effects of changing the inputs (i.e., weight and criterion scores).
Trade-off method:This method was based on the identification of goal
in the criterion space by examining the trade-offs among the criteria. In addition,
it also helped to determine the consistency of the available solutions of the
Case study:In this study, the agricultural investment opportunities
in Sinnar were taken as the case study.
|| Sinnar-location of the study area
|| Potential projects with road and water resources
|| Trade-off evaluation of results
Sinnar extends over most of the Eastern part of the present Sudan, forming
a triangular-shaped territory between the White and Blue Niles. Figure
3 shows the location of the study area. The soil, mainly alluvial, is very
fertile, and wherever cultivated, yields abundant crops.
RESULTS AND DISCUSSION
The AHP method was used to evaluate the criteria. The first result depended on the four types of spatial criteria (i.e., road, landuse, railway and water recourses). The results of analysis indicated that nearly 37% of the study area presented with the darkest area, as shown in Fig. 4 was the most suitable place for investment. However, the suitability for investment decreased as the areas became lighter, giving the results 32, 23 and 8%, respectively. The evaluation results concluded that the closer the agricultural project was to a major road, railway and water sources, the greater was its investment potential. Areas which were less or not suitable for agricultural projects (such as bare rocks and shifting sands), conversely, had the lowest potential for investment. The sensitivity analysis indicated that the change did not significantly affect the outputs, thus ranking was considered to be robust. The trade-off method results (Fig. 5) indicated the best alternative, which had the highest stability among all criteria.
The results presented were in agreement with the earlier studies conducted
using the multi-criteria analysis. Multi Criteria Evaluation (MCE) method was
used to analyze and find the flood vulnerable areas in the west of Black Sea
of Northern Turkey (Yalcin, 2002). In this study, GIS
was integrated with MCE, whereby seven spatial criteria were used. Each criterion
was presented and stored in layer by using Arc View 8.2, and the criterion values
were generated. The criterion maps were converted into grids and the mathematical
processes were applied to the criteria utilizing the Map Calculator. In addition,
Ranking Method was used to rank every criterion under consideration in the order
of the decision makers preference and Pair-wise Comparison Method (PCM),
which was designed as a user interface to calculate the weights from the input
preferences using the Visual Basic Application (VBA) programme embedded in ArcGIS
9.1. At the end of the application, the composite maps were created using Boolean
Approach, Ranking Method and Pair-wise Method. There were three different results
produced from the methods used. Following this, the difference between the three
methods was analyzed. In addition, this study also conducted the sensitivity
analysis. The purpose was to examine how sensitive the choices were to the changes
in criteria weights. This was useful in situations, such as where uncertainties
existed in the definition of the importance of different factors.
Another study in Singapore presented a GIS-based multi-criteria analysis approach
to assess the accessibility of housing development. It applied the multi-criteria
analysis framework to incorporate the buyers opinions into accessibility
assessment with GIS in order to determine the overall attractiveness of an area
for housing development from a demand-side perspective. In the past, the accessibility
analysis for housing development focused on the supply-side perspective. Hence,
the approach proposed here will allow the planners and housing developers to
examine the accessibility requirements from the demand-side perspective, so
that demand and supply issues can be better managed (Zhu
et al., 2005).
Lin et al. (1997) presented GIS-based multi-criteria
evaluation for investment environment to provide the investors and local government
decision makers with more specific information on investment location. Thus,
the aim of this study was to explain how to develop an analysis environment
to support various investment researchers and investors. Furthermore, Antonie
et al. (1997) presented an example application on the integration
of multi-criteria evaluation technique with GIS for sustainable land use in
Kenya by maximizing revenues from crop and livestock production, food output,
district self-reliance in agricultural production and minimizing environmental
damages from erosion.
Other studies used the multi-criteria analysis to find the best location for
the purpose of planning, such as the best places to build hospitals (Malczewski
and Ogryczak, 1990; Malczewski, 1991), a solid waste
transfer station (Gil and Kellerman, 1993), or more
generally, any type of public facility (Joerin, 1995;
Yeh and Hong, 1996).
Another study was also performed utilising the spatial multi-criteria analysis
in order to rank and display the marketability of 32 pay pond businesses in
West Virginia (Aurora, 2003).
This study deals with one important application of GIS technology, i.e. investment mapping, and provides a clear idea for the third world countries (who are dealing with a real problem pertaining to advance technology) in order to encourage the investment there. It integrates GIS and Multi Criteria Method to evaluate the different alternatives. Besides, this study also provides clear indicative areas for agricultural investment. The potential areas of agricultural investment in the study area were evaluated into four classes. The subjective numbers in the weights and values of the criteria can be changed according to the study area characteristics and experts opinions.
From this study, several conclusions were made. When performing the sensitivity analysis on all the criteria, it revealed that the accuracy in estimating weights needed to be examined carefully. In addition, the sensitivity analysis helped to see the roles of attribute and weight, while the trade-off method assisted in determining the consistency of the available solutions of the criteria that could lead to the best solution. The classification map of agricultural investment projects could be produced by using GIS and multi-criteria techniques. Finally, this map could give planners and investors the tool for assessing and minimizing investment risks.