apicultural site zonation using gis and multi-criteria ... papers/jtas vol. 31 (2) aug... ·...

Apicultural Site Zonation Using GIS and Multi-Criteria Decision Analysis

Nisfariza Mohd Noor Maris1*, Shattri Mansor2 and Helmi Zulhaidi M. Shafri3

1Department of Geography, Faculty of Arts and Social Sciences,University of Malaya, 50603 Kuala Lumpur, Malaysia

2Institute of Advanced Technology (ITMA), 3Geomatics Unit, Department ofCivil Engineering, Universiti Putra Malaysia, 43400 UPM, Serdang, Selangor, Malaysia

E-mails: [email protected];[email protected]; [email protected]

ABSTRACTThis paper discusses the application of Geographical Information System (GIS) and Multi-Criteria DecisionAnalysis (MCDA) technology as a tool to aid decision-making in a case study to locate beekeeping zones in thestate of Selangor. The combination of GIS capabilities with MCDM technique provides greater effectiveness andefficiency of decision making while solving spatial decision problems. In this research, land suitability analysisand zoning was carried out with respect to the bee’s biotic needs and some other important factors in apiarymanagement. Suitability weighting was determined using the pairwise comparison matrix of the AnalyticalHierarchy Process (AHP) and suitability score using Weighted Overlay function in ArcGIS9. The overallconsistency ratio value of AHP pairwise comparison was 0.01 which indicates a reasonable level of consistencyin the deployment of the pairwise comparisons. The results of the analysis are presented and verified with actualdata of the existing apiaries in Selangor. The integration of AHP model with GIS resulted in Non-Suitable, MostSuitable, Moderately Suitable and Suitable beezones. The total Non Suitable Areas (NS) was 34.73%, leaving theremainder as potential areas (65.27%). The remaining are the Most Suitable Areas (S1) 13.72 %, Suitable Areas(S2) of 27.24% and Moderately Suitable Areas of 24.32 %.

Keywords: Geographical Information Systems, Multi-Criteria Decision Analysis (MCDA), Analytical HierarchyProcess (AHP)

economic and ecological security to ruralcommunities in Asia cannot be overlooked as ithas always been linked with their cultural andnatural heritage (Matsuka, 1998).

Bees play a key role in the functioning ofagricultural ecosystems as pollinators of cropsand flowers. Malaysian Ministry of Agriculture &Agro-Based Industry have started the ‘HoneybeeProject’ to encourage the honeybee industry infarm families as a main/side income, exploitingthe existing resources of plantation. Thishoneybee industry is expanding and a profitablecommercial industry and side income for farmers.The prospect to expand this industry is bright inMalaysia considering that the demand for beeproducts in Malaysia and worldwide hasincreased.

INTRODUCTIONApiculture or beekeeping is a large area of studyand application by itself. It is a huge field ofagriculture and has been practised by man sincethe primitive age. The term apiculture as definedby Food and Agriculture Organization of theUnited Nations (FAO, 2003) is “the science andart of bees and beekeeping”, which uses bees asmicro-manipulators to harvest plant foods fromenvironmental resources that would otherwisebe wasted (FAO, 1986).

Beekeeping is an important component ofagriculture and rural development programmesin many Asian countries. Honeybees are nativesto the IndoMalaya region where diverse floralsources are available throughout the year. Therole of beekeeping in providing nutritional,

Pertanika J. Trop. Agric. Sci. 31(2): 147 - 162 (2008) ISSN: 1511-3701©Universiti Putra Malaysia Press

Received: 5 Jun 2007Accepted: 2 September 2008* Corresponding Author

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Nisfariza Mohd Noor Maris, Shattri Mansor and Helmi Zulhaidi M. Shafri

This report demonstrates how GIS can playa role to aid decision-making in locating suitablezones for beekeeping. Land suitability analysisand zonation involve a multiple criteria analysistechnique. In this research, land suitabilityanalysis was carried out in respect of the bee’senvironment and modelled into GIS systemsincorporated with Analytical Hierarchy Process(AHP) model for the analysis of the criteriaweightage.

Historical OverviewThe history of commercial beekeeping in Malaysiastarted since the establishment of MalaysianBeekeeping and Research and Development Team(MBRDT) in 1981 (IDRC, 1987). The teamconsists of several institutions namely UniversitiPutra Malaysia (UPM), Universiti Malaya (UM),Malaysian Agricultural Research DevelopmentInstitute (MARDI), Malaysian Industry Small-holders Development Authority (RISDA), RubberResearch Institute Malaysia (RRIM) andDepartment of Agriculture (DOA).

MBDRT was funded by IDRC (InternationalDutch Research Council) and the objective ofMBRDT is to undertake research and extensionactivities in promoting modern beekeeping inMalaysia. Although it has been more than 20years since the establishment of MBDRT, modernbeekeeping in movable hives is still notprominent in Malaysia.

Types of bee plants and pollen which arefavourable to honeybees have been identified inprevious MBDRT research, but the location ofthe source has not been identified and there isno map for suitable beekeeping locations or zonescreated using Multi Criteria Decision Analysis(MCDA) and GIS (Geographical InformationSystem) technology. Existing flowering calendarsonly provides time (month) of blooms but doesnot contain information such as location andspecific time of blooms. Thus, time of the mostnectar and pollen production is not known forcommercial beekeeping.

The integration of beekeeping with othercrop production has been practised in othercountries and shown to yield high revenue.According to Akranatul (1987), productivebeekeeping depends on good colony managementand good beekeeping areas. In order to promoteit as a profitable agricultural occupation, areaswith a good potential for beekeeping must belocated and evaluated.

GIS and MCDA for Land Suitability AnalysisGIS has long been used as a tool for developingalternative uses of agricultural land, precisionfarming, crop yield or land suitability mappingin determining the best alternatives foragricultural production. It is the capability ofGIS for supporting decision making that is ofparticular importance for the landuse suitabilitymapping and modelling (Malczewski, 2004). Theability of GIS to integrate, display, and querymany types of information at the same timemakes it an important tool for decision supportin agriculture. Perhaps the most useful tool ofall in GIS is its ability to form overlay operationsbetween layers especially in selecting or locatingsuitable area for agricultural purposes.

The terms Multi-Criteria Decision Making(MCDM) and Multi Criteria Decision Analysis(MCDA) are used interchangeably in referringto the multi-criteria evaluation (MCE) technique,usually carried out for land suitability analysis orin determination of site fitness for any specificapplication (Malczewski, 2004). The criticalaspect of spatial multi-criteria analysis is that itinvolves evaluation of geographical events basedon the criterion values and preferences set withrespect to a set of evaluation criteria. Thecombination of GIS capabilities with the MCDMtechnique provides greater effectiveness andefficiency of decision making while solving spatialdecision problems.

According to FAO (1976), suitability is ameasure of how well the qualities of a land unitmatch the requirements of a particular form ofland use. The process of land suitabilityclassification is the evaluation and grouping ofspecific areas of land in terms of their suitabilityfor a defined use. De la Rosa (2000) stated thatland suitability is a component of sustainableevaluation of land use. Suitability together withvulnerability defines the suitability of a land use.The sustainable land use should have maximumsuitability and minimum vulnerability, as shownin Fig. 1.

Fig. 1: Land use sustainability (after de la Rosa 2000)

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Land suitability analysis deals withinformation, which is measured in different scaleslike ordinal, nominal, ratio scale etc. Based onthe scope of suitability there are two types ofclassifications in FAO (1976) framework.• Current suitability: refers to the suitability for adefined use of land in its present condition,without any major improvements in it.• Potential suitability: for a defined use, of landunits in their condition at some future date,after specified major improvements have beencompleted where necessary.

Agricultural land suitability is aninterdisciplinary approach thus; determinationof optimum land use type for an area involvesintegration of data from various domains andsources like soil science to social science,meteorology to management science. All thesemajor streams can be considered as separategroups; further each group can have variousparameters (criteria) pertaining to that group.However all the criteria are not equallyimportant, every criterion will contribute towardsthe suitability at different degrees (Prakash,2003).

There are several decision makingapproaches for analysing land suitability for land-use or land suitability purposes. Today, the widelyused methods for land suitability analysis includeranking and ratings, weighted summation (AHP),Simple Additive Weighting Method (SAW),Boolean overlays, Fuzzy techniques and GAMratings.

Analytical Hierarchy ProcessThe Analytical Hierarchy Process (AHP) is acomprehensive, logical and structural frameworkwhich enhances the understanding to decompose

a complex decision making into a more feasibleone to resolve hierarchical structure. AHP wasdeveloped by Professor Thomas L. Saaty of theUniversity of Pittsburgh in 1980. AHP is based onthree basic principles which are decomposition,comparative judgment and synthesis of prioritiesas shown in Fig. 2.

Comparative judgment by a decision makerrequires pairwise comparison assessment betweentwo criteria, including the sub-criteria. Thefundamental concept of AHP lies in proceedingfrom a pairwise comparison of criteria to evaluatethe weights that assign relative importance tothese criteria. The Pairwise Comparison methodwas developed in the context of the AHP tocreate a ratio matrix. The procedure involvesinput of the pairwise comparison matrix andproduces the relative weight as output.Identification of each criterion weightage canbe interpreted easily through PairwiseComparison Matrix; it requires rating scalepreferences between two different criteria withvalues from 1 - 9 as shown in Table 1.

Fig. 2: Three major steps of Saaty’s AHP

TABLE 1Scale for AHP comparisons (Saaty, 1980)

Intensity of DescriptionImportance

1 Equal importance of both elements3 Weak importance of one element over another5 Essential or strong importance of one element over another7 Demonstrated importance of one element over another9 Absolute importance of one elements over another2,4,6,8 Intermediate values between two adjacent judgements

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The synthesis principle uses the derived ratioscale of the local priorities in the various levelsof the hierarchy and constructs a composite setof alternatives (Malczewski, 2004). Theprocedures involve the utilisation of geographicaldata, the decision preferences and themanipulation of the data and preferencesaccording to specific decision rules. The criticalaspect of spatial multi-criteria analysis involvesevaluation of geographical data based on thecriteria values and preferences set with respectto a set of evaluation criteria.

generation of suitability zones, ground truthingand verification, and finally evaluation andrecommendations.

Criterion FactorIn multi-criteria analysis using AHP, criteriaidentification and determination are the mostimportant elements in achieving any set aim orgoal. In this research, a suitable bee zone wasidentified and weighted using AHP mathematicalmodel. Several criterion factors were identifiedfor locating the bee zone. The criterion factortook into account every single factor for locatingan apiary for beekeeping as well as other factorssuch as ensuring migratory beekeeping issuccessful.

Site assessment of suitable bee settlementareas is important to ensure bees are placed insuitable and favourable areas regardless ofabundant sources of nectar and pollen. Thecriteria used to determine suitable areas forbeekeeping was identified based on numerousliterature reviews including the FAO (1987b)guidelines on apiculture (beekeeping) anddiscussions with expert in apiculture. Based onthe FAO guidelines, an apiary site should ideallybe:• away from playgrounds and noisy commercial

or industrial areas• near a fresh water supply: the banks of a

river, lake or fish-pond, or even a drippingfaucet

• near food sources• fairly dry, away from swampy or flooding

valley or any bottom land with stagnant water• accessible to good roads• on the leeward side of a hill• with annual rainfall between 1275 mm and

1875 mm• away from smoke and fire,• away from danger of vandalism and

unfriendly neighbours

Consequently, information about the relativeimportance of the criteria is required. This isachieved by assigning a weight to each criterion.After the weightage are derived, these evaluationcriteria have to be integrated using multi-criteriadecision rules. The decision rules provide thebasis for ordering the decision alternatives andfor choosing the most preferred alternative.

Fig. 3: Analytical Hierarchy Process (modifiedfrom Malczewski, 2004)

METHODOLOGYThere are many research methodologies thathave been used by various researchers for landsuitability analysis, which are all based on theuse of GIS and several MCDM techniques as atool and require certain evaluation criteria.

The methodology framework focuses ondecision making as a process which involves asequence of activities. It starts with problemrecognition, criteria and constraints evaluation,data acquisition, AHP weightage analysis,manipulation of the AHP results using GIS and

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AHP Weight Analysis Using Expert Choice! SoftwareLand suitability in this research consisted ofgenerating pairwise comparison matrix for eachcriterion and sub-criterion of the beekeepingfactor using AHP technique. The generation ofweight and rank for the suitable area is carriedoutside the GIS environment or termed as ‘loosecoupling’ using Expert Choice software. Severalapiculture experts from the Department ofAgriculture Malaysia (DOA) and Universiti PutraMalaysia (UPM) were consulted to determinethe preferences and the ratings of AHP.Explanations were given to the experts on thebasis of AHP implementation. Several criteriarankings were given as ratios and percentages bythe experts and then synthesised to reflect theAHP ratings.

The information on AHP weightage was used toproduce a suitability map. Pairwise comparisionmatrix for each sub-criteria is shown in Table 2and summary results of pairwise comparison ofsub-criteria and criteria are shown in Fig. 5.

The estimation of the consistency ratio isone of the important steps in determining thelevels of inconsistency in the pairwise comparison.According to Malczewski (2004), the consistencyratio (CR) is designed in such a way that if CR< 0.10, the ratio indicates a reasonable level ofconsistency in the pairwise comparisons.However, if CR " 0.1, the values of the ratioindicate inconsistent judgement (Saaty, 1980,1982; Malczewski, 1999), for a complete technicaldescription of AHP technique). The overallweighting values and its consistency of the AHPanalysis are shown in Fig. 6.

Fig. 4: Research methodology

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TABLE 2Pairwise comparison matrix

Comparing the relative importance with respect to : HYDROLOGY FEATURES

0 - 200m 200 - 500m 500 - 700m > 700m0 - 200m from source 2.0 3.0 9.0200 - 500m from source 2.0 7.0500 - 700m from source 4.0>700m from source

Incon = 0.01

Comparing the relative importance with respect to : ROAD NETWORKS< 5 km 5 - 10 km 10 - 15 km >15 km

< 5 km 2.0 4.0 9.05 - 10 km 2.0 7.010 - 15 km 4.0>15 km

Incon = 0.01

Comparing the relative importance with respect to : TOPOGRAPHY FEATURES< 150m 150 - 300m > 300m

< 150m 2.0 7.0150 - 300m 4.0> 300m

Incon = 0.00

Comparing the relative importance with respect to : NECTAR CLASS< 150m 150 - 300m > 300m

< 150m 2.0 4.0150 - 300m 2.0> 300m

Incon = 0.00

Comparing the relative importance with respect to : POLLEN CLASS< 150m 150 - 300m > 300m

< 150m 2.0 4.0150 - 300m 2.0> 300m

Incon = 0.00

Comparing the relative importance with respect to : GOAL- SUITABLE ZONENECTAR POLLEN HYDROLOGY ROAD TERRAIN

NECTAR CLASS 1.0 2.0 5.0 8.0POLLEN CLASS 2.0 3.0 6.0HYDROLOGY FEATURES 2.0 3.0ROAD NETWORKS 2.0TERRAIN

Incon = 0.01

Linear TransformationThe weight values produced by AHP in ExpertChoice software are as pointers. The values needto be transformed as commensurate criterionmaps so that they could be further analysed in aGIS environment. Maximum score lineartransformation is used to generate theproportional magnitude to the original weight.

Linear scale transformation is a frequently useddeterministic method for transforming input datainto measurable criterion maps. The linear scaletransformation method converts the raw datainto standardised criterion scores. The two mostoften used procedures for linear transformationare maximum score and score range procedures.Linear scale transformation formula formaximum score is

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x

x

xij

ij

ij

'max

=

where, x’ij is the standardised score for the ithobject (alternative) and the jth attribute, xij isthe raw score and xij

maxis the maximum score forthe jth attribute. The value of the standardized

scores would then range from 0 to 1 andaccording to (Malczewski, 1999) the beststandardized score is always equal to 1. Theadvantage of this method is that it performsproportional (linear) transformation of the rawdata. In order to capture the magnitude of thestandardised method in a GIS system, it has tobe multiplied with an arbitrary multiplier andthe formula is

x'ij =

x

xij

ijmax

* m

The multiplier value in this instance is nine (9)to reflect the maximum values as applied inSaaty’s (1980, 1982) AHP. The final valuegenerated was then rounded to the nearestinteger value to allow the weight to be inputinto a GIS environment. Linear transformationresults of the final value corresponding to thecriterion are shown in Table 3.

Data Preparation for Available AreaIn order to determine available areas forbeekeeping zoning several steps have to beaccomplished, which include sieving of unsuitableareas according to the guidelines provided byFAO (1987b), expert’s opinion and availabilityof bee plants listed by Atim (1981). Only areasthat have major bee plants as listed by Atim

Fig. 5: Pairwise comparison in Expert Choice Software

Fig. 6: Overall consistency ratio of AHP

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(1981) are selected as available areas. All otherlanduse occupancies such as built-up areas,mining areas, urban, towns and associated areasand other agricultural areas that are not classifiedas having bee plants are sieved. The selectedagricultural areas which consist of several majorbee plants are grouped into classes todetermining the volume of nectar or pollen.N1/P1 indicates High Nectar/Pollen Source,N2/P2 indicates Medium Nectar/Pollen Source

while N3/P3 indicates Low Nectar/Pollen Sourceas shown in Table 4.

Data Conversion and RankingData conversion consists of converting map layers,which are in vector format to raster layer. Thelayers are then ranked accordingly as previouslyidentified through AHP analysis. This isimportant because the AHP extension tool inArcGIS9 only allows raster datasets for analysis.

TABLE 3Criteria and commensurate weight

CRITERION SUB-CRITERIA DESCRIPTION WEIGHT

CLIMATIC Rainfall Dry Season 9Wet Season 3

PHYSICAL Topography/ Elevation Low Land : Below 150m 9Hill Land : 150 -300m 5High Land: Above 300m 1

Hydrology Bodies Best Suited: Below 200m 9(Water Network) Moderately Suited : 200-500m 5

Slightly Suitable : 500-700m 2Modifiable : More than 700m 1

EXISTING INFRA- Road Networ Best Suited: Below 5 km 9STRUCTURE Moderately Suited : 5-10 km 5

Suitable : 10- 15 km 2Unsuitable : More than 15 km 1

FOOD SOURCE/ Nectar High nectar source : N1 9BEE PLANT Medium nectar source : N2 5

Low nectar source : N3 3Pollen High nectar source : N1 9

Medium nectar source : N2 5Low nectar source : N3 3

OVERALL CRITERION WEIGHT Terrain/Topography 1Road Networks 2Hydrology Networks 4Pollen Class 7Nectar Class 9

TABLE 4Classification of bee plants according to nectar and pollen class

CATEGORY LANDUSE Nectar Class Pollen Class

Agriculture Grassland N2 P2Agriculture Coconut N1 P1Agriculture Coconut/Cocoa N1 P1Agriculture Coffee N1 P2Forest Forest N1 P1Agriculture Orchards N1 P1Agriculture Rubber N1 P3Agriculture Mixed Horticulture N2 P2Agriculture Oil Palm N3 P2

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Spatial analysis for land evaluation comprisesof overlaying several thematic layers to findlocations that encompass all desired criteria.The relative weights of factors for beekeepingsuitability were used as multi-factors to rank andclassify the GIS database map layers of the studyarea in order to generate the suitability map ofbeekeeping zones.

Criterion 1: TopographyNaturally, bees inhabit lowlands or highlands. Inthis analysis, topography is divided into threeclasses; the highest rank is for lowlands which areeasier for apiary management in comparison tohighlands. The elevation map of the study areais divided into three regions. The topographicfeatures are divided into three classes’ lowland (0-150 m above sea level), hilly land (150 - 300 mabove sea level) and highland (more than 300 mabove sea level) (refer to Fig. 7).

Criterion 2: HydrologyA suitable beekeeping zone must be locatednear to water resources. Therefore, 200 m, 500m and 700 m buffers were input into the GISsoftware to generate surrounding water surfaces.In this research, the main contributing factorwas the hydrologic features because in tropicalclimate bees need water to cool their hives andthe water resources must be at least 500 m from

their hives. Areas nearest to water resources areof the highest rank. Values for 700 m or moreare still acceptable if a proper apiary managementcould be established by placing dripping faucetsnear hives in the apiary (refer to Fig. 8).

Criterion 3: Road NetworkIn terms of logistics, a good road network is veryimportant to the location of an apiary or beezone.The highest ranked placed areas are nearest toroad networks. Buffering of road networks isexecuted using Spatial Analyst Tool in which theprocesses include reclassification of requiredbuffer zone followed by another reclassificationaccording to the AHP weight. (refer to Fig. 9).

Criterion 4: Nectar ClassThe nectar class map of the study area is dividedinto three ranks. Each bee plant is classifiedaccordingly to its corresponding nectar class;the highest rank has the most nectar production.The ranking for High Nectar Source (N1) is 9,Medium Nectar Source (N2) is 5 and for LowNectar Source (N3) is 3 (refer to Fig. 10).

Criterion 5: Pollen ClassThe pollen class map of the study area is dividedinto three ranks. Each bee plant is classifiedaccordingly to its corresponding pollen class;the highest rank has the most pollen production.

Fig. 7: Topography - Classification and ranking

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The ranking for High Pollen Source (N1) is 9,Medium Pollen Source (N2) is 5 and for LowPollen Source (N3) is 3 (refer to Fig. 11).

RESULTSThe ranks for each criterion maps were producedusing Linear Transformation according to AHPvalue; and used for further suitability analysis.The are several methods in performing suitability

analysis in ArcGIS9, for instance using mapcalculator, Weighted Overlay function or usingtools that have been developed for AHP analysis.

Suitability Analysis - Weighted OverlayWeighted Overlay is a technique for applying acommon measurement scale of values to diverseand dissimilar inputs in order to create anintegrated analysis. Geographic problems often

Fig. 8: Hydrology - Classification and ranking

Fig. 9: Road network - Classification and ranking

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require the analysis of many different factors,for instance, choosing the sites in the beekeepingzones. Every single layer of data must beprioritized accordingly.

Continuous (floating-point) rasters must bereclassified to integer before they can be utilisedin ArcGIS9 which has been done during thelinear transformation process. Each range must

be assigned a single value before it can be usedin the Weighted Overlay tool. The assignedweight need to be inserted in each input raster.The weight of each criterion is stated in Table 5.

For suitability analysis, five factors wereconsidered: nectar class, pollen class, hydrology,road network and topography. The goal was tofind suitable zones for beekeeping activity. The

Fig. 10: Nectar class - Classification and ranking

Fig. 11: Pollen class - Classification and ranking

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weighted overlay model is displayed in Fig. 12 asa process generated using Model Builder.

The weighted overlay function dialogue boxis shown in Fig. 13, the integer values of eachcriterion was inserted in the percentage ofinfluence column, while the rank wasautomatically inserted according to existing rankof each layer.

The results of weighted overlay analysis arethe creation of a suitability map as shown in Fig.14.

The most suitable areas are ranked as 9followed by rank 3 for the least suitable area.The classification is accomplished using aReclassification tool, Equal Interval anddetermines 3 classes. This analysis producesresults in raster format and the suitability mapwas reclassified to S1 - Most Suitable, S2 -Suitable, S3 - Moderately Suitable as shown inFig. 15.

A calculation of the total area was done todetermine the effective suitable area forapiculture in Selangor as shown in Table 6.

DISCUSSIONThe site verification was carried out at apiariesin Kuala Selangor and Mardi, Serdang to verifythe results of the model. Several areas werevisited and visually captured. The coordinate ofeach beekeeping site was recorded using a SilvaMulti-Navigator GPS System. Information aboutthe sites are shown in Table 7. The coordinateswere then transferred into GIS system andevaluation of the area assessed in terms of itssuitability according to the model developed.

The result is comparatively acceptablewhereby the site verification data of the apiariescorresponded to S1 which is the Most Suitablearea, as verified with the existing apiaries locationas shown in Fig. 16.

TABLE 5Percentage of weight

Criteria Factor Weight % (Expert Choice) Weight (%) WeightedOverlay

Nectar Class 37.4 37Pollen Class 31.8 32Hydrology Feature 16.8 17Road Network 9 9Topography 5 5

Fig. 12: Model for Generating Beekeeping Suitability Zone

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Fig. 14: Suitability map using weighted overlay Fig. 15: Beekeeping uitability zone classificationusing weighted overlay

Fig. 13: Weighted overlay function

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TABLE 6Suitability hectarage

Weighted Overlay

Suitability Analysis Hectares Percentage

S1 - Most Suitable 109, 166.67 13.72%S2 - Suitable 216, 757.24 27.24%S3 - Moderately Suitable 193, 498.15 24.32%Total Suitable area 519, 422.0589 65.27%Not Suitable 276, 358.66 34.73%

TABLE 7Information on Apiaries

GPS ID X Y Owner Address/Area # Hives Species1. 3.38 101.20 Misbah b. Yusof Kampung Sungai Gulang- 9 Cerana,

Gulang, Kuala SelangorTrigona

2. 3.48 101.08 Lee Man Fay Kampung Sekinchan, 100 CeranaKuala Selangor

3. 2.98 101.68 Haji Hamzah Mardi, Serdang 20 Cerana,Trigona,Florea

Fig. 16: Verification of suitability maps with existing apiaries

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CONCLUSIONSIntegrating multi-criteria decision-making andGIS technology in locating beekeeping areas isone of the ways to determine or evaluate potentialzones for beekeeping, since there are severalfactors that contribute to potential zones andeach factor has a different weight. This researchachieved its objective of using GIS and MCDA astools to locate suitable zones for beekeeping.Verification of existing apiaries with the modelusing AHP techniques provides satisfactory resultsof weightage of each criterion for beekeepingzones suitability. By using AHP, a mathematicalmodel of criteria that contributes to suitabilityof beekeeping zones could be established. Theanalysis is a guideline of suitable factors ofbeekeeping and the model could be modified tosuit certain needs depending on the area ofinterest. The research outcome could beexpanded in further research to forecast theflowering time for migratory beekeeping.Evaluation and zoning of suitable beekeepingareas can contribute to the implementation ofbeekeeping activity on a large or small scale.

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