Determining Oil Palm Plantation Potential Location Using Spatial Multi-Criteria Evaluation (Case Study Musi Banyuasin Regency, South Sumatra Province)

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EVALUATION

(Case Study in Musi Banyuasin Regency, South Sumatra Province)

YUDI ASTONI

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

BOGOR

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I, Yudi Astoni, hereby stated that this thesis entitled:

DETERMINING OIL PALM PLANTATION POTENTIAL LOCATION USING SPATIAL MULTI-CRITERIA EVALUATION

(Case Study in Musi Banyuasin Regency, South Sumatra Province)

Is result of my own work under the supervision advisory board during the period February until July 2011 and that it has not been published before. The content of the thesis has been examined by the advising the advisory board and external examiner.

Bogor, August 2011

Yudi Astoni

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YUDI ASTONI. Determining Oil Palm Plantation Potential Location Using Spatial Multi-Criteria Evaluation (Case Study in Musi Banyuasin Regency, South Sumatra Province). Under the Supervision of SURIA DARMA TARIGAN and HARTANTO SANJAYA

Indonesia has the potential land to grow oil palm plantation and currently being the largest producer of crude palm oil (CPO). Satellite remote sensing data is very potential to be used in studies of forest conditions and plantation, because remote sensing data gives current and accurate information. Sumatra Island, especially Musi Banyuasin regency is one of area that has potential for oil palm plantation development. The objective of this research is to determine the potential location of oil palm plantation development by considering bio-physical criteria such as climate suitability, land condition, and accessibility in Musi Banyuasin regency, and to evaluate the suitability map of oil palm plantation that obtained compared with existing plantation in Musi Banyuasin regency. The Method of this research is based on multi criteria decision making by implementing GIS model technology using spatial multi criteria evaluation that will determine location of oil palm plantation potential. Each criterion and alternatives should be evaluated and weighted using pairwise comparison method to determine the best location for oil palm plantation potential in Musi Banyuasin Regency. Geographic Information System (GIS) analysis using Spatial Multi Criteria Evaluation (SMCE) in finding the best location for oil palm plantation succeeded to choose Sungai Lilin Sub-District as the best location for oil palm plantation development. Sungai Lilin Sub-District has the total suitability area of 62,246.37 Ha or 20.30% of Musi Banyuasin area. Based on the calculation area and percentage of suitability map and existing oil palm plantation indicated that area Highly Suitable (S1) for development plantation area is 306,612.42 ha or 21.23% of Musi Banyuasin regency and for the existing plantation area is 223,699.66 ha or 15.49%.

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YUDI ASTONI. Menentukan Lokasi Potensial Perkebunan Kelapa Sawit menggunakan evaluasi spasial multi kriteria (Studi Kasus di Kabupaten Musi Banyuasin, Propinsi Sumatera Selatan). Dibimbing oleh SURIA DARMA TARIGAN dan HARTANTO SANJAYA

Indonesia memiliki lahan yang potensial untuk pertumbuhan perkebunan kelapa sawit dan saat ini menjadi produsen terbesar minyak sawit mentah (CPO). Data satelit penginderaan jauh sangat potensial untuk digunakan dalam penelitian tentang kondisi hutan dan perkebunan, karena data penginderaan jauh memberikan informasi terbaru dan akurat. Pulau Sumatera, khususnya Kabupaten Musi Banyuasin adalah salah satu daerah yang memiliki potensi untuk pengembangan perkebunan kelapa sawit. Tujuan dari penelitian ini adalah untuk menentukan lokasi potensial pengembangan perkebunan kelapa sawit dengan mempertimbangkan kriteria bio-fisik seperti kesesuaian iklim, kondisi lahan, dan

aksesibilitas di Kabupaten Musi Banyuasin, dan untuk mengevaluasi peta

kesesuaian perkebunan kelapa sawit yang diperoleh dibandingkan dengan perkebunan yang ada di Kabupaten Musi Banyuasin. Metode penelitian didasarkan pada keputusan multi-kriteria yang menerapkan teknologi permodelan Sistem Informasi Geografis dengan menggunakan evaluasi spasial multi-kriteria yang akan menentukan lokasi potensial perkebunan kelapa sawit. Setiap kriteria dan alternatif harus dievaluasi dan berbobot menggunakan metode perbandingan berpasangan untuk menentukan lokasi terbaik untuk potensi perkebunan kelapa sawit di Kabupaten Musi Banyuasin. Analisis Sistem Informasi Geografis (GIS) dengan menggunakan evaluasi spasial multi kriteria (SMCE) dalam mencari lokasi terbaik untuk perkebunan kelapa sawit berhasil memilih kecamatan Sungai Lilin sebagai lokasi terbaik untuk pengembangan perkebunan kelapa sawit. kecamatan Sungai Lilin memiliki luas daerah kesesuaian 62.246,37 Ha atau 20.30% dari total luas kabupaten Musi Banyuasin. Berdasarkan perhitungan luas area dan persentase peta kesesuaian dan perkebunan kelapa sawit yang ada menunjukkan bahwa wilayah Sangat Cocok (S1) untuk pengembangan daerah perkebunan 306.612,42 ha atau 21.23% dari kabupaten Musi Banyuasin dan untuk daerah perkebunan yang ada 223.699,66 ha atau 15.49%.

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The objective of this research is to determine the potential location of oil palm plantation development by considering bio-physical criteria such as climate suitability, land condition, and accessibility in Musi Banyuasin regency. And to evaluate the suitability map of oil palm plantation that obtained compared with existing plantation in Musi Banyuasin regency. This research was conducted from February to July 2011 including the data preparation, data processing, and developing method. The processing of this data was conducted in the laboratory of MIT IPB SEAMEO BIOTROP, Bogor. The study site in this research is in Musi Banyuasin Regency, located at 1.30 - 40 S, 1030 - 1050 E.

The Method of this research is based on multi criteria decision making by implementing GIS model technology using spatial multi criteria evaluation that will determine location of oil palm plantation potential. Each criterion and alternatives should be evaluated and weighted using pairwise comparison method to determine the best location for oil palm plantation potential in Musi Banyuasin Regency. Multi-criteria evaluation is a technique in decision making using multiple criteria or scenarios. Multi-criteria evaluation function is to assist policymakers in choosing from several alternatives based on the priority scale. in this study made three alternative scenarios or weighting based on the priority scale for the development of potential locations of oil palm Plantations in Musi Banyuasin regency by assigning the weights based on the opinion of expert , local knowledge and giving equal weight to each criterion (average value). Based on the results of the calculation of the area by comparing several alternatives it can be concluded that alternative or scenario from expert is the best solution for the development of oil palm plantations in Musi Banyuasin regency.

Geographic Information System (GIS) and Multi Criteria Evaluation as tools have proven to be useful to determine location of priorities development of oil palm plantation in Musi Banyuasin Regency. GIS analysis using Spatial Multi Criteria Evaluation (SMCE) in finding the best location for oil palm plantation succeeded to choose Sungai Lilin Sub-District as the best location for oil palm

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58,558.68 ha or 19.10% of suitability area, while Sekayu Sub-District only covers 56,466.3 Ha or 18.4% of the total Musi Banyuasin Regency. Based on the calculation area and percentage of suitability map and existing oil palm plantation indicated that area Highly Suitable (S1) for area plantation development is 306,612.42 ha or 21.23% of Musi Banyuasin regency and for the existing plantation area is 223,699.66 ha or 15.49%.

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1. It is prohibited to cite all of part of this thesis without referring to and mentioning the source;

a. Citation only permitted for the sake of education, research, scientific writing, report writing, critical writing or reviewing scientific problem

b. Citation does not inflict the name and honor of Bogor Agricultural University.

2. It is prohibited to republish and reproduce all part of this thesis without written permission from Bogor Agricultural University.

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EVALUATION

(Case Study in Musi Banyuasin Regency, South Sumatra Province)

YUDI ASTONI

A thesis submitted for the degree Master of Science in Information Technology for Natural Resources Management Study Program

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

BOGOR

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Musi Banyuasin Regency, South Sumatra Province)

Student Name : Yudi Astoni

Student ID : G051090041

Study Program : Master of Science in Information Technology for

Natural Resources Management

Approved by, Advisory Board

Dr. Ir. Suria Darma Tarigan, M.Sc

Supervisor Co-Supervisor

Hartanto Sanjaya, S.Si, M.Sc

Endorsed by,

Program Coordinator Dean of the Graduate School

Dr. Ir. Hartrisari Hardjomidjojo, DEA Dr. Ir. Dahrul Syah, M.Sc.Agr

Date of Examination: Date of Graduation:

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Alhamdulillah, Thanks to The Almighty ALLAH SWT, which has given me the opportunity and ability to complete this thesis. The success of this study would not have been possible without various contributions and supports from many people and I will not be able to mention them one by one. Of course, I would like to express my highly appreciation to all of them.

I would like to express my thanks and sincere appreciation to Ministry of Agriculture Indonesia for giving me the opportunity to continue my studies and providing the financial support essential for overall expenses during the whole study at Master of Science in Information Technology for Natural Resources Management, Bogor Agricultural University.

I wish to thank my supervisor, Dr. Ir. Suria Darma Tarigan, M.Sc and my co-supervisor, Hartanto Sanjaya, S.Si, M.Sc for their guidance, supporting and kindly provided me the valuable advice of my research. Moreover, I would like to express my gratitude to the external examiner Prof. Dr. Ir. Lilik Budi Prasetyo, M.Sc for giving advice and valuable correction to make my thesis acceptable.

I would like to thank Regional Planning Agency (BAPPEDA) of Musi Banyuasin Regency for supporting in obtained field data during my research.

I would like to thank all staffs at MIT secretariat that support our administration, technical and facility. I would also thank all MIT lecturers who taught me with very important knowledge during my study.

I would like also thank all my colleagues in MIT (especially MIT 2009) for helping, supporting, and togetherness in finishing our assignment and study during accomplish our master degree.

Finally, I would like to thank my lovely wife Nurlaili my boy Muhammad Giovanni Vallendra and my parents for all moral their support, patience, prayer and love during my study.

Hopefully, this thesis could give positive and valuable contribution for anyone who reads it.

Bogor, August 2011

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The Author, Yudi Astoni was born in Sekayu, Musi Banyuasin Regency, South Sumatra, Indonesia on January 02, 1980. He finished his Elementary, Junior and High School in Government school, in Sekayu. Finished his bachelor degree from Sriwijaya University Faculty of Agricultural Engineering in 2003. He entered the IPB Graduate School in year 2009. He was enrolled as Scholarship student from ministry of agriculture in Master of Science in Information Technology for Natural Resources Management, Bogor Agricultural University in 2009 and completed his master study in 2011. Currently, he is working as Teacher in Development and Agricultural School in Sembawa, Banyuasin Regency, South Sumatera Province. His final thesis is “Determining Oil Palm Plantation Potential Location Using Spatial Multi-Criteria Evaluation (Case Study in Musi Banyuasin Regency, South Sumatra Province)”

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Page

TABLE OF CONTENTS ………. xi

LIST OF TABLE……….. xiv

LIST OF FIGURES……….. xv

LIST OF APPENDICES……….. xvi

I. INTRODUCTION ………..……… 1

1.1 Background………..…….……… 1

1.2 Problem Statement ……… 2

1.3 Research Objective ………...………… 2

1.4 Research Scope……….……….…… 2

II. LITERATURE REVIEW ……… 3

2.1 Geographic Information System ………..……… 3

2.2 GIS for land Suitability Analysis………..…… 3

2.3 Spatial Multi Criteria Evaluation (SMCE) Method…….…… 5

2.4 Oil Palm and Potential Land Suitability……… 6

III. METHODOLOGY………..… 9

3.1 Time and Location ……….………….. 9

3.2 Data and Tools Requirements………...

3.2.1 Data Needs……….… 3.2.2 Hardware and Software Requirements………..

10 10 11

3.3 Method……….……….………

3.3.1 Definition of Goal and Objectives……..………...

3.3.2 Identification and Grouping of Criteria (Factor and Constrains)……….….………...… 3.3.2.1 Climate Factor……….……… 3.3.2.2 Land Condition Factor ……… 3.3.2.3 Accessibility Factor ………..…..…… 3.3.2.4 Constrains Factor ……… 3.3.3 Preparation of Maps for Each Factors and Constraints.…

11 13 13 13 13 14 14 15

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3.3.5 GIS-Spatial Multi Criteria Evaluation………...… IV. RESULTS AND DISCUSSIONS ………..……

4.1 Suitability Evaluation ………. 4.1.1 Climate Suitability Map ………..… 4.1.1.1 Rainfall Map ……….… 4.1.1.2 Temperature Map ……….… 4.1.2 Land Suitability Map ………..

4.1.2.1 Soil Type Map ……….…. 4.1.2.2 River Map ……….…… 4.1.2.3 Slope Map ……….… 4.1.3 Access Suitability Map ………

4.1.3.1 Road Map ………..… 4.1.3.2 Factory Location Map ……… 4.1.4 Generating Constrains Map ……….… 4.1.4.1 Settlement Map ………..… 4.1.4.2 Protected Forest Map………..… 4.1.4.3 Water Body Map ……..………..…… 4.1.4.4 Paddy Field Map ……… 4.2 Weighting Based on Pairwise Comparison Matrix ...

4.2.1 Climate Suitability Factor ……… 4.2.2 Land Condition Factor ………. 4.2.3 Accessibility Factor ………. 4.2.4 Suitability of Oil Palm Plantation Factor ……….…

4.3

GIS-Spatial Multi Criteria Evaluation……….…

4.3.1 Suitability Assessment ……….… 4.3.1.1 Assessment based on Expert ………..… 4.3.1.2 Assessment based on Same Weight ……… 4.3.1.3 Assessment based on Local Knowledge ………… 4.3.2 Alternative Comparison for Final Selection ………

17 19 19 19 19 20 21 21 22 23 24 24 25 27 27 28 28 29 30 30 31 31 32 32 33 33 35 37 38

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4.3.4 Development Area of Oil Palm Plantation……….…..… V. CONCLUSIONS AND RECOMMENDATIONS………

5.1 Conclusions ……….… 5.2 Recommendations ……… REFERENCES………..…. APPENDICES……….…

41 43 43 43 45 44

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Page

Table 1 Growing requirements of oil palm plantations…………….…… 7

Table 2 Type and sources of spatial data ………. 10

Table 3 Hardware and software requirements……….. 11

Table 4 Pairwise comparison scale ………...…... 16

Table 5 Example of pairwise comparison matrix ………..…. 16

Table 6 Pairwise comparisons matrix on climate factor……..………… 30

Table 7 Pairwise comparisons matrix on land condition factor…...…. 31

Table 8 Pairwise comparisons matrix on accessibility factor……..…… 31

Table 9 Pairwise comparisons matrix on Suitability of oil palm factor.. 32

Table 10 Weight value based on expert assessment…………..…………. 34

Table 11 Weight value based on the same value………...…… 36

Table 12 Weight value based on local knowledge……….…………. 37

Table 13 Suitability area comparison………...…... 39

Table 14 The area and percentage of suitability and existing oil palm…... 41

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Page

Figure 1 Land use sustainability (de la Rosa 2000)…………...………. 4

Figure 2 Spatial multi criteria evaluation scheme ………. 5

Figure 3 Map of Musi Banyuasin regency……….…………... 9

Figure 4 General framework of the study……….… 12

Figure 5 Rainfall suitability map………. 19

Figure 6 Climate suitability map………. 20

Figure 7 Soil suitability map……….……….. 21

Figure 8 River suitability map………. 22

Figure 9 Slope suitability map……… 23

Figure 10 Land suitability map……….… 24

Figure 11 Road suitability map……….… 25

Figure 12 Factory location suitability map……….. 26

Figure 13 Access suitability map……….………. 27

Figure 14 Settlement map……….. 27

Figure 15 Protected forest map………. 28

Figure 16 Water body map………... 29

Figure 17 Paddy field map……….... 29

Figure 18 Schematic diagram of SMCE suitability modeling………….. 33

Figure 19 Criteria tree in the SMCE module……….... 34

Figure 20 Suitability of oil palm based on expert assessment………... 35

Figure 21 Suitability of oil palm based on the same value………. 36

Figure 22 Suitability of oil palm based on local knowledge………….. 38

Figure 23 Histogram of alternative comparison………... 39

Figure 24 Map of suitability oil palm and existing plantation…………. 40

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Page Appendix 1 Input pairwise comparison matrix (PCM) of all criteria……. 47 Appendix 2 Geometric mean of input PCM for all criteria…………... 51 Appendix 3 ILWIS Step in spatial multi-criteria evaluation (SMCE)….... 52

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1.1 Background

A significant change in the oil palm industry has taken place during the past season, as Indonesia surpassed Malaysia in production of palm oil and is now the world leader. Indonesia has become the first largest world producer of crude palm oil (CPO) followed by Malaysia (GAPKI, 2006). Together these two countries account for 84% of total world production and 88% of global exports (Guerin, 2006). The demand for oil palm significantly increased both local and export, so that land clearing for plantation in Sumatra and Kalimantan regions expanded.

Satellite remote sensing data is very potential to be used in studies of forest conditions and plantation, because remote sensing data gives current and accurate information. Sumatra Island, especially Musi Banyuasin regency is one of area that has potential for oil palm plantation development. Suitability for plant cultivation need to be considered in order to obtain optimal growth, although the plants seem to grow together in a region, but each type of plant has a character that requires different requirements, so that optimum production can be observed among the suitability of land for farming and growing requirements for each type of plant.

Potential land for plantation development basically determined by the physical properties and the environment which include: soil, topography, hydrology and climate. Suitability between the physical properties with the requirements of the use of a commodity to be evaluated will give an idea or information that the land has potential for the development of these commodities. This means that if the land is used for a specific use by providing the necessary input it will give results as expected.

Geographic Information System (GIS) is a powerful tool of acquisition, management and analysis of spatially-referenced data. With the combination of Multi-Criteria Analysis (MCA), GIS provides a decision support tool that help decision making process. The use of GIS-MCA or commonly known as Spatial Multi-Criteria Evaluation (SMCE) in providing alternative locations of potential land for palm oil plantation will give decision maker the ability to choose the best location for plantation intensification from many criteria.

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1.2 Problem Statement

Musi Banyuasin regency has large potential area for the development of oil palm plantations. With this condition, the main problem in this research is: "where are optimal locations (priority) for oil palm development by considering the criteria for climate suitability, land condition, and accessibility using Geographic Information System (GIS) technology?”

1.3 Research Objectives

The objectives of this research are:

1. To determine the potential location of oil palm plantation development by considering bio physical criteria such as climate suitability, land condition, and accessibility in Musi Banyuasin regency.

2. To evaluate the generated suitability map of oil palm plantation with

existing plantation in Musi Banyuasin regency.

1.4 Research Scope

This research is intended to search for the potential areas which can be exploited for oil palm plantation development in Musi Banyuasin regency. In this research, land suitability analysis for oil palm plantation development will be carried out using GIS with processing spatial data such as intersect and buffer processing for a few maps and Spatial Multi-Criteria Evaluation (SMCE) technique and used pairwise method as significant tool in decision making.

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I. LITERATURE REVIEW

2.1 Geographic Information System

Geographic Information System (GIS) is a collection of tools, which can be used for conducting some procedures to collect, store, retrieve at will, transform, and display geographical data from the real world (Burrough 1986). Functionally, GIS can be subdivided into four main components, namely: data input, data storage and management, data manipulation and analysis and data output (Malczewski 1999).

In GIS database, the major objects at conceptual level are geographic object. These objects relate to an entity in real word, which consist of two elements: spatial data and attribute data. The capability to process spatial data differentiates GIS and other kinds of database application. The spatial data is represented in form of points, lines, and polygons and is stored in one of two methods: raster and vector. The spatial data can be obtained either by field survey using GPS equipment (Global Positioning System) or by the result of satellite image interpretation.

2.2 GIS for Land Suitability Analysis

GIS has been used as a tool for developing alternative uses of agricultural land, precision farming, crop yield or land suitability mapping in determining the best alternative for agricultural production. The ability of GIS to integrate, display, and query many types of information at the same time makes it an important tool for decision support in agriculture. Information derived from remote sensing and GIS technologies are being effectively utilized in several areas for sustainable agricultural development and management. One of the most useful tools in GIS is its ability to form overlay operations between layers especially in selecting or locating suitable area for agricultural purposes.

According to FAO (1976), suitability is a measure of how well the qualities of a land unit match the requirements of a particular form of land use. The process of land suitability classification is the evaluation and grouping of specific areas of

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land in terms of their suitability for a defined use. De la Rosa (2000) stated that land suitability is a component of sustainable evaluation of land use. Suitability together with vulnerability defines the suitability of a land use. The sustainable land use should have maximum suitability and minimum vulnerability, as shown in Figure 1.

Figure 1 Land use sustainability (de la Rosa 2000)

Land suitability analysis deals with information, which is measured in different scales like ordinal, nominal, ratio scale etc. Based on the scope of suitability there are two types of classifications in FAO (1976) framework.

 Current suitability: refers to the suitability for a defined use of land in its

present condition, without any major improvements in it.

 Potential suitability: for a defined use, of land units in their condition at some

future date, after specified major improvements have been completed where necessary.

According to Malczewski (1999), spatial multi criteria decision analysis can be thought as a process that combines and transforms geographical data (input) into a resultant decision (output). The critical aspect of spatial multi-criteria analysis that it involves evaluation of geographical event based on the criterion values and preferences set with respect to a set of evaluation criteria. The combination of GIS capabilities with Multi Criteria Decision Making (MCDM) technique provides greater effectiveness and efficiency of decision making while solving spatial decision problems.

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2.3 Spatial Multi Criteria Evaluation (SMCE) Method

Spatial multi criteria decision problems characteristically require a set of geographically-defined alternatives (events) from which a choice of one or more alternatives is made with respect to be given to set of assessment criteria (Malczewski 1999).

Figure 2 Spatial multi criteria evaluation scheme

SMCE consists of two components standardization and weighting. Standardization of constraints is similar to standardization of factors but it is not completely the same and weighting are the relative importance that are part of the main goal or how the considerable of factor influence the main goal.

SMCE method can be the idea of a process that combines and transforms spatially referenced data (input) into a resultant vulnerability score (output). The advantage of using SMCE are the user will easily compare criteria and criteria trees with each other to determine differences and discover what are exactly the reasons that result to a significantly different composite map, which may lead to a different decision or different rank at certain area.

This study uses ranking method. This method is the simplest method to investigate weighting based on ranking. In Ranking Method, every criterion under consideration is ranked in the order of the decision maker’s preference. There are two common ways of doing this; Straight ranking (most important =1,

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second important=2) or the inverse ranking (the least important = 1, next least important = 2 etc.) The Inverse ranking was applied to these factors.

In ranking method, every criterion under consideration is ranked in the order of the decision maker’s preference. To generate criterion values for each evaluation unit, each factor was weighted according to the estimated significance for most potential for oil palm plantations and pairwise comparison method to determine the weights for the criteria. This method involves the comparison of the criteria after criteria is assigned, and then conducted some procedure to make numeric weighting form available ranking information. One method used for this study is rank sum.

The formula of Rank sum is:

Where Wj are normalization weighting for the criteria j, n to the number of

considered criteria (k=1,2,…,n) and rj are importance criteria rank (Malczewski

1999). The purpose from criteria weighted is to express the level interest based on the value that is given by decision maker. Interest weighting to calculate the criteria is depending on:

 Interest variation range change to criteria calculation

 Degree of interest difference to variation of this range (Kirkwood in

Malczewsky 1999).

2.4 Oil Palm and Potential Land Suitability

Oil palm is a plant that originally grew wild in the forests, but now it is cultivated. Palm oil plantations require good environmental conditions to be able to grow and produce optimally. Climatic conditions and soil are the main factors for the growth of oil palm. Oil palm can be grown successfully if in accordance with the terms grow that desired as seen in Table 1.

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Table 1 Growing requirements of oil palm plantations

Requirement

Class of land suitability

S1 S2 S3 N

Temperature (0C) 25 - 28 22 - 25 or

28 - 32

20 - 22 or 32 - 35

< 20 or > 35 Annual

Rainfall (mm)

1700 - 2500 1450 - 1700 or

2500 - 3500

1250-1450 or 3500- 4000

< 1250 or > 4000

Slope (%) < 8 8 - 16 16 - 30 > 30

Soil Type Sandy clay loam Loam, sandy

loam

Sandy loam Sand

(Source: Ministry of Agricultural.) Description:

S1 = Highly suitable S2 = Moderately Suitable S3 = Marginally Suitable N = Not Suitable

According to FAO land suitability classification is divided into four classes, namely:

• Class S1, Highly Suitable: Land has no significant limitations to sustained application of a given use. It may include minor limitations that will not reduce productivity, benefits or cost below the lower boundary set for the class.

• Class S2, Moderately Suitable: Land has limitations that in aggregate are moderately severe for sustained application of given use. The limitations may reduce physical productivity, benefits or cost compared with S1 land to a lower limit set for the class.

• Class S3, Marginally Suitable: Land has limitations which in aggregate are severe for sustained application of given use and will so reduce physical productivity, benefits or cost that the expenditure will only be marginally justified.

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II. METHODOLOGY

3.1 Time and Location

This research was conducted from March to June 2011 including the data preparation, data processing, and developing method. The processing of this data was being conducted in the laboratory of MIT IPB SEAMEO BIOTROP, Bogor.

The study site in this research is in Musi Banyuasin Regency, located at 1.3° - 4o S, 103° - 105o E. The total area is ± 14,265.96 km² or 1,426,596 Ha, the annual mean minimum and maximum temperature is 23,4° C and 31,7° C; and the mean humidity is 75 – 89 % (BAPPEDA, Musi Banyuasin. 2010).

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3.2 Data and Tools Requirements 3.2.1 Data Needs

The data in this research came from several sources. Some information were collected by conducting an interview, questionnaire and discussion with expert from Plantation Office, BAPPEDA Musi Banyuasin Regency and Assessment Institute for Agricultural Technology (BPTP) South Sumatra Province. They have a good knowledge and experience in the study area. Things discussed with experts were concept and procedure of land suitability evaluation, and implementation of spatial multi criteria evaluation in land evaluation.

Spatial data and literature were gathered from several institutions, based on field survey of previous research. The data consist of vector or raster format and non spatial data. Description of type and sources of spatial data is given in Table 2.

Table 2 Type and sources of spatial data

Data Data Format Scale Sources

Landuse Map Shapefile 1 : 250.000 BAPPEDA Musi Banyuasin

Administration Boundary Map

Shapefile 1 : 250.000 BAPPEDA Musi Banyuasin

Digital Elevation Model (Slope Map)

Grid Data Format

30 x 30 m Downloaded from

G-TOPO

Spatial Plan Map Shapefile 1 : 250.000 BAPPEDA Musi Banyuasin

Soil Map Shapefile 1 : 250.000 BBSDLP

Temperature Map Shapefile 1 : 250.000 BMKG

Rainfall Map Shapefile 1 : 250.000 BMKG

Road Map Shapefile 1 : 250.000 BAPPEDA Musi Banyuasin

River Map Shapefile 1 : 250.000 BAPPEDA Musi Banyuasin

Oil Palm Factory Shapefile 1 : 250.000 BAPPEDA Musi Banyuasin

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3.2.2 Hardware and Software Requirements

This research used some hardware’s and software’s to support field data recording and analysis process. The hardware and software needed are shown in the Table 3.

Table 3 Hardware and software requirements

Hardware/Software Specification Function

Hand held GPS Receiver

Garmin 60i Positioning

Camera Digital Pocket Camera Digital Documentation

Mobile Computer Acer Notebook with Intel Core

i5-450M processor, 2 GB Memory and 640 GB HDD

Processing Unit

Color Printing HP DeskJet F2180 all in-One

printer, Scanner and copier

Hardcopy

ILWIS 3.4 Version Spatial Data Analysis

Microsoft Office

Word and Excel 2007 Version Writing Report

3.3 Method

The Method of this research was based on multi criteria decision making by implementing GIS model technology using spatial multi criteria evaluation that will determine location of oil palm plantation potential. Each criterion and alternatives should be evaluated and weighted using pairwise comparison method to determine the best location for oil palm plantation potential in Musi Banyuasin Regency. The research framework is show in Figure 4.

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Figure 4 General framework of the study

Factory Location Distance from Factory Accessibility Suitability Map Musi Banyuasin Regency

Rainfall Map Temperature Map Soil Map River Map

DEM Roads Map Distance from River Slope Map Distance from Roads Land Condition Suitability Map Weighting Climate Suitability Map Spatial Analyst Weighting Criteria Landuse Constrain Set of Alternatives Spatial Plan Map

Oil Palm Plantation Potential Map of Musi Banyuasin

Regency

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3.3.1 Definition of Goal and Objectives

Definition the goal and objectives were based on interview with the stakeholders by considering the entire criteria and possible alternatives. In order to reach the goal, there are some sets of objective that also should be considered as stated below:

• Where the optimal location for development of oil palm plantation

• All factors that may be considered for the affecting plantation location, such as road, river and factory location of Crude Palm Oil (CPO).

3.3.2 Identification and Grouping of Criteria (Factor and Constrains) 3.3.2.1 Climate Factor

a) Rainfall

The major climatic factor influencing plant growth is rainfall, the plants can grow well if the water supply is enough. The quantity of water needed to grow plant will depend upon soil and climatic conditions and as well as the nature of the plant itself, the soil moisture must be retained at levels high enough that the plants will not suffer during dry periods.

b) Temperature

Temperature is one of the most important climate factors in the development plantation. It influences rate of photosynthesis from which the plant can be use the necessary food for the growth. The temperature requirement of plant is different from another depend on the type of plant and origin of a particular plant.

3.3.2.2 Land Condition Factor a) Soil Type

Soil is a physical factor that serves as a planting medium; each region has the characteristics of different soil types. Plants grow well in an area where it satisfies the requirements grow.

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b) Distance from River

River is one factor to consider in determining the location of plantations as a source of water needed for crops and also serves as a pathway of water transportation.

c) Slope

Slope information is needed for land management. Particularly, the information is required in relation to activity of agriculture, erosion risk hazard and the protection function of a land. The relationship with other parameter is quite dominant as well. Usually, if the slope is different, automatically the topography is also different.

3.3.2.3 Accessibility Factor a) Distance from Road

Roads as a means of transportation are a factor to be considered for plantation development. Distances by road will determine the value of plantation locations accessibilities suitability. Distance of plantation location close to the road will provide a high value of accessibility.

b) Distance from Factory Location

Factory location is also one factor to consider in determining the location for the development of plantations. Distance plantation area with the location of the plant is very influential on transportation costs. The closer the plantation to the factory location then the value of accessibility will be higher.

3.3.2.4 Constrains Factor

Constrains factor in the development of oil palm plantations should be based on regulations and government policies. Constraint factors include: settlement, water body, paddy fields and protected forest areas will not be converted into oil palm plantation area.

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3.3.3 Preparation of Maps for Each Factors and Constraints

All the maps prepared for the process of spatial analysis, data obtained from various sources in the vector and raster formats and using a projection system using the UTM (Universal Transverse Mercator), with the same map scale is 1: 250.000.

3.3.4 Weighting Factor and Constrains

In principle, the weighted method gives value to each factor which influences location suitability for palm oil plantation. In this observation there are three kinds of value, climate factor, land condition factor and accessibility factor. Weighting method used in this study using the pairwise comparison method.

3.3.4.1 Pairwise Comparison Method (PCM)

Pairwise Comparison Method (PCM) was developed by Saaty (1991) as part of analytical hierarchy process (AHP) concept for multi-criteria decision approach. The main concept for PCM involves one-on-one comparisons between each of Indicators (CIFOR 1999). The comparison between each indicator asked from expert teams, which have objective to make comparative judgment on the relative importance of each pair of indicators in term of the criterion they measure. Therefore numerical values expressing a judgment of the relative importance (or preference) of one factor against another have to be assigned to each factor. Saaty (1988) suggested a scale for comparison consisting of values ranging from 1 to 9 which describe the intensity of importance, where by a value of 1 expresses “equal importance” and a value of 9 is given to those factors having an “extreme importance” over another factor (Table 4).

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Table 4 Pairwise comparison scale Intensity of

Importance

Description

1 Equal importance

3 Moderate importance of one factor over

another

5 Strong or essential importance

7 Very strong importance

9 Extreme importance

2,4,6,8 Intermediate values

Reciprocals Values for inverse comparison

The simple comparison between criteria describe in simple matrix comparison, which is form in order 3 where three criteria C1, C2, and C3. They are compared against each other. For instance; criterion C1 has been regarded strongly more important than C3, hence a value of 5 has been assigned to the corresponding matrix position. The transpose position automatically gets the reciprocal value, in this case 1/5 which equals 0.2 (Table 5).

Table 5 Example of a pairwise comparison matrix

Criteria C1 C2 C3

C1 1 7 5

C2 1/7 1 1/3

C3 1/5 3 1

For the next step of the calculation process, the assigned preference values are synthesized to determine a ranking of the relevant factors in terms of numerical values which are equivalent to the weights of the factors. Therefore the eigen values and eigenvectors of the square preference matrix revealing important details about pattern in the data matrix are calculated.

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Some experience in AHP process, the values of the pairwise comparison matrix will normally be well considered and not set arbitrarily. However, people’s feeling and preferences remain inconsistent and intransitive and may then lead to perturbations in the eigenvector calculations (Marinoni, 2004). Saaty (1986) defined a consistency ratio (CR) as a ratio of the consistency index CI to an average consistency index RI, thus;

RI or resulting average consistency index, also called the random index, was calculated by Saaty (1986) as the average consistency of square matrices of various orders n which he filled with random entries.

When we faced multidiscipline expert with various judgments, it’s important to check one by one those consistency of judgments. After that process, the selected result has good consistency should merging into one value. To merging values of expert judgment use geometric mean method (Marimin, 2008):

Where; XG = Geometric mean

n = Total Respondent

Xi = Judgment value from respondent i

The result from calculate with used by geometric average method will calculate as there is in PCM process.

3.3.5 GIS-Spatial Multi Criteria Evaluation

Multi-criteria evaluation is a technique in decision making using multiple criteria or scenarios. Multi-criteria evaluation function is to assist policymakers in choosing from several alternatives based on the priority scale. SMCE method is to give a weight to each criterion to bring up the priority scale. Input from SMCE are maps of an area which later referred to as criteria and a "tree criteria" which contains the information grouping criteria, standardization of criteria, and weights for each criterion, then the output is a map in the same region of the suitability which helps in making policy.

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IV. RESULTS AND DISCUSSIONS 4.1 Suitability Evaluation

Suitability evaluation is an assessment process of suitability class for a particular purpose. Evaluation results will provide information about the land suitability (FAO,1976). Results of suitability evaluation represented as a map layer in the GIS database. The suitability map represents the spatial distribution of an attribute that measure the degree of suitability for development of oil palm plantation in Musi Banyuasin regency. The generated suitability maps are climate suitability map, land condition suitability map, accessibility suitability map, and constrains map.

4.1.1 Climate Suitability Map 4.1.1.1 Rainfall Map

Rainfall is classified into 3 based on grow requirement for oil palm namely the class of rainfall Highly Suitable (S1) (1700 - 2500 mm), Moderately Suitable (S2) (1450 - 1700 mm) or (2500 - 3500 mm), Marginally Suitable (S3) (1250 – 1450 mm or 3500 - 4000 mm) and Not Suitable (N) (> 4000 mm). Based on the data obtained from BMKG annual rainfall in Musi Banyuasin Regency is (1673 -2829 mm/year). Classification map for the rainfall is presented in Figure 5.

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Figure 5 shows the rainfall in the Musi Banyuasin regency has suitability classes very well, which has a suitability class S1 (40.82%) and S2 (59.18%). It means that oil palm plantations could be developed in all areas in Musi Banyuasin regency.

4.1.1.1 Temperature Map

Temperature are also classified into three classes based on requirement grows of oil palm, namely the class S1 (250 - 280C), class S2 (220 - 250C or 280 -320C), class S3 (200 - 220C or 320 - 350C), and class N (< 200 or > 350C). Temperature classification for Musi Banyuasin regency based on data from BMKG obtained that the temperature for Musi Banyuasin regency suitable for oil palm namely S1 class with temperatures of 260 - 280C.

Map of climate suitability was created by combining a rainfall map and temperature maps based on suitability classification. The weights from several experts with pairwise comparison method was added into the attribute map using weighted overlay with a value of 89 percent weight for rainfall and temperatures of 11 percent, in order to get suitability climate (Figure 6).

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Figure 6 shows the climate suitability map obtained from overlaying process between rainfall and temperature that have highly suitable (S1) and moderately suitable (S2) suitability classes. Moderately suitable (S2) class dominate all area with 59.19 % and 40.81 % area is highly suitable (S1). Based on climate suitability classes, oil palm crops can grow and develop in the Musi Banyuasin regency.

4.1.2 Land Suitability Map 4.1.2.1 Soil Type Map

Soil types are classified into 3 based on growing required for oil palm, namely the class S1 (sandy clay loam), S2 (loam, sandy loam), S3 (sandy loam) and class N (sand). Maps of soil suitability are presented in Figure 7.

Figure 7 Soil suitability map

Figure 7 shows the soil suitability classes in the study area that showed the class S1 (37.92%) and S2 (62.08%). Different suitability class spread out in some areas of study, it means that the soil suitability factors are not a limiting factor for the development of oil palm plantations in Musi Banyuasin regency.

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4.1.2.2 River Map

River is one of the determining factors for the development of plantations. It functions as a source of water as well as transportation facility that needed for development of oil palm plantation. Suitability criteria of the distance of the river from the plantation were obtained from interviews with experts and also from the regulatory regions in the study area (BAPPEDA Musi Banyuasin). It is made as the basis of classification, namely: 0 - 100 M as the boundary of river that should not be built. Furthermore, suitability classes for river distance is made based on the boundary of river that are distance for S1 (0 - 5 km), S2 (5 - 10 km) and S3 (> 10 km). Buffer function in GIS was used in the analysis to create river distance classes.

Figure 8 River distance suitability map

Figure 8 shows the buffer of river distance that divided into three classes, namely S1 (29.93%), S2 (23.33%) and S3 (46.73%). The buffer map indicates that the area with yellow color has a range of (0 - 5 km) from the river line (S1), whereas the area in red color has a distance between( 5 - 10 km) from the river line (S2), then the area with blue color has a range that exceed 10 km from the river line (S3).

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4.1.2.3 Slope Map

Slope data was derived from the contour line by using DEM (Digital Elevation Model). Elevation is important information to determine suitable area for plantation crop. Every crop has its own requirements to the soil level above sea surface. Some crops prefer mountain regions more than plain but some are not. Moreover, other environment factor like weather condition, which is very important to be considered in agriculture, is also changing with the elevation.

Slope was classified into 3 classes based on suitability for growing requirements of oil palm plantations, namely S1 slope classes (0 8%), S2 (8 -16%), S3 (16 - 30%) and class N (> 30%). Slope map is presented in Figure 9.

Figure 9 Slope suitability map

Figure 9 shows the slope suitability classes in the study area. The class S1 (18.90%), S2 (11.98%), S3 (31.99%), and N (37.11%) showed spread distributions in some areas of study.

Land suitability map can be generated from three criteria map among other; soil map, distance from river map and slope map. The weight from several experts with pairwise comparison method is inserted into the attribute map.

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Weighted overlay was carried out based on a value of 65 percent weight for soil, 23 percent for distance from river and slope of 12 percent, in order to get a land suitability map. Overlay results of suitability maps can be seen in Figure 10.

Figure 10 Land suitability map

Figure 10 shows the land suitability classes in the study area that showed the class S1 (27.27%) and S2 (72.72%).

4.1.3 Access Suitability Map 4.1.3.1 Road Map

Road is important factor in the development of oil palm plantations, because the road is necessary as a means of transportation. Classification assessment of road distance from the plantation location obtained from interviews with experts and also from local regulations and policies in the study area (BAPPEDA Musi Banyuasin). It is used as the basis of classification, namely: 0 - 100 M as the boundary of road that should not be built. Furthermore, suitability classes for road distance is made based on the boundary of road that are distance for S1 (0 - 5 km), S2 (5 - 10 km) and S3 (> 10 km). Buffer function in GIS is utilized in the analysis of road distance.

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Road map can be presented in Figure 11.

Figure 11 Road suitability map

Figure 11 shows the buffer of road that divided into three classes, namely S1 (71.64%), S2 (15.57%) and S3 (12.78%). Buffer map indicates that the area with yellow color has a range of (0 - 5 km) from the road line (S1), whereas the area in red color has a distance between(5 - 10 km) from the road line (S2), then the area with blue color has a range that exceed 10 km from the road line (S3).

4.1.3.2 Factory Location Map

Factory location is also one of the criteria needed to be considered in the development of oil palm plantations. Factory location close to the plantations would be very beneficial, because transportation fee can be minimized.

Classification assessment of the distance of the factory location from the plantation was resulted from interviews with experts and also from the regulatory regions in the study area (BAPPEDA Musi Banyuasin), distance factory location to the plantation can be classified, S1(0 - 5 km), S2 (5 - 10 km) and S3 (> 10 km). Factory location map is presented in Figure 12.

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Figure 12 Factory location suitability map

Figure 12 shows that the buffer of factory location was divided into three classes, namely S1 (5.78%), S2 (35.25%) and S3 (58.97%). Buffer map indicates that the area with yellow color has a range of (0 - 5 km) from the factory location (S1), whereas the area in red color has a distance between(5 - 10 km) from the factory location (S2), then the area with blue color has a range that exceed 10 km from the factory location (S3).

Map of access suitability can be created by combining road map and factory location maps based on suitability classification, the weights of several expert with pairwise comparison method was added into the attribute map and do weighted overlay with a value of 42 percent weight for road and factory location of 58 percent, in order to get a map of access suitability. Access suitability map can be seen in Figure 13.

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Figure 13 Access suitability map

Figure 13 shows the access suitability classes in the study area. It showed the class S1 (5.45%), S2 (71.44%) and S3 (23.10%).

4.1.4 Generating Constrains Map 4.1.4.1 Settlement Map

One of constrain factor in the development of plantations based on regulations and government policies is that the area designated as a settlement cannot be converted into plantation areas. Figure 14 shows a map of settlement areas in Musi Banyuasin regency.

Figure 14 Settlement map

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4.1.4.2 Protected Forest Map

Protected forests are also one of factor constraining the development of plantation area. In accordance with regulations and government policies that protected forest and wildlife sanctuary conservation area should not be converted into plantation areas. Distribution of protected forest areas in Musi Banyuasin regency served in the Figure 15.

Figure 15 Protected forest map

Figure 15 shows the forested forest area (7.88%) and not forest area (92.12%).

4.1.4.3 Water Body Map

Water body is also one area that should not be converted to plantation areas. All area of water bodies including river stream is area that become constrains in development of plantation area. Distribution of water body areas in Musi Banyuasin regency presented in the Figure 16.

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Figure 16 Water body map

Figure 16 shows the water body area in the study area. It showed the water body area (2.94 %) and not water body area (97.06%).

4.1.4.4 Paddy Field Map

Based on the regulations and government policies, zones of paddy field also cannot be converted into plantation areas, areas with a zone of paddy field are also a constraint in the development of plantations. Distribution of paddy field areas in Musi Banyuasin regency served in the Figure 17.

Figure 17 Paddy field map

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4.2 Weighting Based on Pairwise Comparison Matrix (PCM)

Pairwise comparison method basically creates a ratio matrix, which takes inputs from experts to produce the relative weights as output (Malczewski, 1999). The weight is produce from normalizing the eigenvector associated with the maximum eigenvalue of the reciprocal ratio matrix.

The questionnaire of the expert group is the main input data for the matrix calculation in determining the weight to each factor and the criteria in determining the location of oil palm plantations. The criteria in determining the location of oil palm plantations, among others: climatic factors, land conditions and accessibility. Results of questionnaires from some experts can be seen in detail in Appendix 1.

4.2.1 Climate Suitability Factor

Climatic factors including rainfall and temperature is one factor that must be considered in determining the location of the plantation. These factors become important due to water requirement in growing oil palm plantations. Plantations would be better if located in areas that have high rainfall and moderate to high temperature. Scale assessment of each criteria and the main eigenvector value (priority) of a combination of several experts to climate factors can be presented in Table 6.

Table 6 Pairwise comparisons matrix on climate factor

Criteria Rainfall Temperature Priority

Rainfall 1 8.3 0.890

Temperature 1/8.3 1 0.110

Table 6 shows the criteria for rainfall to be the top priority in the development of plantations with the highest eigenvector 0.890 while the temperature criteria scored lower eigenvector 0.110.

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4.2.2 Land Condition Factor

Land condition factors including soil type, distance from river and slope are physical factors to be considered in the development of plantations. The weight value from the expert helped in conducting spatial analysis. Scale assessment of each criteria and the main eigenvector value (priority) of a combination of experts to land condition factors can be presented in Table 7. Table 7 Pairwise comparisons matrix on land condition factor.

Criteria Soil Type

Distance

from River Slope Priority

Soil Type 1 3.45 4.59 0.650

Distc From River 1/3.45 1 2.45 0.230

Slope 1/4.59 1/2.45 1 0.120

Table 7 shows that the criteria for soil type to be the top priority in the development of Plantations with the highest eigenvector 0.650, then followed by the criteria of distance from river 0.230 and the lowest value eigenvector value is the slope with a value 0.120.

4.2.3 Accessibility Factor

Accessibility factor is also considered in the development of plantation. Road distance and location of the factory near the plantation is consideration for accessibility factor. The result of several expert assessments for accessibility factor is shown in Table 8.

Table 8 Pairwise comparisons matrix on accessibility factor

Criteria Distance road Distance factory Priority

Distance road 1 0.71 0.420

Distance factory 1/0.71 1 0.580

Table 8 shows that the criteria for distance from factory to be the top priority in the development of plantations with the highest eigenvector 0.580, while the distance from road criteria scored lower eigenvector 0.420.

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4.2.4 Suitability of Oil Palm Plantation

Value of the weight given to suitability of palm oil plantation is also one deciding factors in spatial analysis for the development of plantations. The result from several expert assessments for suitability of palm oil plantation factor is shown in Table 9.

Table 9 Pairwise comparisons matrix on suitability of oil palm plantation factor

Criteria Climate Land condition Accessibility Priority

Climate 1 1.57 4.58 0.530

Land condition 1/1.57 1 3.55 0.360

Accessibility 1/4.58 1/3.55 1 0.110

Table 9 shows that the criteria for climate to be the top priority in the development of Plantations with the highest eigenvector 0.530, then followed by the criteria of land condition 0.360 and the lowest value eigenvector value is the accessibility with a value 0.110.

4.3 GIS-Spatial Multi Criteria Evaluation

GIS have capability to store and analyze spatial data effectively, and it could spatial modeling to support decision analysis. GIS has long experience in decision making and map design, and it can integrate with SMCE system to support decision maker’s (Zhao and Garner, 2009). In this research, GIS-SMCE capabilities need to simulate different criteria to making suitability map.

The concept spatial analysis based on weighted sum using grid system (cell based modeling). The step one is prepared the spatial data to input as an attribute spatial data. The value of attribute spatial data was transformed into value between values 0 to 1. The advantages of using standardized data will facilitate the calculation of each attribute value is very varied. The step two was transform all vector data into raster based or grid system before weighted sum process. The step three was run spatial model by used weighted sum in ILWIS software (Figure 18).

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Figure 18 Schematic diagram of SMCE suitability modeling

4.3.1 Suitability Assessment

Suitability analysis for the development of oil palm plantations in Musi Banyuasin regency in this study is based on an assessment of the factors that have a high value on the influence of other factors. It make the three scenarios (alternative) with weighting based on the opinions of expert, local knowledge, and giving value to the weights same for each criteria.

4.3.1.1 Assessment based on expert (Alternative 1)

Based on the expert assessment for the criteria of climate, rainfall is a factor that considered more important than temperature. For the criteria of land conditions, the slope is considered less important than soil type and distance from the river, soil type is considered more important than the distance from the river.

Distance From Factory Rainfall Temperature Soil Type _{From River}Distance Slope

Distance From Road Standartdized Criterion Standartdized Criterion Standartdized Criterion

Convert to Grid System

Convert to Grid System Weighted Sum (W1) Weighted Sum (W2) Weighted Sum (W3) Weighted Sum

(W1,W2,W3) Reclassification: _{S1, S2, S3, N}

Suitability Map

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For the criteria of accessibility, distance from palm oil factory is considered quite more important than the distance from the road. The weights based on expert assessments shown in Table 10.

Table 10 Weight value based on expert Assessment

Criteria/Sub Criteria Weight Value

Climate 0.530

1. Rainfall 0.890

2. Temperature 0.110

Land Condition 0.360

1. Soil Type 0.650

2. Distance From River 0.230

3. Slope 0.120

Accessibility 0.110

1. Distance From Road 0.420

2. Distance From Factory 0.580

The calculation of the matrix pairwise comparison method for the weight of each criterion would be used as input weighting values in the process model in SMCE module. Input data in ILWIS Module for each factor and constrains based on expert assessment is show in Figure 19.

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The results of the analysis process suitability of oil palm plantation using SMCE model in ILWIS module based on expert assessment shown in Figure 20.

Figure 20 Suitability of oil palm based on expert assessment

Figure 20 shows the suitability classes based on expert assessment in the study area that showed the class S1 (29%), S2 (50.41%), S3 (4.98%) and N (15.61%).

4.3.1.2 Assessment Based on Same Weight Value (Alternative 2)

Assessment based on the value of the same weight is the weighting scenarios created with the assumption that any criteria for the suitability of the location of oil palm plantations in Musi Banyuasin regency has a value equal importance with the other criteria. The weights based on the same value shown in Table 11.

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Table 11 Weight based on the same value Criteria/Sub Criteria

Suitability

Weight Value

Climate 0.333

1. Rainfall 0.500

2. Temperature 0.500

Land Condition 0.334

1. Soil Type 0.334

2. Distance From River 0.333

3. Slope 0.333

Accessibility 0.333

1. Distance From Road 0.500

2. Distance From Factory 0.500

The results of the analysis process suitability of oil palm plantation using SMCE model in ILWIS module based on the same value shown in Figure 21.

Figure 21 Suitability of oil palm based on the same value

Figure 21 shows the suitability classes based on the same value in the study area that showed the class S1 (26.95%) S2 (56,28%), S3 (1.14%) and N (15.61%).

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4.3.1.3 Assessment Based on Local Knowledge (Alternative 3)

Assessment based on local knowledge is one scenario weighting that obtained from the local communities in the study area which makes the assumption that for the criteria accessibility is a factor that considered more important than land condition and climate. Accessibility factor has a weighting value is higher because distance of plantation that near with the road and plant site will minimize the amount of transportation costs. Weights based on local knowledge are shown in Table 12.

Table 12 Weight value based on local knowledge

Criteria/Sub Criteria

Suitability Weight Value

Climate 0.200

1. Rainfall 0.500

2. Temperature 0.500

Land Condition 0.300

1. Soil Type 0.334

2. Distance From River 0.333

3. Slope 0.333

Accessibility 0.500

1. Distance From Road 0.500

2. Distance From Factory 0.500

The results of the analysis process suitability of oil palm plantation using SMCE model in ILWIS module based on the local knowledge shown in Figure 22.

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Figure 22 Suitability of oil palm based on local knowledge.

Figure 22 shows the suitability classes based on local knowledge in the study area that showed the class S1 (21.45%) S2 (60.38%), S3 (2.53%) and N (15.61%).

4.3.2 Alternative Comparison for Final Selection

The composite index maps can be compared to each other in several ways, in order to rank-order the sites. The most common way is to aggregate the composite indexes of each site through their histogram values (e.g., maximum, average, sum, and connectivity index) and rank-order the sites accordingly. The one with the most favorable selected histogram value becomes the site recommended by the SMCE process.

Histograms of the composite maps are showing the distribution of pixel value in the result maps. The value then can be grouped into suitability classification and calculate the areas for each suitability class. The alternative maps are compared based on the distribution of suitability areas to find the best alternative to be chosen for oil palm plantation location. The first preference is given to the site with the largest area with highly suitable values, if there is equality with other alternatives; preference is given to the site with the smallest area with low value or not suitable areas.

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Figure 23 shows the histogram for alternative comparison.

Figure 23 Histogram of alternative comparison Table 13 Suitability area comparison

Suitability Area (Ha)

Alternative1 Alternative2 Alternative3

Highly Suitable (S1)

418,497.6 (29 %) 389,193.8 (26.95 %) 309,860.9 (21.45 %) Suitable (S2) 728,006.9 (50.41 %) 812,767.2 (56.28 %) 872,020.3 (60.38 %) Moderate Suitable (S3)

72,034.6 (4.98 %) 16,578.1 (1.14 %) 36,657.8 (2.53 %) Not Suitable (N)

225,500.0 (15.61 %) 225,500.0 (15.61 %) 225,500.0 (15.61 %)

Grand Total 1,444,039.1 1,444,039.1 1,444,039.1

Table 13 shows a class of highly Suitable (S1) on alternative 1 has the highest area is 418,497.6 ha or 29% of the total area of Musi Banyuasin regency, alternative 2 has area 389,193.8 ha or 26.95% of the total area of Musi Banyuasin regency, while for the alternative 3 only covers 309,860.9 ha or 21.45% of the total area of Musi Banyuasin regency. Based on the results of the calculation of the area by comparing several alternatives it can be concluded that alternative 1 is the best solution for the development of oil palm plantations in Musi Banyuasin regency.

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4.4 Comparison Map of Suitability with Existing Oil Palm Plantations

Map of results for the spatial overlay of generated suitability map of oil palm and existing plantations can be seen in Figure 24. (Appendix 4). It can be seen that existing plantation situated in all types of suitability classes (S1, S2 and S3). It seems that the existing plantations do not follow pattern of classes in the field. Some plantations are situated at S2 and S3 classes, although S1 class is still available. But there is a general pattern, that all existing plantation are situated follow river and road infrastructure.

Figure 24 Map of oil palm suitability and existing plantation

Overlay results of suitability map with the landuse map shows that the existing plantation areas for oil palm development by suitability class S1 is still broad enough 306,612.42 or 21.23% of total Musi Banyuasin regency. The total area and percentage results of overlay can be seen in Table 14.

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Table 14 Area and percentage of suitability and existing oil palm plantation

Suitability Class

Area

Ha Percentage

Not Suitable (N) 225,500.0 15,61

Highly Suitable (S1) 306,612.42 21,23

Suitable (S2) 622,157.41 43,08

Moderate Suitable (S3) 66,079.53 4,57

Existing Plantation 223,699.66 15,49

4.5 Development Area of Oil Palm Plantation

Based on the results overlay process of suitability map with administrative boundaries indicates that the suitability classes (S1) which has the potential for oil palm development in Musi Banyuasin regency are in some region. Figure 25 shows the distribution area of suitability class in each district area.

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Table 15 Development plantation area

Sub District Development Plantation Area

Ha Percentage

Sungai Lilin 62,246.37 20.30

Sungai Keruh 9,622.26 3.13

Sekayu 56,466.3 18.4

Plakat Tinggi 1,987.58 0.64

Lais 45,198.52 14.74

Keluang 33,312.03 10.86

Batang Hari Leko 360,18 0.11

Babat Toman 5,386.06 1.75

Bayung Lalan 33,475.37 10.91

Bayung Lencir 58,558.68 19.10

Grand Total 306,612.42 100

Table 15 shows the area of oil palm plantation development which has the greatest area of 62,246.37 ha or 20.30% of the total suitability class S1 is located in Sungai Lilin sub-district, and area of 58,558.68 ha or 19.10% are located in Bayung Lencir sub-district, while for the third priority lies in Sekayu sub-district with area that has the potential of 56,466.3 ha or 18.4% of the total suitability class S1.

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V. CONCLUSIONS AND RECOMMENDATIONS

5.1. Conclusions

• Geographic Information System (GIS) analysis using Spatial Multi Criteria Evaluation (SMCE) in finding the best location for oil palm plantation succeeded to choose Sungai Lilin Sub-District as the best location for oil palm development. Sungai Lilin Sub-District has the total suitability area of 62,246.37 Ha or 20.30% of Musi Banyuasin regency area.

• Based on the calculation area and percentage of suitability map and existing oil palm plantation indicated that area Highly Suitable (S1) for development plantation area is 306,612.42 ha or 21,23% of Musi Banyuasin regency and for the existing plantation area is 223,699.66 ha or 15,49%. Existing plantation do not follow exactly generated suitability classes, most of them situated nearby road and river. Therefore road and river must be given higher weighting criteria for suitability classification.

5.2. Recommendation

• Information about land suitability area can be used by local government in making a map of spatial plans and for the investors or farmers to determine which areas could be suitable for oil palm plantation.

• Productivity of existing plantation in S1, S2 and S3 class should be compared based on fresh bunches fruit data in the field.

• To obtain accurate results in the spatial analysis, data used must be have more detailed scale such as the map scale 1: 25,000.

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REFERENCES

[BPS] Badan Pusat Statistik. 2010. Hasil Sensus Penduduk 2010 Kabupaten Musi Banyuasin: Data Agregat per kecamatan.

Burrough PA. 1986. Principles of Geographic Information System for Land

resources Assessment. Oxford. Oxford University Press.

[CIFOR] Center for International Forestry Research. 1999. Guidelines for

Applying Multi-Criteria Analysis to the Assessment of Criteria and Indicators. Jakarta; CIFOR

De la Rosa D. 2000. Conceptual Framework Agro-ecological Land Evaluation. Institudo de Recursos Naturales y Agrobiologia, CSIC, Avda. Reina Mercedes 10,41010 Sevilla,Spain.Available :

[FAO] Food and Agriculture Organization. 1976. A Framework for land

Evaluation. Available:

[GAPKI] Indonesian Palm Oil Associati

Available

Government of Musi Banyuasin Regency 2010. Profil Kabupaten Musi

Banyuasin.

Guerin, B. 2006. ‘Jakarta eyes palm oil for fuel’ Asia Times Online.

Handoko, I. 1994. Basic Construction and Application of Computer Simulation Models in Agriculture, department of Meteorology and Geophysics, Faculty of Mathemathics and Natural Science, Bogor Agricultural Unyversity.

Hardjowigeno,S. Widiatmaka. 2001. Kesesuaian Lahan dan Perencanaan

Tataguna Tanah. Bogor.

ILWIS User’s Guide .2004 :

Looijen,J. 2009. Site Selection for Waste Disposal Using SMCE. Institute for Geoinformation and Earth Observation (ITC). The Netherlands.

(64)

M. A. Shari_ and H. van Keulen,1994. A decision support system for land use planning", Agricult. Syst., vol. 45, pp. 239-257.

M. A. Shari_ and M. van Herwijnen. 2003 Spatial Decision Support System: Theory and Practice. Enschede: ITC Lecture Serie.

Malczewski J. 1999. GIS and Multicriteria Decision Analysis. John Wiley & Sons. USA and Canada

Marinoni, O. 2004. Implementation of the analytical hierarchy process with VBA in ArcGIS. Computers & Geoscience 30 (2004) 637-646

Marimin. 2008. Pengambilan Keputusan Kriteria Majemuk. Jakarta: Grasindo.

Saaty, L.T. 1988. Decision Making for Leaders: The Analytical Hierarchy

Process for Decisions in a Complex World. Pittsburg: University of Pittsburg.

Turban E. and Jay E. Aronson. 2001. Decision Support Systems and Intelligence Systems, 6th Edition. New Jersey .Prentice Hall, Upper Saddle River. Zhao, L. and B. J. Garner. 2001. Developing GIS Tools to Integrate MCDM for

the Analysis of Bank Branch Closures. 6th International Conference on GeoComputation, Brisbane, Australia.

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Appendix 1: input Pairwise Comparison Matrix (PCM) of all criteria 1. Respondents 1

Criteria Rainfall Temperature Priority

Rainfall 1 5 0.834

Temperature 1/5 1 0.166

CR = 0.000

Criteria

Soil Type

Distance

from River Slope

Priority

Soil Type 1 7 3 0.686

Distance From River 1/7 1 1 0.135

Slope 1/3 1 1 0.179

CR = 0.0703

Criteria Distance road Distance factory Priority

Distance road 1 1/3 0.25

Distance factory 3 1 0.75

CR = 0.000

Criteria Climate Land condition Accessibility Priority

Climate 1 5 7 0.723

Land condition 1/5 1 3 0.193

Accessibility 1/7 1/3 1 0.084

CR = 0.0568 2. Respondents 2

Criteria Rainfall Temperature Priority

Rainfall 1 7 0.875

Temperature 1/7 1 0.125

CR= 0.000

Criteria

Soil Type

Distance

from River Slope Priority

Soil Type 1 3 5 0.634

Distance From River 1/3 1 3 0.260

Slope 1/5 1/3 1 0.106

CR =0.03338

Criteria Distance road Distance factory Priority

Distance road 1 1/5 0.167

Distance factory 5 1 0.833

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Criteria Climate Land condition Accessibility Priority

Climate 1 1 7 0.510

Land condition 1 1 3 0.390

Accessibility 1/7 1/3 1 0.100

CR = 0.0697 3. Respondents 3

Criteria Rainfall Temperature Priority

Rainfall 1 3 0.75

Temperature 1/3 1 0.25

CR = 0.000

Criteria Soil Type

Distance

from River Slope Priority

Soil Type 1 3 5 0.648

Distance From River 1/3 1 2 0.230

Slope 1/5 1/2 1 0.122

CR = 0.0318

Criteria Distance road Distance factory Priority

Distance road 1 4 0.8

Distance factory 1/4 1 0.2

CR = 0.000

Criteria Climate Land condition Accessibility Priority

Climate 1 1 3 0.428

Land condition 1 1 3 0.428

Accessibility 1/3 1/3 1 0.144

CR = 0.000 4. Respondents 4

Criteria Rainfall Temperature Priority

Rainfall 1 7 0.875

Temperature 1/7 1 0.125

CR = 0.000

Criteria Soil Type

Distance

from River Slope Priority

Soil Type 1 3 5 0.634

Distance From River 1/3 1 3 0.260

Slope 1/5 1/3 1 0.106

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Criteria Distance road Distance factory Priority

Distance road 1 1 0.5

Distance factory 1 1 0.5

CR = 0.000

Criteria Climate

Land

Condition Accessibility Priority

Climate 1 1 3 0.4054

Land condition 1 1 5 0.4796

Accessibility 1/3 1/5 1 0.1150

CR = 0.0251 5. Respondents 5

Criteria Rainfall Temperature Priority

Rainfall 1 7 0.875

Temperature 1/7 1 0.125

CR = 0.000

Criteria Soil Type

Distance

from River Slope Priority

Soil Type 1 3 5 0.634

Distance From River 1/3 1 3 0.260

Slope 1/5 1/3 1 0.106

CR = 0.0333

Criteria Distance road Distance factory Priority

Distance road 1 1/8 0.111

Distance factory 8 1 0.889

CR = 0.000

Criteria Climate Land condition Accessibility Priority

Climate 1 1 3 0.405

Land condition 1 1 5 0.480

Accessibility 1/3 1/5 1 0.115

CR = 0.0251 6. Respondents 6

Criteria Rainfall Temperature Priority

Rainfall 1 8 0.889

Temperature 1/8 1 0.111

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Criteria Soil Type

Distance

from River Slope

Priority

Soil Type 1 3 5 0.619

Distance From River 1/3 1 4 0.284

Slope 1/5 1/4 1 0.097

CR = 0.0747

Criteria Distance road Distance factory Priority

Distance road 1 4 0.8

Distance factory 1/4 1 0.2

CR = 0.000

Criteria Climate Land condition Accessibility Priority

Climate 1 3 7 0.668

Land condition 1/3 1 3 0.243

Accessibility 1/7 1/3 1 0.089

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Appendix 2: Geometric Mean of Input PCM for All Criteria

Criteria Rainfall Temp Priority

Rainfall 1 8.3 0.890

Temp 1/8.3 1 0.110

CR = 0.000

Criteria Soil Type

Distance

from River Slope Priority

Soil Type 1 3.45 4.59 0.650

Distc From River 1/3.45 1 2.45 0.230

Slope 1/4.59 1/2.45 1 0.120

CR = 0.0360

Criteria Distance road Distance factory Priority

Distance road 1 0.71 0.420

Distance factory 1/0.71 1 0.580

CR = 0.000

Criteria Climate Land condition Accessibility Priority

Climate 1 1.57 4.58 0.530

Land condition 1/0.57 1 3.55 0.360

Accessibility 1/4.58 1/3.55 1 0.11

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Appendix 3 : ILWIS Step in Spatial Multi-Criteria Evaluation (SMCE)

(ILWIS 3.4, 2010;

START

Identification of the main Goal

Identification of a hierarchy of sub goals

Identification of criteria or effects,

which measure the performance of the sub goals

Creating and filling a criteria tree, which represents the hierarchy of the main goal,

any sub goals, and the criteria.

Assignment of input maps to criteria for each alternative Identification of alternatives to be evaluated

Determination of a standardization method per criterion

Weighing of criteria in the criteria tree

Calculation of the Composite Index maps and visualization

Inspecting the values in the Composite Index maps

Classifying or slicing the Composite Index maps

Calculation of Shape Index

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Appendix 4. Suitability map of oil palm plantation in Musi Banyuasin regency

a) Map of Musi Banyuasin Regency

b) Map of suitability oil palm and existing plantation in Musi Banyuasin regency c) Map of development area oil palm plantation in Musi Banyuasin regency

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1.1 Background

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1.2 Problem Statement

1.3 Research Objectives

The objectives of this research are:

2. To evaluate the generated suitability map of oil palm plantation with

existing plantation in Musi Banyuasin regency.

1.4 Research Scope

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Appendix 2: Geometric Mean of Input PCM for All Criteria

Criteria Rainfall Temp Priority

Rainfall 1 8.3 0.890

Temp 1/8.3 1 0.110

CR = 0.000

Criteria Soil Type

Distance

from River Slope Priority

Soil Type 1 3.45 4.59 0.650

Distc From River 1/3.45 1 2.45 0.230

Slope 1/4.59 1/2.45 1 0.120

CR = 0.0360

Criteria Distance road Distance factory Priority

Distance road 1 0.71 0.420

Distance factory 1/0.71 1 0.580

CR = 0.000

Criteria Climate Land condition Accessibility Priority

Climate 1 1.57 4.58 0.530

Land condition 1/0.57 1 3.55 0.360

Accessibility 1/4.58 1/3.55 1 0.11

(2)

Appendix 3 : ILWIS Step in Spatial Multi-Criteria Evaluation (SMCE)

(ILWIS 3.4, 2010;

START

Identification of the main Goal

Identification of a hierarchy of sub goals

Identification of criteria or effects,

which measure the performance of the sub goals

Creating and filling a criteria tree, which represents the hierarchy of the main goal,

any sub goals, and the criteria.

Assignment of input maps to criteria for each alternative Identification of alternatives to be evaluated

Determination of a standardization method per criterion

Weighing of criteria in the criteria tree

Calculation of the Composite Index maps and visualization

Inspecting the values in the Composite Index maps

Classifying or slicing the Composite Index maps

Calculation of Shape Index

(3)

Appendix 4. Suitability map of oil palm plantation in Musi Banyuasin regency a) Map of Musi Banyuasin Regency

b) Map of suitability oil palm and existing plantation in Musi Banyuasin regency c) Map of development area oil palm plantation in Musi Banyuasin regency

(4)

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(6)

Determining Oil Palm Plantation Potential Location Using Spatial Multi-Criteria Evaluation (Case Study Musi Banyuasin Regency, South Sumatra Province)

EVALUATION

YUDI ASTONI

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

BOGOR

EVALUATION

YUDI ASTONI

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

BOGOR

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Determining Oil Palm Plantation Potential Location Using Spatial Multi-Criteria Evaluation (Case Study Musi Banyuasin Regency, South Sumatra Province)

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GRADUATE SCHOOL

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