Determination of Agricultural Potential Area Based on Land Suitability and Revenue Cost Analysis. Case study in Bantul Regency, Yogyakarta
DETERMINATION OF AGRICULTURAL POTENTIAL AREA
BASED ON LAND SUITABILITY AND REVENUE-COST
ANALYSIS
Case study in Bantul Regency, Yogyakarta
ARRY AGUNG HANANTO
GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
2007
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DETERMINATION OF AGRICULTURAL POTENTIAL AREA
BASED ON LAND SUITABILITY AND REVENUE-COST
ANALYSIS
Case study in Bantul Regency, Yogyakarta
ARRY AGUNG HANANTO
A Thesis submitted for the degree of Master of Sciences of Bogor Agricultural University
MASTER OF SCIENCE IN INFORMATION TECHNOLOGY
FOR NATURAL RESOURCES MANAGEMENTGRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
2007
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Research Title : Determination of Agricultural Potential Area Based on Land Suitability and Revenue Cost Analysis. Case study in Bantul Regency, Yogyakarta
Name : Arry Agung Hananto
Student ID : G.051040161
Study Program : Master of Science in Information Technology for Natural Resources Management
Approved by, Advisory Board
Dr. Ir. Hartrisari Hardjomidjojo, DEA Ir. M. Arief Syafi’i, M.Eng.Sc Supervisor Co-supervisor
Endorsed by,
Program Coordinator Vice Dean of the Graduate School
Dr. Ir. Tania June, M.Sc Prof. Dr. Ir. Khairil A. Notodiputro, M.S
Date of Examination: Date of Graduation: February 21, 2007
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STATEMENT
I, Arry Agung Hananto, here by stated that this thesis entitled:
Determination of Agricultural Potential Area Based on Land Suitability and Revenue Cost Analysis
(Case Study in Bantul Regency, Yogyakarta)
is result of my own work during the period of May until November 2006 and that it has not been published before. The content of the thesis has been examined by the advising committee and external examiner
Bogor, February 2007
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ACKNOWLEDGMENT
There area many people I should thank in regard to this work and so doubt I will not be able to name them one by one. To these I would beg forgiveness. I wish to thank to:
1. Dr. Hartrisari Hardjomidjojo, DEA and Ir. M. Arief Syafi’i, M.Eng.Sc as my supervisor and co-supervisor for their guidance, technical comments and constructive criticism through all month of my research.
2. BAKOSURTANAL for financial support during two years of my study. Without this support, this research would not be possible.
3. SEAMEO-BIOTROP management and staff, and also IPB post graduate directorate that support our administration, technical and facility.
4. Our lecturer from IPB and all other lecturer from BAKOSURTANAL, ITB and BPPT, who taught us the very important knowledge for our future.
5. My friends in MIT, I really appreciate our togetherness, and how we support each other to finish our study in MIT.
6. My wife Irma Novitasari for her moral support and patience during accompany in my study.
7. And ‘Ndut’ (Najla Lulu Nuraini), which was her naughtiness, could entertain me during my study.
I dedicated this thesis to Bantul Regency local government, my office BAKOSURTANAL, and my country Indonesia. I hope this thesis can give a value for developing of agricultural area in Indonesia especially in Bantul Regency.
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CURRICULUM VITAE
Arry Agung Hananto was born in Malang, East Java, Indonesia, at January 26, 1965. He received his Engineer Diploma in Agriculture from Faculty of Agriculture, Bogor Agricultural University, Bogor in 1988. From 1989 till now he has been working for National Coordinating Agency for Surveys and Mapping (BAKOSURTANAL), Cibinong Bogor, West Java.
In 2004, Arry Agung Hananto received a financial support from the Center for Marine Natural Resource Surveys – BAKOSURTANAL to pursue his graduate study. His thesis entitled “Determination of Agricultural Potential Area Based on Land Suitability and Revenue Cost Analysis (Case study in Bantul Regency)”.
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ABSTRACT
ARRY AGUNG HANANTO (2007). Determination of Agricultural Potential Area Based On Land Suitability and Revenue Cost Analysis, A Case Study of Bantul Regency Yogyakarta Province. Under the supervision of HARTRISARI HARDJOMIDJOJO and M. ARIEF SYAFI’I.
Nowadays, the population of the world is growing dramatically. Under present situations, where the land is a limiting factor, it is impossible to bring more area under cultivation (extensive farming), so farming community should tackle this challenge of producing more and more food with the available land only (intensive farming).
Bantul regency which one of the food source in Yogyakarta has undergone rapidly tremendous economic growth during last few years. This condition caused the decreasing of agricultural areas to industrialized, tourism and settlement areas.
The objective of this research is to explore the geographic information systems on defining potential agricultural area based on land suitability and revenue cost analysis in Bantul Regency.
Land suitability analysis is a prerequisite for sustainable agricultural production. It involves evaluation of the criteria ranging from soil, terrain to socio-economic, market and infrastructure. Many of these factors are vaguely defined and characterized by their inherent vagueness. Multicriteria decision-making techniques like weighting, ranking, rating etc. are employed for suitability analysis. Simple Additive Weighting (SAW) or Weighted Linear Combination (WLC) is the most often used technique in multi-criteria decision making. As this process incorporates expert knowledge and judgment by decision makers at various levels, it is very much subjective in nature. Revenue cost analysis is needed in this research for determining agricultural potential area, in order to get the maximum benefit out of the land in the research area which had several land suitability level of several crops.
The result of this research showed how land suitability and revenue cost analysis approach were very useful to determine the agricultural potential area in Bantul Regency. The agricultural potential area consists of potential area for corn, rice, soybean, and peanut.
Keyword: Agricultural Potential Area, Land Suitability, Revenue Cost Analysis, GIS
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TABLE OF CONTENTS
STATEMENT ... i
ACKNOWLEDGMENT... ii
CURRICULUM VITAE ... iii
Table of Contents ... v
List of Tables ... vii
List of Figures ... viii
List of Appendix ... ix
I. INTRODUCTION ... 1
1.1. Background ... 1
1.2. Statement of the problem ... 3
1.3. Scope of Research ... 4
1.4. Objectives... 4
1.5. Thesis Structure... 5
II. LITERATURE REVIEW... 6
2.1. Land Suitability... 6
2.2. Need for Land Suitability Analysis... 6
2.3. Land Suitability Analysis... 7
2.4. Geographical Information Systems (GIS)... 7
2.5. Remote Sensing... 10
2.5.1. Definition of Remote Sensing... 10
2.5.2. Digital Image Processing ... 11
2.5.3. Geometric Correction... 12
2.5.4. Radiometric Correction... 13
2.5.5. Supervised Classification ... 13
2.6. Role of GIS and Remote Sensing ... 14
2.7. Weighted Method Analysis... 15
2.7.1. Simple Additive Weighting ... 15
III. RESEARCH METHODOLOGY... 17
3.1. Description of Research Area ... 17
3.2. Research Materials and Tools ... 18
3.3. Research Methodology ... 19
3.3.1. Data Collection ... 19
3.3.2. Data Preparation... 19
3.3.3. Spatial Processing and Analysis ... 25
1. Modeling Approach ... 25
2. Revenue Cost Analysis Approach ... 28
IV. RESULT AND DISCUSSION ... 29
4.1. Land Use Map ... 29
4.2. Soil Type Map... 30
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4.4. Temperature Map ... 35
4.5. Rainfall Map ... 38
4.6. Overlay Process and Weighting Analysis ... 41
4.7. Agricultural Potential Area ... 49
V. CONCLUSION AND RECOMMENDATION ... 54
5.1. Conclusion ... 54
5.2. Recommendation ... 55
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LIST OF TABLES
Table 3.1. Data Requirement ... 18
Table 3.2. The human factor value from expert for Environmental factors ... 26
Table 3.3. Factor and Class value of Overlay Weighted Method ... 26
Table 4.1. Land Suitability Area for Corn on the Existing Condition ... 43
Table 4.2. Land Suitability Area for Mungbean on the Existing Condition ... 45
Table 4.3. Land Suitability Area for Peanut on the Existing Condition ... 46
Table 4.4. Land Suitability Area for Rice on the Existing Condition... 48
Table 4.5. Land Suitability Area for Soybean on the Existing Condition ... 49
Table 4.6. Ratio Revenue-Cost Comparison of Commodities... 51
Table 4.7. The Area and Location for Agricultural Potential Area of Investigated Crops ... 53
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LIST OF FIGURES
Figure 3.1. Map of Bantul Regency ... 17
Figure 3.2. Scheme / flowchart of the research... 20
Figure 3.3. Description of Image Processing ... 21
Figure 3.4. Generating of Slope Process... 22
Figure 3.5. Generating Temperature Map... 23
Figure 3.6. Digitizing Soil Map ... 24
Figure 3.7. Digitalizing Rainfall Map ... 24
Figure 4.1. Land Use Map of Bantul Regency... 29
Figure 4.2. Land Map Unit of Bantul regency. ... 30
Figure 4.3. Suitability map of Land Map Unit for each crop... 31
Figure 4.4. Slope class suitability map for Corn... 32
Figure 4.5. Slope class suitability map for Rice... 33
Figure 4.6. Slope class suitability map for Mungbean... 33
Figure 4.7. Slope class suitability map for Soybean ... 34
Figure 4.8. Slope class suitability map for Peanut ... 34
Figure 4.9. Temperature suitability map for Peanut ... 35
Figure 4.10. Temperature suitability map for Corn ... 36
Figure 4.11. Temperature suitability map for Mungbean ... 36
Figure 4.12. Temperature suitability map for Rice ... 37
Figure 4.13. Temperature suitability map for Soybean ... 37
Figure 4.14. Water available suitability map for Mungbean ... 38
Figure 4.15. Water available suitability map for Rice ... 39
Figure 4.16. Water available suitability map for Corn... 39
Figure 4.17. Water available suitability map for Peanut... 40
Figure 4.18. Water available suitability map for Soybean... 40
Figure 4.19. Land Suitability Area for Corn in Bantul Regency ... 42
Figure 4.20. Land Suitability Area for Corn on existing condition in Bantul Regency... 42
Figure 4.21. Land Suitability Area for Mungbean in Bantul Regency ... 44
Figure 4.22. Land Suitability Area for Mungbean on existing condition in Bantul Regency... 44
Figure 4.23. Land Suitability Area for Peanut in Bantul Regency ... 45
Figure 4.24. Land Suitability Area for Peanut on existing condition in Bantul Regency... 46
Figure 4.25. Land Suitability Area for Rice in Bantul Regency... 47
Figure 4.26. Land Suitability Area for Rice on existing condition in Bantul Regency... 47
Figure 4.27. Land Suitability Area for Soybean in Bantul Regency ... 48
Figure 4.28. Land Suitability Area for Soybean on existing condition in Bantul Regency... 49
Figure 4.29. Land Suitability Area for Crop on existing condition in Bantul Regency... 50
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LIST OF APPENDIX
Appendix 1. Land Suitability Criteria for Corn (Zea mays)……….58 Appendix 2. Land Suitability Criteria for Mungbean (Phaseolus radiatus LINN)
………...59 Appendix 3. Land Suitability Criteria for Peanut (Arachus hypogea) ………….60 Appendix 4. Land Suitability Criteria for Rice (Oryza sativa)……….61 Appendix 5. Land Suitability Criteria for Soybean (Glycine maximum)………..62 Appendix 6. Average Yearly Rainfall in Yogyakarta Province……….63 Appendix 7. Average of Revenue Cost Analysis for Corn in Bantul Regency...64 Appendix 8. Average of Revenue Cost Analysis for Mungbean in Bantul Regency
………..65 Appendix 9. Average of Revenue Cost Analysis for Peanut in Bantul Regency.66 Appendix 10. Average Revenue Cost Analysis for Rice in Bantul Regency...….67 Appendix 11. Average of Revenue Cost Analysis for Soybean in Bantul Regency
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I. INTRODUCTION
1.1. Background
Agriculture, being the most primitive profession of the civilized society, draws much on its development starting from shifting cultivation to advanced precision farming. With the advancement in the civilization, people came to know about more crops and started to cultivate many crops. Population increase and advancement in the civilization made man to settle at one place and to cultivate the same area year after year. Now, agriculture became a profession is given the name commercial agriculture, and precision agriculture and sustainable agriculture as being the part of it.
Nowadays, the population of the world is growing dramatically. In order to meet the increasing demand for food, the farming communities have to produce more and more their agricultural yields to meet the food demand. Under present situations, where the land is a limiting factor, it is impossible to bring more area under cultivation (extensive farming), so farming community should tackle this challenge of producing more and more food with the existing available land (intensive farming).
The importance of land as a resource cannot be overemphasized. Land issues have become a concern not only locally or nationally but also globally. There are several agenda of international conferences and treatises that have placed land at the center of development issues. They have underscored the fact that land issues are tied to combating poverty, protecting the environment,
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promoting food security, advancing social equity and improving economic growth.
However, latter the current technologies have the potential to increase the productivity of food production and profit. One of the technologies is by using geographical analysis of land resource base, to analyze suitable land area for agricultural crops. By selecting the crop that should be planted on the area that is most suitable for that crop, it is expected the higher productivity and profitability can be achieved.
Land suitability is a function of crop requirements, climate and soil/land characteristics. Matching the land characteristics and climate with the crop requirements gives the suitability. So, suitability is a measure of how well the qualities of a land unit match the requirements of a particular form of land use (FAO, 1976). Besides all factors above; socio-economic, market and infrastructure characteristics are the other driving forces that can influence the crop selection.
Land suitability information alone is sometimes not enough, if we want to involve in agricultural investment. Other problems will occur when one investigated area fulfill land suitability criteria for several kinds of crops, for instance one investigated area is suitable for corn, soybean and ground peanut. To obtain the information about which crop that will give higher profit among others, revenue cost analysis of each crop is needed and then crop, which has the higher revenue cost ratio or the higher returns is selected in order to get higher profit.
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Revenue cost analysis is a method of comparing alternative by analyzing the monetary income that each alternative would generate in relation to its cost. It means that crops that have high revenue cost ratio, will give the high return.
1.2. Statement of the problem
Bantul regency in Yogyakarta province was area which had the wide agricultural areas, so it could be said that Bantul Regency was one of food producer area, especially for fulfilling the food demand in Yogyakarta province. According to the data from Dinas Pertanian Bantul (Bantul Agricultural Office), in Bantul Regency there were five kinds of crop that became superior commodities. Those crops were corn, rice, mungbean, peanut, and soybean. The superior commodities mean the crops that had the high yield compared with other crops which were planted in Bantul Regency areas.
And as the area which located near the central of Yogyakarta province capitol and tourism area of Yogyakarta city, Bantul regency which one of the food source in Yogyakarta, has undergone rapidly tremendous economic growth during last few years. This condition caused the decreasing of agricultural areas to industrialized, tourism and settlement areas.
Therefore to maintain the sustainable agriculture sector and to increase the farming community income in Bantul regency, its need the exact agricultural operation by selecting the proper crop and land. Selecting proper crops means to select the crop which would gave the highest income compared other crops; and proper land means to plant the crop on the suitable area for certain crops.
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For getting the proper crop and land on agriculture operation, it is needed the analyzing of land suitability and revenue cost for each superior crop.
1.3. Scope of Research
This study intended to integrate together remote sensing and GIS to investigated the land suitability of superior commodity in Bantul Regency. The criteria of land suitability which used in this research were based on Land Suitability Criteria which was published by Center for Soil and Agro Climate Research (Puslittanak) Bogor.
The investigated areas were not on all of Bantul Regency, but only on the areas which from image classification were classified as areas that could be operated as crop fields like rice field, dry land, grass and rice dependent of rain filed.
In this research the revenue cost analysis was applied to monoculture practice of agriculture operation.
1.4. Objectives
The aim of this research is to explore the geographic information systems on defining potential agricultural area based on land suitability and revenue cost analysis. More specific objectives are:
1. to provide information about potential area for a certain agricultural crop in Bantul Regency using land suitability analysis, and
2. to determine the most potential area for a certain agricultural crop using revenue cost analysis.
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5 1.5. Thesis Structure
This research work is explained in five chapters. In chapter 1 a brief background is given to introduce the topic and raise preliminary issue on land suitability and revenue cost. Statement of problem structures real condition issue into a workable research topic.
Chapter 2 describes what available literature has said about land suitability, Geographical Information System, remote sensing, role GIS and remote sensing, and weighted method analysis. Chapter 3 describes how the research is conducted. It first gives a profile of study area, which is Bantul Regency and to implement the methodology thus developed. Chapter 4 presents analyze and discuss the results thus obtained. Chapter 5 gives conclusions on the present study and recommendations
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2.
II. LITERATURE REVIEW
2.1. Land Suitability
Land suitability analysis is to estimate the environment condition in order to determine the crop types that are suitable to be planted on a given area. Generally, factors that can be considered for land suitability analysis are soil, slope, climate, and water availability. Land suitability analysis is intended to determine suitable land for cultivating specific crops or other utilization relating to agricultural activities (FAO, 1976). Analysis of the criteria for land characteristics should be done to get information on land suitability, usually by conducting the land evaluation.
Land suitability is a description of compatibility level of a land for certain utilization. Land suitability evaluation is related to evaluation for certain utilization like rice, corn, etc.
Carter (1988) reported that land evaluation is only part of land use planning. Its precise role varies in different circumstances. In the present context, it is sufficient to represent the land use planning process by following generalized sequence: recognition of a need for change, identification of aims, and selection of a preferred use for each type of land, decision to implement, implementation and monitoring of operation.
2.2. Need for Land Suitability Analysis
Land suitability analysis is needed for various purposes in the context of present day agriculture, for instance, land suitability for sustainable agriculture.
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The concept of sustainable agriculture or farming (SA / SF) involves producing qualityproducts in an environmentally benign, socially acceptable and economically efficient way (Addeo et al. 2001), i.e. optimum utilization of the available natural resource for efficient agricultural production. In order to comply these principles of SA one has to grow the crops where they suit best and for which first and the foremost requirement is to carry out land suitability analysis (Nisar Ahamed et al. 2000). So, land suitability analysis has to be carried out in order to keep the sustainable agriculture.
2.3. Land Suitability Analysis
As stated above, land suitability is the ability of a given type of land to support a defined use. The process of land suitability classification is the evaluation and grouping of specific areas of land in terms of their suitability for a defined use. The main objective of the land evaluation is the prediction of the inherent capacity of a land unit to support a specific land use for a long period of time without deterioration, in order to minimize the socio-economic and environmental costs. Land suitability analysis is an interdisciplinary approach by including the information from different domains like soil science, crop science, meteorology, social science, economics and management.
2.4. Geographical Information Systems (GIS)
One of common geographical information system definition is a computer base software/tool for collecting, storing, retrieving, transforming and displaying spatial data from the real world (ETSU, 1999). Traditionally, GIS have grown
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from several diverse backgrounds such as computer-based mapping, database, remote sensing, and design packages. As a result of this diverse background, GIS have the ability to answer a number of spatial questions that are not possible or very time consuming, using traditional methods.
A geographic information system is a power tool for handling spatial data (Aronoff, 1991). Large quantities data of data can also maintained and retrieve at greater speeds and lower cost per unit when computer-based systems are used. The ability to manipulate the spatial data and corresponding attribute information and to integrate different types of data in single analysis and at high speed is unmatched by any manual method. The ability to perform complex spatial analyses rapidly provides quantitative as well as qualitative advantages. Planning scenarios, decision models, change detection and analysis, and other type of plans can be developed by making refinements to successive analyses.
Geographic data are now identified clearly as that required for geographic information systems. Many researchers claim that between 75% and 90% of information used every day by most organizations are geographically based. For planner and decision makers, geographic information is especially important. The geographic information system (GIS) is one of the most powerful tools in planning and decision making today (Juppenlazt and Tian, 1996).
A geographical information system has four functional components (Marble & Amundson, 1988):
- A data input subsystem: collect and/or processes spatial data derived from sources, such as existing maps, remote sensed data and direct digital input.
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- A data storage and retrieval subsystem: organizes spatial data in a topologically structured form, which permits it to be quickly retrieved on the basis of either spatial or non spatial queries for subsequent manipulation, analysis or display
- A data manipulation and analysis subsystem: performs a number of tasks, such as changing the form of the data through user-defined aggregation rules, or producing estimates of parameters for transfer to external analytical type model.
- A data-reporting subsystem is capable of displaying all or selected portions of the spatial database in terms of standard reports or in a variety of cartographic formats.
The data input component converts data from their existing form into one that can be used by a GIS.
Data to be entered in a GIS are of two types: spatial data and associated non-spatial attribute data. Spatial data represent the geographic location of features. Points, lines, and areas are used to represent geographic features. The non-spatial attribute data provide descriptive information like the name of a street, the salinity of a lake, or the composition of a forest stand. During data input the spatial and attribute data must be entered and correctly linked (i.e. the attributes must be logically attached to the features they describe). Suitable verification procedures are needed to check that data quality standards are met (Aronoff, 1991).
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As most geographic information systems in the developing countries are regional and resource and environment based, they are especially useful for implementing the sustainable development strategy.
2.5. Remote Sensing
2.5.1. Definition of Remote Sensing
According to Juppenlantz and Tian (1996), remote sensing is technology that collects data relating to the earth’s surface without contacting with it, through a sensor mounted in a satellite or high-flying aircraft.
The Earth’s surface and atmosphere emit individual characteristic signatures within the visible light and electromagnetic radiation spectrum. The spectrum is divided into spectral bands ranging from short gamma rays to long radio waves.
The Earth Resources Technology Satellite (ERS-1, later renamed Landsat-1), was the first unmanned satellite designed top provide systematic global coverage of earth resources. Launched by the United States on July 23, 1972. It was designed as an experimental system to test the feasibility of collecting earth resource data from satellites (Aronoff, 1991).
The kind of Landsat that are useful for image interpretation for a much wider range of applications is Landsat Thematic Mapper (TM). The characteristic of Landsat Thematic Mapper (TM) which first loaded on Landsat 4 in 1982 was designed to provide improved spectral and spatial resolution over the Multi Spectral Scanner (MSS) instrument. Landsat TM is designed to capture electromagnetic in 7 spectral bands. It has three bands in visible region (band 1, 2, and 3), one band in near infra red (band 4), two bands in mid infrared (band 5
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and 7), and one in thermal infra red (band 6). Geometrically, TM data are collected using a 30 m IFOV/ Instantaneous Field of View (for all but thermal band which has a 120 m IFOV) (Lillesand and Kiefer, 1987).
2.5.2. Digital Image Processing
Digital image processing involves the manipulation and interpretation of digital images with the aid of a computer. The central idea behind digital image processing is quite simple. The digital image is fed into a computer one pixel at a time. The computer is programmed to insert these data into an equation, or series of equations, and then store the result of computation for each pixel (Lillesand and Kiefer, 1987).
The procedures of digital image processing are following four broad types of computer assisted operations: image rectification and restoration, image enhancement, image classification, and data merging.
Image rectification and restoration are operations aiming at correcting distorted or degraded image data, which stem from image acquisition; to create a more faithful representation of original scene. The procedures of image rectification and restoration consist of geometric correction, radiometric correction, and noise removal.
Image enhancement is procedures that are applied to image data in order to effectively display or record the data for subsequent visual interpretation. Steps that most commonly applied digital enhancement technique can be categorized as contrast manipulation, spatial features manipulation, or multi-image manipulation.
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The objective of image classification is to replace visual analysis of the image data with quantitative technique for automating the identification of features in a scene.
2.5.3. Geometric Correction
Raw digital images usually contain geometric distortions so significant that they cannot be used as maps. The geometric correction process is normally implemented as two-step procedure. First, those distortions that are systematic, or predictable, are considered. Second, those distortions that are essentially random, or unpredictable, are considered (Lillesand and Kiefer, 1987).
As systematic distortions are constant and predicable they do not constitute a problem to the user of satellite imagery. The agencies that supply the imagery do the corrections. The main systematic distortions are: panoramic (or scanner) distortion, scan skew, and change in scanning velocity (Meijerink, et.al., 1994).
Systematic distortion are well understood and easily corrected by applying formulas derived by modeling the sources of the distortions mathematically. Random distortions and residual unknown systematic distortions are corrected by analyzing well-distributed ground control point s (GCPs) occurring in an image. As with their counterparts on aerial photographs, GCPs are features of known ground location that can be accurately located on digital imagery. Some features that make good control points are highway intersections and distinct shoreline features (Lillesand and Kiefer, 1987).
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13 2.5.4. Radiometric Correction
As with geometric correction, the type of radiometric correction applied to any given digital image data set varies widely among sensors. Other things being equal, the radiance measured by any given system over a given object is influenced by such factors as changes in scene illumination, atmospheric conditions, viewing geometry, and instrument response characteristics.
2.5.5. Supervised Classification
In image classification there are two classification technique kinds that commonly known, Supervised classification and Unsupervised classification. The fundamental difference between these techniques is that supervised classification involves a training step followed by classification step. In the unsupervised approach the image data are first classified by aggregating them into natural groupings or clusters present in the scene (Lillesand and Kiefer, 1987).
In supervised classification this is realized by an operator who defines the spectral characteristics of the classes by identifying sample areas (training areas). Supervised classification requires that the operator be familiar with the areas of interest. The operator needs to know where to find the classes of interest in the area covered by the image. This information can be derived from general area knowledge or from dedicated field observations (Janssen and Goerte, 2000).
Supervised classification is the procedure most often used for quantitative analysis of remote sensing data. It rest upon using suitable algorithm to label the pixel in an image as representing particular ground cover types, or classes. A variety of algorithms is available for this, ranging from those based upon
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probability distribution of models for the classes of interest to those in which the multi spectral space in partitioned into class-specific using optimally located surfaces (Richards, 1993).
2.6. Role of GIS and Remote Sensing
GIS is a tool for input, storage and retrieval, manipulation and analysis, and output of spatial data (Marble et al. 1984). GIS functionality can play a major role in spatial analysis. Considerable effort is involved in information collection for the suitability analysis for crop production. GIS has the ability to perform numerous tasks utilizing both spatial and attribute data stored in it. It has the ability to integrate variety of geographic technologies like GPS, Remote Sensing etc. The ultimate aim of GIS is to provide support for spatial decisions making process (Foote and Lynch 1996). In multi-criteria evaluation many data layers are to be handled in order to arrive at the suitability, which can be achieved conveniently using GIS.
Remote sensing provides information about the various spatial criteria/factors under consideration. Remote sensing can provide us the information like land use/cover, drainage density, topography etc. Many of the non-spatial parameters can also be inferred by looking at the various spatial parameters. Remote sensing in combination with GIS will be a powerful tool to integrate and interpret real word situation in most realistic and transparent way. Research by Leingsakul et al. (1993) showed that integrated GIS and remote sensing technologies apart from saving time and yielding good data quality have the ability to locate potential new cropland sites.
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15 2.7. Weighted Method Analysis
The basis of this research is a classification problem in which class definition is done through training samples for a particular class of interest. For labeling samples, it is necessary to define all of the class’s existent in a given data by collecting ground truth or existing data.
Typically multiple criteria have varying importance. To illustrate this, each criterion can be assigned to a specific weight that reflects its importance relative to other criteria under consideration. The weight value is not only dependent the importance of any criterion, it is also dependent on the possible range of the criterion values. A criterion with variability will contribute more to the outcome of the alternative and should consequently be regarded as more important than other criteria with no or little changes in their range. Weights are usually normalized to sum up to 1, so that in a set of weights (w1, w2, w3, … wn), ∑ wi = 1. There are several methods for deriving weights, among them (Malczewski, 1999): ranking, rating, pair wise comparison and trade-off.
The simplest way is the straight ranking (in order of preference: 1 = most important, 2 = second most important, etc). Then, the ranking is converted into numerical weights on a scale from 0 to 1, so that they sum up to 1 (http://journalofvision.org/2/1/6/).
2.7.1. Simple Additive Weighting
Simple Additive Weighting (SAW) or Weighted Linear Combination (WLC) is the most often used technique in multi-criteria decision making (Fisher, 1994). Criteria here may include weighted factors and constraints. Calculating
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the product of weight and factor multiplied with all constraints at any location, and then summing up all products yields a total overall score. The score for each alternative A is:
A = SUM (wi * xi) or
A = SUM (wi * xi) * SUM (cj) if a constraint is part of the decision xi = criterion score of factor i,
wi = weight of factor i,
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3.
III. RESEARCH METHODOLOGY
3.1. Description of Research Area
The research area is located in Bantul Regency, Yogyakarta, Indonesia. Geographically the area is located between 110° 12 34 - 110° 31 08 East, and 07° 44 04 - 08° 00 27 South. The breadth of Bantul Regency has an area of about 50,685 Ha or 506.85 square km and consist of 17 (seventeen) districts. Figure 3.1 shows a map of Bantul Regency.
Figure 3.1. Map of Bantul Regency
Topographically, most of Bantul areas are flat land and some parts are infertile hilly areas. In western part, stretching from north to south is low and some hilly lands of about 89.86 square km. The middle part is flat and low land, but is fertile, covering about 210.942 square km. The eastern part varies from low, undulated to steep areas covering about 206.05 square km. The southern
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part, which is actually part of middle area, is sandy and lagoon area, from Srandakan, Sanden, and Kretek Districts.
The area of Bantul is classified into wet tropical area. The wet season occurs between November – April and the dry season between May – October. In 2004, it was recorded that the number of rainy days of 30 days happened in January. But normally the highest average monthly rainfall occurs in December of about 316 mm and the highest rainy days of 14 days.
3.2. Research Materials and Tools
The data used in this research consist of remotely sensed data, topographic data, soil data, and climate data (Table 3.1). The tools are softwares that required for image processing, spatial preparation process, and spatial analysis.
Table 3.1. Data Requirement
Data Description Topographic:
Research Area Administration Hydrology Contour Land use Soil
Imagery Climate:
Temperature Rainfall
Bantul Sub-district
River and seasonal river Relief
Vector and raster data Soil type in research area Landsat TM
Temperature in research area Data and distribution of rainfall
The tool/software that required consists of: - ER Mapper 6.3 for image process
- Autodesk Map 5 for spatial preparation process - ArcGIS 9 for spatial analysis
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19
The hardware requirement for processing data at least has to fulfill the specification: PC Pentium III, 256 MB RAM and 40 MB Hard disk.
3.3. Research Methodology
The procedures of this research consist of data compilation, data preparation, spatial analysis, modeling approach, and data validation. The flowchart of research procedure is represented in Figure 3.2.
3.3.1. Data Collection
The data input is collected from various sources, e.g.:
- Topographic data which is obtained from National Coordinating Agency for Surveys and Mapping (BAKOSURTANAL),
- Information of soil type is derived from regional soil maps produced by Center for Soil and Agro Climate Research (PUSLITANAK),
- Climate data were obtained from Bureau of Meteorology and Geophysics (BMG) and Puslittanak Bogor,
- and Imagery data. 3.3.2. Data Preparation 1. Image Processing
The first step of data preparation is to process the satellite image of research area, while the activities comprise of image processing and vector data processing and analysis. In image processing the activities consist of identifying the data source (coordinate system, format conversions), radiometric correction, geometric correction, cropping image in research area, and image enhancement.
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Figure 3.2. Scheme / flowchart of the research Respondent Data Revenue Cost Analysis Land Overlay
Tentative Map
Land Suitability Map Weighting
Area Selection with more than one suitabilitycriteria Existing
Condition Spatial Analysis
Land Suitability Rainfall Map Climate Data Image Processing Land use Map Landsat TM Soil Map
Digitizing Generate Rainfall Map Derive Temperature Map Soil Map Generate Slope Map Slope Map Temperature Map Topographic Map Data Collection Agricultural Potential Map 20
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Figure 3.3. Description of Image Processing
The image was then classified by using Supervised Classification technique into several types of land uses. The classification processed was completed by landuse data, which obtained from the Bantul local government.
One of main steps in image classification is the ‘partitioning’ of the feature space. In supervised classification the process is realized by defining the spectral characteristic of the classes by identifying sample areas (training areas). A sample of a specific land use class like rice field, comprising of a number training pixels, form a cluster in feature space.
Classification Result Data Image Enhancement
Cropping Image Landsat Imagery
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After that, all vector data required were extracted using spatial processing software. The landuse data result will be used for the next spatial processing and analysis steps.
2. Generated Slope Map
The topographic data provide varying altitude of the research area. A slope map was derived from the contour of topographic map and was classified into several classes. The topographic data that used in this research were already in digital format, so for generating slope map only took the contour data and processing by 3D analysis tool in ArcGIS 9 application.
Figure 3.4. Generating of Slope Process Contour Data
Slope Map
TIN Slope
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The slopes were classed or grouped depending on the rank that each crop requires (this was done based on available literature). The detail slope class of each crop can be seen in appendix.
3. Generated Temperature Map
Temperature data were required to determine the distribution of temperature area. The temperature data was estimated using a formula with the input of altitude polygon derived from altitude of topographic data. Same with soil and altitude, plant need certain temperature condition to grow optimally. The formula that is used to estimate the temperature data is the Braak formula, and the equation is given below:
T = 26.3 °C – (0.01 * altitude in meters * 0.6 °C)
Contour Data Temperature Map
Braak Formula
Figure 3.5. Generating Temperature Map
In this case, temperatures were divided into 3 classes, based on the limitation of the temperature that can influence to the growth of plants.
4. Soil Map Digitizing Process
Soil type data that was obtained from Puslittanak was a paper map. For further process is needed to change the format of soil type data from hardcopy
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data to digital. This process can be done by digitizing the paper map with Autodesk Map 5 application, and then the digital data result will used for analyzing process by using ArcGIS 9.
Figure 3.6. Digitizing Soil Map Digitized
5. Generated Rainfall Map
Rainfall map of investigated area was generated from digitized process of rainfall map which was obtained from Puslittanak Bogor. Digitalizing process was carried out by using Autodesk Map 5 and the result was used for next spatial analyzing process.
Figure 3.7. Digitalizing Rainfall Map Digitalized
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25 3.3.3. Spatial Processing and Analysis 1. Modeling Approach
There were several criteria involved to determine the growth factor. Multiple criteria typically, have varying importance; each criterion can be assigned to a specific weight that reflects how big each criterion influence to the plant growth relative to other criteria. The principle of weighted method is to give value to each factor, which influence to the land suitability for crops growth. The value of factor can be divided into two kinds of value, they are environmental factor value and human value. The environment factors consist of soil type, water availability, slope, and temperature.
Each crop, which will be investigated in this research, has its own growth requirement. Optimum growth of crop could be reached if the requirements are met. Based on crop tolerance to the environmental value, the degree of suitability can be divided into 4 classes: highly suitable, suitable, marginally suitable, and not suitable.
While the environment factor value depend on the condition of the environment, which meet to the optimum growth of crops; the human factors, which contribute to the assessment of environment factors, are obtained from the questioners that are distributed to experts. The expert in this case consists of policy makers, farmers, and researcher, which have experience or expertise on the land suitability for each investigated crop. The human factor values are set from 0 up to 100 percent. The human factor values applied to each crop is described in Table 3.2.
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Table 3.2. The human factor value from expert for Environmental factors
Crops Slope (%) Water Availability (%) Soil (%) Temperature (%) Total (%)
Rice 25 30 29 16 100
Corn 28 23 31 18 100
Soybean 28 22 33 17 100
Peanut 27 24 32 17 100
Mungbean 26 22 33 19 100
Source: Respondent data
After getting the result of human factors values from respondents above, the weighting method will process all data with the formula that have created. The formula describes the relationship between all factors i.e. environmental factor and human factor in weighted method analysis.
As mentioned before, there are two values for the overlay processed of weighted method i.e. value for each environmental factor (altitude, water availability, soil, and temperature), which were given by experts above, and value for the class of each environmental factor that depend on literature. For instance, the values of overlay weighted method for corn are shown in Table 3.3.
Table 3.3. Factor and Class value of Overlay Weighted Method
Factor Weight value
(%) *)
Class of factor (**)
Class Value (***)
Total Value
Slope 28 < 8 %
8 % – 16 % 16 % - 30 %
3 2 1
84
Water
Availability 23
500 – 1,200 mm 1,200 – 1,600 mm
> 1,600 mm
3 2 1
46
Soil type 31 Very suitable
Suitable Marginal Suitable 3 2 1 93 Temperature 18
20° - 26° C 26° - 30° C 16° - 20° C
3 2 1
36
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27 Note:
*) factor value : from expert
**) Class of environment factor : from literature
**) class value : 1= marginally suitable, 2 = suitable, 3 = highly suitable
The land suitability value is summing up of all factor total values that were applied, and the total value itself is obtained from human factor value multiplied by the environment class value. The minimum and maximum values of land suitability can be calculated as:
a) The maximum value: if all factors have maximum class value. The maximum value: 100 * 3 = 300
b) The minimum value: if all factors have minimum class value. The minimum value: 100 * 1 = 100
As mentioned before, the land suitability areas were divided into 3 classes that are very suitable, suitable, and marginal suitable. Therefore, the range value between land suitability classes is the maximum value minus minimum value divided by number of classes. So, the range value is (300 - 100) / 3 = 66.67, or rounded up to 67. The interval values for each class are:
- Marginally suitable area having value between 100 up to 167; - Suitable area having value between 168 up to 235; and
- Highly suitable area having value between 236 up to 300.
If one or more factors or classes have 0 (zero) value, the result becomes a not suitable areas.
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28 2. Revenue Cost Analysis Approach
Revenue cost analysis is needed in order to get the biggest profit in the area that is suitable for several crops.
The procedure to get the potential area is done by overlaying all of suitable land area for each crop; from this activity the areas that have the suitable criteria for more than one crop in the same suitable criteria level can be found. By inputting revenue cost analysis data for each crop, the potential crop, which could give the maximum return, can be obtained.
For the areas that have the ‘suitable’ criteria for more than one crop in the different suitable criteria levels, for instance: the area is suitable for corn in level S3 and also suitable for rice field but in level S1; this area should be as a potential area for the crop that has higher suitability level (in this case is suitable for rice).
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4.
IV. RESULT AND DISCUSSION
4.1. Land Use Map
The existing condition of research area that was obtained from the classification process and completed/validated by secondary and field data, shows that land cover consists of settlement, agriculture area, dry land, bush, and sand.
Depending on the source data, land use in research area can be divided into several land uses (see Figure 4.1).
Figure 4.1. Land Use Map of Bantul Regency.
Based on the land utilization data from land use map above shows that areas which could be processed refer to scope of research were rice field, dry land, grass, and rice dependent of rain field.
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4.2. Soil Type Map
The Peta Tanah Semi Detail map from Puslittanak classified the soil types as Satuan Peta Tanah (SPT) or Land Map Unit. SPT is the smallest unit of soil type, which had the same characteristics and distinguished element from other SPT.
From the available data used in this research, the research area consists of 78 SPT’s.
Figure 4.2. Land Map Unit of Bantul regency.
Then the 78 SPT’s were analyzed one by one to get the level of suitability of each SPT to the investigated crops. The results of analyzed process for each crop were grouping into highly suitable SPT group, suitable SPT group, marginally suitable SPT group, and not suitable SPT group.
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The suitability classification of SPT group for each investigated crops are shown in Figure 4.3.
Figure 4.3. Suitability map of Land Map Unit for each crop
4.3. Slope Map
Most of Bantul Regency are flat plain areas with slope of less than 2 %, and the distribution of plain area are in the northern, middle and southern parts of
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Bantul Regency covering an area of about 31,421 Ha (61.99 %). Most of the eastern and western areas have slope from 2.1 up to 40 % and cover about 15,148 Ha (30 %), and the rest of the area have slope of more than 40 %.
Based on the criteria of land suitability that published by Puslittanak, some investigated crop have the same the classification as slope suitability. The crops, which have same classification were corn, mungbean, peanut, and soybean. Rice need more flat area for its growth, so the areas, which have slope more than 8 % is classified as not suitable area.
Figure 4.4. Slope class suitability map for Corn
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Figure 4.5. Slope class suitability map for Rice
Figure 4.6. Slope class suitability map for Mungbean
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Figure 4.7. Slope class suitability map for Soybean
Figure 4.8. Slope class suitability map for Peanut 34
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4.4.Temperature Map
Temperature zone in research area is made by using Braak formula with contour data as an input. The classification of temperature is based on the temperature suitability classification for each crop which issued by Puslittanak.
Generally, the temperature of Bantul Regency is suitable for all crops investigated. According to Puslittanak land suitability classification, the suitable temperature needed for almost all investigated crop are between 16° C up to 34° C, except Mungbean needed the temperature cooler than others that is between 8° C up to 30° C for its optimum growth. The temperature suitability map for all investigated crops can be seen in Figure 4.9 to 4.13.
Figure 4.9. Temperature suitability map for Peanut
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Figure 4.10. Temperature suitability map for Corn
Figure 4.11. Temperature suitability map for Mungbean
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Figure 4.12. Temperature suitability map for Rice
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4.5.Rainfall Map
The water availability zones were obtained from the isohyet line of rainfall average data of several rainfall observation stations (rainfall data can be seen in Appendix 6). Based on average hydrological data series, it shows that the water availability were not become a limitation factor for growing the investigated crops. There were no unsuitability areas of water availability level in the research area, the water availability level for all investigated crops at least on marginally suitability level.
According to Puslittanak Land Suitability Criteria and discussion result with expert from Puslittanak, water availability level for rice was not based on rainfall but more from the wet area (rice field irrigation areas). The areas outside the wet area were classified as marginally suitable areas. Areas that are suitable for mungbean were found in the area where water availability has marginally suitable level. Suitability map of water availability for all crops can be seen in Figures 4.14 to 4.18.
Figure 4.14. Water available suitability map for Mungbean
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Figure 4.15. Water available suitability map for Rice
Figure 4.16. Water available suitability map for Corn
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Figure 4.17. Water available suitability map for Peanut
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41 4.6. Overlay Process and Weighting Analysis
After all suitability data of investigated crops for each factor are ready, the next step was cropping to overlay all suitability data for slope, water availability, temperature, and soil. As mentioned before, all unsuitable data for this area were not processed further, but others will be processed for the next step.
Unsuitable areas were not inputted to the overlay process due to need the high effort to increase the level from not suitable to marginal suitable level. And in this research, the determination of suitability level was assumed on the operationally level that usually done by farmer.
The results of overlay process are parcels that are obtained from intersecting between suitability levels of each factor. The overlay process was done to each crop, and the results of this process were used for the weighting process.
In weighting process, land suitability level was generated from summing up of all factor total values that were applied, and the total value itself is obtained from human factor value multiplied by the environment class value.
The results from overlay process above were processed by weighting method to get the land suitability level of each crop. The areas were divided into four parts: highly suitable, suitable, marginally suitable and not suitable area (Figure 4.19).
As mentioned before in the scope of research, the investigated areas were areas, which was obtained from classification process and completed by secondary data that were classified as agricultural area like rice filed, dry land, rice dependent rain field, or area that could be converted into agricultural area easily like grass.
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Figure 4.19. Land Suitability Area for Corn in Bantul Regency
Figure 4.20. Land Suitability Area for Corn on existing condition in Bantul Regency
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Table 4.1. Land Suitability Area for Corn on the Existing Condition
And from overlying process of the land suitability area of each crop with the existing condition on the investigated area, it could be seen that ‘suitable area’ were located on the areas, which were could classified into four existing land utilization.
The areas were: mixed plant areas, rice plant areas, dry field rice areas, and grass areas. Mixed plant areas mean the existing conditions of those areas were already planted by several kind of crop, which were planted in dry field areas. Rice plant areas mean the existing condition areas were already planted by rice plant. Dry field rice areas mean the existing condition were already planted by dry field rice. And the grass areas mean the existing condition was grass.
The land suitability areas for mungbean in Bantul Regency were shown in Figure 4.20, and referred to the scope of research the suitable areas were also applied only on the investigated areas as shown in Figure 4.21.
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Figure 4.21. Land Suitability Area for Mungbean in Bantul Regency
Figure 4.22. Land Suitability Area for Mungbean on existing condition in Bantul Regency
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Table 4.2. Land Suitability Area for Mungbean on the Existing Condition
Figure 4.23 below showed the land suitability areas for peanut in Bantul Regency, and the intersecting area between suitable area and the existing condition was shown in Figure 4.24.
Figure 4.23. Land Suitability Area for Peanut in Bantul Regency
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Figure 4.24. Land Suitability Area for Peanut on existing condition in Bantul Regency
Table 4.3. Land Suitability Area for Peanut on the Existing Condition
Land suitability area for rice in Bantul Regency could be seen in figure 4.25. Most of the suitable areas for rice in investigated area were located in the proper place, which were rice plant areas as can be seen in Figure 4.26.
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Figure 4.25. Land Suitability Area for Rice in Bantul Regency
Figure 4.26. Land Suitability Area for Rice on existing condition in Bantul Regency
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Table 4.4. Land Suitability Area for Rice on the Existing Condition
As other investigated crop, the land suitability for soybean in Bantul Regency can be seen in Figure 4.27. Location of suitable area on the existing condition area was shown in Figure 4.28.
Figure 4.27. Land Suitability Area for Soybean in Bantul Regency
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Figure 4.28. Land Suitability Area for Soybean on existing condition in Bantul Regency
Table 4.5. Land Suitability Area for Soybean on the Existing Condition
4.7. Agricultural Potential Area
Data about ‘suitable area’ of each crop on the investigated area above, indicated that there were several condition which can described the relation
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between the suitable areas of each crop and the existing condition.
The conditions were: suitable area and the existing condition was already match, suitable area and existing condition was not match, and the areas were already suitable but have not managed yet. The instance of first condition can be described as the areas that were suitable for rice and existing condition were rice plant or areas that were suitable for peanut and existing location were in dry land. Second condition was described as the areas that were suitable for soybean and the existing condition were rice plant (rice field) or dry field rice. And the third condition was described as the areas that were suitable for corn and the existing conditions were grass.
Description of suitable areas of all crop that fulfilled the conditions above, were generated by overlaying the suitable areas of all crops that have been obtained before (Figure 4.29).
Figure 4.29. Land Suitability Area for Crop on existing condition in Bantul Regency
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To determine the agricultural potential areas was carried out by investigating all land suitability level of areas that belonging of the conditions which is described above.
Agricultural potential areas means the areas that will give the higher profit if it is operated by a certain crop, which is suitable in those areas, compared other crops that are also suitable in that areas in the same level of crop land suitability.
And for determining a certain area for the most profitable agricultural operation, is by looking to value of revenue cost analysis of each crop. A certain commodity which had the higher revenue cost analysis value was more profitable than others. The areas which are suitable for several crops but have different land suitability level; the agricultural potential area is determined by looking for the crop which has the higher land suitability level.
In Table 4.6, it can be seen the revenue cost analysis of each crop. And the utilization of area for agricultural operation of corn gave the most profitable value. The detail item of revenue cost analysis of each crop can be seen in appendices 7 to 11.
Table 4.6. Ratio Revenue-Cost Comparison of Commodities
No Commodities R/C Ratio
1 2 3 4 5
Corn Rice Soybean Peanut Mungbean
3.45 2.54 2.30 1.87 1.46 Source: Mujihono, 2005. and Sudaryanto, 2004
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For the area, which is suitable for several crops but have different land suitability level; the agricultural potential area is determined by looking for the crop which has the higher land suitability level.
Figure 4.30. Agricultural Potential Area in Bantul Regency.
And the agricultural potential area in Bantul Regency, which was generated from revenue cost and land suitability analysis, can be seen in Figure 4.30.
Potential areas for rice were only grouped into one potential criterion that was areas which were suitable for rice and located in rice field. Other areas, which were suitable for rice but located on the outside of rice field, can not be categorized as potential areas for rice because it needed a lot of effort to irrigate water to those areas.
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Table 4.7. The Area and Location for Agricultural Potential Area of Investigated Crops
And as mentioned before, the areas which have not managed were areas, which were located in grass areas.
Table 4.7 showed that potential areas for rice, which is match with the existing condition, had the highest areas among others. On the other hand, there were no areas which were potential for mungbean. Even though mungbean had enough suitable area, but compared with other crops, agricultural operation by mungbean economically was not profitable enough.
And the data also indicated that there were areas, which still can explored more in order to get more food or benefit, especially for areas that potential for certain crop but were located in other land use areas or areas which have not managed yet.
Grass area as area which potentially can increase the production of food, in this case economically was not significant because the areas was not too wide. Totally area of grass was only 1.1 % of total potential areas.
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5.
V. CONCLUSION AND RECOMMENDATION
5.1. Conclusion
Exploring of geography information system can be applied to provide the information about potential areas of investigated crops in Bantul Regency. Determination of agricultural potential areas was based on land suitability and revenue cost analysis, which is from this analyzing resulted agricultural potential area for corn, rice, soybean, and peanut respectively.
Suitable lands for investigated crops were obtained by overlay process to all environment factors that used, which have been classified according to the land suitability criteria of each environment factor. And by using weighted method for analyzing, suitable area can be classified into four classes: highly suitable, suitable, marginally suitable areas, and not suitable areas.
The result of Agricultural Potential Area in Bantul Regency indicated that the most potential areas for corn were already located in the proper area (dry field), with the areas about 66.8 % of total potential area of corn. The same like corn, 90.9 % of total potential areas of peanut were also already located in the proper area (dry filed). For mungbean, 50.1 % of total potential area of mungbean were located in rice plant area, and only 42.9 % of total potential areas of mungbean were in the proper area (dry field). For rice, all of potential area for rice were located in rice field arera, which were about 79.7 % of total areas of rice field.
However, the revenue-cost analysis could be used as consideration to increase the profit. Crop that will give the highest contribution in increasing the
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profit of agricultural investment in Bantul Regency is corn with revenue cost ratio, of 3.45, followed by rice (R/C = 2.54), soybean (R/C = 2.30), peanut (R/C = 1.87), and mungbean (R/C = 1.46).
Overall, it can be said here that remote sensing and GIS as tools have proven useful to obtain the potential areas for agricultural operation.
5.2. Recommendation
Information about agricultural potential area and land suitability area can be used by local government as a tool for land use planning, and for investor this information can be used to determine which crop would be planted.
The recommendation to local government if want to assess this research, is the local government should take inventory to the land resource area which is included to the agricultural potential area, then suggesting the farmer to plant their land with the suitable potential crop of their land.
The accuracy of data is needed to support the user to get the accurate information about land suitability and agricultural potential area.
Further study need to be carried out to develop spatial database to complete the database in spatial form, and developing the facilities to manage, analyze and provide information in information system.
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6.
REFERENCES
Addeo, G., G. Guastadisegni and M. Pisante. 2001. Land and water quality for sustainable and precision farming. I World Congress on Conservation Agriculture, Madrid.
Aronoff, S. 1991. Geographic Information Systems: A Management Perpecstive, WDL Publications, Ottawa, Canada.
Carter, R.W.G. 1988. Coastal Environment. An Introduction to the Physical, Ecological, and Cultural Systems of Coastlines. Academic Press. Toronto. Djaenudin, D, et all. 2005. Petunjuk Teknis Evaluasi Lahan Untuk Komoditas
Pertanian. Balai Penelitian Tanah. Pusat Penelitian dan Pengembangan Tanah dan Agroklimat. Bogor.
ESRI, Arc ViewGIS, 32. Software, Help. Weighted Method.
ETSU, 1999.What is GIS?. URL: www.etsu.com/gis/html/what is gis.html.
FAO Soils bulletin 32. A framework for land evaluation. 1981, Soil resources development and conservation service land and water development division, FAO AND AGRICULTURE ORGANIZATION OF THE UNITED NATIONS, Rome 1976
http://www.fao.org/docrep/X5310E/x5310e00.htm#Contents
FAO, 1976. A Framework for Land Evaluation. FAO Soil Buletin 32. Soil Resources Management and Conservation Service Land and Water Development Division.
Fisher, P.F. 1994. Probable and Fuzzy Models of The Viewshed Operation Innovations in GIS 1,pp. 161-175.
Foote, K. E. and M. Lynch. 1996. Geographic information systems as an integrating technology: context, concepts and definition. Austin, University of Texas.
http://journalofvision.org/2/1/6/
Janssen, L.L.F. and B.G.H. Gorte. 2000. Digital Image Classification. In Principle of Remote Sensing. ITC, Enschede, The Netherlands.
Juppenlatz, M. and X. Tian. 1996. Geographic Information Systems and Remote Sensing. McGraw-Hill Book Company. Sydney.
(69)
57
Leingsakul, M., S. Mekpaiboonwatana, P. Pramojanee, K. Bronsveld and H. Huizing. 1993. Use of GIS and remote sensing for soil mapping and for locating new sites for permanent cropland - a case study in the highlands of northern Thailand. Geoderma 60(1-4): 293-307.
Lillesand, T.M. and R.W. Kiefer.1987. Remote Sensing and Image Interpretation. Second Edition. John Wiley & Son. Inc, New York.
Malczewski, J. 1999. GIS and Multicriteria decision Analysis. USA and Canada, John Wiley & Sons.
Marble, D. H., H. W. Calkins and D. J. Pequet. 1984. Basic Readings in Geographic Information Systems. Williamsville, NY., SPAD Sysems Limited.
Meijerink. A.M.S., H.A.M. de Brouwer, C.M. Mannzerts, and C.R. Velenzuela. 1994. Introduction to the Use of Geographic Information System for Practical Hydrology. ITC and UNESCO International Hydrological Programme Netherlands.
Mujihono, R. 2005. Pengkajian Uji Adaptasi Variestas Unggul Baru Padi, Jagung dan Kedelai: Laporan Kegiatan Penelitian dan Pengkajian TA 2005. BPTP Yogyakarta. Yogyakarta.
Nisar Ahamed, T. R., K. Gopal Rao and J. S. R. Murthy. 2000. GIS-based fuzzy membership model for crop-land suitability analysis.. Agricultural Systems 63(2): 75-95.
Ricard, J.A. 1993. An Introduction to Remote Sensing Digital Image Analysis. Second Edition. Spinger-Verlag. Berlin.
Sudaryanto, B. 2004. Analisis Indikator Pembangunan Pertanian Daerah Istimewa Yogyakarta. BPTP Yogyakarta. Yogyakarta.
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APPENDICES
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Appendix 1. Land Suitability Criteria for Corn (Zea mays)
Land Suitability Class Using Condition /
Land Characteristics S1 S2 S3 N
Temperature (tc)
Average temperature (°C)
Water availability (wa)
Annual rainfall (mm) Humidity
Oxygen availability (oa)
Drainage
Root media (rc)
Texture
Rough materials (%) Soil solum (cm)
Peat
Thickness (cm)
Thickness (cm), if there is mineral materials / enrichment inserted Maturity
Nutrient retention (nr)
CEC of clayey (cmol) Base saturation (%) H2O pH
Organic-C (%)
Toxicity (xc)
Salinity (dS/m)
Sodicity (xn)
Alcalinity/ESP (%)
Danger of sulfidic (xs) Depth of sulfidic (cm)
Erosion hazardous (eh) Slope (%)
Erosion hazardous
Flood hazardous
Flooded area
Land preparation
Rock in surface Rock exposure
20 - 26
500 – 1,200 > 42 good, rather blocked soft, rather soft, medium < 15 > 60 < 60 < 140 saprik > 16 > 50 5.7 - 7.8 > 0.4 < 4 < 15 > 100 < 8 very low F0 < 5 < 5 - 26 - 30 1,200 – 1,600 400 - 500 36 - 42
rather fast, medium
- 15 - 35 40 – 60 60 - 140 140 - 200
saprik, hemik
≤ 16 35 - 50 5.5 - 5.8 7.8 - 8.2
≤ 0.4 4 - 6 15 - 20 75 - 100 8 - 16
low - medium
- 5 - 15 5 - 15
16 - 20 30 - 32 > 1,600 300 - 400 30 - 36 blocked
rather rough 35 - 55 25 - 40 140 - 200 200 - 400
hemik, fibrik
< 35 < 5.5 > 8.2
4 - 8 20 - 25 40 - 75 16 - 30 hard F1 15 - 40 15 - 25
< 16 > 32 < 300 < 30 extremely blocked rough > 55 < 25 > 200 > 400 fibrik > 8 > 25 < 40 > 30 very hard > F2 > 40 > 25 Source : Puslittanak. 2005.
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Appendix 2. Land Suitability Criteria for Mungbean (Phaseolus radiatus LINN) Land Suitability Class
Using Condition /
Land Characteristics S1 S2 S3 N
Temperature (tc)
Average temperature (°C)
Water availability (wa)
Annual rainfall (mm) Humidity
Oxygen availability (oa)
Drainage
Root media (rc)
Texture
Rough materials (%) Soil solum (cm)
Peat
Thickness (cm)
Thickness (cm), if there is mineral materials / enrichment inserted
Maturity
Nutrient retention (nr)
CEC of clayey (cmol) Base saturation (%) H2O pH
Organic-C (%)
Toxicity (xc) Salinity (dS/m)
Sodicity (xn)
Alcalinity/ESP (%)
Danger of sulfidic (xs) Depth of sulfidic (cm)
Erosion hazardous (eh)
Slope (%)
Erosion hazardous
Flood hazardous
Flooded area
Land preparation
Rock in surface Rock exposure
12 - 24
350 – 600 42 - 75
good, rather blocked soft, rather soft, medium < 15 > 75 < 60 < 140 saprik > 16 > 50 5.6 - 7.6
> 1.2 < 1 < 5 > 100 < 8 very low F0 < 5 < 5
24 - 27 10 - 12 600 – 1,000
300 - 350 36 - 42 75 - 90 rather fast,
medium - 15 - 35 50 – 75 60 - 140 140 - 200
saprik, hemik
≤ 16 35 - 50 5.4 - 5.6 7.5 - 8.0 0.8 - 1.2 1 - 1.5
5 - 8 75 - 100
8 - 16 low - medium
- 5 - 15 5 - 15
27 - 30 8 - 10 > 1,000 230 - 500
30 - 36 > 90 blocked
rather rough 35 - 55 20 - 40 140 - 200 200 - 400
hemik, fibrik
< 35 < 5.4 > 8.0 < 0.8 1.5 - 2
8 - 12 40 - 75 16 - 30 hard
F1 15 - 40 15 - 25
> 30 < 8 < 250 < 30 extremely blocked rough > 55 < 20 > 200 > 400 fibrik > 2 > 12 < 40 > 30 very hard > F2 > 40 > 25
(73)
61
Appendix 3. Land Suitability Criteria for Peanut (Arachus hypogea) Land Suitability Class
Using Condition /
Land Characteristics S1 S2 S3 N
Temperature (tc)
Average temperature (°C)
Water availability (wa)
Annual rainfall (mm) Humidity
Oxygen availability (oa)
Drainage
Root media (rc)
Texture
Rough materials (%) Soil solum (cm)
Peat
Thickness (cm)
Thickness (cm), if there is mineral materials / enrichment inserted
Maturity
Nutrient retention (nr)
CEC of clayey (cmol) Base saturation (%) H2O pH
Organic-C (%)
Toxicity (xc)
Salinity (dS/m)
Sodicity (xn) Alcalinity/ESP (%)
Danger of sulfidic (xs)
Depth of sulfidic (cm)
Erosion hazardous (eh)
Slope (%)
Erosion hazardous
Flood hazardous
Flooded area
Land preparation
Rock in surface Rock exposure
25 - 27
400 – 1,100 50 - 80
good, rather blocked soft, rather soft, medium < 15 > 75 < 60 < 140 saprik > 16 > 35 6.0 - 7.0
> 1.2 < 4 < 10 > 100 < 8 very low F0 < 5 < 5
20 - 25 27 - 30 1,100 – 1,600
300 - 400 > 80 < 50 rather fast,
medium - 15 - 35 50 – 75 60 - 140 140 - 200
saprik, hemik
≤ 16
≤ 35 5.0 - 6.0
7.0 - 7.5 0.8 - 1.2
4 - 6 10 - 15 75 - 100
8 - 16 low - medium
- 5 - 15 5 - 15
18 - 20 30 - 34 1,600 – 1,900
200 - 300
blocked
very soft, rather rough
35 - 55 25 - 50 140 - 200 200 - 400
hemik, fibrik
< 5.0 > 7.5 < 0.8 6 - 8 15 - 20 40 - 75 16 - 30 hard
- 15 - 40 15 - 25
< 18 > 34 > 1,900 < 200 extremely blocked, fast rough > 55 < 25 > 200 > 400 fibrik > 8 > 20 < 40 > 30 very hard > F0 > 40 > 25
(74)
62
Appendix 4. Land Suitability Criteria for Rice (Oryza sativa)
Land Suitability Class Using Condition /
Land Characteristics S1 S2 S3 N
Temperature (tc)
Average temperature (°C)
Water availability (wa)
Annual rainfall (mm) Humidity
Root media (rc)
Drainage
Texture
Rough materials (%) Soil solum (cm)
Peat
Thickness (cm)
Thickness (cm), if there is mineral materials / enrichment inserted
Maturity
Nutrient retention (nr)
CEC of clayey (cmol) Base saturation (%) H2O pH
Organic-C (%)
Toxicity (xc)
Salinity (dS/m)
Sodicity (xn) Alcalinity/ESP (%)
Danger of sulfidic (xs)
Depth of sulfidic (cm)
Erosion hazardous (eh)
Slope (%)
Erosion hazardous
Flood hazardous
Flooded area
Land preparation
Rock in surface Rock exposure
24 - 29
33 - 90 rather blocked’ medium soft, rather soft, < 3 > 50 < 60 < 140 saprik > 16 > 50 5.5 - 8.2
> 1.5 < 2 < 20 > 100 < 3 very low F0,F11,F12, F21,F23,F31, F32 < 5 < 5
22 - 24 29 - 32
30 - 33 Blocked
good, medium 3 - 15 40 - 50 60 - 140 140 - 200
saprik, hemik
≤ 16 35 - 50 4.5 - 5.5
8.2 - 8.5 0.8 - 1.2
2 - 4 20 - 30 75 - 100
3 - 5 low F13,F22,F33,
F41,F42,F43
5 - 15 5 - 15
18 - 22 32 - 35
< 30; > 90 extremely blocked, rather fast, rather rough
15 - 35 25 - 40 140 - 200 200 - 400
hemik, fibrik
< 35 < 4.5 > 8.5 < 0.8 4 - 6 30 - 40 40 - 75 5 - 8 medium F14,F24,F34,
F44
15 - 40 15 - 25
< 18 > 35 Fast rough > 35 < 25 > 200 > 400 fibrik > 6 > 40 < 40 > 8 hard F15,F25, F35,F45 > 40 > 25
(75)
63
Appendix 5. Land Suitability Criteria for Soybean (Glycine maximum) Land Suitability Class Using Condition /
Land Characteristics S1 S2 S3 N
Temperature (tc)
Average temperature (°C)
Water availability (wa)
Annual rainfall (mm) Humidity
Oxygen availability (oa)
Drainage
Root media (rc)
Texture
Rough materials (%) Soil solum (cm)
Peat
Thickness (cm)
Thickness (cm), if there is mineral materials / enrichment inserted
Maturity
Nutrient retention (nr)
CEC of clayey (cmol) Base saturation (%) H2O pH
Organic-C (%)
Toxicity (xc)
Salinity (dS/m)
Sodicity (xn) Alcalinity/ESP (%)
Danger of sulfidic (xs)
Depth of sulfidic (cm)
Erosion hazardous (eh)
Slope (%)
Erosion hazardous
Flood hazardous
Flooded area
Land preparation
Rock in surface Rock exposure
23 - 25
350 – 1,100 24 - 80
good, rather blocked soft, rather soft, medium < 15 > 75 < 60 < 140 saprik > 16 > 35 5.5 - 7.5
> 1.2 < 6 < 15 > 100 < 8 very low F0 < 5 < 5
20 - 23 25 - 28 250 - 350 1,100 – 1,600
20 - 24 80 - 85 rather fast,
medium
- 15 - 35 50 – 75 60 - 140 140 - 200
saprik, hemik
≤ 16 20 - 35 5.0 - 5.5
7.5 - 7.8 0.8 - 1.2 6 - 7 15 - 20 75 - 100
8 - 16 low - medium
- 5 - 15 5 – 15
18 - 20 28 - 32 180 - 250 1,600 – 1,900
< 20 > 85 blocked
rather rough 35 - 55 20 - 50 140 - 200 200 - 400
hemik, fibrik
< 20 < 5.0 > 7.8 < 0.8 7 - 8 20 - 25 40 - 75 16 - 30 hard
F1 15 - 40 15 - 25
< 18 > 32 < 180 > 1,900 extremely blocked, fast rough > 55 < 20 > 200 > 400 fibrik > 8 > 25 < 40 > 30 very hard > F1 > 40 > 25
(76)
64
Appendix 6. Average Yearly Rainfall in Yogyakarta Province
No. Station Location Regency Rainfall (mm/year) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29 30 31 32 33 34 35 36 37 Moyudan Godean Gamping Seyegan Sleman Ngaglik Tempel Turi Pluyon Pakem Cangkringan Adisucipto Depok Kalasan Berbah Prambanan Pajangan Kasihan Sewon Bantul Pleret Barongan Pundong Sanden Dlingo Ngawen Nglipar Playen Semin Wonosari Paliyan Karangmojo Semanu Ponjong Panggang Tepus Rongkop Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Bantul Bantul Bantul Bantul Bantul Bantul Bantul Bantul Bantul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul 1966 1920 2069 2293 1706 1942 2522 3192 4672 2820 2309 2219 2593 1353 1233 1717 1474 1394 1670 1336 1962 1837 1344 3611 4492 2076 2148 2140 1888 1985 1781 1386 1186 2014 2139 2222 1776 Source: Puslittanak, 2005
(1)
64
Appendix 6. Average Yearly Rainfall in Yogyakarta Province
No. Station Location Regency Rainfall (mm/year) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29 30 31 32 33 34 35 36 37 Moyudan Godean Gamping Seyegan Sleman Ngaglik Tempel Turi Pluyon Pakem Cangkringan Adisucipto Depok Kalasan Berbah Prambanan Pajangan Kasihan Sewon Bantul Pleret Barongan Pundong Sanden Dlingo Ngawen Nglipar Playen Semin Wonosari Paliyan Karangmojo Semanu Ponjong Panggang Tepus Rongkop Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Sleman Bantul Bantul Bantul Bantul Bantul Bantul Bantul Bantul Bantul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul Gunung Kidul 1966 1920 2069 2293 1706 1942 2522 3192 4672 2820 2309 2219 2593 1353 1233 1717 1474 1394 1670 1336 1962 1837 1344 3611 4492 2076 2148 2140 1888 1985 1781 1386 1186 2014 2139 2222 1776 Source: Puslittanak, 2005
(2)
65
Appendix 7. Average of Revenue Cost Analysis for Corn in Bantul Regency
No. Component Sum
I
II
III
Cost for labor force Planting
Fertilizing
Clear away processing Weeds control
Pest control Transportation Harvest cost
Means of Production Seed
Fertilizer Urea TSP KCl OM Herbicide Insecticide Others Tax
Dropping off
Total of Production Cost (I+II+III)
Gross revenue 14.43 ton/ha x Rp 900/kg Revenue
R/C ratio (Gross Revenue / Total of Production Cost)
1,960,000 555,000 185,000 500,000 0 0 220,000 500,000 477,525 349,800 6499,800 0 0 888,800 0 0 70,000 70,000 0 3,768,400 12,987,000 9,215,600 3.45 Source: Mudjihono, R. 2005.
(3)
66
Appendix 8. Average of Revenue Cost Analysis for Mungbean in Bantul Regency
No. Component Sum
I
II
III
Cost for labor force Land preparation Making drainage Planting
Fertilizing
Clear away processing Spraying
Harvest After harvest
Means of Production Seed
Fertilizer Urea SP-36 KCl Pesticide Equipment Others Dropping off
Total of Production Cost (I+II+III)
Gross revenue 1.200 ton/ha x Rp 3,000/kg
Revenue
R/C ratio (Gross revenue / Total of Production Cost)
1,560,000 475,000 140,000 262,500 40,000 337,500 50,000 180,000 75,000 660,000 122,500 55,000 150,000 82,500 100,000 150,000 250,000 250,000 2,470,000 3,600,000 1,130,000 1.46
(4)
67
Appendix 9. Average of Revenue Cost Analysis for Peanut in Bantul Regency
No. Component Sum
I
II
III
Cost for labor force Land preparation
Raising seedlings processing Planting
Cultivation Harvest
Means of Production Seed
Fertilizer Urea SP36 TSP OM Others
Transportation
Total of Production Cost (I+II+III)
Gross revenue 1.667 ton/ha x Rp 2,500/kg
Revenue
R/C ratio (Gross revenue / Total of Production Cost)
866,666 433,333 0 216,667 43,333 173,333 1,336,667 400,000 366,667 133,333 186,667 250,000 25,000 25,000 2,228,333 4,167,500 1,939,167 1.87
(5)
68
Appendix 10. Average Revenue Cost Analysis for Rice in Bantul Regency
No. Component Sum
I
II
III
Cost for labor force Land preparation
Raising seedlings processing Bund repairing
Planting Fertilizing
Clear away processing Pest control
Harvest
Means of Production Seed
Fertilizer Urea TSP KCl Herbicide Insecticide Others Tax
Total of Production Cost (I+II+III)
Gross revenue 7.584 ton/ha x Rp 1,200/kg
Revenue
R/C ratio (Gross revenue / Total of Production Cost)
2,731,428.50 162,500.00 500,000.00 107,500.00 400,000.00 60,000.00 195,000.00 35,000.00 1,271,428.50 777,125.00 77,500.00 449,625.00 90,000.00 90,000.00 0.00 70,000.00 70,000.00 70,000.00 3,578,553.50 9,100,800.00 5,522,246.50 2.54
(6)
69
Appendix 11. Average of Revenue Cost Analysis for Soybean in Bantul Regency
No. Component Sum
I
II
III
Cost for labor force Planting
Fertilizing
Clear away processing Weeds control
Pest control Harvest cost
Means of Production Seed
Fertilizer Urea TSP KCl Herbicide Insecticide Others Tax
Dropping off
Total of Production Cost (I+II+III)
Gross revenue 2.205 ton/ha x Rp 2,500/kg
Revenue
R/C ratio (Gross Revenue / Total of Production Cost)
1,603,750 700,000 140,000 312,500 0 0 451,250 477,525 191,250 217,875 68,400 0 0 0 320,000 70,000 250,000 2,401,275 5,512,500 3,111,225 2.30
Source: Mudjihono, R. 2006.