Determination of Agricultural Potential Area Based on Land Suitability and Revenue Cost Analysis. Case study in Bantul Regency, Yogyakarta

Abstract

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 decisionmaking 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.