รายละเอียดบทคัดย่อ


Zaw Wan and Chanchai Sangchyoswat  . Soil Erosion Risk Assessment Using GIS and Farmer’s Perception: A Case Study in Dry Zone Area of Central Region of Myanmar .  ใน: รายงานการสัมมนาระบบเกษตรแห่งชาติ ครั้งที่ 4 :เกษตรเพื่อชุมชนและสิ่แวดล้อม พร้อมรับโลกร้อน . ระหว่างวันที่ 27-28 พฤษภาคม 2551  ณ ศูนย์ประชุมนานาชาติเอ็มเพรส เชียงใหม่.  น.395-405.

บทคัดย่อ

         Soil erosion is the single most important environmental concern in the developing world. It has been become an ecological, social and economic problem. This study explored the influence of major socioeconomic factors on erosion processes and conservation measures in a Dry Zone farming context for producing Erosion Risk Map of the study area. In order to gain an over view on the erosion status of the study area and to identify potential areas where effective erosion protection measures might be useful, erosion risk assessment was carried out based on both farmers’ perception and RS and GIS analysis. The multinomial logit model was chosen to study the identification of major socioeconomic and physical factors influencing on soil erosion in the context of individually farmer’s specific data on multiple choice. The farmers perceive soil erosion and land degradation mainly by yield variability, changing in soil color and appearance, occurrence of stony and pebbles followed by rill, sheet and gullies formation. And trend of changing number of tillage operation is increasing being top soil has been removed due to erosion. To estimate a spatially-explicit of soil erosion risk in the study area, Digital Elevation Model (DEM), digital geological map and Landsat TM image were used with the ICONA model. The final erosion risk map shows that 76.5 % of the area has low erosive status. These areas are including agricultural land and already eroded area in the past.21.8% of area has medium erosion risk and 1.7% of the area in high erosion risk status. The ICONA erosion risk model is useful for forming erosion risk assessment framework of large areas and farmers’ perception may improve estimations and accuracy of the model in the study area.