Determination of Farm Level Soil Erosion Using the Revised Universal Loss Equation (RUSLE)

  • Edgar Allan C. Po College of Agriculture University of Science and Technology of Southern Philippines – Claveria Claveria, 9004 Philippines
  • Mark Alexis O. Sabines College of AgricultureXavier University – Ateneo de Cagayan Cagayan de Oro City, 9000 Philippines
  • Jan Taat College of EngineeringXavier University – Ateneo de Cagayan Cagayan de Oro City, 9000 Philippines
Keywords: soil erosion, digital elevation model, GPS, RUSLE


The authors present this investigation concerning estimate of soil loss at a corn farm. Soil erosion is an enormous concern particularly in sloping agricultural lands. Heavy siltation of any river system after a heavy rain event upstream is indicative of soil erosion. However, it is difficult to convince farmers of the urgency to initiate soil erosion control measures at the individual farm level without providing estimates of soil loss. The objectives of this study were a) to generate terrain or digital elevation models (DEMs); and b) to estimate the extent of soil erosion in a representative cornproducing farm using the Revised Universal Soil Loss Equation (RUSLE). DEMs were generated by extracting data from Google Earth (GE), and conducting a topographic profiling using dual frequency survey-grade Global Positioning System (GPS) receivers on a representative corn-growing farm in Claveria, Misamis Oriental, Philippines. The annual amount of soil erosion estimated by the RUSLE for the modeled corn farm was 16 mTha-1 (4.08 cm) using GE DEM. On the other hand, the use of GPS-derived DEM had about 340% higher estimated soil loss at 55 mTha1 (12.85 cm).The GE elevation was higher than GPS in 22% of the land area while 77% of the study land area had GPS elevation higher than GE. The soil erosion figures obtained in the study serve as an objective starting point in exploring ways and means to mitigate the loss of soil which is an agricultural resource of vital importance, thereby contributing to agricultural sustainability and productivity.