Soil nutrient varibility mapping in UiTM Research Station, Arau, Perlis using landsat TM and geostatistical analysis - Mohd Yusoff, Malek; Jusoff, Kamaruzaman; Ismail, Mohd Hasmadi (2006)
Spatial variability of N, P, and K using a systematic soil sampling were studied in the UiTM Perlis Branch Campus Research Station field at Arau, Perlis. A total of 54 surface soil samples at 0-15 cm depth were collected at 80 m x 80 m grid pattern and analyzed to determine total N, available P and K in the plots. A GeoExplorer II Global Positioning System (GPS) was used to record the coordinates of the soil sampling points. All the samples were then analysed in the Soil Science Laboratory, Universiti Putra Malaysia, Serdang to determine soil selected elements of nitrogen, phosphorous and potassium (N, P, K). Soil mapping variability was later analyzed using the Geostatistics software. Kriging analysis was used to determine the value of each point in the area of study. A semi variogram was developed to describe the spatial relationship between the locations where the value of a soil property was estimated and characterized. It was found that the presence of N, P, and K varied from 0.098 -0.147 %, 10.0 – 24.2 ppm and 62 -129 ppm, respectively. Field maps were prepared with point-kriged estimates and showed that the northern portion of the field (80 % of the study area) is under shortage of nutrients perhaps due to the barren condition of the exposed soil. It was also visually observed that there is a reduction of the planted crop growth in both of the first, second and third quadrant of the of the study plot which were opened-up areas. Therefore, it is strongly recommended that of fertilizer, lime and organic matter be applied in these quadrants of the study area to improve the soil condition compared to the right bottom side of the quadrant. This study implies that site-specific or precision agriculture provides a useful management tool in the forecasting of crop yield and future market intelligence. Further research with respect to integrating the use of remotely sensed data especially airborne hyperspectral sensing with GPS and GIS to improve accuracy of systematic variability mapping in UiTM Farm Research Station should be attempted.