Geospatial Technologies in Precision Farming: A Case Study

The knowledge of spatial variability in soil organic carbon (SOC) is an important consideration in precision agriculture as well as site specific nutrient management. Geostatistical analyses coupled with GIS and GPS are effective tools in assessing the spatial variability and mapping of SOC. A total of 268 soil samples were collected in a systematic grid design (1-minute interval) using GPS covering four sub-districts: Delduar, Melandah, Mirpur and Fultala under two major alluviums - the Ganges and the Brahmaputra. The classical statistics showed that SOC values are normally distributed in the Fultala sub-site whereas in the other sub-sites, the SOC contents were not normally distributed. The semivariogram model also shows that the Fultala sub-site appears to have a strong structure and a gradual approach to the Gausian model providing the best fit where as the other sites show a weak spatial dependency. Due to salinity and other constrains, Fultala sub-site bears a relatively low cropping intensity and hence tillage and crop management are much lower than the other sites. GIS based interpolated values of SOC ranged from 0.39 to 2.02 % in the Fultala sub-site. Interpolated values of SOC ranged from 0.40 to 2.60% in the Delduar sub-site, 0.40 to 1.35% in the Melandah sub-site and 0.38 to 1.39% in the Mirpur sub-site respectively. Clearly, the sites where SOC is low, a pragmatic and location-based policy should be adopted to maximize SOC sequestration. Therefore, the geospatial technologies can help better management of agricultural land by targeting management practices appropriate to the SOC levels.