Geospatial Inputs for Precision Farming in Grape Gardens ABSTRACT Quality and Quantity of grape yield obtained every harvest depends on the quality of the soil, availability of micro nutrients and macro nutrients and the practices adopted during the crop growing period. Precision farming, wherein applying the inputs in appropriate quantity wherever needed to minimize the spatial variability and to reduce the input cost with minimal environmental degradation, has been the recent trend to achieve sustainable development of grape production system. The aim of the present paper is to assess the nutrient variability within the soil of a study farm and to relate it with Normalized difference vegetation index (NDVI) and to produce variable rate input maps. The study area includes grape farms in Chikkaballapur taluk. Each of these farms is divided into 72m*72m grids of 8 management zones. Soil samples to a depth of 30cm were collected from each management zone on March 10th, 2015 and these samples were analyzed for pH, OC, EC, Nitrogen, P2O5, K2O, and micro nutrients for both farms and the values for each grid were generated. LISS-4 sensor data of RESOURCESAT-2 satellite of January 28, 2015 was analyzed and NDVI values generated for both the farms. The NDVI value for farm 1 varies from 0.062 to 0.40 and farm 2 range between 0.031 to 0.42. The yield values for each grid as reported by the respective farm owners (farmers) was collected and correlated with NDVI values. The GIS map of spatial variability in yield levels and soil properties has been compared with that of NDVI variability across farmers’ fields. It is found that the remotely sensed data appropriately reflects the spatial variation. The study shows the potential for identifying the areas where fertility levels are low requiring supply of nutrients. These geospatial variability maps could be used for advising the farmers in managing the nutrient variability within the fields and get good yield at minimal cost.

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Geospatial inputs for precision farming in grape gardens

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