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Dilip Kumar Paul
Physical Planning Consultants India Limited
57/D, Beltola Road, Calcutta 700 025
Email: [email protected]
Precision farming revolves around the idea that any agricultural land can contain wide spatial variations in soil types, nutrient availability, and other important factors and not taking these variations into account can result in a loss of productivity. Precision farming, as such, is a method of farming that allows the farmer to produce more efficiently, thereby realizing gains through economical use of resources. In practical terms, precision farming involves studying and managing variations within fields that can affect crop yield.
What follows is a description of steps involved in the application of geospatial technology in precision farming. The study does not deal with everything about precision farming. It only deals with some of the tasks on the digital side of precision farming for crop production using TNTmips.
Selection of Arapanch village, in West Bengal in eastern India, is based on the premises that
- recent advances in space and information technologies are capable of enabling small farm families achieve sustainable advances in productivity and profitability per unit of land, time, labour, and capital, and
- if the case study is developed for the fields of resource poor farmers, all farmers benefit.
Inputs for the study include two digital maps, the databases on fertility classification, productivity, soil samples and yield samples and a vector (ARC-E00) of plots and multispectral imagery.
Two digital maps are imported into TNTmips. The map, which shows geographic coordinates, are georeferenced manually. The other map is georeferenced via object to object registration process. Study area boundary is digitized using the Spatial Data Editor.
Polygon Grid Sampling process is used for generating polygon grids and points within the field boundary. Polygon grids are smaller sub fields of agricultural fields and represent precision farming management zones. Hexagons best represent the average values for attributes associated with an area. Grid points represent locations for soil sampling. Database for soil samples are imported and attached to the grid points.
Surfaces are generated for soil samples of pH, potassium, and phosphate. Database for yields are also imported and used for generating a surface for yields.
Vector (ARC-E00) of plots along with its geocoordinates is imported. Database for fertility classes of plots and productivity are imported and attached to the vector of plots. Productivity potential field of the database is used for processing the vector to generate productivity potential raster for paddy and vegetables. Field containing fertility details of plots is used for generating fertility class raster – training set – for the study area. A RGB image was classified via automatic classification to create a training set. The training set created using the attributes of vector of plots was used as a reference for ground truths.
Polygon grid cell properties are extracted from productivity potential raster, training set raster and yield surface raster created earlier. A table with computed fields (with implied one-to-one attachment with polygon ID) is created with a view to facilitate modeling the precision farming actions such as application of fertilizers, etcetera.
Theme maps for each of the computed fields were created in order to facilitate visualization of the effect of changes made in the intended action via management formulas for planting, fertilizer and pesticide application rates.
Surface rasters of soil samples of pH and potassium are used to create a management layer via raster expression regions.
Theme map on application of fertilizer vindicates the environmental stewardship of precision farming in resource poor Indian villages. Theme map also vindicates the potential of productivity gains of precision farming.
NormalizeFactor of grid cells is a potential indicator for monitoring sustainability of a precision farming in a given location at a given point of time.
Geospatial applications in precision farming via TNTmips include the following steps: importing, georeferencing, spatial data editing, polygon grid sampling, vector extraction, vector to raster conversion, raster mosaicking, surface modeling, raster extraction via regions, extracting raster cell properties, and creating raster expression regions.
The inputs for study came from Dr Deepak Sarkar of National Bureau of Soil Survey and Land Use Planning, Calcutta and Mr. Bidhan Chandra Roy of Bharat Nursery Private Limited, Calcuuta.
The study – for reasons of paucity of funds – is not exhaustive. Yet it vindicates that precision farming if practiced in Indian villages, could make a lot of difference to India’s efforts in achieving sustainable food security – food for every household for a healthy and productive life – via environmental stewardship and productivity gains of precision farming.
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