As conceived in the “West”, Precision Agriculture (PA) may be suitable for capital-intensive, high turnover economy, high technology monoculture and controlled management of a single major soil borne pathogen or pest across vast cultivated area but not for the peasant farming or the pest complex ridden unaided agriculture as in India. PA is not about an abstract of characterization. The specific values indicating variations in population at exact grids and helping the individual farmer in decision-making in pest management at varied pesticide doses depending on varied soil pest population intensity and spatial distribution. Often it ends up in reducing the quantum of a pesticide threatened with withdrawal. We understand Appropriate PA (Appropriate Precision Agriculture, APA) as Site-Specific Appropriate Precision Agriculture (SSAPA) applied to cropping system management with geoinformatics and agriinformatics as may be available of land, soil, including its nutrient content and availability of requisite water, meteorology, agrometeorology and forecasting inputs and outputs by marketing, production, protection and processing, and other essential information provided through a decision support system (DSS), in an agriinformatics networking such that is not ordinarily available to Indian farmers in general. Due to Precision Farming (PF), production increased by 40 to 60 percent farmers’ margins of the produce and reduction of the commission charged by the middlemen to 7-10 percent. Further, bargaining power, capacity building, optimal crop protection and produce quality were improved and the cost of cultivation has reduced at Agaram Village, Dharamapuri, Tamil Nadu of Adhiyaman Precision Farmers Association (Ramasamy 2007). Their “PA” is a fine example of community or cooperative approach to farming, which has high prospect in India. But it is far from both PA (sensu lato) and SSAPA (sensu Dasgupta 2006, 2007b) as applied by us in this trial for methodology development. As a prelude to SSAPA we are comparing nine rice- and vegetable-based cropping sequences between improved and farmers’ practices in terms of five parameters, viz. crop growth and productivity, soil nutrient management, pest management, (BCA) biocontrol agent balance, and energy balance, economics ~ each with several variables. We are also providing ground and prepare to provide PA –specific data to the farmers. By Return per Rupee invested Rice-Potato-Pumpkin1, Cucumber-Cabbage-Basella alba, and Groundnut–Brinjal+Brinjal sequences were suitable for resource-rich growers, whereas Okra-Chilli+Chilli2 , and Black gram–Parwal+Parwal sequences were suitable for resource-poor growers. Overall, Groundnut–Brinjal+Brinjal, Okra-Chilli+Chilli, Cucumber-Cabbage-Basella alba cropping sequences, over three years in a row, were the best. When information on the detailed soil survey, RS (Remote Sensing) generated geo informatics (Global Positioning System, GPS; Geographical Information System, GIS) including geo-referenced thematic maps, image analysis, geostatistics for spatial analysis, land, soil and physiography, farmers’ socio-economic status, meteorological data, crop suitability, crop and pest management, processing strategy and transport, market arrival, demand and price are provided, farmers’ decision-making as individuals and members of a given community may become easier and more precise than without.