Rural poverty reduction is closely related to increase in agricultural growth and productivity. While, agriculture remains the main occupation in villages, agriculture sector growth has been showing a declining trend in since 1991. One of the key reasons for this is poor crop and soil management practices, and imbalanced application of fertilizers historically over years by farmers due to lack of timely, accurate and reliable information on nutrient management and crop cultivation practices. Mobile phone growth over last few years have made it a ubiquitous device and can help reach out to large number of farmers. Through this action-research a system was designed, developed and implemented at the farm level to answer the question whether providing customized crop cultivation and nutrient management practices to farmers can improve livelihoods and if so what are the implications of such an effort. This study has shown that ICTs when appropriately harnessed can increase farmers' access to information. Tailor-made information to individual farmers can improve farm productivity. Farmers still need to attain greater level of awareness on new crops and management practices and mobile phones can be the vehicle to support this. Farmers are interested in bundled services providing a variety of information related to crop production, processing and sale. This study also indicates the importance of socially embedding the technology with the help of local institutions to effectively address information needs of farmers

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The main aim of the proposed methodology is to design a multi-objective function for replica management system using oppositional gravitational search algorithm (OGSA), in which we analyze the various factors influencing replication decisions such as mean service time, mean file availability, energy consumption, load variance, and mean access latency. The OGSA algorithm is hybridization of oppositional-based learning (OBL) and gravitational search algorithm (GSA), which is change existing solution, and to adopt a new good solution based on objective function. Here, firstly we create a set of files and data node to generate a population by assigning the file to data node randomly and evaluate the fitness which is minimizing the objective function. Secondly, we regenerate the population to produce optimal or suboptimal population using OGSA. The experimental results show that the performance of the proposed methods is better than the other methods of data replication problem.

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