Jayasinghe, J.A.S.S. (author), Hewagamage, K.P. (author), and Department of Information Technology, CINEC Campus, IT Park, Malabe, Sri Lanka
University of Colombo School of Computing, 35, Reid Avenue, Colombo 07, Sri Lanka
Format:
Conference paper
Publication Date:
2017
Published:
Sri Lanka: Institute of Electrical and Electronics Engineers Inc.
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 162 Document Number: D08141
Notes:
article number 7829901, pp. 72-79, 16th International Conference on Advances in ICT for Emerging Regions, ICTer 2016; Jetwing BlueNegombo; Sri Lanka; 1 September 2016 through 3 September 2016; Category numberCFP1686L-ART; Code 126111
The International Association of Agricultural Information Specialists (IAALD)
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 102 Document Number: D10917
Notes:
14 pages., via IAALD website., The study determined farmers’ adoption of Organic agricultural technologies disseminated via radio
farmer agricultural Extension programme in Imo state, Nigeria. An interview schedule was used to
collect data from a sample of 200 farmers. Results show that radio farmer broadcast and co-farmers
were the major sources of information to greater proportion of the farmers. Data on relevance of the
organic agricultural technologies disseminated showed that almost all the technologies were perceived
to be relevant except the use of biological pest control farming. The radio farmer agricultural
programme enhanced the extent of adoption of organic agricultural technologies namely; crop rotation
practice, planting of indigenous varieties, application of compost, mulching of crops, intercropping,
mixed cropping, crop residues, animal manuring, planting of legumes, green manure, off farm organic
waste, minimum tillage and alley cropping. Nevertheless, the adoptions of the technologies were
generally low. Age, farming experience and social participation significantly influenced adoption of
organic agricultural technologies disseminated via radio farm agricultural Extension programme. Major
constraints identified include short duration of programme, inappropriate scheduling of programme,
inability to ask relevant questions and get feed back from the radio presenter. The study recommends
among other things the rescheduling of the radio programme to very late in the evenings when the
farmers will be opportune to listen to the programme.
Social Networks like Facebook, Twitter, YouTube and WhatsApp are now becoming very popular tools that are used for sharing the latest and important farming based information in different part of India. These tools are now widely used by Agricultural Extension and Advisory services to interact with the farmers for exchanging agricultural related information in India. The most influential farmers in a network can disseminate the information to the less central farmers of the network. The extension functionaries that promote the agricultural innovation will share the information with the most central members which in turn will share it with maximum number of the members of a social network. Social Network Analysis (SNA) acts as an efficient analytical tool that helps us to understand the relationship between farmer stakeholders and the importance of a farmer's position in the entire network. In this paper, a structural analysis of the Social Network is performed over two datasets, namely, Facebook-like dataset and Twitter Lists dataset. Here, we have studied the importance of individual nodes in the network through various centrality measures. SNA properties like centralities are used to represent the most central nodes that can act as a good influence spreader in the network. In this paper, initially the k-core decomposition method is used to find a set of influential nodes among all the nodes in the network. It is done to reduce the computational time. Our simulation shows that the nodes with higher Page Rank centrality can activate more members in a network as compared to other centrality measures. For Information diffusion, we have used Linear Threshold (LT) Model to understand the influence spread of the central farmers in the network.
