Beam, Brooke W. (author) and Specht, Annie R. (author)
Format:
Conference paper
Publication Date:
2016-02
Published:
USA
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 162 Document Number: D08135
Notes:
Research paper presented in the Agricultural Communications Section, Southern Association of Agricultural Scientists (SAAS) in San Antonio, Texas, February 7-8, 2016. 26 pages.
Settle, Quisto (author), McCarty, Keelee (author), Rumble, Joy N. (author), and Ruth, Taylor K. (author)
Format:
Conference paper
Publication Date:
2016-02
Published:
USA
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 162 Document Number: D08127
Notes:
Research paper presented in the Agricultural Communications Section, Southern Association of Agricultural Scientists (SAAS) in San Antonio,Texas, February 7-8, 2016. 24 pages.
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.
Isham, Jonathan (author) and Centre for the Study of African Economics, University of Oxford
Format:
Conference paper
Publication Date:
unknown
Published:
United Kingdom
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Document Number: C28221
Notes:
Posted online at http://www.csae.ox.ac.uk/conferences/2000-OiA/pdfpapers/isham.PDF, Presented at "Opportunities in Africa: micro-evidence on firms and households," a conference at the University of Oxford from April 9-10, 2000.
10 pages, Abstract— Smart agriculture involves the use of technology such as drones, GPS, robotics, IoT, AI, big data, and solar energy to improve farming practices. As with any disruptive innovation, however, stakeholder expectations can be misaligned from what the innovation can actually deliver. There can also be varying perspectives on what the innovation entails, related topics of interest, and impediments to large scale adoption. This study examines public perception of smart agriculture and its perceived drivers and challenges as present in social media discourse. We collected online posts from Twitter, Reddit, forums, online news and blogs between January 2010 and December 2018 for analysis. Results show that 38% of social media posts contained emotion with 52% joy, 21% anger and 12% sadness. Through topic analysis, we discovered seven key drivers and challenges for smart agriculture which included: enabling technologies, data ownership and privacy, accountability and trust, energy and infrastructure, investment, job security, and climate change.
Gavitt, A.R., Jr. (author / University of Connecticut, College of Agriculture and Natural Resources, Department of Agricultural Publications) and University of Connecticut, College of Agriculture and Natural Resources, Department of Agricultural Publications
Format:
Conference paper
Publication Date:
unknown
Published:
USA
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 27 Document Number: B02745
Notes:
AgComm Teaching, Mimeographed, [19- ]. 14 p. Paper presented at the 54th Annual Conference of the American Association of Agricultural College Editors; July 15; Cornell University, Ithaca, NY
