Jakku, Emma (author), Taylor, Bruce (author), Fleming, Aysha (author), Mason, Claire (author), Fielke, Simon (author), Sounness, Chris (author), and Thorburn, Peter (author)
Format:
Journal article
Publication Date:
2019-12
Published:
Netherlands: Elsevier
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 203 Document Number: D12272
13 pages, Advances in Smart Farming and Big Data applications have the potential to help agricultural industries meet productivity and sustainability challenges. However, these benefits are unlikely to be realised if the social implications of these technological innovations are not adequately considered by those who promote them. Big Data applications are intrinsically socio-technical; their development and deployment are a product of social interactions between people, institutional and regulatory settings, as well as the technology itself. This paper explores the socio-technical factors and conditions that influence the development of Smart Farming and Big Data applications, using a multi-level perspective on transitions combined with social practice theory. We conducted semi-structured interviews with 26 Australian grain farmers and industry stakeholders to elicit their perspectives on benefits and risks of these changes. The analysis shows that issues related to trust are central concerns for many participants. These include procedural concerns about transparency and distributional concerns about who will benefit from access to and use of "farmers' data". These concerns create scepticism about the value of `smart' technologies amongst some industry stakeholders, especially farmers. It also points to a divergence of expectations and norms between actors and institutions at the regime and niche levels in the emerging transition towards Smart Farming. Bridging this divide will require niche level interventions to enhance the agency of farmers and their local networks in these transactions, and, the cooperative design of new institutions at regime level to facilitate the fair and transparent allocation of risk and benefit in farming data information chains.
13 pages, Agricultural fairs provide one of the last frontiers, and largest stages, for showcasing livestock agriculture to the public. However, public funding, attendance revenue, animal biosecurity, and public health concerns are all aspects worthy of conversation and increased research attention given the interaction between livestock animals and the general public in fair and festival settings. A prominent social media listening and data analytics platform was used to quantify online and social media chatter concerning agricultural fairs during a 27-mo period. A general search for online media referencing agricultural fair keywords was designed; social and online media mentions of agricultural fairs (n = 2,091,350 mentions) were further queried according to their reference to livestock, fair food, or the major agricultural product producing species of dairy and beef cattle (n = 68,900), poultry (n = 39,600), and swine (n = 31,250). Numbers of search results were found to be seasonal and Twitter was the single largest domain for all fair-related results; in contrast, the majority of livestock-related media was generated by news sources rather than from Twitter. On a weekly basis, the percentage of fair livestock mentions with species-specific reference was highly variable ranging from 0% to 86.8% for cattle, 0% to 85.7% for poultry, and 0% to 76.9% for swine. In addition to quantifying total search hits or mentions, the positivity/negativity of the search results was analyzed using natural language processing capabilities. The net sentiment quantified is the total percentage of positive posts minus the percentage of negative posts, which results in a necessarily bounded net sentiment between −100% and +100%. Overall net sentiment associated with mentions of agricultural fairs was positive; the topics garnering the highest positive sentiments were fair food and cattle (both 98% positive). Online discussion pertaining to agricultural fairs and swine was overall positive despite references to swine flu outbreaks. In conclusion, livestock and animal products had positive net sentiment over the time period studied, but there are multiple aspects of agricultural fairs worthy of further investigation and continued vigilance, including zoonotic disease risk and public perceptions of livestock industries.
4 pages., Online via AgEconSearch, Authors explain the basic concepts of Internet+ and big data, analyze the main problems in the application of big data technology in agricultural informationization, summarize corresponding solutions from the aspects of government guidance, financial input, open sharing of agricultural big data, big data storage and processing, data mining, etc., and describe prospects ahead in the province.
3 pages, Big data represent a new productive factor (the "new oil" for advocates) that generates new realities in agriculture. By adding an extra "cyber" dimension to current farming systems, big data lead to the emergence of new, complex cyber-physical-social systems. However, our understanding of the sustainability of such systems is still at a rudimental stage. In this critical review we attempt to shed some light on this topic, by identifying and presenting some issues that put in doubt the sustainability of big data agriculture. By using a punctuated equilibria lens, we argue that despite their contribution to the economic and environmental performance of farming, big data act as a speciation mechanism. Hence, they lead to new forms of intraspecific, interspecific and intergeneric competition, thus putting at risk the most vulnerable players of the game. We conclude by pointing out that to holistically address the interrelation between big data and agricultural sustainability we need a hybrid research line, which will combine the qualities of both technology-oriented research and critical social science.
14 pages, via online journal, Designing effective policies for economic development often entails categorizing populations by their rural or urban status. Yet there exists no universal definition of what constitutes an “urban” area, and countries alternately apply criteria related to settlement size, population density, or economic advancement. In this study, we explore the implications of applying different urban definitions, focusing on Tanzania for illustrative purposes. Toward this end, we refer to nationally representative household survey data from Tanzania, collected in 2008 and 2014, and categorize households as urban or rural using seven distinct definitions. These are based on official administrative categorizations, population densities, daytime and nighttime satellite imagery, local economic characteristics, and subjective assessments of Google Earth images. These definitions are then applied in some common analyses of demographic and economic change. We find that these urban definitions produce different levels of urbanization. Thus, Tanzania's urban population share based on administrative designations was 28% in 2014, though this varies from 12% to 39% with alternative urban definitions. Some indicators of economic development, such as the level of rural poverty or the rate of rural electrification, also shift markedly when measured with different urban definitions. The periodic (official) recategorization of places as rural or urban, as occurs with the decennial census, results in a slower rate of rural poverty decline than would be measured with time-constant boundaries delimiting rural Tanzania. Because the outcomes of analysis are sensitive to the urban definitions used, policy makers should give attention to the definitions that underpin any statistics used in their decision making.