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.
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.
Worthington, Thomas A. (author), Andradi-Brown, Dominic A. (author), Bhargava, Radhika (author), Buelow, Christina (author), Bunting, Pete (author), Duncan, Clare (author), Fatoyinbo, Lola (author), Friess, Daniel A. (author), Goldberg, Liza (author), Hilarides, Lambert (author), Lagomasino, David (author), Landis, Emily (author), Longley-Wood, Kate (author), Lovelock, Catherine E. (author), Murray, Nicholas J. (author), Narayan, Siddharth (author), Rosenqvist, Ake (author), Sievers, Michael (author), Simard, Marc (author), Thomas, Nathan (author), van Eijk, Pieter (author), Zganjar, Chris (author), and Spalding, Mark (author)
Format:
Journal article
Publication Date:
2020
Published:
International
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 201 Document Number: D11696
27 pages., Authors provide an overview of recent and forthcoming global datasets and explore the challenges of translating these new analyses into policy action and on-the-ground conservation of mangrove forests. They describe a new platform for visualizing and disseminating these datasets to the global science community and other audiences - and they highlight future directions and collaborations.
