10 pages, Enormous quantities of data are generated through social and online media in the era of Web 2.0. Understanding consumer perceptions or demand efficiently and cost effectively remains a focus for economists, retailer/consumer sciences, and production industries. Most of the efforts to understand demand for food products rely on reports of past market performance along with survey data. Given the movement of content-generation online to lay users via social media, the potential to capture market-influencing shifts in sentiment exists in online data. This analysis presents a novel approach to studying consumer perceptions of production system attributes using eggs and laying hen housing, which have received significant attention in recent years. The housing systems cage-free and free-range had the greatest number of online hits in the searches conducted, compared with the other laying hen housing types. Less online discussion surrounded enriched cages, which were found by other methods/researchers to meet many key consumer preferences. These results, in conjunction with insights into net sentiment and words associated with different laying hen housing in online and social media, exemplify how social media listening may complement traditional methods to inform decision-makers regarding agribusiness marketing, food systems, management, and regulation. Employing web-derived data for decision-making within agrifood firms offers the opportunity for actionable insights tailored to individual businesses or products.
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.
3 pages, via online journal, Agriculture is the backbone of most developing economies and structural transformation an important vehicle for economic development in low-income agrarian contexts. This special issue brings together a set of high quality academic studies to answer key research questions of importance to understanding agricultural development and change in developing country settings. Using the case of Vietnam, this collection provides comprehensive analytical contributions, that rely on detailed microdata, to understand crucial topics within the fields of agricultural and development economics. Together, these studies provide important insights into the mechanisms underlying structural transformation and its consequences that can contribute to the design of policies to manage the structural transformation process effectively, particularly for the most vulnerable groups in society.
Dobson, A.D.M. (author), Milner-Gulland, E.J. (author), Aebischer, Nicholas J. (author), Beale, Colin M. (author), Brozovic, Robert (author), Coals, Peter (author), Critchlow, Rob (author), Dancer, Anthony (author), Grove, Michelle (author), Hinsley, Amy (author), Ibbett, Harriet (author), Johnston, Alison (author), Kuiper, Timothy (author), Le Comber, Steven (author), Mahood, Simon P. (author), Moore, Jennifer F. (author), Nilsen, Erlend B. (author), Pocock, Michael J.O. (author), Quinn, Anthony (author), Travers, Henry (author), Wilfred, Paulo (author), Wright, Joss (author), and Keane, Aidan (author)
Format:
Journal article
Publication Date:
2020
Published:
International
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 167; Folder: 201 Document Number: D11695
11 pages., Authors present an overview of the opportunities and limitations associated with messy data which conservationists increasingly use (e.g., citizen science records, ranger patrol observations). They also explain how the preferences, skills, and incentives of data collectors affect the quality of the information these data contain and the investment required to unlock their potential.
Subhead: "Are the many alliances and acquisitions among platforms really designed to benefit farmers?" ... "While a data-sharing platform may put growers in the driver's seat when it comes to how their information is distributed, ag tech providers have not always done a good job of communicating that message. 'Problems arise when owners of the data feel as though they no longer understand who is accessing their data and why,' Tatge says."
