Midlands State University Library

Application of Bayesian spatial smoothing models to assess agricultural self-sufficiency (Record no. 160609)

MARC details
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fixed length control field 02732nam a22002417a 4500
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control field ZW-GwMSU
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control field 20221125153947.0
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040 ## - CATALOGING SOURCE
Original cataloging agency MSU
Transcribing agency MSU
Description conventions rda
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Morrison, Kathryn T
Relator term author
245 ## - TITLE STATEMENT
Title Application of Bayesian spatial smoothing models to assess agricultural self-sufficiency
Statement of responsibility, etc. created by Kathryn T. Morrison ,Trisalyn A. Nelson,Farouk S. Nathoo &Aleck S. Ostry
264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Name of producer, publisher, distributor, manufacturer Taylor and Francis
Date of production, publication, distribution, manufacture, or copyright notice 2012
336 ## - CONTENT TYPE
Source rdacontent
Content type term text
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337 ## - MEDIA TYPE
Source rdamedia
Media type term unmediated
Media type code n
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440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE
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520 ## - SUMMARY, ETC.
Summary, etc. With the rising oil prices, climate change, and the ever increasing burden of nutrition-related disease, food security is of growing research interest in academic disciplines spanning agronomy to epidemiology to urban planning. Some governments have developed progressive policies encouraging individuals to consume locally produced foods in order to support local economies, improve agricultural sustainability and community access to food, and to plan and prepare for adverse environmental impacts on food security. However, fundamental methods are lacking for conducting research on food security across these various disciplines. In this article, we first present a method to measure agricultural self-sufficiency, which we refer to as our self-sufficiency index (SSI) for the province of British Columbia, Canada. We then present a Bayesian autoregressive framework utilizing readily available agricultural data to develop predictive smoothing models for the SSI. We find that regional capital investment in agriculture and cropland acreage is the strong predictor of SSI. To accommodate spatial variability, we compare linear regression models with spatially correlated errors to less traditional spatially varying coefficient models, and find that the former class results in better model fit. The smoothed maps suggest that relatively strong self-sufficiency exists only in subset clusters in the Okanagan, Peace River, and lower mainland regions. In spite of policy to promote local food, the existing local agricultural system is insufficient to support a large-scale shift to local diets. Our approach to estimating neighborhood-based self-sufficiency with a predictive model can be extended for use in other regions where limited data are available to directly assess local agriculture and benefit from explicit consideration of spatial structure in the local food system.<br/><br/><br/><br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element spatial analysis
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element census agriculture data
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element spatial autoregressive models
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1080/13658816.2011.633491
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Journal Article
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Serial Enumeration / chronology Total Checkouts Full call number Date last seen Copy number Price effective from Koha item type Public note
    Library of Congress Classification     Main Library Main Library - Special Collections 26/02/2014 Vol 26 .No.7-8 pages 1213-1229   G70.2 INT 25/11/2022 SP14366 25/11/2022 Journal Article For Inhouse use only