Application of Bayesian spatial smoothing models to assess agricultural self-sufficiency

Kathryn T. Morrison, Trisalyn Nelson, Farouk S. Nathoo, Aleck S. Ostry

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1213-1229
Number of pages17
JournalInternational Journal of Geographical Information Science
Volume26
Issue number7
DOIs
StatePublished - Jul 2012
Externally publishedYes

Fingerprint

self sufficiency
self-sufficiency
smoothing
food
food security
predictive model
Nutrition
Agriculture
agriculture
agronomy
Agronomy
alternative agriculture
local economy
Epidemiology
epidemiology
Urban planning
oil price
research interest
urban planning
capital investment

Keywords

  • agricultural self-sufficiency
  • Bayesian analysis
  • census agriculture data
  • spatial analysis
  • spatial autoregressive models

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences

Cite this

Application of Bayesian spatial smoothing models to assess agricultural self-sufficiency. / Morrison, Kathryn T.; Nelson, Trisalyn; Nathoo, Farouk S.; Ostry, Aleck S.

In: International Journal of Geographical Information Science, Vol. 26, No. 7, 07.2012, p. 1213-1229.

Research output: Contribution to journalArticle

@article{c4f76475b7964945b12bf097e94e68f0,
title = "Application of Bayesian spatial smoothing models to assess agricultural self-sufficiency",
abstract = "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.",
keywords = "agricultural self-sufficiency, Bayesian analysis, census agriculture data, spatial analysis, spatial autoregressive models",
author = "Morrison, {Kathryn T.} and Trisalyn Nelson and Nathoo, {Farouk S.} and Ostry, {Aleck S.}",
year = "2012",
month = "7",
doi = "10.1080/13658816.2011.633491",
language = "English (US)",
volume = "26",
pages = "1213--1229",
journal = "International Journal of Geographical Information Science",
issn = "1365-8816",
publisher = "Taylor and Francis Ltd.",
number = "7",

}

TY - JOUR

T1 - Application of Bayesian spatial smoothing models to assess agricultural self-sufficiency

AU - Morrison, Kathryn T.

AU - Nelson, Trisalyn

AU - Nathoo, Farouk S.

AU - Ostry, Aleck S.

PY - 2012/7

Y1 - 2012/7

N2 - 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.

AB - 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.

KW - agricultural self-sufficiency

KW - Bayesian analysis

KW - census agriculture data

KW - spatial analysis

KW - spatial autoregressive models

UR - http://www.scopus.com/inward/record.url?scp=84864688222&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84864688222&partnerID=8YFLogxK

U2 - 10.1080/13658816.2011.633491

DO - 10.1080/13658816.2011.633491

M3 - Article

VL - 26

SP - 1213

EP - 1229

JO - International Journal of Geographical Information Science

JF - International Journal of Geographical Information Science

SN - 1365-8816

IS - 7

ER -