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      Is shrimp farming a successful adaptation to salinity intrusion? A geospatial associative analysis of poverty in the populous Ganges–Brahmaputra–Meghna Delta of Bangladesh

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          Abstract

          The Ganges–Brahmaputra–Meghna delta of Bangladesh is one of the most populous deltas in the world, supporting as many as 140 million people. The delta is threatened by diverse environmental stressors including salinity intrusion, with adverse consequences for livelihood and health. Shrimp farming is recognised as one of the few economic adaptations to the impacts of the rapidly salinizing delta. Although salinity intrusion and shrimp farming are geographically co-located in the delta, there has been no systematic study to examine their geospatial associations with poverty. In this study, we use multiple data sources including Census, Landsat Satellite Imagery and soil salinity survey data to examine the extent of geospatial clustering of poverty within the delta and their associative relationships with salinity intensity and shrimp farming. The analysis was conducted at the union level, which is the lowest local government administrative unit in Bangladesh. The findings show a strong clustering of poverty in the delta, and whilst different intensities of salinization are significantly associated with increasing poverty, neither saline nor freshwater shrimp farming has a significant association with poverty. These findings suggest that whilst shrimp farming may produce economic growth, in its present form it has not been an effective adaptation for the poor and marginalised areas of the delta. The study demonstrates that there are a series of drivers of poverty in the delta, including salinization, water logging, wetland/mudflats, employment, education and access to roads, amongst others that are discernible spatially, indicating that poverty alleviation programmes in the delta require strengthening with area-specific targeted interventions.

          Electronic supplementary material

          The online version of this article (doi:10.1007/s11625-016-0356-6) contains supplementary material, which is available to authorized users.

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              Model selection for geostatistical models.

              We consider the problem of model selection for geospatial data. Spatial correlation is often ignored in the selection of explanatory variables, and this can influence model selection results. For example, the importance of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as applied to a geostatistical model. We offer a heuristic derivation of the AIC in this context and provide simulation results that show that using AIC for a geostatistical model is superior to the often-used traditional approach of ignoring spatial correlation in the selection of explanatory variables. These ideas are further demonstrated via a model for lizard abundance. We also apply the principle of minimum description length (MDL) to variable selection for the geostatistical model. The effect of sampling design on the selection of explanatory covariates is also explored. R software to implement the geostatistical model selection methods described in this paper is available in the Supplement.
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                Author and article information

                Contributors
                +44 238 059 7446 , faj100@soton.ac.uk
                Journal
                Sustain Sci
                Sustain Sci
                Sustainability Science
                Springer Japan (Tokyo )
                1862-4065
                1862-4057
                21 March 2016
                21 March 2016
                2016
                : 11
                : 3
                : 423-439
                Affiliations
                [1 ]Department of Social Statistics and Demography & Centre for Global Health, Population, Poverty and Policy (GHP3), Faculty of Social, Human and Mathematical Sciences, University of Southampton, Southampton, United Kingdom
                [2 ]GeoData Institute, Faculty of Social, Human and Mathematical Sciences, University of Southampton, Southampton, United Kingdom
                [3 ]Faculty of Engineering and the Environment, University of Southampton, Southampton, United Kingdom
                [4 ]School of Oceanographic Studies, Jadavpur University, Kolkata, India
                Article
                356
                10.1007/s11625-016-0356-6
                6106650
                © The Author(s) 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                Funding
                Funded by: Department for International Development (DFID), Economic and Social Research Council (ESRC) and Natural Environment Research Council (NERC) - Ecosystem Services for Poverty Alleviation (ESPA) programme
                Award ID: NE-J002755-1
                Categories
                Original Article
                Custom metadata
                © Springer Japan 2016

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