RESUMO
There is increasing interest in leveraging Earth Observation (EO) and geospatial data to predict and map aspects of socioeconomic conditions to support survey and census activities. This is particularly relevant for the frequent monitoring required to assess progress towards the UNs' Sustainable Development Goals (SDGs). The Sundarban Biosphere Reserve (SBR) is a region of international ecological importance, containing the Indian portion of the world's largest mangrove forest. The region is densely populated and home to over 4.4 million people, many living in chronic poverty with a strong dependence on nature-based rural livelihoods. Such livelihoods are vulnerable to frequent natural hazards including cyclone landfall and storm surges. In this study we examine associations between environmental variables derived from EO and geospatial data with a village level multidimensional poverty metric using random forest machine learning, to provide evidence in support of policy formulation in the field of poverty reduction. We find that environmental variables can predict up to 78% of the relative distribution of the poorest villages within the SBR. Exposure to cyclone hazard was the most important variable for prediction of poverty. The poorest villages were associated with relatively small areas of rural settlement (<â¼30%), large areas of agricultural land (>â¼50%) and moderate to high cyclone hazard. The poorest villages were also associated with less productive agricultural land than the wealthiest. Analysis suggests villages with access to more diverse livelihood options, and a smaller dependence on agriculture may be more resilient to cyclone hazard. This study contributes to the understanding of poverty-environment dynamics within Low-and middle-income countries and the associations found can inform policy linked to socio-environmental scenarios within the SBR and potentially support monitoring of work towards SDG1 (No Poverty) across the region.
Assuntos
Pobreza , População Rural , Agricultura , Conservação dos Recursos Naturais , Países em Desenvolvimento , Humanos , Renda , Índia , Fatores Socioeconômicos , Inquéritos e QuestionáriosRESUMO
Expansion of aquaculture in the Sundarbans Biosphere Reserve (SBR) is irreversibly replacing agricultural land and the drivers of this change are disputed. Based on in-depth interviews with 67 aquaculture farmers, this paper characterizes major aquaculture types in the SBR, their impacts, and identifies drivers of conversion from agricultural land. Aquaculture types included traditional, improved-traditional, modified-extensive, and semi-intensive systems. Extensive capture of wild shrimp larvae is environmentally harmful but constitutes an important livelihood. Semi-intensive aquaculture of exotic shrimp (Litopenaeus vannamei) has much higher unit-area profitability than other types but involves greater financial risk. Profitability is the main driver for the transition from agriculture, but environmental factors such as lowered crop yields and cyclone impacts also contributed. Many conversions from agriculture to aquaculture are illegal according to the stakeholders. Existing legislation, if enforced, could halt the loss of agriculture, while the promotion of improved-traditional aquaculture could reduce the demand for wild seed.
Assuntos
Agricultura , Aquicultura , Motivação , Agricultura/economia , Agricultura/tendências , Aquicultura/economia , Aquicultura/tendências , Fazendeiros , Humanos , Índia , Alimentos MarinhosRESUMO
Tracking the progress of the Sustainable Development Goals (SDGs) and targeting interventions requires frequent, up-to-date data on social, economic, and ecosystem conditions. Monitoring socioeconomic targets using household survey data would require census enumeration combined with annual sample surveys on consumption and socioeconomic trends. Such surveys could cost up to $253 billion globally during the lifetime of the SDGs, almost double the global development assistance budget for 2013. We examine the role that satellite data could have in monitoring progress toward reducing poverty in rural areas by asking two questions: (i) Can household wealth be predicted from satellite data? (ii) Can a socioecologically informed multilevel treatment of the satellite data increase the ability to explain variance in household wealth? We found that satellite data explained up to 62% of the variation in household level wealth in a rural area of western Kenya when using a multilevel approach. This was a 10% increase compared with previously used single-level methods, which do not consider details of spatial landscape use. The size of buildings within a family compound (homestead), amount of bare agricultural land surrounding a homestead, amount of bare ground inside the homestead, and the length of growing season were important predictor variables. Our results show that a multilevel approach linking satellite and household data allows improved mapping of homestead characteristics, local land uses, and agricultural productivity, illustrating that satellite data can support the data revolution required for monitoring SDGs, especially those related to poverty and leaving no one behind.
Assuntos
Pobreza/estatística & dados numéricos , População Rural/estatística & dados numéricos , Agricultura/estatística & dados numéricos , Características da Família , Humanos , Quênia , Tecnologia de Sensoriamento Remoto/métodos , Classe Social , Fatores Socioeconômicos , Inquéritos e QuestionáriosRESUMO
Correlations between particulate organic carbon (POC) and mineral fluxes in the deep ocean have inspired the inclusion of "ballast effect" parameterizations in carbon cycle models. A recent study demonstrated regional variability in the effect of ballast minerals on the flux of POC in the deep ocean. We have undertaken a similar analysis of shallow export data from the Arctic, Atlantic, and Southern Oceans. Mineral ballasting is of greatest importance in the high-latitude North Atlantic, where 60% of the POC flux is associated with ballast minerals. This fraction drops to around 40% in the Southern Ocean. The remainder of the export flux is not associated with minerals, and this unballasted fraction thus often dominates the export flux. The proportion of mineral-associated POC flux often scales with regional variation in export efficiency (the proportion of primary production that is exported). However, local discrepancies suggest that regional differences in ecology also impact the magnitude of surface export. We propose that POC export will not respond equally across all high-latitude regions to possible future changes in ballast availability.