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Record W1608410893 · doi:10.4236/ajcc.2015.43020

Salinity Intrusion in Interior Coast of Bangladesh: Challenges to Agriculture in South-Central Coastal Zone

2015· article· en· W1608410893 on OpenAlexaff
Mohammed Abdul Baten, Lubna Seal, Kazi Sunzida Lisa

Bibliographic record

VenueAmerican Journal of Climate Change · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTropical and Extratropical Cyclones Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSalinityEnvironmental scienceIrrigationSoil salinityHydrology (agriculture)OceanographyAgricultureBayGeologyGeographyAgronomy

Abstract

fetched live from OpenAlex

This paper investigates the impacts of salinity on crop agriculture in south-central coastal zone of Bangladesh, more particularly interior coast. The coastal areas of Bangladesh, with near flat topography and location at the tip of “funnel shaped” Bay of Bengal, are susceptible to a number of natural hazards such as cyclones, tidal surges, salinity intrusion, riverbank erosion, and shoreline recession. The coastal zone of Bangladesh, especially exposed coast has come into focus in a number of policy and academic studies for salinity intrusion, but with the accelerated impacts of climate change salinity extends from the exposed to the interior coast hampering crop production. To investigate extent of salinity level in interior coast and its impact on crop agriculture, this study tested irrigation water collected in between October and December 2011 from the lower Meghna at Gosairhat upazila in Shariatpur district and interviewed experts and local farmers. This study estimated that salinity concentration of surface water was 1.3 dS/m which was 0.8 dS/m higher than the earlier estimation by ICZMP (Integrated Coastal Zone Management Plan) in 2003. The test further revealed that Chloride ion concentration in irrigation water was 500 ppm, pH level was 7.99 and concentration of Carbonate ion was 221 ppm, which were much higher than the desired level. Estimated salinity concentration has already put a threat to the crop production and a significant yield loss has already been noticed in dry season. In the changing scenario of sea level rise, it has been predicted that the increasing concentration of salinity would create more pressure to the farmer by reducing yield on one hand and threatening livelihood, income generation and food security on the other hand. Therefore, to reduce the future loss and prevent the present loss, the study recommends leaching and selecting salinity tolerant crop varieties as adaptation techniques.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.057
GPT teacher head0.277
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations94
Published2015
Admission routes1
Has abstractyes

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