Salinity Intrusion in Interior Coast of Bangladesh: Challenges to Agriculture in South-Central Coastal Zone
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".