Extreme Temperature Events and Rice Production in Bangladesh
Bibliographic record
Abstract
The extreme climatic events are increasing because of climate change impacts and thus likely to influence global agricultural production. Regional assessments on various abiotic factors and its influences on biological entities in diverse geographic locations are needed for understanding uncertainties. Rice grain yields and daily temperature data from 1971–2015 were used to quantify extreme temperature events in different regions of Bangladesh and their impacts on rice yields growing in three seasons of Bangladesh. The regional averaged trends in temperature extremes were consistent with global warming. The occurrence summer days (SU), tropical nights (TR), warm days (TX90), and nights (TN90) and a warm spell duration indicator (WSDI) increased by 0.388 (P0.00001), 0.103 (P0.19), 0.520 (P0.00000), 0.269 (P0.0004), 0.147 (P0.0001), days yr-1, respectively. The frequencies of cold days (TX10) and nights (TN10), and cold spell duration indicator (CSDI) showed decreasing trends of −0.143 (P0.0006), −0.254 (P0.001), and −0.04 (P0.227) day yr-1, respectively. Sharp increases of TR and TN90 indices took place in 1985–2000. Principal component analysis showed that SU, TX90, TN90, WSDI, TX10, TN10 and diurnal temperature range (DTR) were the main influencing factors for seasonal variations in rice yield. Warm and cold nights played a vital role in reducing rice yields. It can be concluded that extreme temperature events will be increased in Bangladesh and thus necessitating heat and cold tolerant rice varieties with appropriate management options for sustained future rice production in Bangladesh.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".