Climate Change and Wheat Production in Drought Prone Areas of Bangladesh – A Technical Efficiency Analysis
Bibliographic record
Abstract
The present study aims at measuring the technical efficiency of wheat production under changing climate in drought prone areas of Bangladesh. The study employed farm level cross sectional data taken from 100 farmers using purposive random sampling technique from three upazilas of Thakurgoan district of Bangladesh. The study considered two successive years 2006 and 2007 as drought and normal year respectively on the basis of farmers’ opinion and information collected from meteorological station. Semi-logarithmic regression model with dummy variable was used to estimate production variability of wheat due to drought. The findings showed that wheat production decreased by 17.4 percent on an average due to drought occurrence in the study areas. Cobb-Douglas stochastic frontier production function was used to determine the technical efficiency of the wheat growers and the factors which influence technical efficiency in wheat production. The empirical results of technical efficiency model showed that the effects of seed, pesticide, tillage, irrigation and fertilizer costs were significant in the production of wheat. Education, family size, farming experience, credit, extension- contact and farm size had negative effects on technical inefficiency of farmers which indicates that technical inefficiency decreases with the increase of these factors in both normal and drought years. The mean technical efficiencies were 67.00 and 86.40 percent in normal and drought years respectively. The results also indicate a good potential for increasing wheat production by 33 and 14 percent in normal and drought years respectively using the available resources and technology. Wheat farmers should give more attention to their farming practices and should take rationale decision for using farm resources efficiently.
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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 | 0.001 | 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".