Coping with Climatic Stress in Eastern India: Farmer Adoption of Stress-Tolerant Rice Varieties
Bibliographic record
Abstract
Cultivating stress-tolerant rice varieties (STRVs) is widely cited as a strategy of rice farmers to cope with climate-induced stresses. In India, dissemination of STRVs started in 2008 through international development initiatives, but only 5 percent of farmers have adopted it after seven years. Using a double-hurdle model, this study estimated the factors influencing simultaneous decisions on land selection and allocation for cultivating STRVs. It developed a framework for assessing the risks faced by farm households due to adverse climatic conditions vis-à-vis the decision to adopt STRVs. Results show that perceived and actual experiences of climate stress are important parameters influencing the decision to adopt STRVs. Farmers who have adopted such varieties are more likely to cultivate them on only a small portion of their land. These farmers are risk takers and very patient. The study recommends the use of a targeted approach to scale up the adoption of STRVs. Farmers affected by climate stresses should be identified and educated about the benefits of STRVs through demonstration. In addition, the accessibility of the seeds must be ensured.
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 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".