Adapting to climate change through crop choice by small and medium farmers in Southern zone of Tamil Nadu, India
Bibliographic record
Abstract
Unpredictable changes in the climate can have a significant impact on crop yield in India in general and in particular in the climate vulnerable state of Tamil Nadu. This study evaluates how farmers in the Sothern Zone of Tamil Nadu adapt crop change as a technique to cope with uncertainty in crop yield. Three districts in the Sothern Zone, viz., Virudhunagar, Thoothukudi and Thriunelveli districts were adopted for this study. The sample size was equally distributed with 60 households randomly selected and who actively engage in agriculture. The results derived from the Multinomial Logit Model indicate that older farmers were more likely to choose sorghum, groundnut and less likely to choose maize, fruits and vegetables. Education had positive and significant influence on growing sorghum groundnut and chillies. Fruits and vegetables are more likely to chosen if farmer has large acreage. The climate variables seem to have neutral effect for sorghum and groundnut, hence farmers lend to choose theses crops for price stability. Farmers are most likely to prefer sorghum, cotton, maize and groundnut when income increases from other non-farm sources. When temperature increases by 1oC, farmers more often tend to choose pulses, sorghum, chilli and groundnut. If precipitation increases by 1 cm, farmers choose to cultivate pulses, maize, cotton, fruits and vegetable. Farmers adaptations may vary across agro climatic zones of Tamil Nadu. Hence local government policies and programs in agriculture should have a built in component to address the climate change issues. DOI: http://dx.doi.org/10.3329/sja.v12i1.21120 SAARC J. Agri., 12(1): 139-149 (2014)
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".