Does Climate Variability Influence Crop Yield ? - A Case Study of Major Crops in Tamil Nadu
Bibliographic record
Abstract
This paper has examined the impact of climate variability on crop yields and variance thereof in different agro-climatic zones of Indian state of Tamil Nadu. The results indicate that yields of paddy, maize, cotton and sugarcane benefit from the increase in temperature, while yield of banana benefits from the variation in temperature. The precipitation has a positive effect on the yield of cotton but a negative effect on the yield of maize. The yields of most crops, however,are negatively associated with precipitation intensity (ratio of highest monthly precipitation to annual precipitation). If temperature increases, the variance in paddy yield also increases. Likewise, excess rainfall increases variability in the yields of paddy and sugarcane. With climate change by 2030 the maize yield is expected to decrease by 1.4 per cent in the North-Western zone, 1.5 per cent in the Cauvery Delta zone and 2.7 per cent in the Southern zone. Cotton is likely to suffer more with about 13 per cent decline in yield in the North-Eastern zone, 8.1 per cent in the North-Western zone and 2.1 per cent in the Western zone. Sugarcane yield is likely to decline in Cauvery delta (3.8%) and Southern zone (4.7%) of the state. The study has suggested that appropriate weather-based crop insurance policy should be developed to reduce financial loss to the farmers.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".