Micro-Climatic effect on Cotton Yield, quality, Bt toxin & GT Gene
Bibliographic record
Abstract
Unsuitable change in climatic conditions cause decline in quality and yield of major crops. Plant growth is directly affected if temperature, rainfall or humidity are not optimum. A multi-location and multi season evaluation of climatic effects on quality and yield may produce a reliable data for future breeding. A set of 39 upcoming varieties of cotton were evaluated on six different Micro-climatic locations of Punjab i.e. Multan, Bahawalpur, Sahiwal, Rahimyar khan, Vehari and Faisalabad in a triplicated trial. The experiment was repeated next year on same locations. Data for three key environmental factors such as temperature, rainfall and humidity was recorded at each station. The crop was analyzed for yield, fiber length, fiber strength and fiber fineness. The genotypes were also evaluated for Bt toxin and Glyphosate tolerance gene (GTG). The analysis revealed that high temperature has negative effect on yield, Bt expression, fineness, uniformity and GTG. Precipitation and humidity had positive effect on fiber fineness and uniformity, whereas, negative effect of both environmental factors was recorded for fiber length and strength. Increase in precipitation at early cropping stage was associated with increase in yield whereas higher humidity has negative impact on yield. As compared to high average temperature and number of days above 400C, cotton yield is more sensitive to heat waves (maximum temperature). Varieties with high temperature tolerance in cotton should be breed for climate change scenario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".