High-Yield Cotton Cultivation Practices in Arid Regions
Bibliographic record
Abstract
This study focused on cotton cultivation in arid regions and analyzed the key factors influencing high cotton yield and water use efficiency. Results showed significant differences in yield and drought resistance between irrigated and non-irrigated cotton varieties. Among them, ‘Tamcot CD3H’ and ‘TX-CABUCS-2-1-83’ maintained high yields even without irrigation, demonstrating good drought tolerance. Moderately reducing irrigation (controlling to 80% of field capacity) can conserve water and improve water use efficiency while maintaining yield. Controlling irrigation timing and water temperature is also crucial, with irrigation at night and water temperatures maintained between 25 ℃ and 28 ℃. Using treated wastewater for irrigation in arid regions has not only increased cotton yields but also reduced reliance on chemical fertilizers and lowered costs. Adopting partial root zone dry irrigation and mulching has reduced water use while also improving yield and quality. This study proposes a series of effective practices for cotton cultivation in arid regions and introduces several promising technologies and regulatory measures.
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 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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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".