Effects of Nitrogen and Planting Seed Size on Cotton Growth, Development, and Yield
Bibliographic record
Abstract
A standardized experiment was conducted during 2009 and 2010 at 20 location‐years across U.S. cotton (Gossypium hirsutum L.)‐producing states to compare the N use requirement of contemporary cotton cultivars based on their planting seed size. Treatments consisted of three cotton varieties with planting seed of different numbers of seed per kg and N rates of 0, 45, 90, and 134 kg ha–1. Soil at each trial location was sampled and tested for nitrate presence. High levels of soil nitrate (>91 N‐NO3– kg ha–1) were found in Arizona and western Texas, and soil nitrate in the range of 45 to 73 kg N‐NO3– ha–1 was found at locations in the central United States. Cotton lint yield responded to applied N at 11 of 20 locations. Considering only sites that responded to applied N, highest lint yields were achieved with 112 to 224 kg ha–1of applied plus pre‐plant residual soil NO3—translating to an optimal N requirement of 23 kg ha–1 per 218 kg bale of lint produced. Among the varieties tested those with medium‐sized seed produced higher yields in response to N than did larger and smaller seeded varieties. Varieties with larger seed had longer and stronger fibers, higher fiber length uniformity than small seeded varieties and decreased micronaire. Seed protein and oil increased and decreased slightly in response to increasing amounts of soil nitrate plus applied N, respectively.
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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 | 0.000 | 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) | 0.001 | 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".