Tillage and nitrogen rate effects on winter wheat yield in a wheat–sorghum rotation
Bibliographic record
Abstract
The objectives of this study were to quantify long-term tillage practice and nitrogen (N) fertilizer rate effects on yield and N use in a winter wheat ( Triticum aestivum L.)–grain sorghum ( Sorghum bicolor L. Moench)–fallow (W–S–F) rotation. The experimental design was a randomized complete block with a split–split-plot arrangement. The main plot treatments were crop rotation phases W–S–F, S–F–W, and F–W–S. The sub-plots were tillage practices, i.e., conventional tillage (CT), reduced tillage (RT), and no-tillage (NT). And the sub-sub-plot treatments were N rates of 0, 45, 90, and 134 kg ha−1. Wheat yield increased at rates of 15.6, 9.3, 22.8, and 25.7 kg ha−1 for a kg N ha−1 increase in very low-, low-, high-, and very high-yielding environments (average yields of ∼2000, 2500, 2800, and 4400 kg ha−1), respectively. On average, winter wheat yields were 7%–9% greater for CT compared with both NT and RT. Winter wheat removed about 52 kg N ha−1 from the unfertilized control treatment, but N uptake varied by N rate and growing conditions. Nitrogen use efficiency, N agronomic efficiency, and applied N recovery decreased as the N rate increased. Across environments, wheat yield increased by 16, 20, and 17 kg ha−1 for each additional kg ha−1 N applied under CT, NT, and RT, respectively, and additional 2–2.5 kg ha−1 yield increases for a mm increase in fallow precipitation. We concluded that wheat yield response to N is highly dependent on growing conditions, and NT required greater N fertilization than CT and RT for similar yields.
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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 | 0.000 |
| 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 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".