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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".