Synchrony of nitrogen supply and crop demand are driven via high maize density in maize/pea strip intercropping
Bibliographic record
Abstract
Abstract Cereal density may influence the balance between nitrogen (N) supply and crop N demand in cereal/legume intercrop systems. The effect of maize ( Zea mays L.) plant density on N utilization and N fertilizer supply in maize/pea ( Pisum sativum L.) strip intercropping was evaluated in a field study with sole maize, sole pea, and intercropped maize/pea with three maize densities (D1, 45,000 plants ha −1 ; D2, 52,500 plants ha −1 ; D3, 60,000 plants ha −1 ) and two N treatments (N0, 0 kg N ha −1 ; N1, 450 kg N ha −1 for maize and 225 kg N ha −1 for pea). Soil mineral N in intercropped strips decreased with increased maize density. Increased maize density decreased N accumulation for intercropped pea but increased it for maize and the sum of both intercrops. The land equivalent ratio for grain yield (LER grain) showed a 24–30% advantage for intercrops than corresponding sole crops, and was greater with D3 than D1 and D2. Maize/pea intercropping had 4–113% greater nitrogen use efficiency (NUE) than sole maize, which was enhanced with increased maize density. Increasing maize density improved the synchrony of N supply and crop demand in maize/pea strip intercropping.
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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) | 0.001 | 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".