Narrow‐wide‐row planting pattern increases the radiation use efficiency and seed yield of intercrop species in relay‐intercropping system
Bibliographic record
Abstract
Abstract Planting arrangements affect radiation use efficiency (RUE) and competitiveness of intercrop species in intercropping systems. Here, we reveal that narrow‐wide‐row planting arrangement in maize‐soybean relay‐intercropping system increases the dry matter and competitiveness of soybean, increased the RUE of maize and soybean, and compensates the yield loss of maize by substantially increasing the yield of soybean. In this field study, maize was planted with soybean in different planting arrangements (P1, 20:180, P2, 40:160; P3, 60:140, and P4, 80:120) of relay intercropping, all the relay‐intercropping treatments were compared with sole crops of maize (SM) and soybean (SS). Results showed that P1 improved the total RUE 3.26 g/MJ (maize RUE + soybean RUE) of maize and soybean in relay‐intercropping system. Compared to P4, treatment P1 increased the soybean competition ratio (CR) values (by 55%) but reduced the maize CR values (by 29%), which in turn significantly improved the yield of soybean by maintaining the maize yield. Generally, in P1, soybean produced 82% of SS yield, and maize produced 88% of SM yield, and it achieved the land equivalent ratio of 1.7. These results suggest that by maintaining the appropriate planting distances between maize and soybean we can improve the competitiveness and yield of intercrop species in relay‐intercropping system.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".