Nitrogen-Related Rotational Effects of Legume Crops on Three Consecutive Subsequent Crops
Bibliographic record
Abstract
Core Ideas Rotational effects of grain and green manure legumes were evaluated for three subsequent crops. The effects of grain legumes were more pronounced in the third crop than the first two. The removal of aboveground crop residues did not affect the rotational effects. Non-N and belowground N related factors seem to have contributed to the effects. Nonlegume crop responses to legume crops are usually studied only in one subsequent crop. We determined the rotational effects of green and forage pea (Pisum sativum L.), faba bean (Vicia faba L.) grown for seed, faba green manure (GM), and chickling vetch (Lathyrus sativus L.) GM on three subsequent crops. The control crop in the legume year was barley (Hordeum vulgare L.), and the subsequent crops grown consecutively were wheat (Triticum aestivum L.), canola (Brassica napus L.), and barley, all fertilized with 0, 30, 60, and 120 kg N ha−1. In wheat, N uptake (73–84 vs. 48 kg grain + straw N ha−1), C accumulation, straw dry matter (DM), and grain yields (2695–3136 vs. 1950 kg DM ha−1) increased where the preceding crop was a legume relative to a barley preceding crop. In canola, grain C accumulation and grain yield did not respond to the legume crop residues. In barley, all the measured parameters were greater on pulse crop residues than on GM residues. Therefore, the rotational effects of pulse crop residues were more pronounced in the third subsequent crop than in the first two. However, removal of aboveground crop residues did not affect N uptake, C accumulation, or yields of any subsequent crop, suggesting that belowground N sources were a major pathway of N transfer from legumes to subsequent crops. With the exception of canola straw C, crops grown on legume residues and those grown on barley residues responded similarly to increasing N rate, suggesting that non-N-related factors contributed to the responses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".