Nitrogen supply from belowground residues of lentil and wheat to a subsequent wheat crop
Bibliographic record
Abstract
Lentil ( Lens culinaris ) production on the Canadian prairies has increased recently with possible benefits to cropping systems in this region. The yield benefit often observed in cereals grown after a pulse crop can be partially attributed to the supply of nitrogen (N) from decomposing pulse crop residues; however, the contribution of belowground residue (BGR) is poorly documented. The purpose of this greenhouse study was to quantify the N input from BGR (i.e., roots plus rhizodeposits) of lentil and wheat ( Triticum aestivum ) using shoot 15 N-labeling and to trace the 15 N from BGR into subsequently grown wheat plants. Belowground N (BGN) comprised 34 and 51 % of total plant N in lentil and wheat, respectively. Whereas wheat produced more root biomass than lentil, total amounts of BGN did not differ between species. However, biomass production and N uptake by wheat grown on lentil BGR were 49 and 14 % higher than wheat grown on wheat BGR. Moreover, a higher proportion of added 15 N from lentil BGR (14.4 vs. 8.5 %) was recovered in the succeeding wheat crop, indicating that lentil BGN was more readily mineralized than wheat BGN. The disproportionately high increase in yield vs. N uptake for wheat grown on lentil BGR, however, indicates that non-N factors also contributed to the increase in wheat yield. This study highlights the importance of including estimates of BGN when evaluating the positive effects of including pulse crops in rotation with cereals, although further research is required to identify non-N benefits of lentil.
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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 | 0.000 | 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 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".