Post‐flowering Biomass and Nitrogen Accumulation of Lentil Substantially Contributes to Pod Production
Bibliographic record
Abstract
ABSTRACT Lentil (Len culinaris L.) is an indeterminate legume crop that continues to grow and accumulate biomass (DM) after flowering. Abundant N can stimulate post‐flowering vegetative growth of lentil and reduce partitioning of DM and N into pods. The effect of inoculant rhizobia, 50 kg N fertilizer ha–1 and non‐treated control treatments on DM and N partitioning of lentil was studied in Saskatchewan. Accumulated DM and N in leaf, stem and pod of eight lentil cultivars were measured at flowering, podding, and maturity. The results showed that more than 85% of DM (5.8 g DM plant–1) and N (143 mg N plant–1) were accumulated after lentil flowering. Of the 167 mg N and 6.5 g DM plant–1 at maturity, 58% DM and 75% N was in pod, 26% DM and 10% N was in stem, and leaf had the remaining 15% of the DM and N. The treatments affected N concentration and occasionally total DM and N content of the plant organs, but partitioning of DM and N among the three parts was independent of the treatments. In comparison, soil moisture significantly affected total and partitioning of DM and N into pod. Among the cultivars, the medium‐maturing cultivar CDC Milestone accumulated similar DM and N as late‐maturing ones, but allocated a greater portion of DM and N to pod than the late‐maturing cultivars. Overall, available N during pod‐filling promotes lentil biomass and pod production, but the efficiency of DM and N partitioning to pod is controlled by plant genotype and soil moisture.
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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".