Genetic Improvement in Short‐Season Soybeans: II. Nitrogen Accumulation, Remobilization, and Partitioning
Bibliographic record
Abstract
Genetic improvement in yield is conditional on surmounting yield-limiting factors. Nitrogen (N) has been considered an important limiting factor to soybean [Glycine max (L.) Merr.] yield. The high demand for N by soybean seed was previously considered to lead to early leaf senescence through accelerated remobilization of N from the vegetative tissue. The consequent reduction in photosynthetic capacity was postulated to limit yield. The objectives of the current experiment were to determine the changes in N accumulation, remobilization, and partitioning associated with genetic yield improvement. Two groups of old, low-yielding ('Pagoda' and 'Mandarin Ottawa') and new, high-yielding ('Maple Glen' and 'OAC Bayfield') soybean cultivars of similar maturity were grown in side-by-side trials at the Elora Research Station, Ontario, in 1996 and 1997. Nitrogen and dry matter accumulation in leaf, stem + petiole, roots, and seeds were determined during the growing season. The newer cultivars had higher yields and higher seed N content. Contrary to the postulated association between leaf senescence and leaf N values, neither leaf N concentration nor leaf N content per unit leaf area (at R6) were association consistently with either yield or leaf area duration (LAD). Although most of the N in the seed was derived from N remobilized from vegetative tissue, the newer cultivars with their higher yields and LAD, remobilized no more N out of the vegetative tissue than did older, lower-yielding ones. The newer cultivars were distinct from their older counterparts in their ability to accumulate more N during the seed filling period (SFP). Genetic improvement of the short-season soybeans tested was a consequence of continued N accumulation during the SFP and was not due to differences in the genotype's capacity to remobilize or partition N to the seed.
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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".