Genetic Improvement in Short‐Season Soybeans: II. Nitrogen Accumulation, Remobilization, and Partitioning
Why this work is in the frame
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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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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 | 0.001 |
| 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 it