Soybean Genetic Improvement in Yield and the Effect of Late‐Season Shading and Nitrogen Source and Supply
Bibliographic record
Abstract
Genetic improvement in soybean [Glycine max (L.) Merr.] yield has been associated with both assimilate and N accumulation [especially from dinitrogen (N2) fixation] during the seed‐filling period (SFP). Therefore, the physiological factors associated with genetic improvement may be dependent on abundant assimilate and N supply. The objectives of this study were to quantify genetic improvement in yield under: (i) assimilate limiting conditions, (ii) under low N fertility, and (iii) when either inorganic N fertilizer or N2 fixation is the main source of N. A randomized complete block experiment was conducted at two locations in 1998 and one in 1999. The main plot factor included no shade or a 63% shade treatment imposed after the R4/R5 developmental stage. The split plot factors were three N treatments: (i) inoculated, (ii) uninoculated with no additional fertilizer, and (iii) uninoculated plus fertilizer N. The split‐split plots were four cultivars, representing a pair of older and a pair of newer cultivars. Shading reduced dry matter (DM) accumulation, and both shading and N limitation reduced N accumulation and seed yield. However, the newer cultivars consistently maintained their yield advantage over the older cultivars under both shading and N limiting conditions as well as when the source of N was soil available N or N2 fixation. The results of the study suggest that the physiological factors that contribute to genetic improvement in yield are not dependent on the level of incident radiation during the SFP, or the source or availability of N during the SFP.
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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".