Organic selection may improve yield efficiency in spring wheat: A preliminary analysis.
Bibliographic record
Abstract
Organic spring wheat (Triticum aestivum L.) breeding programs have been initiated, yet yield efficiency and N economy research is limited. We evaluated the performance of advanced lines selected from an organic breeding program initiated in 2003. Fourteen F8 and F9 lines in 2009 and 11 lines in 2010 were compared with commercial (check) cultivars. Field experiments were conducted under organic management at four site–years in Manitoba and Saskatchewan. Combined analysis showed no difference in biomass accumulation between organic lines and check cultivars; however, harvest index and grain yield were greater in organic lines compared with the checks. Organic lines were shorter than check cultivars, but yield efficiency, defined as kernel number per unit of crop biomass at anthesis, was higher (P < 0.05). Kernel mass was also greater for organic lines. Biomass N uptake was similar for organic lines and check cultivars, although total uptake of N into grain was greater for organic lines. The average grain protein content of organic lines was significantly lower than the check cultivars. This study demonstrated that improved yield under organic management was because of better assimilate partitioning, both at anthesis and crop maturity, for organically selected genotypes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".