Yield structure of highly heterosic winter rye hybrids and features of its formation
Bibliographic record
Abstract
In the conditions of the Central Black Earth Region, the peculiarities of yield formation by highly heterotic hybrids of winter rye were studied for 3 years. The purpose of the research is to establish which structural elements and processes are responsible for the formation of higher yields in highly heterotic winter rye hybrids compared to the population variety. Each year 13-18 first-generation hybrids obtained using P-type CMS served as the starting material. The studied hybrids and the standard were sown in pairs on plots of 5 m2 in 6-fold repetition using the standard (dactyl) method. We studied the yield structure only of those hybrids that exceeded the standard—a high-yielding population variety—by 15 percent or more. The studied material was analyzed according to 53 indicators including in addition to the main elements of productivity, a detailed analysis of the ear and stem, as well as a number of characteristics characterizing the cenosis, the plant and its individual organs, physiological characteristics and the quality of the formed grains. As a result of the studies, it was found that highly heterotic hybrids of winter rye form a larger grain yield due to a larger yield of above-ground mass and a larger proportion of grain in it. Large proportion of grain in the above-ground mass of the crop (Kkhos.) is the result of more efficient use by hybrids of reserve plastic substances accumulated as a result of photosynthesis in the stem and ear, in particular. This is confirmed by the lower specific gravity of the stem and its parts (internodes), and higher values of micro-distribution, grain attraction and filling indices, as well as the Canadian index. Heterotic hybrids are characterized by a different type of organogenesis, which differs from that characteristic of modern population varieties. It was concluded that when breeding population varieties of winter rye to increase yield, it is necessary to select genotypes with higher values of the Canadian index and indices of micro-distribution, attraction and grain filling, as well as with a lower specific weight of the stem and its parts.
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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 | 0.001 | 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) | 0.000 | 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 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".