Optimization of selection of highly productive winter common wheat genotypes using selection indices
Bibliographic record
Abstract
The purpose of the current study was to estimate the informativeness of selection indices based on morphometric indicators and productivity elements of winter common wheat varieties in the Lower Volga region. Field trials were carried out in the selection crop rotation of the FSBSI “FARC of South-East”, Saratov. Climate zone was the Lower Volga region. The objects of the study were 14 varieties and promising lines of winter common wheat harvested in 2023 and 2024. The soil was low-power southern blackearth with solonetz patches; the wheat was sown black fallow. The productivity was recorded in two field repetitions. There were used dispersion and correlation methods of research. There were studied such selection indices as productivity, attraction, prospects, filling, ear potential, linear density of a ear, Mexican, Canadian, Poltava. All indicators were ranked from maximum to minimum. According to weather conditions, 2023 could be called favorable in general, and 2024 unfavorable. High productivity rank was found in the varieties ‘Podruga’, ‘Anastasiya’, ‘Kalach 60’, ‘Zhemchuzhina Povolzhya’ and the line ‘Santa/Kalach 60’. There is a high correlation between grain productivity and productivity index. Under unfavorable growing conditions, there have been also selected high-attraction varieties. In both years of research, one could rely on the productivity indices and attraction. Under more favorable conditions, selection could also be concentrated on indices of prospects, ear potential and filling. Such indices as Poltava, Canadian, linear density, plant productivity should be taken into account in arid conditions, since it is problematic to rely on them in favorable conditions. When analyzing the average rating estimation for selection indices for two years, there were identified 5 groups of variety samples according to the time of their development and plant height. Similar ranking schemes for the studied forms of winter wheat, with some changes, were obtained both in a favorable and an arid year. Thus, based on the experimental data, there can be argued that the use of selection indices in a complex, relying on the identified correlations with productivity, will give a positive result in breeding.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".