EVALUATION OF VARIETIES OF SPRING SOFT WHEAT BY GRAIN QUALITY INDICATORS IN THE CHANGING CONDITIONS OF THE MIDDLE VOLGA REGION
Bibliographic record
Abstract
The research was conducted in 2019-2021 in the forest-steppe zone of Samara region. The purpose of the research is to screen samples of the world collection of spring soft wheat according to grain quality indicators in the conditions of the Middle Volga region to identify sources valuable for breeding. The soil of the site is typical low-humus medium-sized light clay chernozem. Meteorological conditions differed by years and from the long-term norm, but were generally characterized as arid. The hydrothermal humidification coefficient was 0.48 in 2019, 0.52 in 2020, 0.39 in 2021, and 0.73 for many years. The object of research is 130 samples of different ecological and geographical origin, single repetition, standard - zoned variety Tulaykovskaya Nadezhda. Grain quality indicators were evaluated according to the methodology of the State Commission for Variety Testing and the current National standards of the Russian Federation. According to the results of the research, varieties of spring soft wheat with high values of quality indicators were identified. According to physical properties – grain nature (831...864 g/l) and vitreousness (80...89 %), the following varieties were distinguished: Kinelskaya Niva, Kinelskaya Yubileynaya, Kinelskaya 2020 (Kinel), Tulaykovskaya 116, Ekada 113 (Bezenchuk), Burlak, Ulyanovskaya 105 (Ulyanovsk), Saratovskaya 73, Saratovskaya 74, Liniya 666, Albidum 28 (Saratov), Orenburgskaya 23 (Orenburg), Stepnaya Volna (Altai region). The samples exceeded the standard by 1...34 g/l and 1...10%, respectively. According to technological properties, high protein content (17.40...20.56%) and gluten (40.13...49.26 %) were noted varietals: Kinelskaya 59, Erythrospermum 5289 (Kinel), Sibirskiy Alyans, Stepnaya Niva (Altai region), Novosibirskaya 15, Novosibirskaya 31, Novosibirskaya 41, Polyushko (Novosibirsk), Omskaya 37, Omskaya 38, OmGAU 100, Sigma (Omsk), Balkysh (Tatarstan), Nikon (Ulyanovsk), Gunner (Canada), Long Fu 13 (China), Digana (Switzerland). The excess over the standard was 1.88...5.04% and 4.13...13.26%, respectively. The selected cultivars are recommended to be used in the breeding process as parent forms to create varieties with high grain quality indicators.
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 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".