Скрининг коллекционных образцов сои по скороспелости и продуктивности в условиях Рязанской области
Bibliographic record
Abstract
Представлены результаты изучения сортов сои мировой коллекции ВИР в условиях Рязанской области в 2015 2018 гг. Целью исследований является изучение коллекционного материала сои в условиях Рязанской области и выявление скороспелых и высокопродуктивных образцов, адаптированных к условиям Центрального региона России. В коллекционном питомнике изучалось 224 образца сои из 30 стран, в т.ч. 52 сортов отечественной селекции. Ежегодно самыми скороспелыми сортами были сорта российской селекции Эльдорадо, СибНИИК315, Касатка, Светлая сорта шведской селекции Brawalla и 1384 и сорт Прогресс (Польша). Установлено, что продуктивность сортов сои во все годы исследований в большей степени зависела от количества продуктивных узлов на растении (r 0,738) и количества семян на растении (r 0,827). Урожайность семян в 2015 2018 гг. сильно зависела от погодных условий: наиболее урожайными были сорта Мерлин (Австрия) и Gaillard (Канада). При изменяющихся погодных условиях важным показателем сортов является их устойчивость к стрессу. Установлено, что самую высокую устойчивость к стрессу ( 3,7) имеют сорт Елена (Украина) и линия Н17/09 (Россия). Самую низкую стрессоустойчивость имели сорта Мерлин ( 13,6) и MON04 ( 13,5). Полученные новые знания будут использованы в практической селекции при создании новых сортов, адаптированных к условиям Центрального региона России. При селекции сои на продуктивность необходимо учитывать количество продуктивных узлов, бобов и семян на растении. Селекция на скороспелость осуществляется с учётом пригодности сортов к механизированной уборке. The results of the study of soybean varieties of the world collection of VIR in the conditions of the Ryazan region in 2015 2018 are presented. The aim of the research is to study the collectable material of soybeans in the conditions of the Ryazan region and to identify earlyripening and highly productive samples adapted to the conditions of the Central region of Russia. 224 samples of soy from 30 countries were studied in a collection nursery including 52 of varieties of domestic selection. Annually the most earlyripening varieties were varieties of Russian selection Eldorado, SibNIIK315, Kasatka, Svetlaya varieties of Swedish selection Brawalla and 1384 and variety Progress (Poland). It was established that the productivity of soybean varieties in all years of research was more dependent on the number of productive nodes on the plant (r 0.738) and the number of seeds on the plant (r 0.827). Seed yield in 2015 2018 strongly depended on weather conditions: the most productive varieties were Merlin (Austria) and Gaillard (Canada). Under changing weather conditions an important indicator of varieties is their resistance to stress. It was established that the highest resistance to stress ( 3.7) was in the variety Elena (Ukraine) and the line N17/09 (Russia). The lowest stress resistance had varieties Merlin ( 13.6) and MON04 ( 13.5). The new knowledge gained will be used in practical selection to create new varieties adapted to the conditions of the Central region of Russia. When selecting soybeans for productivity it is necessary to take into account the number of productive nodes, beans and seeds on the plant. Selection for early ripeness is carried out taking into account the suitability of varieties for mechanized harvesting.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.019 |
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; both teacher heads agree on what is shown here.
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".