The Impact of RHT Gene Alleles on the Yield of Spring Wheat Under Drought Conditions
Bibliographic record
Abstract
This study aims to assess the effectiveness of dwarfing genes Rht-B1a/B1b and Rht-D1a/D1b on the phenotype and yield of spring soft wheat under drought conditions in Kazakhstan.From 2018 to 2022, laboratory experiments were conducted at S. Seifullin Kazakh Agrotechnical Research University, and field trials were performed at two sites in Kazakhstan.The study involved phenotypic analysis of plant height (PH) and coleoptile length (CL), as well as yield measurement.The RhtB1a and RhtD1a alleles significantly influenced plant traits.Hybrids carrying the RhtB1a allele exhibited a reduction in plant height by 15-20%, while yield improvements reached up to 30% compared to non-dwarf varieties.Specific hybrids, such as WH190, demonstrated yields of 2.5 tons per hectare, which is 80% higher than the country's average yield indicator.Dwarfing genes RhtB1a and RhtD1a are critical for optimizing wheat growth and yield under drought conditions.This study highlights the potential of specific genotypes in wheat breeding programs, emphasizing the importance of integrating genetic and field data to identify optimal genotypes for specific environments.
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".