Selection of rice breeding lines for resistance to biotic and abiotic stresses
Bibliographic record
Abstract
Rice (Oryza sativa L.) grown in many countries around the world with different climatic conditions and a huge number of environmental stresses, both biotic (fungi, bacteria, viruses, insects) and abiotic (cold, drought, salinity) limit rice productivity. In this regard, breeders and scientists are trying to create rice lines that are resistant to multiple stresses. The aim of this work was to screen and select cold and blast resistant rice breeding lines (RBLs) using molecular markers. Molecular screening of RBLs and parental varieties to cold tolerance was carried out using markers RM24545, RM1377, RM231 and RM569 associated with QTLs (qPSST-3, qPSST-7, qPSST-9). It was discovered that the presence of three QTLs characterizes the cold resistance of studied genotypes, and the absence of one of them leads to cold sensitivity. As a result, 21 cold-resistant out of the 28 studied RBLs were identified. These cold resistant 21 RBLs were further tested to blast resistance using markers Pi-ta, Pita3, Z56592, 195R-1, NMSMPi9-1, TRS26, Pikh MAS, MSM6, 9871.T7E2b, RM224 and RM1233. It was revealed that 16 RBLs from 21 studied lines contain 5-6 blast resistance genes. In accordance with the blast resistance strategy, the presence of 5 or more genes ensures the formation of stable resistance to Magnaporthe oryzae. Thus, 16 lines resistant to multiple stresses, such as cold and blast disease were developed. It should be noted that 6 of these selected lines are high-yielding, which is very important in rice breeding program. These RBLs can be used in breeding process as starting lines, germplasm exchange as a source of resistant genes for the development of new rice varieties resistant to multiple stress factors.
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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 | 0.000 | 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) | 0.000 | 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".