Combining Ability for Drought Tolerance in Upland Rice Varieties at Reproductive Stage
Bibliographic record
Abstract
Rice is an important food crop for human population ranking second among the mostly consumed cereal grains worldwide. Upland rice production is greatly constrained by drought stress resulting from rainfall variation patterns. Cultivation of drought tolerant varieties is considered the best option for drought management in rice production. The already released upland rice varieties are drought susceptible and have poor grain attributes hence, the aim of this study was to determine the combining ability for drought tolerance in upland rice. Four upland NERICA and two upland rice varieties were selected as parents for generating F1s crosses following 6 × 6 complete diallel. The generated 30 F1 crosses were advanced to F2 population for field evaluation. The F2 progenies together with six parents were planted in two sites; KALRO-Mwea Center Farm and Kirogo research Farm following a randomized complete block design in three replications. Drought stress was initiated 45 days after sowing after which data was collected on drought and agronomic parameters. The study revealed large genetic variations among the genotypes used. Both GCA and SCA were significant indicating the importance of both additive and non additive gene action in the expression of studied traits. In this study NERICA 2 and NERICA 15 were identified as good combiners for drought tolerance and grain yield under drought conditions. The single crosses namely; NERICA 15 × NERICA 2, NERICA 1 × NERICA 15, NERICA 11 × NERICA 15 and NERICA 2 × NERICA 15 were identified as superior for improving yield under drought conditions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".