Yield and Response of Bean Breeding Lines for Drought Tolerance to Field Diseases
Bibliographic record
Abstract
Climate change has resulted in an increase in the intensity of droughts and rains, and higher temperatures which are adversely affecting crop production in Africa. It has also influenced the distribution and increased the occurrence of disease and pest epidemics. Common bean (Phaseolus vulgaris L.) is especially sensitive to these changes. Most released varieties are not well adapted to environmental extremes and extended periods of drought in particular has become a major constraint. In this study, 462 advanced breeding lines developed for drought tolerance were evaluated for yield, agronomic traits and disease response in two contrasting agro-ecologies, Kawanda in the Lake Victoria crescent and Kachwekano in the southwestern highlands. Analysis of variance indicated significant differences (P < 0.05) among lines for most variables and environments (P < 0.001). Performance was better at Kachwekano than Kawanda with mean yields ranging from 928 to 2251 kg ha-1, and 698 to 2036 kg ha-1 respectively. Angular leafspot, common bacterial blight and rust diseases varied between locations, and among lines with no visible to sever symptoms. Based on Wricke’s ecovalence estimates for stability, SCN20, SCN13, SEN114, SEC40 and SEC35 expressed yield stability and superiority. Of the 462 lines, 6.3 % maintained >1500 kg ha-1, the minimum anticipated commercial yield of new varieties, in all the trials, both seasons and in each year; 5.0 % also maintained above average yields. The most outstanding (1629-3944 kg ha-1) were; DAB299, DAB291, DAB234, DAD34, DAB478, DAB487, DAB543, DAB231, SCN20, SCR66, SCR60, and SER335. These are recommended for development of new varieties.
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".