Selection for drought resistance in early generations of common bean populations
Bibliographic record
Abstract
Availability and use of high-yielding drought-resistant common bean (Phaseolus vulgaris L.) cultivars would reduce dependence on irrigation water and production costs, stabilize yield in drought-prone environments, and potentially increase profit margins for growers. Among various selection criteria, seed yield has been found to be the most effective to improve drought resistance in common bean. Our objective was to determine the effectiveness of selection for seed yield in drought-stressed (DS) and non-stressed (NS) environments in early generations of common bean populations under field conditions. One hundred thirty-four F 2 -derived F 4 (F 2:4 ) families from each of three double-cross populations (SX 12008, SX 12009, and SX 12010), four parents, and six checks were evaluated in replicated field trials in DS and NS environments at the Centro Internacional de Agricultura Tropical, Palmira, Colombia during the January to March growing season (1997A). The 30 highest yielding F 2:5 families from each population and each environment, four parents, and two checks were again evaluated in replicated trials in their respective DS and NS environments during the June to August growing season (1997B). Three F 5:7 lines from each of the four highest yielding F 2:5 families from each population and each environment (a total of 72 lines), and nine parents involved in the three populations were evaluated in replicated trials in both DS and NS environments in 1998B and 1999B. Mean yieldof lines selected in the NS environment was significantly higher than the mean of parents in populations SX 12008 and SX 12010 when tested in the NS environment. In population SX 12009, lines selected in the DS environment had higher mean yield than their parents when tested in NS environment. The mean yields of lines selected in the DS and NS environments did not differ significantly when tested in the DS environment in any population. NS environment seed yields were positively correlated with DS yields, geometric mean yield (GM), percent reduction (PR) due to drought stress, and drought susceptibility index (DSI). In contrast, DS and GM yields were negatively correlated with PR and DSI, whereas the association between the latter two was positive. The 100 -seed weight was slightly reduced and mean maturity was accelerated by 2 to 4 d in DS versus NS environments. Considering the extra costs involved to test early generation selections for seed yield in DS environments, it does not appear to be advisable in common bean. Key words: Drought resistance in common bean, early generation yield test, F 2 -derived family selection, Phaseolus vulgaris, seed yield
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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 | 0.001 |
| 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".