Legume production in semi-arid areas: comparative study of the physiology of drought tolerance in common bean («Phaseolus vulgaris L.») and mungbean «(Vigna radiata (L.) Wilczek)»
Bibliographic record
Abstract
Context: Approximately one billion people live in semi-arid and arid regions, and of these about 40% live on less than a dollar a day. Legumes crops are an important component of sustainable agriculture, but they are often grown under intermittent or terminal droughts. Thus, improving drought tolerance in legumes has the potential to improve food security and sustainability of agricultural systems. Objective: This study compares the response of two legume crops, common bean (Phaseolus vulgaris L.) and mungbean (Vigna radiata (L.) Wilczek), to water stress conditions under field and controlled-environment conditions. Methods: Field experiments were conducted in the Fergana valley, Uzbekistan, and controlled environment experiments were conducted at the Macdonald campus of McGill University, Ste-Anne-de-Bellevue, Canada, and at the Hermitage Research Station in Warwick, Australia. Results: Our results demonstrate that alternate furrow irrigation maintains yields, yet decreases water applied by 25%. In addition, mungbean showed the highest yields in the moderate deficit irrigation treatment in 2003 and severe deficit irrigation treatment in 2004 under field conditions in Uzbekistan. Common bean also showed a capacity to maintain yields under moderate deficit irrigation in both years. Further characterization of the legume responses to water deficit stress in controlled-environment experiments indicated that mungbean’s higher tolerance is attributable to higher transpiration efficiency, a more conservative water use in the vegetative stage, and a higher root-to-shoot ratio when compared to common bean. Root characteristics might also play an important role, although we have observed a large variability between genotypes. An additional field experiment in Uzbekistan demonstrated that an early maturing Canadian soybean cultivar could be grown after the harvest of winter wheat, and thus contribute to food security. It has also been demonstrated that i
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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 | 0.000 | 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 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".