A Preliminary Evaluation of Faba Bean as a Green Shell Bean in Virginia, USA
Bibliographic record
Abstract
Even though faba bean (Vicia faba L.) is an important food crop on worldwide basis, its use as a vegetable i.e. use of green seeds as food is limited. We were interested in characterizing production and food quality traits of faba bean grown in eastern USA. Our main objective is to develop faba bean as a winter alternative crop for this region to diversify cropping system that currently mainly depends upon cereal grain crops. This necessitates existence of winter-hardy faba bean varieties. We conducted a preliminary experiment with two winter-hardy faba bean breeding lines to record production and seed composition traits. Two faba bean lines (VSX-BL and VSX-BH) were planted in the field in November 2022 and green pods were harvested in June 2023 at physiological maturity. Values of most of the traits under study were statistically similar between the two lines except for concentration of phosphorus. Results demonstrated that faba bean green pod and green seeds yields could be approximately 5700 and 2100 kg ha-1. The shelling percent in green faba bean pods was approximately 37 whereas seed number per pod was approximately 2.6. Concentrations (g 100 g-1) of fructose, glucose, and sucrose were 0.245, 0.668, 2.9021, respectively whereas concentrations of Fe and Zn (mg kg-1) were 103.7 and 69.7, respectively. Green faba bean seeds contained small amounts of RFO carbohydrates. Seed composition traits of green faba bean seeds compare well with green seeds of pea, soybean, and white lupin. Our results provide a positive proof of concept that production of green faba bean as an alternate winter food legume crop in eastern USA is possible.
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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 | 0.001 | 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".