Trends in production, consumption, trade, and research of dry beans across the globe and Canada
Bibliographic record
Abstract
Dry beans ( Phaseolus vulgaris L.) are known as a significant component of global agri-food systems, in regions such as Southern Asia, Eastern Africa, and South America, where they also serve as a valuable source of feed. Over the past few decades, global production has grown significantly, driven by rising demand, technological advancements, improved yields, and expanded cultivation areas. Canada, in particular, has become a significant player in the dry bean industry, leveraging its rich agricultural landscape and advanced agricultural technologies. Canadian research initiatives, financially supported by both governmental and private funding, have concentrated on developing new bean varieties with higher yields, resistance to pests and diseases, better adaptation to local growing conditions, and improved nutritional profiles. This study reviews trends in dry bean production, consumption, and international trade over the past decades, emphasizing the implications for research on both global and Canadian scales. Collaborative efforts between Canadian institutions and international research organizations have facilitated the exchange of genetic resources and agronomic techniques, thereby enhancing productivity and sustainability. By investing in these innovative endeavors, Canada not only bolsters its strengthened agricultural sector but also contributes significantly to global food security and the achievement of sustainable development goals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.026 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".