Conservation Management Practices and Rotations for Irrigated Dry Bean Production in Southern Alberta
Bibliographic record
Abstract
Dry bean (Phaseolus vulgaris L.) production on the Canadian prairies has traditionally used wide rows, inter‐row cultivation, and undercutting at harvest. Recent breeding efforts have produced cultivars with more upright growth which are better suited to solid‐seeded narrow‐row production systems. A 12 yr (2000–2011) study compared conservation (CONS) and conventional (CONV) management for dry bean in 3‐ to 6‐yr rotations. The CONS rotations included reduced tillage, cover crops, feedlot manure compost, and solid‐seeded narrow‐row dry bean. Effects of CONS management on plant density were inconsistent with some years showing lower density when seeded into high‐residue conditions. On average, there was a 3 d maturity advantage with CONS (103 d) vs. CONV (106 d) management. The CONS rotations showed significantly higher mean incidence (19%) of white mold [Sclerotinia sclerotiorum (Lib.) de Bary] than CONV rotations (6%). Averaging across 12 yr, there was no significant rotation effect on yield (P = 0.19) showing that CONS production performed as good as CONV production. In the last 2 yr (2010–2011) of the study, in an attempt to reduce harvest losses, CONS dry bean was undercut rather than direct combined. This led to significantly higher (25%) yield with CONS (3311 kg ha−1) vs. CONV management (2651 kg ha−1). Our results provide incentive for more rapid adoption of conservation‐oriented soil and crop management practices for dry bean production on the Canadian prairies, including narrow rows, reduced tillage, cover crops, and feedlot manure compost addition.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".