Performance and legacy effect of crop rotations on eastern Canadian dairy farms
Bibliographic record
Abstract
Abstract Crop rotations on dairy farms in eastern Canada nowadays include fewer perennial crops and more nitrogen‐demanding annual crops. This study compared, over a 7‐year rotation cycle, the agronomic performance and the legacy effect of six crop rotations that varied in crop types (perennial or annual) and nutrient sources (mineral or organic). Crop yield and nutritive value were determined on a yearly basis and cumulated over the rotation cycle. The legacy effect was assessed by growing forage corn (Zea mays L.) in year 6 and soybean [Glycine max (L.) Merr.] in year 7 in all rotations. The legacy effect of perennial forage crops manifested with a 78% lower weed biomass at harvest of forage corn in year 6 and a 14% greater soybean yield in year 7. A greater soil‐derived corn nitrogen uptake in year 6 after repeated slurry applications indicated a modest legacy effect of organic fertilization on soil N supply capacity. The presence of perennial forage crops or the use of organic fertilization did not affect cumulative dry matter or crude protein production over the 7‐year rotation cycle. The addition of alfalfa (Medicago sativa L.) in mixture with perennial grasses improved forage yield (+26%) and nutritive value (greater digestible energy and crude protein concentration) in post‐seeding years. In perennial‐based rotations, adding alfalfa to grasses resulted in greater dry matter (+22%) and crude protein (+46%) productions over the 7‐year cycle despite a fourfold reduction in N fertilizer input, attesting to the high N use efficiency of perennial legume‐based cropping systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".