Forage yield trend of alfalfa cultivars in the Canadian prairies and its relation to environmental factors and harvest management
Bibliographic record
Abstract
Abstract Alfalfa ( Medicago sativa L.) is one of the most important forage crops in the world. The objectives of this study were to assess alfalfa yield improvement in the Canadian prairies, and determine critical climatic factors influencing alfalfa yield in different soil zones. Forage yield data of alfalfa cultivars tested from 1997 to 2011 in the Western Forage Variety Testing System were used for the analysis. There was no significant trend of alfalfa yield increase in western Canada except at Saskatoon, SK. Regrowth yield of alfalfa cultivars released from 2000 to 2011, however, showed a significant ( p ≤ 0.05) increase under irrigation. Based on structural equation modelling ( SEM ), at rain‐fed sites, precipitation from April to June was the most important driver for the farm hay yield (1st‐cut). Forage yield of alfalfa, however, was not associated with winter extreme temperatures, or number of days with freeze‐thaw temperatures in April. Alfalfa yields were greatest under a 3‐cut than 1‐ or 2‐cut systems in the first production year, but this difference declined as stands became older. Alfalfa stands that were harvested more frequently were less responsive to growing season rain, but responded more strongly to increased snow cover, which may indicate reduced growth and less winder hardiness. At the irrigated sites, 1st‐cut forage yield increased with accumulated temperatures above 5°C from April to June. Development of alfalfa cultivars with tolerance to early season drought and improved regrowth, without reducing winter hardiness, would be necessary to stabilize alfalfa production under changing climate in the Canadian prairies.
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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 | 0.001 | 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".