Development and evaluation of grazing-tolerant alfalfa cultivars: A review
Bibliographic record
Abstract
Plant breeders have long sought to improve grazing tolerance of alfalfa without sacrificing the beneficial yield and quality attributes of this species. Most efforts have focussed on selecting for traits (e.g., creeping rootedness) related to grazing tolerance and/or simulated grazing, but these efforts failed to account for the multiple stresses caused by grazing animals. Trait selection often led to sacrifices in yield and other desirable characteristics resulting in cultivars that were not robust across grazing management systems and environments. An innovative selection procedure was recently developed at the University of Georgia which incorporated intensive grazing with continuous stocking by beef cattle. The development of "Alfagraze" using this procedure showed that grazing tolerance and high yields can be incorporated into the same cultivar, along with consistent performance across grazing management systems and environments. Subsequent research has shown that grazing tolerance can be improved within elite, high-yielding, multiple-pest-resistant cultivars and breeding populations. Selection using intensive grazing with continuous stocking has been summarised in a "Standard Test Protocol" that is now being successfully used by public and private alfalfa breeders and in cultivar evaluation programs in the USA, Canada, and other countries. Key words: Medicago sativa, Medicago sativa ssp. falcata, persistence, lucerne, grazing tolerance, Alfagraze
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".