Effect of fall harvest time on forage yield and nutritive value of three alfalfa cultivars with different root types
Bibliographic record
Abstract
Abstract Warmer than normal late fall temperatures may change standard fall harvest management of alfalfa (Medicago sativa L.) in western Canada. The objective of this study was to evaluate the forage yield and nutritive value of three types of alfalfa cultivars under different harvest managements. A field experiment was conducted from 2013 to 2017 using rhizomatous type (RR), cv. AC Yellowhead (M. sativa subsp. falcata), tap root type (TR), cv. Equinox (ssp. sativa) and creeping root type (CR), cv. Spredor 4 alfalfa (ssp. sativa), at Melfort, SK, Canada. The five harvest treatments were two 1‐cut systems (early July or late July) and three 2‐cut systems (early July + early or late September, late July + late September). Total forage yield was greater in TR and CR alfalfa than RR alfalfa; however, RR alfalfa had higher total forage yield under dry growing conditions versus CR alfalfa. The three alfalfa cultivars had a similar response to 2‐cut harvest managements. The first cut yield was affected by the timing of the second cut of the previous year with the early September harvest reducing yield more than the late September. Three alfalfa cultivars had similar crude protein (CP), total digestible nutrient (TDN), and acid detergent fiber (ADF) concentrations. There was a significant nutrient yield benefit of TR and CR alfalfa under a 2‐cut system over the RR alfalfa. Although the prairie climate is warming, the findings suggest that a fall rest period until late September is recommended regardless of alfalfa types to maintain high hay yield over multiple years.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".