Responses of <i>Medicago sativa</i> and <i>M. falcata </i> type alfalfas to different defoliation times and grass competition
Bibliographic record
Abstract
Incorporating alfalfa into rangelands can enhance the quantity and quality of forage production. We evaluated the impact of clipping timing and selective clipping on two M. falcata (Anik and Yellowhead) and one M. sativa type alfalfas (Vernal) near Mandan, North Dakota, USA. Cultivars were space-planted into an existing mixed grass prairie and clipped at the mid-bud, flower or flower and subsequent vegetative stages. In each clipping treatment, half of the plots had only the alfalfa clipped and half had both the alfalfa and the associated native vegetation clipped. Plots without alfalfa were also included to evaluate the impact of alfalfa on grass and forb biomass. Including Yellowhead increased total productivity by 38 to 185% without lowering the productivity of the grass or forb biomass components. Plots with Yellowhead produced 17 to 26% more total biomass than the next highest entry every year. In 2003 and 2005, alfalfa biomass was increased 1.5 to 2.7 times by clipping only alfalfa in the flower and regrowth stages compared with a mid-bud clipping of only alfalfa. Vernal and Yellowhead generally produced more total biomass when clipping was deferred to the flower stage, but clipping Anik in the mid-bud stage produced as much or more total biomass than did the later clipping treatments. Selective clipping of the alfalfa did not have a consistent impact on yield. Yellowhead appeared to be a good choice for incorporating into rangelands. Producers with Yellowhead or Vernal should consider delaying defoliation until flowering to maximize productivity. Key words: Grazing-type alfalfa, hay-type alfalfa, defoliation timing, biomass productivity
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".