Mixtures of native perennial forage species produce higher yields than monocultures in a long-term study
Bibliographic record
Abstract
To evaluate the forage yield and quality of seven perennial native species in monoculture and binary mixtures under a range of climate conditions, a 6-yr field experiment was conducted at the Swift Current Research and Development Centre (SCRDC), Agriculture and Agri-Food Canada (AAFC), in Swift Current, SK. Seven native perennial forage species from three functional groups (C3, C4 grasses, and legumes) were seeded in 2010 in monocultures and binary mixtures. Forage yield and quality [crude protein, acid detergent fiber (ADF), neutral detergent fiber (NDF), phosphorus (P), calcium (Ca), and copper (Cu)] were measured during the first week of July and last week of August in 2011–2016. Mixtures that included western wheatgrass [Pascopyrum smithii (Rydb.) Löve] (WWG) tended to produce a greater yield when 90% of the composition within these mixtures was WWG. Adding bluebunch wheatgrass [Pseudoroegneria spicata (Pursh) Löve] (BBW), little blue stem [Schizachyrium scoparium (Michx.) Nash] (LBS), and prairie clovers (Dalea spp.) to the binary mixtures can increase the positive aspects of species diversity on stability and productivity in seeded pastures. Among the grasses, WWG contained higher crude protein and lower ADF and NDF concentration. Mixtures of forage species produced higher forage yield compared with monocultures. Native forage species can produce stable forage yield across very different climate situations. In mixtures, WWG showed promising results in forage productivity and quality and can be a suitable option for seeded pastures.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".