Native Perennial Grassland Species for Bioenergy: Establishment and Biomass Productivity
Bibliographic record
Abstract
Proposed perennial bioenergy cropping systems include both native grass monocultures and polycultures of grasses and forbs. We determined the effect of species richness and composition on establishment and initial biomass production of native plant polycultures. Twelve treatments with varying levels of species richness (1–24 species) were established. Establishment success and yield varied over eight locations. The number of species established in polyculture increased linearly as the number of species seeded increased. Average biomass yield ranged from 1.2 to 6.0 Mg ha−1 with the highest yielding treatments being grass monocultures or an eight species grass–legume mixture. An increase in species richness from one to eight species increased yield an average of 28%, but increasing species richness from 8 to 12 or 24 species had no yield advantage at most locations. Early successional species, Canada milkvetch (Astragalus canadensis L.) and Maximilian sunflower (Helianthus maximilian Schrad.), were dominant in mixtures and contributed a majority of the biomass to the yield. Even in high diversity plots, biomass was from only a few plant species with a single species dominating the mixture. Our results suggest that selected low diversity mixtures (one to five species) likely offer the best combination of species establishment and high yield during stand establishment. However, we expect that early successional species that were dominant during the establishment phase of our experiment will contribute less biomass as stands mature and later successional species will become dominant and provide greater biomass.
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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) | 0.002 | 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".