Long‐Term Biomass Yield and Species Composition in Native Perennial Bioenergy Cropping Systems
Bibliographic record
Abstract
Biomass yield is an important factor when recommending native perennial plants and mixtures for bioenergy production. Our objective was to determine long‐term biomass yields in fertilized and unfertilized native plant monocultures and mixtures that show promise for bioenergy across diverse environments in the Upper Midwest. We measured biomass yields, species composition, and diversity annually in monocultures and mixtures ranging from 4 to 24 planted species including grasses, legumes, and other forbs; each managed with and without 67 kg N ha −1 fertilizer applied annually at nine locations for 7 yr. Without N fertilization, switchgrass ( Panicum virgatum L.) monocultures and an eight‐species mixture of grasses and legumes produced the most biomass over locations and years (5.1 Mg ha −1 ). With N fertilizer, switchgrass monocultures and a four‐species mixture of grasses produced the highest yields (6.8 and 6.4 Mg ha −1 ). Over time, biomass yields increased for switchgrass, decreased for Canada wild rye ( Elymus canadensis L.), and remained stable for the high diversity mixtures. Other mixtures had nonlinear changes in yield, likely related to changes in species composition. Although the relative abundance of individual species changed over time, Shannon diversity was constant except for the four‐species legume mixture where it decreased. Contrary to other studies, N fertilization did not decrease species diversity through time. Diversity was positively related to biomass yield following establishment, but the strength of the relationship diminished with stand age. Native plant mixtures managed with and without N fertilizer can yield similar biomass compared with highly productive monocultures in the Upper Midwest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".