Dual-Use Bioenergy-Livestock Feed Potential of Giant Miscanthus, Giant Reed, and Miscane
Bibliographic record
Abstract
High yielding perennial grasses could integrate bioenergy-livestock operations, thereby, offsetting diversions of cropland to lignocellulosic crops, but research is needed to determine chemical composition and digestibility of leaf and stem fractions that might affect downstream reside uses. The objective of this study was to compare feedstock quality of leaf and stem tissues of dedicated bioenergy feedstocks: giant miscanthus (Miscanthus × giganteus), giant reed (Arundo donax), and miscane (Saccharum hybrid × Miscanthus spp.) when grown with or without supplemental irrigation on an upland site. Three species were space-planted on a silt loam soil in March 2007 and harvested prior to the first freeze in plant-cane, first ratoon, and second-ratoon crops for three years. Giant miscanthus leaf tissue had greatest acid detergent lignin and cellulose, and lowest concentrations of nitrogen (N) and total nonstructural carbohydrates (TNC) in ratoon crops. Giant reed leaf tissue had greatest concentrations of in vitro digestible dry matter (IVDMD), TNC, and N (P ≤ 0.05). Conversely, miscane stem tissue had greatest concentrations of IVDMD, TNC, hemicellulose, and low dry matter and combustible energy (P ≤ 0.05). Results suggest all species’ residue has positive feedstock attributes for thermochemical bioenergy conversion, and albeit giant miscanthus has very little potential value as fodder. Miscane stem and giant reed leaf tissue have potential value as livestock feed, although giant reed is not currently recommended for planting. Further research is needed on dietary composition, acceptability, voluntary intake, and live weight gain before any of these species are recommended as livestock feed sources.
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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 | 0.001 | 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".