Switchgrass as a biofuels feedstock in the USA
Bibliographic record
Abstract
Switchgrass (Panicum virgatum L.) has been identified as a model herbaceous energy crop for the USA. In this review, we selectively highlight current USDA-ARS research on switchgrass for biomass energy. Intensive research on switchgrass as a biomass feedstock in the 1990s greatly improved our understanding of the adaptation of switchgrass cultivars, production practices, and environmental benefits. Several constraints still remain in terms of economic production of switchgrass for biomass feedstock including reliable establishment practices to ensure productive stands in the seeding year, efficient use of fertilizers, and more efficient methods to convert lignocellulose to biofuels. Overcoming the biological constraints will require genetic enhancement, molecular biology, and plant breeding efforts to improve switchgrass cultivars. New genomic resources will aid in developing molecular markers, and should allow for marker-assisted selection of improved germplasm. Research is also needed on profitable management practices for switchgrass production appropriate to specific agro-ecoregions and breakthroughs in conversion methodology. Current higher costs of biofuels compared to fossil fuels may be offset by accurately valuing environmental benefits associated with perennial grasses such as reduced runoff and erosion and associated reduced losses of soil nutrients and organic matter, increased incorporation of soil carbon and reduced use of agricultural chemicals. Use of warm-season perennial grasses in bioenergy cropping systems may also mitigate increases in atmospheric CO 2 . A critical need is teams of scientists, extension staff, and producer-cooperators in key agro-ecoregions to develop profitable management practices for the production of biomass feedstocks appropriate to those agro-ecoregions. Key words: Bioenergy, biomass conversion technologies, Panicum virgatum L., stand establishment, switchgrass improvement, USDA-ARS
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".