Improving biomass yield of giant Miscanthus by application of beneficial soil microbes and a plant biostimulant
Bibliographic record
Abstract
Sustainable production of biomass crops is important in the development of feedstocks for the production of biofuels and other bioproducts. This study investigates the use of nine beneficial soil microbes and a plant biostimulant (i.e., Ascophyllum nodosum seaweed extract) to increase the growth of two giant Miscanthus (Miscanthus × giganteus) cultivars, ‘Amuri’ and ‘Nagara’, under greenhouse conditions and in the field on poor-quality, marginal land. Greenhouse trials indicated increases in shoot dry weight (DW) in ‘Amuri’ in treatments with Gluconacetobacter diazotrophicus PAL5T LsdB++, Gluconacetobacter johannae UAP-Cf-76, and Variovorax paradoxus JM67 by 15%–24% compared with untreated controls. In ‘Nagara’, shoot DW was increased in the treatments with Penicillium bilaiae by 11% and the seaweed extract by 10%. The nutrient content of shoot tissues increased in the same treatments in which biomass was increased. Despite a lack of treatment effects on shoot DW in ‘Amuri’ in the field, several treatments increased Fe and Zn content in shoots by up to 1.9×. In ‘Nagara’ in the field, treatment with G. johannae UAP-Cf-76 and the seaweed extract resulted in increases in shoot DW by 16% and 23%, respectively, and several treatments resulted in increases in shoot Fe and Zn concentrations. The productivity enhancements in giant Miscanthus by beneficial soil microbes and the seaweed extract may be associated with increasing access to limited soil nutrients. These findings suggest that the use of beneficial soil microbes and plant biostimulants may aid in the sustainable production of giant Miscanthus on marginal lands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.001 | 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".