Biofertilizers for the sustainable production of herbaceous biomass crops in southern Ontario
Bibliographic record
Abstract
Cultivation of switchgrass (Panicum virgatum) and miscanthus (Miscanthus spp.) as dedicated biomass crops on Ontario’s marginal agricultural lands is increasing, and producers are seeking opportunities to enhance the sustainability of their operations. Therefore, we conducted a field study addressing the knowledge gap regarding field scale agronomic and environmental impact of four biofertilizers compared to a synthetic nitrogen fertilizer and a control for mature switchgrass and miscanthus. Biomass yield, plant morphology, soil fertility and biological health, and greenhouse gas fluxes were measured. Synthetic nitrogen and AGTIV® biofertilizer produced the highest yield for switchgrass and miscanthus, respectively. AGTIV® and Optimyc + MooR also increased bacterial and fungal gene abundance in the top 10 cm of soil under switchgrass cultivation in 2020. All fertilizers increased the release of key macronutrients under controlled conditions. In conclusion, this research shows that certain biofertilizers may be an alternative option to synthetic fertilizers for biomass crop production.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".