Biomass Production and Environmental Considerations from Reed Canarygrass Fertilized with Organic Residues in Northern Environments
Bibliographic record
Abstract
Core Ideas Liquid swine manure and municipal biosolid provide sufficient N for reed canarygrass. Liquid swine manure and municipal biosolid use do not cause nitrate leaching and heavy metal accumulation. Kura clover with reed canarygrass improves DM yield but less than fertilizers. Sustainable biomass production on marginal lands of northern areas using cool‐season perennial grasses and under‐exploited N sources requires development. We determined the biomass production of reed canarygrass (Phalaris arundinacea L.) fertilized with municipal biosolid (MB), liquid swine manure (LSM), mineral fertilizer (M), or grown with a legume species and harvested either in July or October along with the consequences of soil contamination by nitrates and heavy metals. The experiment, conducted at two sites from 2009 to 2011, included three target N rates (40, 80, and 120 kg total N ha−1) applied in spring as either MB, LSM, or M along with an unfertilized control treatment and a treatment with kura clover (Trifolium ambiguum M. B.). Reed canarygrass responded positively to N application from all sources. Both sources of organic fertilization resulted in DM yield close to that obtained with mineral fertilization but seasonal DM yield was greater with LSM than with MB. Kura clover improved DM yield compared with reed canarygrass without N fertilization, but it was not sufficient to reach DM yields obtained with N fertilization. The three N sources did not affect residual soil nitrates in the fall at both sites, the soil solution nitrate concentrations measured during the growing season at one site, nor the soil accumulation of heavy metals (Cu, Zn, and Cd) in the fall of the last year at both sites. Our results confirm that, as an alternative to M use, MB and LSM are valuable N sources for reed canarygrass biomass in northern areas.
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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 | 0.000 |
| 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".