Biomass Yield and Feedstock Quality of Prairie Cordgrass in Response to Seeding Rate, Row Spacing, and Nitrogen Fertilization
Bibliographic record
Abstract
Field establishment and management of prairie cordgrass as a dedicated bioenergy crop. Evaluate the effect of seeding rate and row spacing on biomass yield of prairie cordgrass. Determine effects of N fertilization on biomass yield and feedstock quality of prairie cordgrass. Prairie cordgrass (Spartina pectinata Link) shows potential as a bioenergy feedstock in marginal croplands across much of the United States and Canada. Objectives of this study were to: (i) evaluate the effects of seeding rate and row spacing on biomass yield and (ii) determine effects of N fertilization on biomass yield and feedstock quality of prairie cordgrass. During 2012, a field trial composed of three seeding rates (162, 323, and 484 pure live seed [PLS] m−2) and three row spacing treatments (19, 38, and 76 cm) was established in Urbana, IL. In the same year, another field trial was established with four N rates (0, 84, 168, and 84/84 [equal split applications during spring and after V6 stage] kg N ha−1). During 2013 to 2016, no differences in biomass yields were observed under all combinations of seeding rate and row spacing treatment, except for a higher yield under 76‐cm spacing in 2014. Biomass yields increased as N applications increased from 0 to 84 kg N ha−1, but no additional response occurred above this rate. Feedstock quality (cellulose, hemicellulose, and ash concentrations) was not affected by N rate. Biomass nutrient removal increased as N fertilization caused an increase in biomass yield except for biomass P. Our results indicated that prairie cordgrass could be successfully established in 76‐cm row spacing with a seeding rate of 162 PLS m−2. The recommended N rate for maximum yield is 84 kg N ha−1 based on a post‐killing frost harvest.
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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 | 0.001 |
| 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".