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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".