Effect of nitrogen fertilizer application rate on yield, methane and nitrous oxide emissions from switchgrass (<i>Panicum virgatum</i> L.) and reed canarygrass (<i>Phalaris arundinacea</i> L.)
Bibliographic record
Abstract
Wile, A., Burton, D. L., Sharifi, M., Lynch, D., Main, M. and Papadopoulos, Y. A. 2014. Effect of nitrogen fertilizer application rate on yield, methane and nitrous oxide emissions from switchgrass (Panicum virgatum L.) and reed canarygrass (Phalaris arundinacea L.). Can. J. Soil Sci. 94: 129–137. This 2-yr (2008–2009) study conducted in Truro, Nova Scotia, measured plant biomass production (yield and ash content) and greenhouse gas emissions (methane and nitrous oxide), from the bioenergy crops switchgrass (SG; Panicum virgatum L.) and reed canarygrass (RCG; Phalaris arundinacea L.) receiving spring application of nitrogen fertilizer at 0, 40 and 120 kg N ha−1. In both years, crop yields were unresponsive to N fertilizer. In 2008, SG average yields were greater than RCG producing 7.0 vs. 4.6 Mg ha−1, respectively, while ash content was significantly greater for RCG in both years. Cumulative seasonal (May–November) N2O emissions were<1 kg N2O-N ha−1 in 2008 and<0.2 kg N2O-N ha−1 in 2009 with crop (SG>RCG) and N fertilizer (N120>N40=N0) effects found in 2008 only. Nitrate exposure was greater for SG in 2008 only, but responded to N fertilization in both years (N120>N0). These crops were net sinks for methane and the magnitude of the sink was not influenced by crop type, N fertilization or year. Despite lower yields, the greenhouse gas intensity calculated for RCG (−2 to 20 kg CO2e t−1 biomass) was lower than for SG (8 – 60 kg CO2e t−1 biomass) as a result of lower N2O emissions.
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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 | 0.001 |
| 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".