Effects of Nitrogen Fertilizer on Switchgrass Productivity and Soil Trace Gas Production
Bibliographic record
Abstract
Atmospheric greenhouse gas (GHG) concentrations continue to increase and one of the major culprits is the continued elevation and use of fossil fuels for energy. Using bioenergy, a renewable and sustainable source of natural energy, could help to reduce the effect that fossil fuels are having on the planet by slowing the rate of input of atmospheric GHG’s. Perennial crops such as switch grass can be grown and used as a bioenergy crop. In some cases, nitrogen fertilizers are used to increase the growth of bioenergy crops with potential negative environmental consequences. For example, nitrogen fertilizer can impact soil chemical processes and lead to an increase in the production of greenhouse gases, mainly N2O and CH4. Production of these gases would negate some of the benefits achieved by substituting bioenergy crops for fossil fuels. When I examined the amount of gas flux being produced by switchgrass fields, with 0 lbs/acre, 50 lbs/acre and 150lbs/acre fertilizer treatments we observed, as predicted, an increase in N2O production with more fertilization. In some cases the increase in N2O production in the 150lbs/acre treatment was as extreme as being over 200% larger compared with no fertilization. I also observed some very interesting results with methane production, which has been showing production of methane, along with after around 30 minutes of gas collection in a chamber. Based on the results of my research, I have created a cost benefit analysis of using nitrogen fertilizer on switchgrass crops.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".