Measurement and simulation of nitrous oxide fluxes from perennial forage grasses and annual crops amended with pig manure and inorganic fertilizer
Bibliographic record
Abstract
Crop and nutrient management on agricultural soils are essential considerations for mitigating greenhouse gas emissions in our environment. This thesis aimed to simulate and compare nitrous oxide (N2O) fluxes from perennial forage grasses (FPP) and annual crops (ANN) amended with solid pig manure (SPM), liquid pig manure (LPM) and inorganic urea fertilizer (FER). Two field studies were carried out at different sites Carman and Carberry, Manitoba, Canada. At Carman, N2O fluxes were monitored from FPP following its termination and restoration. At the Carberry, N2O fluxes were measured from LPM applied to soil annually at a rate of 56,000 L ha-1, FER applied at the equivalent rate as total available N from the LPM and un-amended control (CON) plots. At Carberry, in 2011 and 2014 when applied manure N was low, emission factor and emission intensity from LPM was one-half of that from FER. At Carman, the result showed that the termination of perennial forage grasses in combination with applied manure leads to increased soil nitrogen content and N2O fluxes. However, when FPP were replanted in 2014, N2O emission from FPP was 30% less than that from ANN treatments. The data from the Carman site were used to evaluate the performance of the DeNitrification-DeComposition (DNDC) model to predict soil moisture and N2O fluxes. The DNDC model output compared well with the field observed values on the ANN (cumulative N2O flux and daily soil moisture Nash–Sutcliffe efficiency (NSE) > 0.7) but not on the FPP (cumulative N2O flux and daily soil moisture NSE < 0.2). In spite of the wide use of the DNDC model, some routines in the model still needs work as seen on the FPP. In conclusion, perennial forage grass planted in rotation with annual crops can provide N saving benefits, but the N would be lost when the forage grasses are converted to annual cropland. Also, LPM can provide nutrient to crop more efficiently than FER, as less N2O was emitted to produce a unit grain of wheat, but the nutrient would be lost when applied beyond crop need. Consequently, adequate consideration for mineralizable residue from plowed down forage grasses and applied manure N may help prevent future losses.
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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) | 0.000 | 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 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".