Effect of manure application methods on nutrient and metal mobilization with snowmelt flooding in a manured agricultural land
Bibliographic record
Abstract
Accumulation of phosphorus (P), nitrogen (N), and metal(loid)s with manure applications to agricultural lands and their subsequent losses via runoff and leaching pose a potential risk of water quality deterioration. In the Canadian prairies, snowmelt flooding contributes to the majority of annual runoff. Injection of liquid swine manure (LSM) has been well documented as a manure application method to reduce nutrient loss with rainfall-runoff; however, its effectiveness in reducing snowmelt-driven nutrient and metal(loid) runoff has not been studied to our knowledge. This thesis examined the (a) release of P and metal(loid)s [zinc (Zn), manganese (Mn), iron (Fe), magnesium (Mg), calcium (Ca), and arsenic (As)] to floodwater under simulated snowmelt flooding, and (b) losses of P, N, Zn, Mn, Fe, Mg, and Ca to snowmelt runoff from field plots, with LSM injection and surface application. LSM was applied in the Fall of 2021 to four replicated field plots with three treatments: manure injected, surface-applied, and unmanured (control). For the simulated snowmelt study, intact soil columns were extracted from each plot after two weeks of manure application. The columns were flooded with ultrapure water at 4±1 °C to simulate snowmelt flooding. Porewater and floodwater samples were extracted for 8 consecutive weeks and analyzed for dissolved reactive P (DRP), pH, and metal(loid)s concentrations. For the field study, snowmelt was collected from installed boxes in each plot in the 2022 spring for 10 days when the temperature was above 0 °C and snowmelt was present. The snowmelt samples were analyzed for DRP, nitrate-N, metals (Zn, Mn, Fe, Mg, and Ca), and pH. Significant differences in DRP concentrations in porewater or floodwater were not observed among the three treatments in the simulated snowmelt study; however, concentrations of DRP in floodwater increased (1.5-fold and 5-fold in porewater and floodwater, respectively) under prolonged flooding conditions irrespective of the treatment. Metal concentrations in porewater or floodwater did not show significant differences among the three treatments in the simulated snowmelt study, with the exception of porewater Mg concentration on day 0 and floodwater Zn concentration on day 7. In the field, the DRP concentrations in snowmelt were significantly higher in surface-applied manure treatment than in injected and control treatments from day 7 to 8. The concentrations of nitrate-N or any of the metals were not significantly different among the treatments. However, DRP, nitrate-N, and metal concentrations increased with time during the snowmelt period. There was a dramatic spike in nitrate-N concentration towards the end of the sampling period, where the increase in nitrate-N concentration was 44-fold compared to day 1. We also found significant correlations and positive linear relationships between snowmelt volume and DRP and metals loads, suggesting that snowmelt volume was the main driving factor of DRP and metal (Zn, Mn, Fe, and Ca) loss with snowmelt. The results of this research suggest that the management practices should be focused not only on reducing concentrations of nutrients and metals in snowmelt but also on reducing the snowmelt volume and snowmelt flooding duration.
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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 | 0.001 | 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".