SOIL PHOSPHORUS AND NITROGEN EXPORT AND RETENTION IN RAINFALL RUN-OFF FROM MANURED LANDSCAPES
Bibliographic record
Abstract
Rainfall run-off from manured agricultural fields can be high in nitrogen (N) and phosphorus (P) due to excess nutrient application that is unused by crops. This can pose the risk of eutrophication of nearby surface water bodies receiving run-off. The objective of this study was to determine the effectiveness of precision variable rate application of feedlot cattle manure compared to traditional constant rate applications on nutrient losses in rainfall run-off. A silage barley field at the Livestock and Forage Center of Excellence (LFCE) research facility near Clavet, SK was used for the field study. Additionally, the ability of gypsum and biochar to reduce nutrient losses in leachate was evaluated in a controlled environment study. For the field study, three zones utilizing different nutrient application strategies with three replicate micro-watersheds within each zone were used. The zones were precision variable rate manure application with setbacks from micro-watershed basins, traditional constant rate manure application with no setbacks, and a commercial fertilizer (control) application zone which only received annual synthetic fertilizer applications. The manured zones received manure applications in the spring of 2019 and 2021. Rainfall simulations were conducted in each micro-watershed at different landscape positions (upper east, upper west and depression) in the summers of 2021 and 2022 and nutrient concentrations were measured in the collected run-off. For the controlled environment study, nutrient concentrations in actual rainfall collected from the constant rate and variable rate manure zones was measured before and after being added to columns of soil that were either unamended, or amended with cattle manure-derived biochar or gypsum to determine the effectiveness of these amendments in removing nutrients from leachate. The mean concentrations of total dissolved and soluble reactive P (SRP) in simulated rainfall run-off from the landscape positions were significantly lower in the variable rate manure zone than the constant rate zone in the year of manure application. Biochar did not sorb any nutrients from leachate except for nitrate-nitrogen (NO3--N) in the 2022 season. Both biochar and gypsum contributed ammonium-nitrogen (NH4+-N) to leachate consistently, and gypsum sorbed SRP from leachate in both study years. The contributions of nutrients from biochar to leachate is attributed to the biochar feedstock material (manure) containing high concentrations of nutrients. Variable rate manure application with setbacks may be considered a suitable management practice to limit the export of nutrients in run-off, thus limiting potential negative environmental impacts. Further research is needed to better characterize the contribution/ sorption of nutrients by biochar and gypsum.
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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".