The seasonality of nitrate and phosphorus leaching from manure and chemical fertilizer added to a chernozemic soil in Canada
Bibliographic record
Abstract
Abstract Identifying seasons sensitive to nutrient losses could help farmers and policymakers to formulate effective nutrient loss reduction strategies. This long‐term study monitored water percolation as well as nitrate (NO 3 –N) and total phosphorus (TP) leaching from liquid swine manure and chemical fertilizer applied to intact core lysimeters in a sandy loam soil in Manitoba, Canada. Water percolation, NO 3 –N, and TP leaching were monitored from 2005 to 2016. Chemical fertilizer showed greater average annual mean water percolation ( p = .01), annual flow‐weighted mean concentration (FWMC) of NO 3 –N (22 mg L –1 ; p < .001), and annual NO 3 –N leaching (36 kg N ha –1 ; p = .002) compared with the manure treatment (FWMC NO 3 –N, 15 mg L –1 ; NO 3 –N leaching load, 22 kg N ha –1 ). Average annual mean TP loss did not differ between treatments ( p = .86). Spring (April–June) was the most sensitive season, when >75% of annual percolation, >80% of annual NO 3 –N, and >68% of annual TP leaching losses occurred from both manure and chemical fertilizer. Annual NO 3 –N and TP leaching increased exponentially with cumulative winter and spring precipitation (control, r 2 = .69; manure, r 2 = .79; chemical fertilizer, r 2 = .63) and decreased with winter and spring air temperatures. The largest spring NO 3 –N and TP leaching losses were observed in 2013, which followed the dry year of 2012, indicating the potential for nutrient flushing. The findings emphasize the need for environmentally sound N and P management strategies in cold North American regions underlain by coarse‐textured soils, particularly during the spring season.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".