Nongrowing season soil surface nitrate and phosphate dynamics in a corn–soybean rotation in eastern Canada: in situ evaluation using anionic exchange membranes
Bibliographic record
Abstract
Determining how agricultural management practices affect changes in soil nitrogen (N) and phosphorus (P) could further our understanding of soil N and P cycle. The main objective of this study was to assess in situ nongrowing season soil nitrate and phosphate dynamics as adsorbed on anionic exchange membranes (AEM-N and AEM-P, respectively). The membranes were buried in the surface horizon (5 cm below the soil surface) over the nongrowing season (mid-November to mid-April) in five consecutive years (2009–2010 to 2013–2014) in a long-term corn–soybean rotation experiment established in 1992 in eastern Canada. The treatments consisted of two tillage systems, namely moldboard plow (MP) and no-till (NT), and nine combinations of fertilizer applications, namely three N rates (0, 80, and 160 kg N ha−1) and three P rates (0, 17.5, and 35 kg P ha−1) in a split-plot design with four replications. The results showed that AEM-N and AEM-P averaged 1.8 µg cm−2 d−1 and 7.4 ng cm−2 d−1 under MP, respectively, and 2.8 µg cm−2 d−1 and 67.8 ng cm−2 d−1 under NT, respectively. Nitrogen application increased AEM-N in 2010–2011, 2011–2012, and 2012–2013, but decreased AEM-P mainly under NT. Phosphorus fertilization had no effect on AEM-N, but increased AEM-P under both MP and NT. We conclude that AEM can be used as a technique to study N and P dynamics under cold winters of eastern Canada.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".