Tillage Management Impacts on Soil Phosphorus Variability under Maize–Soybean Rotation in Eastern Canada
Bibliographic record
Abstract
Conservation tillage, including no-tillage (NT), is being used increasingly with respect to conventional tillage (CT) to mitigate soil erosion, improve water conservation and prevent land degradation. However, NT increases soil phosphorus (P) stratification, causing P runoff and eutrophication. For sustainable P management, fertilization must be balanced between P sources and actual crop demand. To reduce P losses to the environment, it is important to better understand P spatial variability in NT fields. Little is known about tillage impacts on field-scale P spatial variabi-lity in precision agriculture. This study examines tillage impacts on spatial variability of soil-avai-lable P in a maize–soybean rotation, in two commercial fields, denoted CT (10.8 ha) and NT (9.5 ha), with the aim of improving P fertilizer recommendations in Eastern Canada. NPK fertilizers were applied to the soils (Humic Gleysols) following local recommendations. Soil samples were collected in fall 2014 in regular 35 m by 35 m grids, at 0–5 and 5–20 cm depths, providing 141 and 134 geore-ferenced points for CT and NT fields, respectively. Available P and other elements were analyzed by Mehlich-3 extraction (M3), and the P saturation index (P/Al)M3 was calculated. Variability of soil-available P in both fields ranged from moderate to very high (32% to 60%). A mean (P/Al)M3 of 3% was found in both layers under CT, compared to 8% in the 0–5 cm layer and 6% in the 5–20 cm layer under NT. Relationships between P indices and other elements differed between tillage practices. This study highlights the need to improve P fertilizer recommendations in 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".