Management zone delineation strategies for phosphorus fertilizer recommendations under a no-till field in Eastern Canada
Bibliographic record
Abstract
Soil phosphorus (P) spatial variability under no-tillage (NT) management should be taken into account for developing P fertilization programs while avoiding P losses and stratification. Little is known about controlling field-scale spatial variability of P using management zones (MZs), particularly under no-tilled soils. The general objective of this study was to delineate MZs in a corn–soybean rotation (9.5 ha) under NT for 20 years, exploring the soil apparent electrical conductivity (ECa)–P relationship to reduce soil P spatial variability for site-specific P fertilizer recommendations in Eastern Canada. To address this, an intensive grid sampling of 35 m by 35 m (total: 134 soil samples) was conducted in fall 2014. Mehlich-3 extractable P and aluminum (Al) were determined in the upper soil profile (0–5 cm). The (P/Al)M3 index was then calculated. The ECa data were measured in two depths (0–30 and 0–100 cm; ECa30 and ECa100) using a commercial Veris-3100 galvanic contact resistivity sensor system. The MZs were delineated using (P/Al)M3 or the ECa alone or in combination. Mean (P/Al)M3 was 7.9%. Variability (coefficient of variation) of soil P was moderate (32% to 36%), indicating that uniform P fertilizer recommendation is not adapted to this large NT field. Mean values of ECa30 and ECa100 were 15.8 and 32.6 mS m−1, respectively. A significant ECa30–soil P correlation ( r = 0.22–0.23) was observed. Delineating two to three MZs using (P/Al)M3 measurements represented the best agronomic strategy, generating the highest P fertilizer recommendation reductions (40–74 kg P2O5). This study highlighted a potential for reducing spatial variability of soil P, by delineating MZs, while reducing P losses from crop fields.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".