Decimetric‐Scale Two‐Dimensional Distribution of Soil Phosphorus after 20 Years of Tillage Management and Maintenance Phosphorus Fertilization
Bibliographic record
Abstract
Core Ideas 2‐D distribution of Mehlich‐3 P across seeding row had no spatial pattern in no‐till and moldboard plow. Horizontal distribution of Mehlich‐3 P was less sensitive to extrinsic factors. Soil‐surface P accumulation in no‐till is due in part to P recycled by corn and soybean. Surface P accumulation in no‐till is also due to the replenishment of solution P by residual P. Improving soil test P assessment at plot scale is essential for productivity in conservation agriculture systems. We characterized the distribution of Mehlich‐3 P (P M3 ) concentrations at the decimetric scale with depth on either side of the sowing row in no‐till (NT) and moldboard plow (MP) plots fertilized with 35 kg P ha –1 every 2‐yr in a corn–soybean rotation (20‐yr). A total of 996 soil samples (83 samples × 2 depths [0–5 and 5–20 cm] × 6 plots [3 blocks each MP and NT]) were collected at corn harvest in 2012. The average P M3 concentrations in the 0‐ to 5‐cm layer were 35.7 and 63.4 mg kg –1 in MP and NT, respectively. The P M3 concentration in the 5‐ to 20‐cm depth was similar between MP and NT and averaged 32.0 mg kg –1 . The horizontal distribution of P M3 concentrations in these plots was less sensitive to extrinsic factors including tillage, P fertilization and soil depth. High coefficients of variation were associated with P M3 data in both MP (77 and 63% at 0–5 and 5–20 cm, respectively) and NT plots (46 and 66% at 0–5 and 5–20 cm, respectively). It is possible that this strong overall variability overshadowed any P M3 pattern that could have been introduced by NT management. Geostatistical semivariance analysis indicated a predominance of random spatial dependence in most plots, except two plots (one MP and one NT) with moderate spatial structures. The 2‐D geospatial model related to tillage was not detected by the sampling grid used at this experimental site. Therefore, a similar sampling strategy would be appropriate and could be recommended for these two tillage systems in this long‐term corn–soybean rotation.
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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) | 0.001 | 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".