Characterizing the fine‐scale spatial distribution of soil phosphorus for efficient phosphorus management in an Illinois tile‐drained field
Bibliographic record
Abstract
Closed depressions in post-glacial landscapes can accumulate phosphorus (P) due to repeated flooding and become hotspots for P loss when underlain by subsurface (tile) drainage. Soil P mapping is routinely based on the interpolation of samples from a 1-ha grid, which may miss closed depressions and underestimate soil P levels leading to overfertilization and nutrient loss. Our objective was to improve the characterization of the spatial distribution of soil P at the sub-field scale by accounting for depressions and assess their importance for fertilizer prescriptions and tile P loss. We evaluated the effectiveness of stratified sampling that included closed depressions within a 1-ha grid and nonstationary interpolation (external drift kriging) that leverages information about depression depth to estimate the distribution of soil P (0-16 cm) under a corn (Zea mays L.) and soybean (Glycine max (L.) Merr) rotation in Douglas County, IL. Our novel approach produced an improved soil P map, which resulted in a 47% increase in land area that does not require P fertilizer (a reduction of 7.14% or ca. 4 metric tons of P). Additionally, soil P estimated from the improved map was a stronger predictor of the flow-weighted mean concentration of dissolved reactive P during the non-growing season than soil P estimated from ground-based sampling and other interpolation approaches. These results demonstrate that improved characterization of the spatial distribution of soil P through stratified sampling and interpolation with depression depth can better match soil P with crop P requirements, protecting water quality and conserving a finite resource.
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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) | 0.000 | 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".