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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".