Long-term fertilization restructured spatial patterns of soil phosphorus, organic matter, and bulk density in Florida soils
Bibliographic record
Abstract
Decades of fertilization have led to the accumulation of phosphorus (P) in soils, complicating its management and contributing to degradation in Florida’s agricultural systems. Our objective was to quantify the impact of years of fertilization on P stocks and distribution within the soils of this agriculturally significant region. The study employed traditional methods and geostatistics to analyze data gathered from an intensive grid sampling approach across a network of paired sites in Lake City, St. Augustine, and Grandin, FL. The soil samples were analyzed for total P using X-ray fluorescence, bulk density (Db), and soil organic matter (SOM). Our findings revealed an average 44% increase in P due to long-term fertilizer application, with variability in accumulation across different sites. Agricultural practices also led to lower organic matter levels (21–68% decrease) and higher Db (7–24% increase) in cultivated soils. Fertilized fields showed a stronger spatial dependence for Db and SOM compared to unfertilized fields, measured by the nugget: sill (NS) ratio. Additionally, soil P exhibited stronger spatial correlations in fertilized fields, varying with crop type, soil type, and fertilizer application methods. The greater within-plot variances observed for most soil properties, especially in unfertilized fields, indicate the need for scientists to better match sample sizes to the variability of soil properties being studied. This research offers resource and land managers, policymakers, and scientists with quantitative insights into legacy P, guiding site-specific monitoring efforts (e.g., sample size requirements and field-scale variability) and regional forecasting and management strategies (e.g., regional variability).
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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 | 0.000 | 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".