Agricultural Practices Influence Dissolved Nutrients Leaching through Intact Soil Cores
Bibliographic record
Abstract
Nitrogen and P leaching from agricultural land to ground water poses a threat to water quality, but it may be possible to control dissolved nutrient leaching by choosing appropriate management practices. The objective of this study was to evaluate the effects of agricultural practices on dissolved N and dissolved P leaching from topsoil to subsurface soil after crop harvest. Intact soil cores and small disturbed soil columns were collected from a factorial (tillage × crop × fertilizer source) field experiment, 3 yr after the treatments were established. Soils were leached with synthetic rainwater in the laboratory and nutrient loads (kg ha −1 ) were calculated. Dissolved N and dissolved P loads were not affected by tillage and were similar following corn ( Zea mays L.) (in a continuous corn rotation) and soybean [ Glycine Max (L.) Merr.] (in a soybean/corn rotation) production. Soils receiving inorganic fertilizer had a 70% greater nitrate (NO 3 –N) load and 48% less dissolved reactive P than soils receiving organic fertilizer, suggesting that fertilizing soils with a combination of inorganic and organic fertilizers might be a good way to reduce both NO 3 –N and dissolved reactive P transport to water systems. The NO 3 –N load increased as the soil NO 3 –N concentration increased ( R 2 = 0.36) while the dissolved reactive P load was positively related to the soil Mehlich‐3 P concentration ( R 2 = 0.50) and soil P saturation ratio (M3‐PSR) ( R 2 = 0.55). These results suggest that the leaching of dissolved N and dissolved P compounds is influenced more by the type of fertilizer applied than tillage or cropping practices.
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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".