Dissolved Organic Carbon in Runoff and Tile‐Drain Water under Corn and Forage Fertilized with Hog Manure
Bibliographic record
Abstract
Dissolved organic carbon (DOC) export from soils can play a significant role in soil C cycling and in nutrient and pollutant transport. However, information about DOC losses from agricultural soils as influenced by management practices is scarce. We compared the effects of mineral fertilizer (MF) and liquid hog manure (LHM) applications on the concentration and molecular size of DOC released in runoff and tile-drain water under corn (Zea mays L.) and forage cropping systems. Runoff and tile-drain water samples were collected during a 2-mo period (October to December 1998) and DOC concentration was measured. Characterization of DOC was performed by tangential ultrafiltration with nominal cut-offs at 3 and 100 kDa. Mean concentration of DOC in runoff water (12.7 mg DOC L(-1)) was higher than in tile-drain water (6.5 mg DOC L(-1)). Incorporation of corn residues increased the DOC concentration by 6- to 17-fold in surface runoff, but this effect was short-lived. In runoff water, the relative size of the DOC molecules increased when corn residues and LHM were applied probably due to partial microbial breakdown of these organic materials and to a faster decomposition or preferential adsorption of the small molecules. The DOC concentration in tile-drain water was slightly higher under forage (7.5 mg DOC L(-1)) than under corn (5.4 mg DOC L(-1)) even though the application rates of LHM were higher in corn plots. We suggest that preferential flow facilitated the migration of DOC to tile drains in forage plots. In conclusion, incorporation of corn residues and LHM increased the concentration of DOC and the relative size of the molecules in surface runoff water, whereas DOC in tile-drain water was mostly influenced by the cropping system with relatively more DOC and larger molecules under forage than corn.
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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 | 0.001 | 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".