Short-term legacy effects of feedlot manure amendments on water-extractable organic carbon in a clay loam soil profile
Bibliographic record
Abstract
Limited research exists on legacy effects of land application of feedlot manure on accumulation, redistribution, and leaching potential of water-extractable organic carbon (WEOC) in soil profiles. We sampled a clay loam soil at six depths (0–1.50 m) 2 yr after the last application (2014) of 17 continuous annual manure applications (since 1998). The amendment treatments were stockpiled (SM) or composted (CM) feedlot manure containing straw (ST) or wood-chip (WD) bedding at three application rates (13, 39, and 77 Mg ha−1 dry basis). There was also an unamended control (CON) and inorganic fertilizer (IN) treatment. The soil samples were analyzed for concentrations of WEOC. The total mass or accumulation of WEOC in the soil profile was greater (P ≤ 0.05) by 1.2–3.3 times for the CM-ST-77 treatment than 12 of 14 other treatments, and it was significantly greater for amended than CON or IN treatments. The total WEOC mass was 14%–20% greater for CM-ST than CM-WD, SM-ST, and SM-WD treatments, and it was 16%–22% greater for CM than SM at the 39 and 77 Mg ha−1 rates. The 77 Mg ha−1 rate of the four manure type-bedding treatments had the significantly greatest (by 37%–527%) concentrations of WEOC at the six depths compared with other treatments, suggesting greater redistribution and leaching potential. Significant manure effects occurred on soil WEOC 2 yr after the manure was last applied following 17 continuous applications, and it indicated an increased risk of leaching potential at the higher application rate.
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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".