Short-term legacy effects of feedlot manure amendments on earthworm abundance in a clay loam soil
Bibliographic record
Abstract
Long-term application of feedlot beef cattle manure amendments to cropland may enhance earthworm abundance by increasing soil organic carbon. The objective of this study was to determine the legacy effects of feedlot manure type [stockpiled (SM) vs. composted (CM)], bedding material [straw (ST) vs. woodchips (WD)], manure rate (13, 39, or 77 Mg ha−1), unamended control, and inorganic fertilizer treatments on earthworm abundance in a clay loam soil after 3–4 yr of discontinued applications following 17 annual applications. Earthworms were sampled (20 cm depth) in 2 yr (2017–2018), and ancillary soil properties also determined. The Aporrectodea genus was the dominant earthworm identified. Earthworm abundance was similar (P > 0.05) for amended and unamended or inorganic fertilizer treatments. Abundance at the 39 Mg ha−1 rate in 2018 was significantly (P ≤ 0.05) greater by four times for SM than CM with ST, but it was two times greater for CM than SM with WD. Abundance at the 13 Mg ha−1 rate in 2017 was significantly greater by 91% for ST than WD, but at the 39 Mg ha−1 rate, it was 10 times greater for WD than ST. In 2018, abundance was five times greater for WD than ST with CM, but it was similar with SM. Overall, short-term legacy effects occurred on earthworm abundance, but these effects varied with manure rate. Earthworm abundance was not increased by manure application, which suggested a carrying capacity not directly related to food resource.
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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) | 0.001 | 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".