Influence of feedlot manure amendments on dissolved organic carbon in runoff during transition from continuous to legacy applications
Bibliographic record
Abstract
Little research has compared land application of stockpiled (SM) or composted (CM) beef feedlot manure with straw (ST) or wood-chip (WD) bedding on loss of dissolved organic carbon (DOC) in runoff. We conducted a 6 yr (2013–2018) rainfall simulation-runoff study and utilized surface (0–5 cm) soil collected from a long-term (since 1998) field experiment on a clay loam soil in southern Alberta, Canada. The treatments consisted of SM or CM with ST or WD bedding applied at 13, 39, and 77 Mg·ha−1 (dry weight), as well as an unamended control and mineral fertilizer treatment. Surface soil was collected from all treatments after 15–17 (C15, C16, and C17; 2013–2015) continual annual applications and then after one to three legacy years (L1–L3, 2016–2018) after manure applications were first discontinued in 2015. The soil was packed into runoff trays, and flow-weighted mean concentrations (FWMCs) and mass loads of DOC in runoff water were determined during rainfall simulations. Mean DOC losses were generally significantly (P ≤ 0.05) lower for CM with ST bedding compared with the other manure type – bedding treatments in certain years and were consistent with this amendment having the lowest total carbon (C) content. The total C content of the amendments explained 92% of the variation in DOC concentration. Termination of long-term manure applications reduced FWMCs by 85%–91% and mass loss by 76%–89% from the C17 to L3 year. Therefore, our findings suggested that composting manure with ST or discontinued long-term manure application may reduce DOC loss in runoff.
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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) | 0.001 | 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".