Influence of feedlot manure amendments on runoff hydrology and sediment loss during transition between continuous and legacy phases
Bibliographic record
Abstract
Little research has been conducted on the effect of beef feedlot manure amendments on runoff hydrology and soil loss during the transition between continuous and legacy application phases. We conducted a 6 yr (2013–2018) 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 were stockpiled or composted feedlot manure with straw (ST) or wood-chip (WD) bedding applied at 13, 39, and 77 Mg ha−1 (dry weight). There was also an unamended control and inorganic fertilized treatment (IN). Disturbed surface soil was collected from all treatments after 15 (C15), 16 (C16), and 17 (C17) continual annual applications (2013–2015), and then 1 (L1), 2 (L2), and 3 (L3) yr (2016–2018) after manure applications were discontinued in 2015. The soil was packed into runoff trays, and a Guelph rainfall simulator (70 mm h−1) was used to generate 30 min of runoff. The time to runoff, total runoff depth, and flow-weighted mean concentrations (FWMCs) and mass loads of total suspended solids (TSS) were determined. Mean values of runoff variables were significantly greater for ST than WD in certain years for time to runoff (by 28%–127%), runoff depth (44%), TSS FWMCs (58%–137%), and FWMC loads (28%–230%). Mean TSS was generally significantly greater at the two lower than highest manure rates for FWMC (48%–135%) and loads (21%–253%). Overall, choice of bedding material showed the most potential to maximize infiltration and reduce water and sediment loss.
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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".