Influence of long-term application of stockpiled feedlot manure with straw or wood-chip bedding on net nitrogen mineralization and nitrification in a clay loam soil
Bibliographic record
Abstract
Little research has been conducted on the influence of land application of stockpiled feedlot manure (SM) containing either wood-chip (WD) or straw (ST) bedding on soil net N mineralization (Nm) and nitrification (Nn) rates during the barley (Hordeum vulgare L.) silage growing season. The stockpiled manure containing ST or WD bedding at 77 Mg (dry weight) ha−1 yr−1 was annually applied for 13–16 yr to a clay loam soil in a field experiment in southern Alberta. The net Nm and Nn rates were measured using the “in situ-soil core” method over 30–33 d (Nm1, Nn1) and 46–50 d (Nm2, Nn2) in each of 4 yr (2011–2014). Net Nm1 rates were generally significantly (P ≤ 0.05) greater for ST (1.0–1.9 mg N kg−1 d−1) than WD (0.1–0.9 mg N kg−1 d−1). Net Nn1 rates were also generally significantly greater for ST (0.9–2.0 mg N kg−1 d−1) than WD (0.03–0.9 mg N kg−1 d−1). Similar trends were found for Nm2 and Nn2. The Nn rates, however, were limited by NH4 supply during the incubations as Nm:Nn ratios were typically <1 with relatively low initial ammonium levels. Hence, the nitrification rates reflected the Nm rates and they would have been considerably lower than potential nitrification rates. A shift from ST to WD bedding by feedlot producers may decrease crop N supply 2- to 10-fold by lowering Nm (2- to 10-fold) and Nn (2- to 30-fold), and supplemental inorganic N fertilizer may be required when WD bedding is used.
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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".