Influence of continuous application of feedlot manure and legacy treatments on soil organic carbon, soil hydrophobicity, and soil water repellency
Bibliographic record
Abstract
Continuous or discontinued manure applications to agricultural soils may impact soil organic carbon (SOC) and water balances because of manure carbon inputs and the potential for manure-induced soil hydrophobicity (SH) and soil water repellency (SWR). A laboratory study was conducted using a long-term (44 yr) field experiment on a clay loam soil to determine the effect of application rate of feedlot manure under dryland (0, 30, 60, and 120 Mg·ha−1 wet weight) and irrigation (0, 60, 120, and 180 Mg·ha−1) on SOC, SH, and SWR. In addition, we compared the effect of 44 yr of continuous annual manure applications (C44) to legacy treatments which had discontinued applications for 14 (D14) or 30 yr (D30). Laboratory measurements were conducted on air-dried and sieved (<2 mm) soil to determine SOC, SH using Fourier transform infrared spectroscopy, and SWR using the repellency index (RI) method. Mean RI values for all treatments ranged from 2.20 to 13.0, indicating subcritical (RI > 1.95) SWR. Manure application rate had a significant (P ≤ 0.05) and positive effect on SOC and SH, and both followed an exponential model. In contrast, RI had a negative response to the application rate under dryland and had no response under irrigation. Overall, positive responses of SOC and SH to application rate supported our hypothesis, but it was not supported for RI. The hypothesis of greater SOC, SH, and RI for continuous versus discontinued treatments was also supported for SOC and SH but not for RI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".