Cattle manure loadings and legacy effects on copper and zinc availability under rainfed and irrigated conditions
Bibliographic record
Abstract
Long-term cattle manure applications build up nutrient pools and can lead to trace element enrichments in soils. The objectives of this study were to evaluate copper (Cu) and zinc (Zn) loadings in the soil during continuous annual cattle manure applications and determine the time required for soil to return to its pre-manure available Cu and Zn levels after manure is discontinued. The manure application rates were 0, 30, 60, and 90 Mg·ha −1 for rainfed and 0, 60, 120, and 180 Mg·ha −1 (wet weight) for irrigated plots. Although manure was applied for 45 yr in some plots, applications were terminated in one subset of treatments after 14 yr and in another subset after 30 yr to study legacy effects after 31 and 15 yr, respectively. Soil samples were collected in the fall of 2003, 2008, 2013, and 2018 and analyzed for available Cu and Zn. Crops were grown in all years continuously with Cu and Zn concentrations measured in both silage and grains harvested. The regression model developed using data collected suggests long legacy effects with recovery time to pre-manure levels ranging from 10 to 20 yr for Cu and 23 to 41 yr for Zn at irrigated and 10–24 for Cu and 21–32 yr for Zn under rainfed, respectively. Long-term applications of cattle manure could lead to accumulation of Cu and Zn, creating long-lasting legacy effects in soils with the increased environmental risk of leaching to groundwater.
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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) | 0.000 | 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".