Trace Element Changes in Soil after Long‐Term Cattle Manure Applications
Bibliographic record
Abstract
Manure application supplies plant nutrients, but also leads to trace element accumulation in soil. This study investigated total and EDTA-extractable B, Cd, Co, Cu and Zn in soil after 25 annual manure applications. The residual effect of 14 annual manure applications followed by 11 yr with no applications was also investigated. Manure was applied at 0, 30, 60 and 90 Mg ha(-1) yr(-1) (wet weight) under rainfed (treatments Mr0, Mr30, Mr60, and Mr90) and at 0, 60, 120 and 180 Mg ha(-1) yr(-1) under irrigated conditions (Mi0, Mi60, Mi120, and Mi180). The manure applications had no significant effect on soil B, Cd and Co content under both rainfed and irrigated conditions, but significantly increased total Cu and Zn content under irrigated conditions with Zn in Mi120 and Mi180 reaching the lower maximum concentration (MAC) level set by the European Community. Manure application also significantly increased EDTA-extractable Cd and Zn content in soil. Up to 27% of the total Cd (0.156 mg kg(-1)) and 21% of total Zn (38 mg kg(-1)) are found in EDTA-extractable form (Mi180 at 0-15 cm). EDTA-extractable Cd and Zn content was also significantly elevated in the irrigated residual plots due to the higher manure rates used. Thus, the impacts of cattle manure application on trace elements in soil are long lasting. Elevated Cd and Zn are a concern as other studies have linked them with certain types of cancers and human illnesses.
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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".