Influence of long-term feedlot manure and inorganic fertilizer application on selected metal and trace elements in a clay loam soil
Bibliographic record
Abstract
Long-term application of feedlot manure and inorganic fertilizer to cropland may increase metals and trace elements in soils, and negatively impact agricultural land use. We sampled a surface clay loam soil at Lethbridge in southern Alberta after 16 annual applications (2014) of feedlot manure or inorganic fertilizer, as well as an unamended control. The manure treatments were stockpiled (SM) or composted (CM) feedlot manure with barley straw (ST) or wood-chips (WD), and were applied at 13, 39, and 77 Mg ha−1 (dry weight). The soil was analyzed for strong-acid extractable concentrations of selected metals (Al, Fe) and trace elements (As, Ba, Cd, Co, Cr, Cu, Li, Mn, Ni, Pb, Sr, Ti, V, and Zn). Manure type (SM versus CM) had little or no significant (P > 0.05) effect on the elements. Significantly greater As, Co, Cu, Fe, Li, and V were found for ST than WD at all or higher rates, and the reverse trend occurred for Cd at the highest rate. Cadmium (ST only), Cu, Sr, and Zn were increased by greater application rates, whereas most of the other elements were decreased. Concentrations were significantly increased by manure (Cu, Sr, and Zn) and inorganic fertilizer (Cd) compared with the unamended soil. Copper, Sr, and Zn were greater for manured than inorganic fertilizer treatments, and the reverse trend occurred for Al, As, Co, Cr, Fe, Li, Ni, and V. Although manure and fertilizer may increase certain elements in the soil, no concentrations exceeded the federal soil guidelines for agricultural land use.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".