Surface application of cement kiln dust and lime to forage land: Effect on forage yield, tissue concentration and accumulation of nutrients
Bibliographic record
Abstract
Cement kiln dust (CKD), a by-product of the cement industry currently being landfilled, may be a beneficial soil amendment. A 2-yr field forage trial compared the effect of surface applications of CKD and lime on forage yield and the concentration and accumulation of N, P, K, Ca, Mg, Mn, Cu, Zn and B in the forage tissue. Seven soil treatments in three blocks were established on three low pH sites: (1) a check plot with neither lime or CKD added; (2) lime application based on the recommendation from soil analysis; (3) lime at 1.5 times the recommended application; (4) CKD applied at the recommended application; (5) CKD at 1.5 times the recommended application; (6) CKD applied on a neutralizing equivalent basis at the recommended application rate; and (7) CKD on a neutralizing equivalent basis at 1.5 times the recommended lime application; equivalence based on CKD’s apparent neutralizing value at 75% that of lime. In 1998, as tissue concentrations of K, Ca, Cu, and Mn increased with CKD application, the concentrations of N, P and Mg decreased, and in 1999, as the concentrations of Ca, K increased, the concentrations of N, P, Mg Mn, Zn decreased. Lime application increased the concentration of Mg (1998) and Ca and Mg (1999). The concentration of the other nutrients either decreased or were not significantly affected. In1998, forage yield and accumulation of Ca, K, Mn, Cu, Zn and B all increased with CKD addition, whereas the accumulation of Mg increased with lime addition. In 1999, forage yield and accumulation of N, P, K, Ca, Cu, Zn and B in the forage tissue all increased with CKD addition. These results were identified by principal component analysis (PCA). Surface applied CKD increased forage yield to a greater extent than lime. It was also a more readily available source of Ca and K than lime.Key words: Cement kiln dust
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".