Biosolids from Treated Swine Manure and Papermill Residues Affect Corn Fertilizer Value
Bibliographic record
Abstract
Biosolids derived from treatment of animal manure or industrial effluents can be used on farms, but their fertilizer value must be assessed. A 3‐yr field study was conducted on a clay soil in Quebec, Canada, to evaluate the effect of several biosolids on silage corn dry matter (DM) yield; N‐use efficiency; and soil N, P, Cu, and Zn availability. Raw liquid swine (Sus scrofa) manure (LSM), biosolids from four swine manure treatments (aerobic digestion [AER], anaerobic digestion [DIG], filtration [FIL], anaerobic digestion followed by chemical flocculation [DIG+FLO]), combined papermill biosolids (CPB), de‐inking paper biosolids (DPB), and mineral N fertilizer (MIN) were applied before corn planting at a targeted rate of 150 kg total N ha−1. The DIG and DIG+FLO biosolids resulted in silage corn DM yield, N accumulation, and early season soil N availability comparable to LSM with an N‐use efficiency about 70% of that for MIN. The AER biosolid resulted in low DM yield with an N‐use efficiency only 10% of that for MIN; FIL and CPB had an N‐use efficiency almost 40% of that for MIN, whereas DPB caused a decline in DM yield compared to a no‐N control. The LSM‐derived biosolids increased availability of soil P (0.34 kg kg−1 excess P) and, to some extent, Cu and Zn. The CPB and DPB biosolids had little impact on soil P, but DPB markedly increased Zn availability. Manure‐derived biosolids and CPB were satisfactory N sources for silage corn while manure‐derived biosolids caused soil P enrichment when applied based on crop N requirement.
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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".