Combined de-inking paper sludge and poultry manure application on corn yield and soil nutrients
Bibliographic record
Abstract
Application of combined de-inking paper sludge and poultry manure may be an appropriate way to dispose of these residues and restore fertility of highly degraded soils. An experiment was initiated to determine the effects of a single application of this material, using two different application techniques, on corn yield and soil properties of two 12-ha fields of contrasting textures located in the Nicolet watershed in the province of Québec, Canada. De-inking paper sludge was mixed with poultry manure (PP) at a ratio of 25:1 and strip-applied before corn seeding. The study included three treatments: untreated control, a constant application rate, and variable application rates according to initial soil organic C content. The variable application rates were 10, 20 and 30 Mg dry weight ha-1. The strips were randomized within blocks, and the strips ran lengthwise in the blocks. Soil N immobilization and P release occurred on both sites at least 2 mo after PP incorporation, as measured at the corn eight-leaf and silk apparition stages by the anion-exchange membranes. Despite early N immobilization, grain yield was not affected whereas plant P uptake was increased by PP. At both sites, a single application of PP increased soil pH and major nutrient levels at harvest but had no significant effect on soil organic C. The application of PP also increased P saturation indices but the measured values were well below critical thresholds above which environmental risks would be high. The variable application rate treatment did not differ from the equivalent constant rate treatment for improving soil status and crop growth. This study indicates that mixing de-inking paper sludge and poultry manure may provide a cost-effective and environmentally friendly approach to land disposal of these wastes. Key words: Corn, paper sludge, poultry manure, precision farming, soil nutrients
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".