Soil Properties and Crop Yields in Response to Mixed Paper Mill Sludges, Dairy Cattle Manure, and Inorganic Fertilizer Application
Bibliographic record
Abstract
The contribution of organic wastes to crop yields and soil fertility may be influenced by their composition and the soil type. This 6‐yr study (2001–2006) evaluated the effects of repeated additions of mineral fertilizers (MF), mixed paper mill sludges (PMS) (18, 36, and 54 Mg ha −1 ), dairy cattle manure (DCM) (36 Mg ha −1 ) alone or with reduced mineral fertilizer (60% NPK) (RMF) and a control, on soil properties and corn ( Zea mays L.), barley ( Hordeum vulgaris L.), and soybean ( Glycine max L. Merr.) yields in a clay loam and sandy loam. The applications of PMS and DCM increased mostly N mineralization and crop yields in the sandy loam than in the clay loam. However, increases of soil C contents, water‐stable aggregates and MWD following their application were higher in the clay loam than in the sandy loam. The DCM effects on the soil property changes were of less magnitude than those of PMS. Except in the first year, the PMS applications at rates of 36 and 54 Mg ha −1 without NPK, and PMS applied at a rate of 18 Mg ha −1 with 60% NPK, produced highest crop yields in both soils and were comparable to those obtained with MF. The increase in yield following DCM additions (36 Mg ha −1 ) was lower than that obtained with PMS. Annual MF applications increased crop yields in both soil without significant changes on soil properties. The benefits of PMS and DCM on soil properties and crop yields varied depending on organic wastes and soil type.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".