Comparison of Nutrient and Metal Loadings with the Application of Swine Manure Slurries and Their Liquid Separates to Soils
Bibliographic record
Abstract
The accumulation of phosphorus (P) and metals is a serious concern with the continuous application of manure to agricultural soils. Solid–liquid separation of swine slurry is a promising approach to reduce P and metal loadings through application of separated liquid (SL) as a nutrient source. However, little information is available on nutrient and metal loadings with the application of SL compared with unseparated raw manure (RM). We analyzed element concentrations and calculated nutrient and metal loadings for RM and their respective SL applications, considering an application rate of 100 kg total nitrogen (N) ha −1 . Samples of SL were obtained through three separation techniques: (i) centrifugation without a flocculant, (ii) centrifugation with a flocculant, and (iii) rotary press with a flocculant. Irrespective of separation technique, calculated P loadings with the application of SL were only 50 to 70% of that of RM at equivalent rates of total N yet exceeded crop removal rate. In contrast, calculated K and Na loadings with SL application were significantly greater than with RM, indicating a possible build‐up of K and Na in soil. Calculated Ca and Mg loadings were significantly greater with RM than with SL. Loadings of Al, As, Ba, Cd, Cr, Fe, Mn, Ni, Pb, Sn, Se, Ti, and V were low, whereas Cu and Zn loadings were above crop removal rates for RM and SL. For solid–liquid separation to provide a lasting solution to the problem of P and metal accumulation, the SL must be supplemented with commercial N fertilizer to meet crop N demand. Core Ideas P loading with SL was only 50 to 70% of that of swine slurry applied at equivalent rate of N. Loadings of K and Na with SL were higher, indicating a possible salt build‐up in soil. Cu and Zn loading with application of swine slurry and SL were above crop removal rates. Loadings of other trace metals were low with both SL and raw swine slurry application.
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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 | 0.001 |
| 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".