Nitrogen Mineralization in Chernozemic Soils Amended with Manure from Cattle Fed Dried Distillers Grains with Solubles
Bibliographic record
Abstract
Core Ideas Manure from distillers grains with bedding from construction waste had reduced N mineralization. Addition of construction waste to manure from distillers grains diet reduced the Q10 of N mineralization. Nitrogen mineralization was greater at the higher temperature (25°C) than at 15°C. Inclusion of dried distillers grain with solubles (DDGS) in cattle diets, coupled with the increasing use of construction and demolition waste (CDW), particularly the wood and drywall fractions as bedding in beef cattle feedlots, may affect nitrogen (N) dynamics when the resulting manure is applied to soil. This laboratory incubation study was conducted to evaluate the mineralization of N in contrasting Chernozemic soils amended with regular manure (RM) from cattle fed a grain‐based diet versus manure from cattle fed a diet containing DDGS (DGM). The effect of adding CDW to DGM (DGM CDW ) was also assessed. The soils (a Black Chernozem and a Brown Chernozem) were amended with manure (40 g kg soil –1 , dry wt.) and incubated at 15 and 25°C. Nitrogen mineralization in the manure‐amended Brown Chernozem exhibited negative net mineralization. In the Black Chernozem, the first‐order mineralization rate constant varied among manure treatments and decreased in the order DGM CDW > DGM > RM. The rate constants were not significantly affected by temperature, but the temperature sensitivity (Q 10 ) of N mineralization was significantly greater for RM (1.0) and DGM (1.3) than DGMCDW (0.3). The percentages of total organic N mineralized from RM and DGM were greater than that for DGM CDW , with RM producing the greatest mineralization. This suggests that adding CDW to manure will affect N dynamics by lowering the amount of N mineralized, which may necessitate either applying higher manure rates (and risking excess phosphorus build‐up) or supplementing with inorganic fertilizers to minimize N deficiency in receiving crops.
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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) | 0.000 | 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".