Soil microbial biomass and enzyme activity following liquid hog manure application in a long-term field trial
Bibliographic record
Abstract
Liquid hog manure (LHM) addition to soils in corn silage (Zea mays L.) production may benefit microbial activity by providing C and other nutrients. The objective of this study was to compare the effects of a LHM application to that of inorganic fertilizers (IF) on the biological activity of a Le Bras silt loam soil (Humic Gleysol) under continuous corn production and LHM fertilization. Amounts of 0 to 120 m3 LHM ha−1 and 150 N–150 P2O5–150 K2O kg ha−1 were applied to silage corn. The 0- to 15-cm and 15- to 30-cm soil layers were sampled 28 d after the 18th yearly LHM application. The LHM inputs, particularly at 90 m3 ha−1, resulted in higher enzyme activities and microbial biomass C (MBC) than IF or the unamended control in the 0- to 15-cm soil layer. The 90 m3 LHM ha−1 also gave higher microbial biomass N (MBN) than IF in this soil layer. Application of LHM had no effect on the activities of the enzymes studied or on the MBC and MBN contents in the 15- to 30-cm layer. Ammonifier population was highest with 60 m3 LHM ha−1 in both soil layers. Nitrifier population was not affected by LHM in the top soil layer, but was linearly increased by LHM rates in the 15- to 30-cm layer. This study showed that LHM addition may enhance enzyme activities, microbial biomass and the N mineralizer population in the plow layer of a soil in a corn silage monoculture. Key words: Corn, pig slurry, microbial biomass, monoculture, soil enzyme
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".