Soil microbial biomass, functional diversity and crop yields following application of cattle manure, hog manure and inorganic fertilizers
Bibliographic record
Abstract
Soil biological properties can be significantly impacted by land management. Cattle manure, hog manure or inorganic fertilizers were applied annually or triennially in field trials conducted at two sites over 3 yr. A control treatment without manure or fertilizer was also included. Canola (Brassica napus) was grown in year 1, hulless barley (Hordeum vulgare) in year 2, and wheat (Triticum aestivum) in year 3. Where effects were significant, cattle manure increased soil microbial biomass C (MBC) by 26% to three-fold, hog manure by 31% to two-fold, and inorganic fertilizers reduced MBC by 20–64%. Similar effects, except the reduction by inorganic fertilizers, were observed for functional diversity of soil bacteria (Shannon index, H′ ). At one site, where crop yields were recorded for 3 yr, hog manure produced greater grain yields (75% increase over the control) than the rest of the treatments (49% increase by cattle manure) in year 1, but cattle manure out-yielded other treatments thereafter, when it increased yields by 25–50%. Cattle manure out-yielded other treatments even when nutrient uptake from inorganic fertilizers was the highest, implying that other factors also influenced crop yields. At the other site, crop yields were recorded only in year 1, and all soil amendments increased canola yields about three-fold relative to the control. Frequency of application usually had no effects on MBC or H′, but the triennial application rate of inorganic fertilizers or hog manure produced lower crop yields than annual applications in year 1. Key words: Functional diversity, Gray Luvisol, manure quality, NPK fertilizers, soil microbial communities
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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 | 0.001 |
| 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 | 0.001 |
| 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".