Relating Crop Productivity to Soil Microbial Properties in Acid Soil Treated with Cattle Manure
Bibliographic record
Abstract
Cattle ( Bos taurus ) manure can be used to correct soil acidity, supply plant nutrients, and increase soil organic matter. It usually affects soil microbial properties and crop production, but the relationship between the two effects is not always demonstrated. In a 4‐yr study in which barley ( Hordeum vulgare L.) was rotated with canola ( Brassica rapa L.) on an acid soil, we investigated the effects of cattle manure on soil microbial characteristics and related them to other soil properties and crop productivity. The treatments were: (i) Control (no treatments), (ii) nitrogen and phosphorus fertilizer applied annually (NP), (iii) Lime (applied once) + nitrogen and phosphorus applied annually (Lime+NP), (iv) fresh manure applied once at 80 t ha −1 (Manure80), and (v) manure applied once at 160 t ha −1 (Manure160). The treatment order in microbial biomass carbon (MBC) was: NP ≤ Control ≤ Lime+NP < Manure80 < Manure160. Manure160 increased MBC by 34 to 150% in bulk soil, and by 49 to 117% in crop rhizosphere. Microbial activity ranged from 10.80 to 27.55 µg CO 2 –C m −2 d −1 and was in the order: Control = Lime+NP ≤ Manure160 ≤ NP ≤ Manure80. Bacterial community structures differed between manure treatments and non‐manure treatments. There were positive correlations of soil microbial characteristics with soil nutrient contents or crop nutrient uptake, and negative correlations with soil Mn and Na. The positive correlations sometimes translated into positive correlations with crop yields, and they underscore the crucial role of soil microorganisms in nutrient cycling.
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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".