Soil microbial dynamics in response to tillage and residue management in a maize cropping system
Bibliographic record
Abstract
The impact of tillage and residue management on soil microorganisms was studied over the maize (Zea mays L.) growing season in southwestern Quebec. Tillage and residue treatments were imposed on a sandy loam to loamy sand soil in fall 1991. Treatments consisted of no till, reduced tillage, and conventional tillage with crop residues either removed from (-R) or retained on (+R) experimental plots, laid out in a randomized complete block design. Soil microbial biomass carbon (SMB-C), soil microbial nitrogen (SMB-N) and phospholipid fatty acid (PLFA) concentrations were measured four times, at two depths (0--10 cm, 10--20 cm), over the 2001 growing season. Sample periods were: May 7th (pre planting), June 25 th, July 16th, and September 29th (prior to corn harvest). The effect of time was of a greater magnitude than those attributed to tillage or residue treatments. While SMB-C showed no seasonal change (160 mug C g-1 soil); SMB-N was responsive to mineral nitrogen fertilizer; and PLFA data showed an increase in fungi and total PLFA throughout the season. PLFA profiles showed better distinction between sampling period, and depth, than treatments. Of the two treatments, the effect of residue was more pronounced than that of tillage, with increased SMB-C and SMB-N (6.1% and 96%) in +R plots compared to -R plots. This study illustrated that measuring soil quality based on soil microbial components must take into account seasonal changes in soil physical, chemical conditions, and nutrient supply.
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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) | 0.000 | 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) | 0.001 | 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".