Effects of ridge‐furrow and plastic mulching planting patterns on microflora and potato tuber yield in continuous cropping soil
Bibliographic record
Abstract
Abstract Planting patterns have distinctive effects on the soil micro‐ecological environment and soil quality. To explore the effects of film mulch ridge‐furrow ( FMRF ) cropping on soil microbial properties and potato yield, a study was conducted in 2013 and 2014 in a continuously cropped field under nonfilm‐mulched flat plot ( CK ), half‐mulched flat plot (T1), fully mulched ridge cropping (T2), fully mulched furrow cropping (T3), half‐mulched ridge cropping (T4) and half‐mulched furrow cropping (T5) planting patterns. Our results indicate that T3 increased the average bacteria/fungi (B/F) ratio by 253% compared to CK . On average, half‐mulched ridge cropping increased the bacteria population and aerobic Azotobacter by 9 and 19%, respectively, compared with CK . On average, T3 had the greatest inhibitory effect on fungi populations. Half‐mulched furrow cropping had the most anaerobic Azotobacter and nitrifying bacteria. The study showed that FMRF increased soil bacteria, especially Azotobacter but reduced fungi and actinomycetes. Treatment T2 gave the greatest potato yield, followed by T4, whereas the greatest biomass yield was recorded in T4. Full‐mulch furrow cropping methods produced the greatest nutrient use efficiency. The findings of this study enhance our understanding of soil microbe and plant responses to plastic mulch and planting patterns under semi‐arid conditions.
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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".