Ground cover management enhances soil extracellular enzyme activities across Chinese orchards
Bibliographic record
Abstract
The impacts of ground cover management (GCM) on orchard soil properties have been extensively studied. However, the quantitative assessment of soil extracellular enzyme activities (EEAs) in mulch agriculture remains understudied. In this study, we investigated EEAs related to GCM to assess microbial metabolic activity, soil health, and nutrient status, based on 81 studies focusing on orchards in China. Our findings show that GCM significantly increases carbon acquisition (C-acq, 37%), nitrogen acquisition (N-acq, 34%), phosphorus acquisition (P-acq, 26%), and oxidative decomposition (OX, 14%) enzymes compared to continuous clean tillage. A subgroup analysis and a random forest model were conducted to further identify the effects and potential mechanisms through which soil EEAs respond to GCM in orchards under various moderators. The significant changes in EEAs induced by GCM vary with experimental and environmental factors. Tree age, climate conditions, and soil depth are the primary contributors to the variation in soil EEAs. Overall, our results suggest that the implementation of GCM positively affects EEAs, thereby enhancing microbe-mediated soil ecosystem functions and soil fertility. This meta-analysis provides comprehensive evidence of GCM-induced effects on hydrolase and oxidase activity, improving our understanding of the underlying mechanisms by which orchard mulching impacts soil nutrient cycling. • A meta-analysis on soil extracellular enzyme activities (EEAs) in Chinese mulched orchards. • Orchard ground cover management (GCM) enhances (14–37%) carbon-, nitrogen- and phosphorus-acquisition, and oxidative enzyme activities. • Effects of GCM on EEAs depend on management and environmental factors. • The relative influence of 17 variables on GCM-induced changes in EEAs is assessed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".