Enhancing soil ecological stoichiometry and orchard yield through ground cover management: A meta-analysis across China
Bibliographic record
Abstract
Ground cover management (GCM) is a critical agricultural practice that influences soil ecological stoichiometry (SES) and orchard productivity. However, its effects on soil carbon (C), nitrogen (N), and phosphorus (P) dynamics and their implications for fruit yield remain poorly understood. This study synthesizes 12,486 paired observations from 415 studies to assess the impact of GCM on soil SES and orchard yield across China. Results indicate that GCM significantly increases soil C (20.0 %), N (15.0 %), and P (13.0 %) concentrations, as well as C:N (4.9 %), C:P (6.6 %), and N:P (2.6 %) ratios, leading to a 13.9 % improvement in fruit yield. The effects of GCM vary with various management practices and environmental factors. Mowing enhances soil C (20.0 %) sequestration and yield (17.4 %) more effectively than no mowing (19.0 % C, 1.9 % yield). A random forest model identifies mean annual precipitation (MAP) and mean annual temperature (MAT) as key climatic drivers of SES and yield, with maximum yield benefits (14.5 %–18.2 %) observed in cooler, drier regions (MAP ≤ 600 mm, MAT ≤ 15 °C). These findings highlight GCM as a sustainable strategy for improving soil health and maintaining orchard productivity under variable climatic conditions. • GCM enhances soil ecological stoichiometry balance and increases fruit yield by 13.9 %. • GCM’s yield benefits are most pronounced in cooler and drier regions (MAP ≤ 600 mm, MAT ≤ 15 ℃). • Leguminous cover crops significantly reduce soil pH, influencing nutrient availability. • Regular mowing enhances soil organic carbon and boosts fruit yield. • GCM serves as a resilient strategy to sustain orchard productivity under extreme climatic conditions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".