Depth-driven responses of soil organic carbon fractions to orchard cover crops across China: A meta-analysis
Bibliographic record
Abstract
Cover crops (CC) have been widely recognized and implemented as one of the most effective agronomic practices for enhancing soil organic carbon (SOC) sequestration in orchard ecosystems. However, considerable uncertainty remains regarding the effect of CC on specific SOC fractions, posing challenges for accurate prediction of carbon (C) dynamics, which requires further comprehensive study at regional and national scales. Based on 615 paired-comparisons from 47 studies across China, we investigated the effects of CC management on SOC fractions, including microbial biomass C (MBC), dissolved organic C (DOC), particulate organic C (POC), easily oxidizable organic C (EOC), light fraction organic C (LFOC), and heavy fraction organic C (HFOC). In addition, we quantified the effects of various environmental factors (e.g., climatic conditions), soil properties (e.g., soil characteristics and depth), and agronomic variables (e.g., experiment duration, tree age, cover type, source and species of grass, cover pattern, mowing practices, and residue management) on the changes in SOC fractions. Compared to conventional clean (bare ground) tillage, CC significantly increases MBC (35.4 %), DOC (23.7 %), POC (36.2 %), EOC (18.4 %), LFOC (99.9 %), and HFOC (5.4 %). Random forest modeling demonstrates that soil depth is the dominant driver of SOC fractions responses to CC, and the CC effects are weakened with soil depth. It is therefore crucial to consider the various drivers of SOC fractions between soil depths in order to accurately forecast soil C dynamics and its potential feedback on global warming. Overall, this study systematically assessed the effects of CC on SOC fractions changes in China and identified CC as a promising practice for increasing SOC in orchards. These findings further indicate that the response of SOC fractions to CC is predominantly influenced by specific climatic, edaphic, and agronomic variables. These results not only reveal the ecological benefits of CC, but also highlight the importance of developing site-specific CC practices for the sustainability of agroecosystems. • Cover crops (CC) significantly increase soil organic carbon (SOC) fractions. • The effect of CC on SOC fractions varies with climatic, edaphic, and agronomic factors. • Soil depth is the dominant driver of SOC fractions response to CC. • Stabilized organic carbon fraction (i.e., HFOC) show the least response to CC.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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".