Effects of introduced macadamia on soil organic carbon and water stocks in subtropical agroforestry systems of southwestern China
Bibliographic record
Abstract
Macadamia ( Macadamia integrifolia )-based agroforestry systems,widely adopted in Yunnan Province, China since 1981, are critical for soil organic carbon (SOC) sequestration. However, their effects on SOC stocks and coupling mechanisms with soil water dynamics remains poorly understood. This study quantified changes in SOC and soil water stocks following the conversion from primary forest to macadamia monoculture and intercropping systems (dasheen, konjac, and maize) in Yongde County, southwestern Yunnan province. Soil profiles (0–100 cm, 0–20 cm, and 20–100 cm) were analyzed to assess vertical SOC and soil water stocks interactions and driving factors. Results revealed that macadamia-based agroforestry systems reduced significantly SOC stock compared to primary forest (261 t/hm 2 ) but enhanced soil water stock. Among intercropping systems, macadamia+dasheen plantation exhibited the highest recovery of SOC and soil water stock. Surface soil layers (0–20 cm) showed strong SOC-water coupling, whereas this relationship weakened in deeper layers (20–100 cm). Simultaneously, woody above-ground biomass directly increased SOC stock, while specific leaf area and leaf phosphorus content regulated SOC stock directly or indirectly via woody above-ground biomass. Conversely, herb above-ground biomass negatively influenced SOC stock in surface soil layer, but had no significant effect in the deeper layer. Woody above-ground biomass exhibited a consistent negative influence on soil water stock across all soil layers, with the standardized path coefficients of −1.23 (0–100 cm), −1.153 (0–20 cm), and −1.23 (20–100 cm). This relationship was further modulated by indirect effects mediated through specific leaf area and leaf phosphorus content. This study provides mechanistic insights into the interdependencies of SOC and water stock in the subtropical macadamia-based agroforestry systems, providing valuable guidance for sustainable carbon management practices in China’s subtropical mountainous region.
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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) | 0.000 | 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".