Carbon accumulation in agroforestry systems is affected by tree species diversity, age and regional climate: A global meta‐analysis
Bibliographic record
Abstract
Abstract Aim Agroforestry is a globally practised system of land use for achieving greater and more diverse biomass production, but it has other ecological benefits, such as mitigation of climate change. Despite this, long‐term carbon (C) accumulation in different components of agroforestry systems, the drivers for C accumulation and the linkages between tree biomass and soil C stocks remain unclear. Location Global. Time period From 1989 to 2019. Major taxa studied Trees. Methods Here, we report on a global meta‐analysis based on 141 studies to identify patterns of C accumulation in tree‐based agroforestry systems compared with sole cropland and pasture. Results We found that agroforestry systems had, on average, 46.1 Mg/ha (95% confidence interval, 36.4–55.8 Mg/ha) more C in tree biomass compared with sole cropland‐ or pasture‐based land uses without trees. Furthermore, agroforestry systems with multiple tree species contained greater biomass C stocks and accumulated biomass C faster than systems with a single tree species. The effect of agroforestry practices on soil C stock increased with tree age, although such increases varied among climatic zones. Agroforestry systems in tropical zones had the ability to increase soil C to peak levels quickly, whereas soil C in temperate zones increased at a slower rate but peaked at a greater overall soil C level. Our structural equation model did not detect a direct linkage between biomass C and changes in total soil C stock in agroforestry systems. Main conclusions Our results demonstrate that planting multiple tree species in agroforestry systems is an important strategy to increase biomass C sequestration, with regional climate affecting the temporal change of soil C in response to agroforestry practices.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.028 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".