Enhancement of Agroecosystem Multifunctionality by Agroforestry: A Global Quantitative Summary
Bibliographic record
Abstract
In comparison with conventional agriculture, agroforestry systems improve the delivery of multiple ecosystem services and support greater biodiversity. Yet, the effects of agroforestry on various ecosystem services and biodiversity vary worldwide between climatic regions and system types, and studies often focus on measuring a small number of services or biodiversity indicators. We conducted a quantitative summary of multiple large-scale meta-analyses comparing service delivery or biodiversity between agroforestry systems and conventional agricultural systems to capture the global effect of agroforestry on agroecosystem multifunctionality. Data were aggregated from 20 meta-analyses, many of them global, and response ratios were calculated to assess relative effects of agroforestry systems worldwide on multiple categories of biodiversity indicators and production, regulation, and support services. By combining different datasets that addressed specific ecosystem services or biodiversity indicators and analyzing 3075 comparisons between agroforestry systems and their conventional counterparts, we found that agroforestry enhanced ecosystem service delivery and biodiversity globally by an average of 23%. The effects were more pronounced on supporting and regulating services and biodiversity than on production services. The vast majority of analyzed services and biodiversity indicators were enhanced in agroforestry systems, while few were either not affected or affected negatively. Among ecosystem services analyzed along an aridity gradient, only soil organic carbon (SOC) stocks and forage production varied significantly. The positive effects of agroforestry on SOC stocks were stronger in drier conditions, while those on forage production followed a quadratic trend, with maximal benefits in arid conditions. Our results suggest that broad-scale adoption of agroforestry in conventional agriculture could benefit agroecosystem multifunctionality globally without sacrificing productivity and would help support sustainable food production.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".