Agroforestry potential for adaptation to climate change: A soil‐based perspective
Bibliographic record
Abstract
Abstract Agricultural systems face several challenges that threaten their capacity to feed the world while maintaining a healthy and functional environment. Climate change, together with soil degradation, biodiversity loss, resource scarcity and invasive species, is a major threat to agricultural systems worldwide. In this context, new practices have been proposed to circumvent or minimize these threats. Yet, these mostly focus on the farm or plant level (e.g., breeding for stress‐tolerant species), while frequently overlooking belowground components (e.g., soil organic carbon accrual). By interlinking above‐ and below‐ground components, the likelihood of limiting the negative effects of current threats to agricultural systems can be maximized. This review explores current knowledge regarding agroforestry and its effects on belowground components as a key property in the reducing effects of climate change. We first review tree effects on key soil properties of agricultural systems. We synthesize evidence regarding agroforestry systems response to current environmental threats that are related to climate change. We continue by discussing how soil processes play a fundamental role in the capacity of agroforestry systems to cope with climate change. We conclude by proposing options on how resilience of agroforestry systems could be further enhanced.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".