Drivers and consequences of archetypical shifting cultivation transitions
Bibliographic record
Abstract
Abstract Shifting cultivation remains an important land system in many tropical landscapes, but transitions away from shifting cultivation are increasingly common. So far, our knowledge on the social–economic and environmental drivers and consequences of such shifting cultivation transitions is incomplete, focusing on certain transitions, drivers, consequences or regions. Here, we use an archetype approach, validated through systematically identified literature, to describe eight archetypes encompassing the transitions from shifting cultivation to (1) perennial plantation crops, (2) permanent agroforestry, (3) regrown secondary forest, (4) permanent non‐perennial crops, (5) pasture, (6) wood plantation, (7) non‐cultivated non‐forested land and (8) restored secondary forest (ordered in decreasing prevalence). We then discuss social–economic and environmental factors favouring and disfavouring each archetype. This reveals that higher expected land rents, resulting from increased market access, crop price surges, secure land tenure and state interventions, are the main drivers of archetypical transitions to perennial plantation crops, permanent agroforestry, permanent non‐perennial crops and wood plantation. The prioritisation of other activities, both on‐ and off‐farm, favours transitions to regrown secondary forest and non‐cultivated non‐forested land, depending on plot‐level environmental conditions. Active forest restoration is typically implemented through state or NGO interventions. Turning to the consequences of archetypical transitions for biodiversity, the environment and livelihoods, we find that positive environmental outcomes prevail for transitions to permanent agroforestry, regrown secondary forest and restored secondary forest. Negative environmental outcomes dominate for four typically economically profitable transitions to perennial plantation crops, permanent non‐perennial crops, pasture and wood plantation. Non‐income‐related social–economic outcomes are heterogeneous within all archetypes and highly context‐dependent. Our archetype analysis shows that shifting cultivation transitions are diverse in themselves, in their drivers and their consequences. This calls for a critical and contextualised appraisal of the continuation of shifting cultivation, as well as the transition away from it, when designing land system policies that work for people and nature. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".