Fallow, labour and livelihood in shifting cultivation: implications for deforestation in northern Lao PDR
Bibliographic record
Abstract
Shifting cultivation is often blamed for deforestation in tropical upland areas. Based on a case study of three villages in northern Lao PDR, this paper combines household surveys with a remote sensing based analysis of forest cover, covering the period 1989–1999, in order to analyse changes in shifting cultivation practices and livelihood strategies and the impact of these on deforestation. Due to population pressure and relocation of villages, easily accessed land is increasingly scarce and fallow periods have been shortened during the 1990s. A net annual deforestation of about 1% was found in the area during the study period. This deforestation rate reflects shorter fallow periods in secondary forests rather than encroachment on mature forests, which are not used for cultivation by the farmers in the three villages. Farmers rate scarce labour as a major constraint on shifting cultivation; nonetheless, a tendency towards lower labour input with shorter fallow periods is observed, contradicting conventional intensification theory. Livelihoods are diversifying through the establishment of plantations, cultivation of wet rice and adoption of animal husbandry, but given the socio-economic conditions in the area, shifting cultivation is likely to remain the most suitable farming system in the near future.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".