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Fallow, labour and livelihood in shifting cultivation: implications for deforestation in northern Lao PDR

2003· article· en· W2085175075 on OpenAlexfundno aff
Charlotte Seidenberg, Ole Mertz, Morten Bilde Kias

Bibliographic record

VenueGeografisk Tidsskrift-Danish Journal of Geography · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
FundersUdenrigsministerietUniversity of Saskatchewan
KeywordsDeforestation (computer science)Shifting cultivationLivelihoodAgricultureGeographyAgroforestryPopulation pressureSlash-and-burnPopulationPopulation growthEnvironmental science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.653

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.211
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations37
Published2003
Admission routes1
Has abstractyes

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