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Record W2291259119 · doi:10.5539/enrr.v6n1p110

Assessing the Impacts of Land Use and land cover change on Pastoral Livestock Farming in South-Eastern Burkina Faso

2016· article· en· W2291259119 on OpenAlexvenueno aff
Sophie Agnes Kima, Appollonia Aimiosino Okhimamhe, André Kiema

Bibliographic record

VenueEnvironment and Natural Resources Research · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
FundersWest African Science Service Centre on Climate Change and Adapted Land Use
KeywordsLivestockGeographyAgricultureAgroforestryVegetation (pathology)PasturePopulationGrazingLand useEnvironmental protectionForestryEnvironmental scienceAgronomyEcology

Abstract

fetched live from OpenAlex

<p class="1Body">Conversion of pastures to cropland is one of the most important issues facing livestock farming in Burkina Faso. This study examined the impact of land use/cover change on pastoral livestock farming in Boulgou province between 1980 and 2013. Landsat satellite images (1989, 2001 and 2013) and socio-economic data were analysed. The interpretation of the classified Landsat images revealed an increase in cropland from 20.5% in 1989 to 36.7% in 2013. This resulted mainly from the conversion of woody savannah and shrub and grass savannah to cropland. Pastoral livestock farmers reported that the major drivers of vegetation loss were drought (95.1 %), population growth (91.8%), cropland increase (91.4%), extraction of fuel wood (69.8%) and increase in livestock population (65.4). These changes affect livestock farming through reduction of pasture, poor access to water and reduction of livestock mobility routes according to the farmers. This calls for regional and national policies to protect grazing areas in Burkina Faso that are similar to policies being implemented for forest and other types of vegetation cover in other countries. For such pastoral policies to be successful, issues concerning the mobility of livestock farmers must be enshrined into such policies and this study is an example of information source for these policies.</p>

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 score0.087
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.062
GPT teacher head0.293
Teacher spread0.231 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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