Assessing the Impacts of Land Use and land cover change on Pastoral Livestock Farming in South-Eastern Burkina Faso
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".