Influence of Agro-pastoral Activities on Land Use and Land Cover Change in Karamoja, Uganda
Bibliographic record
Abstract
The land use and/or land cover changes (LULCC) caused mainly by human beings for their benefits play a pivotal role in a global environment, resulting in significant ecosystem changes. Iriiri, Matany and Rengen sub-counties in Karamoja sub-region of Uganda have undergone rapid LULCC in the past three decades. Nevertheless, the extent to which these changes have occurred have not been quantified. Establishing the extent of LULCC in the study area between 1986 and 2015 formed our objective. Supervised LANDSAT image classification for years 1986, 1996, 2005 and 2015 was done using ENVI 4.7 software. The classification resulted into six land use classes; Bareland, Farmland, Woodland, Grassland, Settlement, and Wetland. The area under each LULCC was subjected to a change detection analysis using Arc-GIS (ESRI, 2009) in ten years strata. The results revealed that settlement in Iriiri expanded significantly (p < 0.05) by 71.3%, while farmland increased by 45%. Woodland and grassland significantly (p < 0.05) declined by 68% and 30% respectively. Bareland increased by 56%, while wetland decreased by 54%. Woodland and grassland significant (p < 0.05) shrunk by 87% in Matany and Rengen sub-counties. Farmland expanded significantly (p < 0.05) by 147% and Woodland shrunk significantly (p < 0.05) by 79% in Rengen sub-county. Generally, farmland and settlement increased while woodland and grassland shrunk due increased human population and farming. Expansion of farming is partially due to increased human settlement to pursue agriculture following advocacy by the government of Uganda. The removal of natural vegetation is expected to negatively impact soil quality by exposing it to agents of erosion. However, the extent of these impacts is unknown. Hence, further studies on LULCC and their impact on soil quality at sub-counties level are crucial in guiding land use policy and sustainable management practices in the area.
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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 | 0.000 | 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 | 0.001 |
| 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".