Quantifying forest degradation rates and their drivers in Alle district, southwestern Ethiopia: Implications for sustainable forest management practices
Bibliographic record
Abstract
Forest ecosystems contribute significantly to global climate regulation. Nonetheless, vulnerability has emerged as a multifaceted topic in the scientific world. Despite the importance of forest ecosystem services, there has been little quantification of worldwide forest change. The main objective of this study was to quantify forest degradation rates and drivers in the Alle district in Southwest Ethiopia. A mixed-research design was used to collect data on forest degradation rates and drivers in Alle district, incorporating both quantitative and qualitative method. The Land Use/Cover (LULC) for 1990, 2010, and 2022 derived from Landsat Thematic Mapper (TM) and Landsat Operational Land Imager (OLI) were used to detect changes and rates of forest degradation. Using a simple random sampling technique, 284 respondents were selected and questionnaires, interviews, and field observations were used to collect survey data. The results indicated that the forest cover of Abidibor per hectare was 2467.5 ha (97.2 %), 2268.3 ha (89.4 %) and 2203.9 ha (86.8 %) during 1990, 2010, and 2022, respectively. The forest coverage of Aba Gamta was 8296.7 ha (96.7 %), 6796.9 ha (79.2 %), and 6654.5 ha (77.6 %) in 1990, 2010, and 2022, respectively. Agricultural and grazing land increased, whereas forests and wetlands decreased during the respective years. The majority (39.36 %) of the sampled respondents reported that the conversion of forest land to agriculture by a rapidly growing population resulted in the expansion of agricultural land and rural settlements, resulting in forest degradation manifested by deforestation, overgrazing, and overexploitation. Thus, the forest coverage of the area decreased rapidly with time. As a solution to the devastating problems of diminishing forests, the local government and other stakeholders should consider conserving and managing depreciating forests by controlling direct drivers and determinants through participatory and institutionalized mechanisms. • Forest cover has dramatically declined over the last three decades, specifically Abidibor and Aba Gamta areas. • Conversion of forest land to agriculture, overgrazing, and overexploitation were identified as primary drivers. • Expansion of agricultural land and rural settlements contributes to the decline in forest coverage. • Institutionalized and participative approaches to forest management and protection were emphasized. • The local government and stakeholders have to move promptly to address the root causes of forest degradation.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".