Assessment of Sustainable Livelihoods of Small Holder Farmers in the Densely Populated Highlands of South-Western Uganda
Bibliographic record
Abstract
The paper uses cross sectional data from 281 farm households collected through farmer interviews, focus group discussions and field observation from the densely populated highlands of southwestern Uganda. It is derived from a baseline study of a project aimed at developing agricultural intensification models for sustainable livelihoods in the study area. We assess livelihood activities and use the Sustainable livelihood framework, to assess livelihood vulnerability of the small holders focusing on the social and demographic profiles, livelihood strategies, social networks, financial capital and food. The details of the subcomponents for each of the major components are presented and discussed. Crop production is the major source of livelihood for 93.1% of the sampled households. The study shows that households in the study area have a relatively low livelihood vulnerability index of 0.33. Female headed households are more vulnerable with an index of 0.36 compared to male headed households with LVI of 0.32. About 19% of the sampled households are highly vulnerable with a LVI of 0.5. High vulnerability was recorded for social networks at 0.43 and the lowest recorded is 0.29 for food. Results indicate an average household dietary diversity score of 6, implying limited nutrients for some households. A more detailed study on food and nutrition security might be important to identify the gap in nutrient levels. Pests and diseases, unpredictable weather and low soil fertility are the major factors limiting production. Replication of this study including data on other key components specifically climate change might provide information about how the exposure, adaptive capacity, and sensitivity of the region will change as intensification adaptation practices are initiated.
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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 | 0.002 |
| 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 | 0.002 |
| 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".