L CONSERVATION TECHNIQUES IN ENHANCING FOOD SECURITY;THE CASE OF AWADA KEBELE DALE WOREDA SIDAMA REGION-ETHIOPIA
Bibliographic record
Abstract
Land degradation was a significant global issue during the 20th century and remains of high importance in the 21st century as it affects the environment, agronomic productivity, food security, and quality of life. About 99.7% of food is produced from the soil; thus, food security depends directly on soil productivity.Accelerated soil erosion is among the principal causes of the decrease in soil productivity and the increase in risks of global food insecurity.This study aimed to analyze the role of soil conservation practices in household food security in Awada Kebele, Dale Woreda-Sidama Region, Ethiopia. A research methodology, a cross-sectional and descriptive survey involving a qualitative and quantitative approach, was used by the researchers. Using the simple random sampling technique employed for this study,85 respondents were selected as the sample size. The primary data were obtained through a questionnaire, personal observation, a focus discussion group,and an interview. Secondary sources of data were collected from different published as well as unpublished documents, the internet, and reports available in the study area. The quantitative data were presented through the use of percentages, frequencies, tabulations, distributions, figures, and simple descriptive methods, and the qualitative data were interpreted using different words in order to achieve the end objectives of the study and answer the formulated research questions. The study confirmed that the predominant activities commonly identified as contributing to land resource degradation through anthropogenic and topographic factors. Even though soil conservation has many challenges, including the severity of soil erosion due to climatic factors, a lack of management skills and technical support, the steep topography of the land, and the lack of capital for practicing SCT, this study finally recommends that soil conservation is a multidimensional impact worthy of consideration to be incorporated into policy interventions by NGOs or government-designed projects. Development agents of the woreda or other project officers should give maximum attention to the dissemination of information about soil conservation technology to combat food insecurity.
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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 | 0.002 | 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 | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".