Agricultural and Extension Education for Sustainability Approach
Bibliographic record
Abstract
The study analyzed the emerging land rights and the extent of the relationship between agricultural and extension education and soil conservation practices. A survey of 376 household heads randomly sampled respondents was administered using a well-structured questionnaire. Results from correlation analysis revealed that the relationship between "agricultural and extension education" and the soil conservation variables "mulching, zero tillage, and the use of crop residues or household refuse" was positive, moderate in strength, and statistically significant. However, the relationship between "agricultural and extension education" and "slash and burn agriculture" was negative, moderate in strength, and statistically significant. The results from the linear probability model show that the coefficients of "Agricultural and Extension education" are statistically significant at a 1% level of significance for all the model specifications except the case where "organic fertilizer" is used as the dependent variable. Specifically, the results indicate that Agricultural and Extension education increases the probability of farmers practising mulching, use of crop or household residues, and zero tillage by 59.4, 16.1, and 33.6 percentage points, respectively. Also, Agricultural and Extension Education decreases the probability of farmers practising slash and burn agriculture by about 16.2 percentage points. Agricultural and Extension education increases the probability of farmers practising at least two of the soil conservations by 25 percentage points, while it increases the probability of farmers practising at least three of those soil conservations by 5.5 percentage points. Based on the results, we propose the Agricultural and Extension Education for Sustainability approach. This approach consists of knowledge, skills, motivation, awareness, concern, responsibility, and action. Therefore, policies geared towards agricultural and extension services should be highly prioritized.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".