Study of Extension Teaching Methods Adopted through Crop Maximization Project: A Case Study of Sindh Province
Bibliographic record
Abstract
Farmers' decisions to adopt a new agricultural technology depend on complex factors. One of the factors is farmers' perception.Ministry of Food and Agriculture (MINFA) launched an integrated development programme entitled “Crop Maximization Project (CMP)” in 15 districts of the country. A successful extension teaching method can play a vital role to transfer the technology to the farming community. The role of the extension agent cannot be ignored for dissemination of information at field level. This study was conducted six districts of Sindh province comprising Mirpurkhas, Sanghar, Shaheed Benazirabad, Naushahro Feroze, Khairpur and Larkana. Five villages were selected from the each district through multistage sampling techniques. Ten famers were selected from each village. Thus making a sample of 300 farmers was randomly selected for the study. A well structured interview schedule was used to collect information from the small farmers on their personal and socio-economic characteristics and effectiveness of extension teaching method at farm level. Statistical techniques like mean scores and percentages were used to analyze the data. The findings of the study showed that majority 35% of the small farmers were youth having age group (26-35 years). About 57% of the respondents were married; majority (40%) maintained a range of small farmers having land 6-12 acres. 45% of the respondents having their primary education. Almost 47% of the respondents were experienced between 6 to 10 years. The result of the study showed that land management practices and selection and sowing of certified seed at proper time ranked highest in the order of acquiring knowledge. The majority 80% farmers perceived farmers field school, while 70% farmers were identified result demonstration, 68% farmers through method demonstration as the extension teaching method used by extension personnel. It is recommended that farmers should be trained through farmer’s field school for the adoption of the technology at field level. Crop maximization project should be extended to other districts of Sindh province and knowledge of the farmers should be enhanced through extension teaching methods for crop productivity enhancement and better livelihood for the farming community. .
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".