Adoption of Good Agronomic Practices (GAP) Among Smallholder Rice Farmers in Nigeria Agricultural Transformation Agenda
Bibliographic record
Abstract
This study assessed the adoption rate and identified factors influencing adoption of rice technologies among participants of Agricultural Transformation Agenda across the targeted implementation zones of Adani-Omor, Bida-Badeggi, Kano-Jigawa and Kebbi-Sokoto. Multi-stage sampling procedure was used in selecting eighty respondents for the study. The data were collected with the aid of structured questionnaire. Descriptive statistics and Tobit regression model were employed in the analysis of data. The study revealed that majority of farmers participating in Agricultural Transformation Agenda Project (ATASP-1) are youths and still in their active age as indicated by the average age of 42 years. About 62% have secondary and tertiary education. On the gender distribution of the people engaged in ATASP-1 project, it was revealed that about 92% were male while only 8% were female. Substantial numbers of technologies were disseminated on rice being promoted under ATASP-1 project and the adoption rate of these technologies was very high. More than three-quarter of the respondents have adopted technologies introduced to them. Adoption of rice technologies among participating farmers is largely depends on socioeconomic characteristics of farmers such as age, education and gender of the respondents. The study recommends that there should be continuous training of farmers on the importance of these technologies as well as techniques behind their utilization to ensure continuous usage of the adopted technologies. Women should be encouraged to participate more in the project and to take up farming as a business. Also, adequate attention should be given to farmers socioeconomic characteristics as these are the determinants of technology adoption. Keywords: Adoption, GAP, Rice Farmers, Agricultural Transformation Agenda DOI : 10.7176/JESD/10-15-02 Publication date : August 31 st 2019
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".