Adoption and Factors Affecting Farmer’s Adoption of Technologies in Farming System: A Case Study of Improved Technologies in ICARDA’s Arabian Peninsula Regional Program
Bibliographic record
Abstract
The main objective of this study is to assess the rate of adoption of innovations introduced by the Arabian Peninsula Regional Program (APRP) of ICARDA and identify main constraints that limit the adoption process of these new technologies in the GCC countries and Yemen through using ADOPT (Adoption and Diffusion Outcome Prediction Tool). ADOPT predicts the proportion of a target population that might adopt an innovation over time. A focus group discussion (FGD) methodology was used to apply the ADOPT with a panel of farmers in each country and for each introduced technology. In the FGD we streamlined 22 discussion questions around four categories of influences on adoption: characteristics of the innovation, characteristics of the target population, the relative advantage of using the innovation, and learning of the relative benefit of the change.The results showed that predicted peak of adoption and time for reaching that peak vary between technologies, countries, and within the same country. The technology specific variables (e.g. yield potential and acceptability) are significant for explaining adoption behavior, implying that it is important to take farmers’ preferences to varietal characteristics into consideration in the design of a research and development program. Given the significant role played by extension and access related variables, increased emphasis on information dissemination, field demonstration, and farmers’ participatory research and training programs to popularize these technologies and enhance their adoption rate are required. This also suggests that policy intervention should be made on improving the knowledge status of farming households, and developing programs on the technological package which offer farmers a variety of choices among the appropriate pools of technology options. Such programs ultimately help farmers to develop more profit-oriented behavior (both economically and environmentally) which are necessary to enhance adoption rate, production, and food security in the long run for the GCC countries and Yemen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".