Fish Farmers’ Willingness to Pay for Improved Information and Communication Technologies During COVID-19: A Case of Ibadan, Nigeria
Bibliographic record
Abstract
Coronavirus has disrupted aquaculture activities at all levels. The pandemic has had effect on farmer’s input, output, market, revenue, and contact with Extension officers. To reduce the growing effect of the pandemic, the use of Information Communication Technologies has become necessary as farmers can get easy access to extension agents and monitor farm activities while reducing exposure to the virus. Hence, this research was conducted to determine fish farmer’s willingness to pay for improved Information Communication Technologies in bridging the gap caused by the Coronavirus outbreak. The study used cross-sectional survey with data collected from Ibadan, Nigeria. Simple random sampling technique was used to select a sample size of 40 farmers. Primary data was analysed using StataSE13.0 and the results revealed that; 80% of farmers were affected by Coronavirus and acknowledged that Information Communication Technologies play a role in their activities (55%). The probit regression revealed that the scale of operation, age of farmer, household size, status in the household, and usage of Information Communication Technologiess were found to be statistically significant determinants of farmer’s willingness to pay. These points to the fact that improved Information Communication Technologies are relevant to sustain aquaculture output in the face of Coronavirus. The study recommends that the government, the ministry for aquaculture, and stakeholders in aquaculture should support small-scale in the form of training, credit and provision of support systems to help them acquire and use improved ICTs.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".