Impact of Farmers’ Climate Risk Perception and Socio-Economic Attributes on Their Choice of ICT-Based Agricultural Information Services: Empirical Evidence from Pakistan
Bibliographic record
Abstract
In Pakistan, research on information and communication technologies-based agricultural information services (ICTbAIS) have gained significant attention owing to the overwhelming population of smallholder farmers (whose information needs are unable to be met by the conventional extension services) and the increasing incidence of climatic risk. This study is, therefore, conducted in the Punjab province of Pakistan (mixed cropping region) to explore farmers’ use of ICTbAIS and understand the relationship between farmers’ socio-economic attributes, risk perception, and choices of ICTbAIS. A sample of 480 farmers was drawn using a multistage sampling approach, and farmers were interviewed face-to-face. To analyze the dataset, a multivariate Probit (MVP) model was employed. The results show that Television (TV) and mobile-based advisory and mobile-based consultations appeared to be the most used ICTbAIS, followed by radio and internet-based advisory. The estimates of the MVP model showed that farmers’ age, education, farmland, tenancy status, off-farm income, and climate risk perception are significant determinants of their choices of ICTbAIS. Based on our results, we suggest policymakers and extension agencies to improve the content of ICTbAIS and make efforts for the awareness and training of farmers regarding the use of contemporary 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".