Adoption of ICT-in-Agriculture Innovations by Smallholder Farmers in Kenya
Bibliographic record
Abstract
Agricultural development is a powerful tool for raising incomes for deprived population in developing countries, such as Kenya and to support livelihoods by ensuing food security. This has seen the agricultural sector position itself as an engine for sustainable development and economic growth in Kenya. However, the profile of the farming community in Kenya is mainly female smallholders who are illiterate and that practice traditional farming methods. Therefore, there is urgent need to examine emerging digital tools that can support these farmers, and to adopt these tools accordingly to meet their farming needs. In this regard, this paper seeks to explore the willingness and ability of these smallholder farmers to accept and adopt ICT-in-agriculture innovations towards supporting their farm operations, improving their farm productivity, and providing readily and accessible market for their produce. Specifically, this study identifies the factors that influence smallholder farmers’ decision on ICT innovations adoption in agriculture, and examines how these factors are perceived by smallholder farmers on adoption of ICT innovations. The study was carried out in Siaya County, Kenya with sample population of 100 smallholder farmers. A simple random sampling was used, with questionnaires used to collect data. The findings from this study indicate cost, illiteracy, ICT skills, quality of the information and gender as some of the key factors that influence smallholder farmers’ choice and decision on ICT-in agriculture innovations to adopt.
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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 | 0.000 | 0.000 |
| 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".