Determinants of Use of Information and Communication Technologies in Agriculture: The Case of Kenya Agricultural Commodity Exchange in Bungoma County, Kenya
Bibliographic record
Abstract
Access to markets by Smallholder farmers has conventionally been constrained by lack of market information. Efforts to strengthen access of farmers to markets has triggered the mushrooming of several projects that embrace ICT tools in promoting access to competitive market information. Nevertheless, most farmers still lack access to accurate market information, such as existing commodity prices. This study examines the determinants of the use of ICT tools among smallholder farmers for agricultural transactions. The study uses Kenya Agricultural Commodity Exchange (KACE), one of the ICT-based marketing platform, as the case study. The objectives of the research are to determine the factors that influence access to agricultural information, and establishing factors that determine the intensity of use of ICT tools in accessing agricultural information. Survey was conducted among 136 smallholder farmers in Bungoma County. Both purposive, and multi-stage sampling were used to obtain the sample for this research. The study finds that several farmer characteristics, farm and capital endowment factors affect the use of ICT tools, particularly mobile phones. Gender, age, literacy level, affordability, perceived importance, mobile ownership and group membership were found to be significant in influencing the decision to use KACE ICT tools and the intensity of use of these tools for agricultural transaction activities. The study further recommends for policies that support the expansion of ICT projects, training on their applications and sensitization on the use of these platforms. The study suggests for policies to address gender disparities on access and use of ICT tools for agricultural transaction.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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".