Mobile Based Agriculture and Climate Services Impact on Farming Households in Rural Kenya
Bibliographic record
Abstract
Rural farming households in semi-arid regions in Kenya are vulnerable to climate change impacts due to overreliance on rain fed agriculture and low adaptive capacity. Farming households’ adaptive capacity development is detrimental to enable them cope with short and long term impacts. Information Communication and Technology (ICTS) play an essential role in adaptive capacity development by ensuring access to information and knowledge related to agriculture and climate. The mobile phone is one of dominant ICT tool with wider ownership and promising technology for information accessibility. The increasing mobile penetration rate in Kenya has initiated the development of a wide range of agricultural related mobile phone services and applications targeting rural households to increase their agricultural productivity and strengthen their adaptive capacity in the face of climate change. This study examines households use and benefit from the developed mobile phone services and applications to access information related to agriculture and climate change. Using data of 120 households’ multinomial probit analysis is applied to identify factors that determine the adoption of the mobile phone. Findings reveled that through developed mobile phone services and applications, Kenyan rural households are able to access; mobile money banking, extension services, obtain credit, agriculture information, weather information and market information. Access to these services increases household capacity and reduce information asymmetry. Feature phone and smart phones are the types of mobile phone used across households. Multinomial probit analysis elicits that probability of feature phone adoption increases with a decrease in household income while that of smart phone increases on male headed households, increases with an increase in household income and accessibility to credit.
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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 | 0.000 | 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 | 0.000 |
| 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".