Understanding The Purpose and Potential Popularity of Mobile Phone Use in Zambia’s Maize Production
Bibliographic record
Abstract
There is a growing use of mobile phones in rural areas on account of its relative inexpensiveness and lack of requirement for an urban environment. As a platform for accessing information through text messages and voice calls, farmers are able to collect agricultural information which may lead to higher productivity due to technology spillover. If the use of mobile phones contributes to the improvement of farmers’ productivity, their agricultural output level should increase. In this study, we investigate the adoption of mobile phones to obtain agricultural information and its effect on smallholder maize farmers’ production using cross-sectional data from Zambia. Understanding such causal effects is indispensable especially against the background of vision 2030. The propensity score matching (PSM) method was adopted to estimate the average treatment effect of treated of mobile phone adoption in agriculture. We found that the use of mobile phones significantly increases farmers’ productivity, by about 30%. If farmers start to adopt mobile phones to collect agricultural information, the total maize output would also increase by 30.36%, which would culminate in feeding two more people per household daily for the whole year. Therefore, we cautiously conclude that mobile phone use in agriculture serves the purpose of contributing to the fight against hunger via enhancing maize production and this is driving its popularity among smallholder farmers in rural Zambia.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".