An analysis of health factors as predictors of agricultural technology adoption:the case of improved maize seeds and inorganic fertilizers in Malawi
Bibliographic record
Abstract
Agricultural technologies are considered as efficient instruments to support rural and economic development. In general, socio-economic factors are considered as the main determinants that delay or encourage the adoption of improved technologies. Other predictors such as health factors have rarely been included in studies as determinants of (non)adoption (Ersado et al 2004). As a result, this study attempts to determine whether or not health factors (farmers’ health status and accessibility to healthcare services) negatively and significantly influence the adoption of improved agricultural technologies. A Tobit model and a bivariate probit model were designed to evaluate the hypothesis that health factors significantly and negatively impact the adoption of improved maize seeds and inorganic fertilizers in Malawi (Southern Africa). The study uses data from the 2010-2011 Malawi Third Integrated Household Survey (Malawi 2010-2011 IHS3). Overall, findings from this analysis support the hypothesis that longer distances to places were farmers can buy medication negatively and significantly impact the adoption of improved technologies; especially in the case of inorganic fertilizers. The results, however, do not indicate that chronic sickness significantly impact adoption behavior; although the impact is negative. Additionally, the results suggest that socio-economic factors that significantly influence the adoption of hybrid maize seeds and inorganic fertilizers include: education, plot ownership, hired labor, and the price of chemical fertilizers. These results, in part, support policies designed to improve healthcare systems and strengthen collaborative work between the health and agricultural sectors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".