Analysis of efficiency in sugarcane production: the case of men and women headed households in SONY sugar out -grower zone, Rongo and Trans-Mara districts, Kenya
Bibliographic record
Abstract
About one quarter of cane producers in SONY Outgrower Zone are women headed households. However, a number of studies have suggested that women in rural areas are more disadvantaged in terms of accessing education, land, credit, and extension services. If this is the case, women cane farmers would be expected to be less efficient compared to men farmers. Before this can be concluded, there was need to establish whether differences in economic efficiency between men and women headed households exist in cane growing. The objectives of the research were; to characterize men and women headed cane growing households, to evaluate the relationship between institutional factors and gender, and to determine the differences in economic efficiency between men and women managed sugarcane farms. A multi stage sampling procedure was employed to select 205 active sugarcane farmers. A dual parametric stochastic decomposition technique was employed to disaggregate the components of economic efficiency. FRONTIER 4.1 program was used to derive maximum likelihood estimates and farm level technical efficiencies. A two limit Tobit model was then used to determine the influence of selected socio-economic and institutional variables on farm level technical, allocative and economic efficiency. Results showed that men headed households had a mean technical efficiency of 67.6%, a mean allocative efficiency of 82.48% and a mean economic efficiency of 58.0%. Women headed households had a mean technical efficiency of 72.0%, a mean allocative efficiency of 83.15% and a mean economic efficiency of 62.5%. Land under sugarcane cultivation was the single most important contributor to farmers’ efficiency. Women managed farms were on average more technically, allocative and economically efficient than men managed farms. Membership to outgrower associations in addition to encouraging increase in human capital will be important in enhancing farmers’ efficiency.
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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 | 0.001 | 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) | 0.002 | 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".