Factors Affecting Technical Efficiency of Rubber Smallholders in Negeri Sembilan, Malaysia
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Bibliographic record
Abstract
The main objective of the study was to figure out, identify and analyse the technical efficiency of rubber smallholders’ production in Negeri Sembilan, Malaysia. Multi-stage data collection procedures, comprising both purposive and random sampling techniques, were used. Using structured questionnaires, farm-level information with cross sectional data from five districts of Negeri Sembilan, were employed in the study. A parametric Stochastic Frontier Analysis (SFA), with a transcendental logarithmic (Translog) functional form, was used in the study. The descriptive statistics results revealed that, the mean rubber yield was 5465 kg while that of the seven inputs used include 1.2 ha, 602.7, 2.33, 363.6 kg, 13.0 lit, 13.2 man days and 2.47 respectively for farm size, task, farm tools, fertilizer, herbicides, labour and rubber clones.The inferential statistics showed that, the mean technical efficiency was found to be 0.73 with a standard deviation of 0.089. Thus, this translates that 27% accounted for technical inefficiency. Both the sigma square and gamma coefficients were found to be statistically significant at 1% level. The Log Likelihood Function (LLF) and the Log Rati (LR) test were found to be respectively 167.7 and 34.07. The results further revealed that, although none of the farms were found to be on the frontier, however, 9 farms were very near the frontier with efficiency score range between 0.90-0.99. And twenty (20) firms have range 0.80-0.90. Race, Tapping experience, household number and extension agent’s visits were found to be technically significant and are thus critical in determining technical efficiency of rubber smallholders in Negeri Sembilan, Malaysia.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 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 it