Technical Efficiency of Rice Production in Tidal Swampland: A Stochastic Model Approach
Bibliographic record
Abstract
Tidal swamplands have great potential for rice production, but their contribution to rice production in Indonesia could be more significant.This study aimed to identify factors that could significantly increase rice yield in tidal swamplands, measure technical efficiency (TE), and determine the factors influencing efficiency.The survey was carried out in two districts of Central Kalimantan Province, Indonesia: Kapuas and Pulang Pisau.The stochastic frontier model was used to estimate TE, defined as the ratio of actual output to the maximum possible output given the input levels, while accounting for random shocks beyond the farmers' control.The TE estimates provide suggestions for farmer-targeted interventions and for optimizing input allocation to enhance rice production.The study found that expanding the area under rice cultivation and applying NPK fertilizers significantly increased rice yields.Farmers' efficiency levels ranged widely, from 0.24 to 1.0, with an average of 0.755, depending on the estimation method.Age and experience are important variables in determining efficiency.Relaxing the assumption of independence between the two error components in stochastic frontier models had no meaningful effect on estimated efficiency.The conventional model outperformed three copula-based models based on the Akaike and Bayesian Information Criterion, neither overestimating nor underestimating efficiency.It is proposed to encourage new generation participation in farming and upgrading their expertise through training to boost efficiency.Expanding the cultivated area and implementing intensive fertilization strategies are also proposed to increase rice production in tidal swamplands.
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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".