Technical Efficiency of Local Rice Farming in Tidal Swamp Areas of Central Kalimantan, Indonesia: Determinants and Implications
Bibliographic record
Abstract
Rice, as a staple food of Indonesia, is facing increasing demand while its availability stagnates due to the conversion of agricultural land in Java.This study explores a strategy to counteract this shortfall by accelerating rice production in non-Java tidal swamp areas.A key challenge in this context is to enhance the technical efficiency of local rice farming.Conducted in 2021 in the Kapuas and Pulang Pisau districts of Central Kalimantan province, Indonesia, this study aims to quantify the technical efficiency of local rice farming in these tidal swamp areas and identify factors contributing to its inefficiency.Empirical data were collected through surveys and focused group discussions, and subsequently analyzed using stochastic frontier production.The findings suggest that the average technical efficiency level is 0.58, albeit with variations across villages, ranging from 0.45 to 0.71.It was found that the size of landholding, the use of pesticides, labor, and harvesting tools have a significant positive impact on rice production.On the other hand, inefficiency is influenced by factors such as the number of household members aged 15 or above, education level, and the proportion of total household income derived from rice farming.These insights are valuable for policymakers and program planners aiming to improve the efficiency of rice farming in tidal swamp environments.It is recommended that government programs focus on the prerequisites for tidal farming, specifically water management infrastructures.Additionally, the application of locationspecific technology may enhance the productivity of local rice varieties in tidal swamps.
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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 | 0.003 |
| 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 | 0.001 |
| 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".