Identifikasi Wilayah Unggulan Lumbung Pangan Komoditas Padi Provinsi Sumatera Selatan (LQ dan Shift-Share Analysis 2020-2024)
Bibliographic record
Abstract
Population growth and land-use conversion continue to pressure rice production in Indonesia, increasing the need to identify priority regions for Kawasan Sentra Produksi Pangan (KSPP), Indonesia’s Food Production Center Zones under the 2025–2029 National Medium-Term Development Plan. This study aims to identify priority areas for rice-based food production in South Sumatra Province using a descriptive-quantitative approach. The analysis applies Location Quotient (LQ) and Shift-Share Analysis (SSA) to assess specialization, competitive performance, and structural growth of rice production across districts and cities from 2020 to 2024. The results indicate that South Sumatra records an average rice LQ of 0.92 relative to the national level, showing that rice has not yet become a national-scale base commodity despite notable spatial disparities. SSA results reveal strong regional contrasts. East OKU District records the highest Competitive Effect at 59,533 tons, while Banyuasin District records the highest Industrial Mix Effect at 37,545 tons, indicating rapid growth aligned with the provincial food-crop structure. The integration of LQ and SSA identifies six priority areas: Ogan Ilir, Ogan Komering Ilir, Pagar Alam, Muara Enim, Penukal Abab Lematang Ilir, and East OKU District. The novelty of this study lies in producing a province-wide priority mapping based on a single strategic commodity. The analysis is limited by its focus on three food crops and by the exclusion of biophysical factors. Despite these limitations, the findings provide an empirical basis for determining KSPP priority zones and guiding rice-based regional development planning.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".