A Dynamic Model to Uphold Rice Self-Sufficiency Policies, Case Study; Karawang Regency, West Java Province
Bibliographic record
Abstract
Paddy field conversions (PFCs) in Indonesia have become a major threat facing agricultural lands on the local, province, and national levels causing a reduction in rice production and leading to food insecurity.Hence, information on the impacts of PFCs on rice supply is crucial for future policy planning and management.This paper aimed to simulate four policy scenarios to view the impact of paddy fields' conversion on rice production to enhance the planning of future food security-related policies.Based on system dynamics modelling (SDM), the study used primary and secondary data to develop a quantitative Stock Flow Diagram from the qualitative Causal Loop Diagram.Available historical data; paddy cultivated area, population, and harvested areas have been used to test and validate the SDM.The study indicated that the overall maximum relative errors, coefficient of determination, and mean absolute error of tested variables were 1.33%, 0.760, and 1.49, respectively.Under the Business-as-usual scenario, it was found that the population of Karawang will reach 6.1 million people, and the paddy fields will be reduced to only 169 thousand ha by the end of the century.Thus, Karawang rice Availability Per Person, Surplus, and Self-sufficiency will be reduced by 66.20, 50.55, and 359%, respectively.Consequently, the obligation of 1.5 million tons a year cannot be met.Besides that, the Rice demand scenario indicated the Regency will be out of rice surplus in 2094 and 2085 when the demand for rice increases by 80% (SNO#3.3)and 100% (SNO#3.4),respectively.It's been concluded that paddy field conversion is the main threat that may undermine future rice production for Regency.The study recommended that proposed possible interventions should be considered along with formulating and implementing upcoming Regional Short, Medium, and long-term Development Plans.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".