COVID-19 pandemic and food security: Strategic agricultural budget allocation in Indonesia
Bibliographic record
Abstract
The COVID-19 pandemic has exposed significant vulnerabilities in Indonesia's food security, highlighting the need for more strategic agricultural budget allocation. This study addresses the issue of inadequate budgeting techniques that fail to effectively support food production and distribution during crises. The primary objective is to develop a tailored framework that optimises budget allocations to strengthen Indonesia's food security in the face of socio-economic and geographical challenges. Using a multi-dimensional research approach, the study involves key stakeholders from the Ministry of Agriculture, the Ministry of Finance, and the Ministry of National Development Planning, employing methods such as in-depth interviews, focus group discussions, and data collection from primary and secondary sources. The research utilises the Analytical Hierarchy Process (AHP) with the Expert Choice 2000 program to systematically evaluate decision-making options. Findings reveal the necessity of a resilient framework that balances short-term emergency responses with long-term strategies, focusing on increasing production capacity, improving seed quality, expanding land availability, and adopting advanced agricultural technologies. The study contributes to the policy landscape by offering a structured agricultural budgeting framework and policy recommendations aimed at mitigating disruptions, enhancing productivity, and supporting sustainable agricultural practices. It also emphasizes the importance of sensitivity analysis in budget planning to inform strategic decisions. The study recommended targeted investments in production capacity, a reevaluation of rice import regulations, and the adoption of innovative technologies to ensure a resilient and sustainable food system in Indonesia.
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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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".