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Record W4404049108 · doi:10.1016/j.jafr.2024.101494

COVID-19 pandemic and food security: Strategic agricultural budget allocation in Indonesia

2024· article· en· W4404049108 on OpenAlexaff
Akbar Akbar, Rahim Darma, Andi Irawan, Mahyuddin Mahyuddin, Feryanto Feryanto, Rida Akzar

Bibliographic record

VenueJournal of Agriculture and Food Research · 2024
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsArtificial Insemination Center of Quebec
FundersMinistry of Agriculture of the People's Republic of China
KeywordsFood securityAgriculturePandemicCoronavirus disease 2019 (COVID-19)BusinessGeographyMedicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

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, focussing 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 emphasises 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. • Agricultural budget strategy aims to improve food security resilience during crisis. • Integrating technology and community empowerment optimise budget for food security. • Key stakeholders offer insights for enhancing food security decision-making. • Policymaker guidance helps navigate food security and disaster resilience. • Budget allocation strategies enhance disaster preparedness and food security.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.283
GPT teacher head0.495
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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