Ensuring Urban Food Security in Malaysia during the COVID-19 Pandemic—Is Urban Farming the Answer? A Review
Bibliographic record
Abstract
Urbanisation and related insufficiency of food sources is due to the high urban population, insufficient urban food sources, and inability of some urban communities to afford food due to rising costs. Food supply can also be jeopardised by natural and man-made disasters, such as warfare, pandemics, or any other calamities which result in the destruction of crop fields and disruption of food distribution. The COVID-19 pandemic exposed the impact of such calamities on the fresh food supply chain in Malaysia, especially when the Movement Control Order (MCO) policy was first implemented. The resulting panic buying caused some food shortage, while more importantly, the fresh food supply chain was severely disrupted, especially in urban areas, in the early stages of implementation. In this regard, urban farming, while a simple concept, can have a significant impact in terms of securing food sources for urban households. It has been used in several countries such as Canada, The Netherlands, and Singapore to ensure a continuous food supply. This paper thus attempted to review how the pandemic has affected Malaysian participation in urban farming and, in relation to that, the acceptance of urban farming in Malaysia and the initiatives and approaches of local governmental and non-governmental organisations in encouraging the urban community to participate in urban farming through peer-reviewed journal articles and other articles related to urban agriculture using the ROSES protocol. About 93 articles were selected after screening to ensure that the articles were related to the study. During the COVID-19 pandemic, the surge in Malaysians’ awareness of the importance of urban farming has offered great opportunities for the government to encourage more Malaysian urban communities to participate in urban farming activities. Limitations such as relevant knowledge, area, and space, however, are impediments to urban communities’ participation in these activities. Government initiatives, such as the Urban Community Garden Policy (Dasar Kebun Komuniti Bandar (DKKB)), are still inadequate as some issues are still not addressed. Permanent Food Production Parks (TKPM) and technology-driven practices are seen as possible solutions to the primary problem of land and space. Additionally, relevant stakeholders play a crucial role in disseminating relevant and appropriate knowledge and methodology applicable for urban farming. Partnerships between government agencies, the education sector, and the private sector are necessary to develop modern urban agricultural technologies as well as knowledge, knowhow, and supports to build and sustain urban community participation in urban farming activities.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| 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 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".