Food security and disruptions of the global food supply chains during COVID-19: building smarter food supply chains for post COVID-19 era
Bibliographic record
Abstract
Purpose The COVID-19 pandemic has revealed the fragility of the complex global food supply chains (GFSCs) which has drastically affected the essential flow of food from the farms and producers to the final consumers. The COVID-19 outbreak has served as a great lesson for the food businesses and companies to re-strategize toward the post-COVID-19 era. This paper examines the impact of COVID-19 pandemic on food security and global food supply chains using the two countries (Canada and the United States) in North America as the case studies and provides appropriate strategy or framework to build smarter and resilience food supply chains for post-COVID-19 era. Design/methodology/approach This paper is a general review of the impacts of COVID-19 pandemic on food security and disruptions of the GFSCs. This paper conducted a comprehensive literature review to have a complete understanding of the study, identify the research problem and missing gaps in literature and to formulate appropriate research questions. This study uses two countries from North America (Canada and the US) as case studies by analyzing the available open data from Statistics Canada and some recent studies conducted on food insecurity in the US. Finally, based on the findings, a proposed approach or framework was presented. Findings The findings from this study establishes that COVID-19 pandemic has greater impacts on the food security and GFSC due to disruption of the food supply chain leading to increase food insecurity in Canada and the US. The findings clearly show how the COVID-19 pandemic has disrupted the GFSC in the following ways – poor economy, shortage of farm worker, limitation to food accessibility, restriction in the transportation of farm commodities, changes in demand of consumers, shutdown of food production facilities, uncertainty of food quality and safety, food trade policies restriction, delays in transportation of food products, etc. The main findings of this study show that food and beverages sector needs to re-strategize, re-shape and re-design their food supply chains with post-COVID-19 resilience approach in mind. As a result, this study presents a proposed approach or framework to build a smarter and resilience GFSCs in the post-COVID-19 era. The findings in this study highlights the way the proposed framework provide solutions to the identified problems created by the COVID-19 pandemic in affecting the GFSC. Originality/value The contribution of this study towards the existing body of knowledge in food security and GFSC is in the form of a proposed approach or framework for building smarter and resilience GFSC that would assist the key players in the food industry to respond better and faster to the ongoing COVID-19 pandemic, including post-COVID-19 era.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".