Agrifood systems' resilience for sustainable food security amid geopolitical tensions: a systematic literature review
Bibliographic record
Abstract
Introduction This study examines the resilience of the agrifood systems amid geopolitical tensions with a primary focus on the Ukraine-Russia war and its increased effects on global food security, climate change, and post-pandemic recovery. The study explores different resilience elements, scenarios, and behaviors of agrifood systems, highlighting how geopolitical conflicts disrupt resource availability and economic stability. Further, it explores the existing Resource Nexus and its influence on sustainable food and nutrition security amid geopolitical tension. Much research focuses on agrifood systems' resilience in the context of climate change and pandemics, repeatedly overlooking the impacts of geopolitical tensions and related policies enacted for sustainable food security. Methods Focused on geopolitical tension as an influence on food security, 76 articles were systematically reviewed to identify key resilience elements and scenarios enacted based on countries' development, discovered major vulnerability indicators, and Resource Nexus of agrifood systems. Results This review leads to the identification of four key resilience scenarios of the agrifood system amid geopolitical tensions: fragility reduction, robustness building, adaptive strategies, and transformative change over time. In general, the reduction of agrifood system fragility was more prevalent compared to the other three scenarios. There was a decline in the agrifood system's performance due to the existence of some policies that increased the system's instability over time. The study further identifies that the impact of enacted resilience policies on sustainable food security is not uniform. It often influences positive or negative outcomes depending on its feedback nature at different operational levels of the agrifood system. During geopolitical tensions, food, energy, and finance are the most affected sectors, followed by other interconnected resources such as land, water, food (LWF), and water, energy, and food (WEF). Discussion In the presence of effective policies and scenarios, the agrifood system experiences improved resilience and sustainability that contribute to the beneficial relationship between resources, and all pillars of food security.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".