The resilience of domestic transport networks in the context of food security – A multi-country analysis
Bibliographic record
Abstract
This study aims to help bring the domestic food transport network into focus for The State of Food and Agriculture 2021 – Making agrifood systems more resilient to shocks and stresses. Transport infrastructure and logistics, not least domestic food transport networks, are an integral part of agrifood systems, and play a fundamental role in ensuring physical access to food at the local level, as well as in producing non-food agricultural output. The flow of food from farm to fork is vulnerable to various shocks; however, the resilience of this flow has rarely been studied. This study aims to fill that gap; in doing so, it develops a spatial analysis framework that realistically characterizes the physical transport network, and uses this framework to then analyse the network’s ability to transport enough food to meet demand. The analysis builds on a preliminary spatial workflow and on evaluated resilience metrics to analyse the structure of transport networks in the context of national food transport network resilience. For a total of 90 countries, it considers road, river and rail transport infrastructure, along with trade ports, border crossings and their respective import and export quantities. It then measures food transport network resilience for each country through three main indicators: proximity-based resilience, relative detour cost and alternative route availability. Findings show that where food is transported more locally and where the network is denser, disturbances have a much lower impact. This is mostly the case for high-income countries, as well as for densely populated countries like China, India, Nigeria and Pakistan. Conversely, low-income countries have much lower levels of transport network resilience, although some exceptions exist. A simulation of the impact of localized 1-in-10-year flooding events in Mozambique, Nigeria and Pakistan is also used to capture the effect of potential disruptions to food transport networks for crops in the three countries. The simulation illustrates the loss of network connectivity that results when links become impassable, potentially affecting millions of people.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 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".