Economic dynamics of agri‐food supply chains at a macrolevel
Bibliographic record
Abstract
Abstract This paper employs a comprehensive methodology that integrates demand and supply elasticities, performance metrics, and data envelopment analysis to analyze agri‐food supply chains at a macroeconomic level. It utilizes data from a global sample of 53 countries spanning the years 2000 to 2019. The findings from various levels of analysis reveal several key insights: first, Australia or Cameroon should allocate all their food resources to enhance the capacity utilization of their Food Supply Chains (FSC). Second, Ghana or New Zealand may concentrate 100% of their resources on improving the Responsiveness of their FSC. Third, Switzerland, Turkey, and the United States should allocate 100% of their food resources to adjust resource allocation within their FSC. Fourth, for countries like Costa Rica and Canada, the allocation should be 19% to capacity utilization and 81% to resource allocation. Fifth, countries such as Cyprus, the Dominican Republic, Ecuador, and El Salvador should prioritize 12% of inputs for resilience and allocate the remaining 88% to resource allocation. Sixth, for countries like Nicaragua, Niger, Nigeria, Russia, South Korea, Serbia, Senegal, and Singapore, the allocation should be 28% of inputs to responsiveness, with the majority, 72%, directed to resource allocation. Sixth, Nigeria, Ethiopia, and Ghana have better performance across critical variables. This study introduces an innovative framework that facilitates the interaction of agri‐food supply chains with various macroeconomic variables, including GDP, inflation, corruption, food security, and nutrition, opening new avenues for a holistic understanding of these systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".