Different places, different challenges: mapping global variations in agrifood-system burdens
Bibliographic record
Abstract
Abstract The global agrifood system is central to many challenges humanity faces today. Despite significant growth in total production, it fails to ensure food security for all, drives biodiversity decline, and majorly contributes to climate change. Research on agrifood-system burdens often focusses on the national level and isolated burdens, ignoring their systemic complexity. We address this knowledge gap by combining global subnational datasets proxying four key dimensions of agrifood-system burdens: environmental footprint, climate change, income poverty, and malnutrition. We map global hotspots of co-occurring agrifood-system burdens for 2017. We overlay these with data on ambient population counts, agricultural areas, farm size distributions, and lands inhabited by Indigenous peoples to identify spatial correspondence between people in vulnerable contexts and food production regions facing these burdens. We further assess countries’ relative burden against their inequality and governance indicators. Burden hotspots occupy many regions worldwide, especially in low-income, (sub)tropical regions. Single burdens occupy regions harbouring about 5 billion people (∼66% of the global population) and 1.8 billion ha of agricultural lands (∼40%), while multiple burdens occupy regions with about 1.9 billion people and 470 million ha agricultural lands. Environmental footprint is the strongest contributor to these burden profiles. Regions with traditionally marginalised communities (i.e. small-scale farmers and Indigenous peoples) disproportionally face multiple burdens. Agrifood-system burdens are more prevalent in countries with higher economic inequality and poorer governance. Burden profiles vary substantially within and between countries, necessitating regionalised and context-specific policies for effective, bundled, and targeted solutions. Addressing agrifood-system burdens can also synergise with tackling other current global challenges, like biodiversity loss and environmental justice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".