Trade in the US and Mexico helps reduce environmental costs of agriculture
Bibliographic record
Abstract
Increasing international crop trade has enlarged global shares of cropland, water and fertilizers used to grow crops for export. Crop trade can reduce the environmental burden on importing countries, which benefit from embedded environmental resources in imported crops, and from avoided environmental impacts of production in their territory. International trade can also reduce the universal environmental impact of food production if crops are grown where they are produced in the most environmentally efficient way. We compared production efficiencies for the same crops in the US and Mexico to determine whether current crop trade between these two countries provides an overall benefit to the environment. Our economic and environmental accounting for the key traded crops from 2010 to 2014 shows that exports to Mexico are just 3% (∼16 thousand Gg) of the total production of these crops in the US, and exports to US represent roughly 0.13% (∼46 Gg) of Mexican total production of the same crops. Yields were higher in US than Mexico for all crops except wheat. Use of nitrogen fertilizer was higher in US than in Mexico for all crops except corn. Current trade reduces some, but not all, environmental costs of agriculture. A counterfactual trade scenario showed that an overall annual reduction in cultivated land (∼371 thousand ha), water use (∼923 million m ^3 ), fertilizer use (∼122 Gg; ∼68 Gg nitrogen) and pollution (∼681 tonnes of N _2 O emissions to the atmosphere and ∼511 tonnes of leached nitrogen) can be achieved by changing the composition of food products traded. In this case, corn, soybeans and rice should be grown in the US, while wheat, sorghum and barley should be grown in Mexico. Assigning greater economic weight to the environmental costs of agriculture might improve the balance of trade to be more universally beneficial, environmentally.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".