Regional Differences in Phosphorus Budgets in Intensive Soybean Agriculture
Bibliographic record
Abstract
Fertilizer-intensive agriculture has been integral to increasing food production over the past half century but has been accompanied by environmental costs. We use case studies of phosphorus fertilizer use in the world's most productive soybean-growing regions, Iowa (United States), Mato Grosso (Brazil), and Buenos Aires (Argentina), to examine influences of management and soil type on agriculture's most prevalent phosphorus-related environmental consequences: eutrophication and consumption of Earth's finite phosphorus reserves. With increasing phosphorus inputs, achieving high yields on tropical soils with high phosphorus-binding capacity is becoming more common. This system has low eutrophication risks but increases demands on phosphorus supplies. In contrast, production in traditional breadbaskets, on soils with lower phosphorus-binding capacities, is being sustained with decreasing phosphorus inputs. However, in these regions, historical overuse of phosphorus may mean continued eutrophication risk even as pressures on phosphorus reserves diminish. We focus here on soybean production but illustrate how achieving sustainable agriculture involves an intricate optimization of local, regional, and global considerations.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".