Agronomic phosphorus imbalances across the world's croplands
Bibliographic record
Abstract
Increased phosphorus (P) fertilizer use and livestock production has fundamentally altered the global P cycle. We calculated spatially explicit P balances for cropland soils at 0.5° resolution based on the principal agronomic P inputs and outputs associated with production of 123 crops globally for the year 2000. Although agronomic inputs of P fertilizer (14.2 Tg of P·y(-1)) and manure (9.6 Tg of P·y(-1)) collectively exceeded P removal by harvested crops (12.3 Tg of P·y(-1)) at the global scale, P deficits covered almost 30% of the global cropland area. There was massive variation in the magnitudes of these P imbalances across most regions, particularly Europe and South America. High P fertilizer application relative to crop P use resulted in a greater proportion of the intense P surpluses (>13 kg of P·ha(-1)·y(-1)) globally than manure P application. High P fertilizer application was also typically associated with areas of relatively low P-use efficiency. Although manure was an important driver of P surpluses in some locations with high livestock densities, P deficits were common in areas producing forage crops used as livestock feed. Resolving agronomic P imbalances may be possible with more efficient use of P fertilizers and more effective recycling of manure P. Such reforms are needed to increase global agricultural productivity while maintaining or improving freshwater quality.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".