Global Opportunities to Increase Agricultural Independence Through Phosphorus Recycling
Bibliographic record
Abstract
Abstract Food production hinges largely upon access to phosphorus (P) fertilizer. Most fertilizer P used in the global agricultural system comes from mining of nonrenewable phosphate rock deposits located within few countries. However, P contained in livestock manure or urban wastes represents a recyclable source of P. To inform development of P recycling technologies and policies, we examined subnational, national, and global spatial patterns for two intersections of land use affording high P recycling potential: (a) manure‐rich cultivated areas and (b) populous cultivated areas. In turn, we examined overlap between P recycling potential and nation‐level P fertilizer import dependency. Populous cultivated areas were less abundant globally than manure‐rich cultivated areas, reflecting greater segregation between crops and people compared to crops and livestock, especially in the Americas. Based on a global hexagonal grid (290‐km2 grid cell area), disproportionately large shares of subnational “hot spots” for P recycling potential occurred in India, China, Southeast Asia, Europe, and parts of Africa. Outside of China, most of the remaining manure‐rich or populous cultivated areas occurred within nations that had relatively high imports of P fertilizer (net P import:consumption ratios ≥0.4) or substantial increases in fertilizer demand between the 2000s (2002–2006) and 2010s (2010–2014). Manure‐rich cultivated grid cells (those above the 75th percentiles for both manure and cropland extent) represented 12% of the global grid after excluding cropless cells. Annually, the global sum of animal manure P was at least 5 times that contained in human excreta, and among cultivated cells the ratio was frequently higher (median = 8.9). The abundance of potential P recycling hot spots within nations that have depended on fertilizer imports or experienced rising fertilizer demand could prove useful for developing local P sources and maintaining agricultural independence.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".