Characterizing the Spatial Patterns of Global Fertilizer Application and Manure Production
Bibliographic record
Abstract
Abstract Agriculture has had a tremendous impact on soil nutrients around the world. In some regions, soil nutrients are depleted because of low initial soil fertility or excessive nutrient removals through intense land use relative to nutrient additions. In other regions, application of chemical fertilizers and manure has led to an accumulation of nutrients and subsequent water quality problems. Understanding the current level and spatial patterns of fertilizer and manure inputs would greatly improve the ability to identify areas that might be sensitive to aquatic eutrophication or to nutrient depletion. The authors calculated spatially explicit fertilizer inputs of nitrogen (N) and phosphorus (P) by fusing national-level statistics on fertilizer use with global maps of harvested area for 175 crops. They also calculated spatially explicit manure inputs of N and P by fusing global maps of animal density and international data on manure production and nutrient content. Significantly higher application rates were found for both fertilizers and manures in the Northern Hemisphere, with maxima centered on areas with intensive cropland and high densities of livestock. Furthermore, nutrient use is confined to a few major hot spots, with approximately 10% of the treated land receiving over 50% of the use of both fertilizers and manures. The authors’ new spatial disaggregation of the rich International Fertilizer Industry Association (IFA) fertilizer-use dataset will provide new and interesting avenues to explore the impact of anthropogenic activity on ecosystems at the global scale and may also have implications for policies designed to improve soil quality or reduce nutrient runoff.
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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 | 0.004 |
| 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".