Contribution of anthropogenic phosphorus to agricultural soil fertility and food production
Bibliographic record
Abstract
Abstract Agricultural intensification over the last few decades has been accompanied by the extensive use of anthropogenic phosphorus (P) derived from mined phosphate rock. Given the increasing scarcity of P resources, accurate estimates of the reliance of agriculture on anthropogenic P are required. Here we propose a modeling approach for assessing the contribution of anthropogenic P to agricultural soil fertility and food production. We performed computations at country level, and France was chosen as a typical western European country with intensive agriculture. Four soil P pools were identified based on their bioavailability (labile versus stable) and origin (anthropogenic versus natural). Pool evolution between 1948 and 2009 was estimated by combining international databases and a simple biogeochemical model. An optimization procedure demonstrated the necessity of representing a stable P pool capable of replenishing the labile pool within 14 to 33 years in order to match country‐scale observations. Mean simulated P pool sizes for 2009 (0–35 cm soil horizon) were 146, 616, 31, and 156 kgP/ha for natural stable, anthropogenic stable, natural labile, and anthropogenic labile pools, respectively. We found that, on average, 82% (min‐max: 68–91%) of soil P (sum of labile and above defined stable) in that year was anthropogenic. The temporal evolution of this contribution is directly related to the integral of chemical fertilizer use over time, starting from 1948. The contribution of anthropogenic P to food production was similar at 84% (min‐max: 72–91%), which is greater than budget‐based estimates (~50–60%) commonly reported in the literature. By focusing on soil fertility and food production, this study provides a quantitative estimation of human perturbations of the P cycle in agroecosystems.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".