Frequent failure of nutrients to increase plant biomass supports the need for precision fertilization in agriculture
Bibliographic record
Abstract
Implementing precision fertilization to maximize crop yield while minimizing economic and environmental impacts has become critical for agriculture. Variability in biomass response to fertilization within fields, among regions, and over time creates simultaneous risks of under-yielding and overfertilization. We quantify factors determining fertilization responsiveness (i.e., biomass increases with fertilization) up to 15 years in 61 unfertilized rangelands on six continents. We demonstrate widespread multi-year variability in responsiveness, with fertilization increasing average yield by 43% but failing to improve biomass 26% of the time. All sites were responsive at least once, but only four of 61 responded in all plots and years. Modelled management scenarios highlighted that fertilizer cessation is likely to generate sizable economic savings but always reduces yield because of the difficulty in predicting when and where biomass will be unresponsive. This work reveals substantial scale-dependent variability in fertilization responsiveness globally, while clarifying the prospects and pitfalls of managing more spatially and temporally precise nutrient application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".