Mitigating life-cycle multiple environmental burdens while increasing ecosystem economic benefit and crop productivity with regional universal nitrogen strategy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Nitrogen fertiliser is critical for increasing crop yields worldwide, but excessive use causes significant N losses in various forms and subsequent environmental issues, such as greenhouse gas (GHG) emissions. Establishing regional universal nitrogen strategy (RUNs) is indispensable for technology adoption, resource conservation, and pollution mitigation in crop production. OBJECTIVES: This study aims to develop a regional universal nitrogen fertilizer strategy to address variations in N application effectiveness, balancing agricultural productivity with environmental and eco-economic benefits. METHODS: We conducted a total of 48 site-year field experiments including no nitrogen application (Control), farmers' practice (FP), and the implementation of the RUNs with optimized nitrogen recommended formulas and one-off application method. RESULTS: The RUNs significantly increased yields by 5.9%, 12%, and 11% for grain, sweet, and silage maize, respectively, compared with FP. Further, RUNs reduced life-cycle potentials of global warming, soil acidification, water eutrophication, and energy depletion by 22-45%, 63-76%, 51-73%, and 46-67%, respectively. The RUNs increased economic benefits by 11%-58.2%, and net ecosystem-economic benefits by 11.3-77.5%, particularly through the reduction of nitrogen fertiliser and labour-associated agricultural and ecological costs. CONCLUSION: We propose that the RUNs reconciled crop yield, resource efficiency, environmental impacts, and ecosystem economic benefits, demonstrating a regional sustainable N strategy for global food security and resource conservation.
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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 | 0.000 |
| 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 it