Responsible plant nutrition: A new paradigm to support food system transformation
Bibliographic record
Abstract
The coming 10–20 years will be most critical for making the transition to a global food system in which mineral nutrients in agriculture must be managed in a more holistic manner. Fertilizers play a particular role in that because they are among the key drivers for securing global food security and improving human nutrition through increased crop yields and nutritional quality. A new paradigm for responsible plant nutrition follows a food systems and circular economy approach to achieve multiple socioeconomic, environmental and health objectives. Achieving that requires utilizing all available organic and inorganic nutrient sources with high efficiency, tailored to the specific features of food systems and agroecosystems in different world regions. Critical actions include: (i) sustainability-driven nutrient roadmaps, (ii) digital crop nutrition solutions, (iii) nutritious crops, (iv) nutrient recovery and recycling, (v) climate-smart fertilizers, and (vi) accelerated innovation. The outcome of this transformation will be a new societal plant nutrition optimum rather than a purely economic optimum. New partnerships and sustainability-focused business models will create added value for all actors in the nutrient chain and benefit farmers as well as consumers. Research needs to become more problem-driven and merge excellent science with entrepreneurial innovation approaches in order to develop robust solutions faster and at larger scale. Evidence-based policies should focus on creating and supporting the necessary nutrient stewardship roadmaps, including realistic national targets, progressive regulation and incentives that support technology and business innovation.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".