Development, Efficiency, and Impact Factors of Phosphorus Nanofertilizers in Agriculture: A Review
Bibliographic record
Abstract
ABSTRACT Modern intensive agriculture to feed the growing population of the world has been practiced in view of the limitations of arable land and water resources. However, overapplication of chemical fertilizers may pose severe environmental impacts, including soil degradation, water eutrophication, and pollution of groundwater. One of the current trends to reduce the adverse effects of fertilizers is the application of nanotechnology, which has an exciting potential to increase fertilizer efficiency and sustainability. We carried out a systematic literature review through Web of Science and Scopus. The used keywords were “nanofertilizers,” “phosphorus nanoparticles,” “nutrient‐use efficiency,” and “crop development.” Only studies that meet strict inclusion criteria regarding synthesis, application, plant uptake mechanism, and detailed insight into nanofertilizers were considered. To better understand the conditions that most foster nanofertilizer application, we reviewed the factors affecting nutrient‐use efficiency in plants. On this basis, focusing on phosphorus (P), we analyzed recent progress made on using P nanofertilizers and their effect on the development of various crops. The economic and environmental benefits and drawbacks of using nanofertilizers are both presented to make informed decisions by both farmers and industries on the use and manufacturing of the substances. Our review has established that nanofertilizers hold great promise for improving nutrient‐use efficiency and crop productivity, with special mention of phosphorus nanofertilizers, which can offer improved nutrient uptake, reduced environmental pollution, and possibly reduced fertilizer application rates. However, P nanofertilizers face adoption barriers such as unstudied long‐term environmental impacts, costly and unsustainable production methods, inconsistent performance across soils and crops requiring tailored formulations, and lack of regulations and safety guidelines. Transitioning to practical use demands prioritized research using long‐term trials and affordable green synthesis methods to address these gaps.
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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".