Review on enhancing the efficiency of fertilizer utilization: Strategies for optimal nutrient management
Bibliographic record
Abstract
Abstract The increasing global population and growing demand for food and mineral fertilizers underscore the urgent need to enhance fertilizer efficiency. This imperative emphasizes the importance of sustainable fertilizer utilization while mitigating environmental impacts, particularly in addressing agricultural water pollution. Excessive fertilizer use contributes significantly to water contamination and food shortages worldwide. In 2018, food shortages were reported in many nations, including the United States (2.3%), Canada (4.6%), the United Kingdom (8.2%), Germany (2.6%), Japan (2.9%), Ethiopia (23.4%), Ivory Coast (22.4%), Bangladesh (12.7%), Pakistan (17.2%), Haiti (45.6%), and India (14.3%). Moreover, agricultural activities, particularly the use of mineral fertilizers, are major contributors to greenhouse gas emissions. Inefficient fertilizer practices lead to economic losses, environmental degradation, and food insecurity. Studies reveal that in sub-Saharan Africa, farmers receive only about $0.50 in increased productivity for every dollar spent on fertilizer due to inefficiencies. The economic cost of nutrient pollution in the European Union is estimated to range between €7 billion and €10 billion annually. Effective strategies like precision nutrient management, best practices, and innovative technologies optimize fertilizer efficiency and support agricultural sustainability. Besides, promising methods include the combined use of organic and inorganic fertilizers, the application of remote sensing and geographical information system technologies, and the implementation of biological approaches to enhance nutrient management. Moreover, monitoring and evaluation are essential for assessing strategy effectiveness, guiding decision-making, and taking corrective actions. Hence, this review aims to address strategies for improving fertilizer efficiency, sustainable agriculture practices, and addressing food security and environmental concerns related to fertilizer use comprehensively.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".