Nanofertilizers and Nanopesticides for Sustainable Agriculture, Food Security and Environmental Quality
Bibliographic record
Abstract
During human history, sustainable agriculture has been strongly advocated as one of the most practicable solutions that can effectively address the burning issues of food security and environmental degradation. In this context, the research on nanofertilizers and nanopesticides has gained significant momentum due to their potential to revolutionize agricultural practices. The current chapter introduces modern agriculture’s pressing challenges, such as declining soil fertility, nutrient depletion, increasing pest resistance, and environmental pollution caused by the non-judicious use of conventional agrochemicals. It has been strongly advocated that the unwise use of chemical fertilizers and pesticides is the main culprit of the high input cost of farming, lower net economic returns of the farming community, and environmental degradation. Keeping these facts in mind, the “Montreal-Kunming Agreement”—adopted by the UN Biodiversity Forum (2022)—has strongly urged addressing the menace of soil and water bodies’ pollution to achieve healthy environments by 2030. Hence, there is a dire thrust to adopt all possible environmentally friendly and economically viable approaches to realize the human dream of sustainable agriculture and healthy environments. Nanofertilizers are designed with precision and tailored to specific plant nutrient requirements to offer enhanced nutrient uptake and use efficiency, increased crop productivity, reduced environmental impacts, and improved soil health, promoting sustainable crop production and food security scenarios. An intelligent nutrient release system, including nanofertilizers in plant nutrition programs, may positively impact crop yield and product quality, enhance crop biotic and abiotic stress tolerance, and mitigate greenhouse gas emissions. Various case studies and field trials have been reported in the literature to advocate the effectiveness and practical applications of nanofertilizers. Moreover, nanopesticides appeared as an innovative solution for eco-friendly pest management since they are designed to target pests more effectively, exhibit enhanced efficacy, and reduce environmental persistence. By ensuring the site-specific application of pesticides where they are required, the nano-formulations offer significant potential to reduce chemical exposure to non-target organisms, especially crop-friendly insects, and minimize ecological degradation. This chapter critically analyzes the potential benefits and challenges of using nanofertilizers and nanopesticides for sustainable agriculture and a healthy environment. Environmental concerns, toxicity, and long-term effects on ecosystems are discussed to ensure the responsible and sustainable adoption of nanobiotechnology in food production. Finally, the chapter highlights the prospects of nanofertilizers and nanopesticides in sustainable agriculture, along with the need for rigorous research and development in nanotechnology, developing the regulatory frameworks, and public awareness and acceptance to harness the full potential of nanobiotechnology for achieving the goals of sustainable agriculture, food security, and benign environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".