Coating of corn seeds: scientific advances and global collaborations towards agricultural sustainability
Bibliographic record
Abstract
Seed coatings play a critical role in modern agriculture by incorporating protectants, fertilizers and beneficial microorganisms to enhance germination, plant establishment and resistance to biotic and abiotic stresses. In recent years, scientific interest in these technologies has increased, driven by the need for more efficient and sustainable production systems. However, gaps remain in understanding key research trends, leading authors, and international collaborations in the field. This study aimed to conduct a detailed bibliometric analysis of the scientific production on corn seed coating to identify research trends, collaborative networks, and scientific impact. A total of 239 articles from the Web of Science and Scopus databases were analyzed, revealing significant growth in publications, particularly in China, the USA and Brazil. Key topics include biological control, seed treatment efficacy, and environmental impact mitigation, reflecting the balance between productivity and agricultural sustainability. The analysis identified the most influential authors, institutions and journals, with Crop Protection and Pest Management Science playing a central role. A strong international collaborative network was also observed, with countries such as Canada and Denmark showing high relevance and citation impact despite lower publication volumes. Future trends point to the development of coatings that integrate biological control agents and controlled-release nutrient technologies to promote environmentally friendly agricultural practices. This study provides a strategic perspective to guide scientific advances and influence global policy toward sustainable maize production.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.050 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".