Sustainable Agriculture: Impact on Public Health and Sustainable Development
Bibliographic record
Abstract
Sustainable agriculture involves the use of organic farming technologies, which excludes the use of growth stimulants, hormones and chemical additives in animal feeding as well as fertilisers with pesticides and other hazardous chemicals. Sustainable land management practices are aimed at preserving soils, water resources and biodiversity. They mitigate the effects of climate change by reducing greenhouse gas emissions and preserving natural environments. Organic agriculture produces environmentally friendly food that contains fewer harmful chemicals, potentially reducing the incidence of diet-related disorders such as cardiovascular diseases, diabetes and cancer. This study conducts a comprehensive bibliometric analysis (via VOSviewer 1.6.16, Bibliometrix / Biblioshiny App) to explore the sustainable agriculture impact on public health and sustainable development of nations. Applying the Scopus database, 427 relevant papers were reviewed to identify trends, influential works, and key research themes. The analysis reveals that sustainable agricultural practices, which aim to reduce environmental impact, conserve resources and enhance productivity, have gained increasing attention since the mid-1990s. Noteworthy, contributions include fundamental works, influential studies and various UN and WHO publications. The research highlights keywords such as agriculture, public health, nutrition, and food security. Leading countries in this research domain include the USA, China, Canada, India, and the UK, demonstrating extensive international collaboration. The findings underscore the critical role of sustainable agriculture in addressing global challenges, promoting environmental stewardship, and supporting socio-economic progress, which aligns with the UN Sustainable Development Goals. The study provides valuable insights into the development of sustainable practices and policies essential for future research and implementation.
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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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".