Trends in Agricultural Products Marketing: A Bibliometric Analysis and Future Research Agenda
Bibliographic record
Abstract
This study aims to explore and demonstrate sustainable improvements in the domain of marketing and agricultural products by conducting a comprehensive bibliometric analysis. The bibliographic data for this research were meticulously sourced from the Scopus database, an internationally recognized platform known for its inclusion of high-quality, peer-reviewed academic publications. A precise and well-defined search query was employed to ensure the integration of a robust and relevant body of literature. The search string used was “Marketing” AND “Agriculture product,” which allowed the study to encompass a wide range of themes related to agricultural marketing, including aspects of consumer behavior, market dynamics, and innovation in the agricultural sector. The analysis was conducted using R Studio and VOSviewer software, which facilitated the mapping and visualization of bibliometric networks and trends within the dataset. The findings of the study reveal key thematic trends in marketing agricultural products, such as risk management, transaction costs, consumer preferences, agricultural markets, corn prices, Africa, and product quality. Moreover, the results highlight significant geographical interest from countries including the USA, France, Germany, Canada, India, Spain, Greece, and Italy. Strong co-occurrence patterns were identified between keywords such as marketing and innovation, agricultural marketing and food, cooperatives and India, as well as farmers and India. These insights offer valuable guidance for future research and policymaking.
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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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.125 | 0.237 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".