ARTIFICIAL INTELLIGENCE AS A CATALYST FOR EFFICIENCY IN AGRICULTURAL MANAGEMENT AND MARKETING IN THE CONTEXT OF EUROPEAN INTEGRATION
Bibliographic record
Abstract
The article examines the potential of using artificial intelligence (hereafter – AI) as a tool for increasing efficiency in agricultural management and marketing. The emphasis is placed on the need to critically examine the opportunities and challenges of integrating AI into the agricultural business. The factors that influence the success of implementation, including investments in technological infrastructure, staff training, process adaptation, as well as financial and organizational constraints faced by agricultural enterprises in the current conditions of global transformations, are investigated. The author analyzes current achievements and prospects of using AI to improve the accuracy of demand forecasting, optimize supply chains, automate business processes, and improve marketing strategies. Particular attention is paid to the role of AI in the context of Ukraine’s European integration, which opens up access to innovative technologies, investments, and European markets. The author analyzes the main aspects of the impact of European integration processes on the development of Ukraine’s agricultural sector, including standardization, financial support, staff development, and infrastructure modernization. It was substantiated that AI has significant potential to optimize resource management, increase the competitiveness of agricultural enterprises and ensure the sustainable development of the industry. Recommendations for the effective implementation of AI in the agricultural sector are presented, taking into account the challenges and opportunities created by modern technologies. The author analyzes successful cases of AI use in the agricultural sector and its role in increasing the productivity, efficiency and sustainability of agricultural production. International experience is analyzed, in particular in the United States, Israel, Canada, and the Netherlands, where AI helps to predict yields, optimize resources, and develop sustainable technologies in agriculture. The importance of European integration for the development of the domestic agricultural sector through the introduction of modern standards, innovations and financing is emphasized. The results of the study can be used to develop the marketing strategies for the introduction of AI in the field of agricultural management, which will ensure the sustainable development of the industry and increase its competitiveness in international markets.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".