Marketing strategies for the development of agricultural exports of Kazakhstan in world markets: Interregional comparative analysis
Bibliographic record
Abstract
The present research was aimed at analysing the marketing strategies of agricultural exports and identifying effective approaches to the promotion in world markets. In the course of the work, the methods of comparative analysis of marketing strategies of leading exporting countries, research of official statistical data, assessment of the impact of international trade agreements and standards were used, as well as analysis of state export support programmes. The results of the study revealed key features of marketing approaches in different regions and the applicability to the Kazakhstan market, where wheat exports in 2023 amounted to 6.5 million tonnes, or 3.13% of the world volume of 208 million tonnes. Successful models of agro-product exports from countries such as the USA (19.8 million tonnes of wheat in 2023), Canada (22 million tonnes) and Australia (24.5 million tonnes) were studied to identify the most effective promotion tools, including branding and adaptation to market standards. The analysis showed that improving the competitiveness of Kazakhstani products required adapting strategies to the requirements of specific regions, taking into account consumer preferences and improving certification to international standards. It was noted that Kazakhstan's participation in the Eurasian Economic Union contributed to the doubling of mutual trade since 2015. The development of government export support programmes was also noted as an important factor, which also needed to be strengthened: financial and advisory assistance to exporters, modernisation of logistics (where wheat transportation costs were USD 80-100 per tonne versus USD 50-60 for competitors), and digitalisation of processes remained priorities. Based on the data obtained, recommendations were developed to improve Kazakhstan's marketing strategies for agrarian exports, aimed at optimising logistics, developing partnerships with international distributors, increasing brand awareness, and actively using digital technologies to promote products
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".