The Inductiveness of Agricultural Village-Type Cluster Creation in Developing Countries
Abstract
The assessment of emerging risks is substantial risk in implementing and creating various types of clusters used in the agricultural sector of the economy. In this regard, the goal is to develop practical measures to ensure the creation of a cluster of an agricultural settlement at the regional level, taking into account various types of risk that directly affect its creation and development. The study revealed that within the framework of the policy of substitution for domestic production and marketing of agricultural products during the formation of a cluster, it would allow combining more into a standard established system from production, processing to the sale of finished agricultural products both at the local level and at the federal level. This approach will significantly harmonize the interests of all participants of the agroindustry, as well as significantly simplify and expand access to external export markets, thereby reducing the cost of marketing research. At the same time, clustering will increase the overall economic impact on individual farmers, which will have a more significant impact on the development of non-resource zonal territories employed to produce agricultural products. Therefore, it will affect the increase in jobs in small villages.
JEL Classification: F63, O13, Q18
How to Cite:
Petrova, L. I., Glubokova, N. Y., Akhmadeev, R. G., Bykanova, O. A., Artemova, E. I., & Gabdulkhakov, R. B. (2021). The Inductiveness of Agricultural Village-Type Cluster Creation in Developing Countries. Etikonomi, 20(2), xx– xx. https://doi.org/10.15408/etk.v20i2.22014
Keywords
References
Ahmed, K., Ahmed, N., Shahbaz, M., Ozturk, I., & Long, W. (2016). Modelling Trade and Climate Change Policy: a Strategic Framework for Global Environmental Negotiators. Journal of Water and Climate Change, 7(4), 731–748
Aleksandrova, Y.A., & Agapova, A.A. (2019). Peculiarities of Tax Monitoring in the Russian Federation. Scientific Almanac of Association France-Kazakhstan, 2, 170-174
Antimanon, S., Chamkhuy, W., & Sutthiwattanakul, S. (2018). Efficient Production of Arachidonic Acid of Mortierella sp. by Solid-state Fermentation Using Combinatorial Medium With Spent Mushroom Substrate. Chemical Papers, 72, 2899–2908.
Bacaria, J., El Aynaoui, K., & Woertz, E. (2015). Introduction to The Special Section “Food Trade Relations of the Middle East and North Africa with Countries of the Tropics: Opportunities and Risks of South-South Cooperation”. Food Security, 7, 1097–1099.
Baldos, U. L. C., & Hertel, T. W. (2015). The Role of International Trade in Managing Food Security Risks from Climate Change. Food Security, 7, 275–290.
Bellamy, A. S., Furness, E., Nicol, P., Pitt, H., & Taherzadeh, A. (2021) Shaping More Resilient and Just Food Systems: Lessons from The COVID-19 Pandemic. Ambio, 50(4), 782–793.
Bouët, A., & Debucquet, D. L. (2012). Food Crisis and Export Taxation: The Cost of Non-Cooperative Trade Policies. Review of World Economics, 148, 209–233.
Brisbois, M. C., Morris, M., De Lo, R. (2019). Augmenting the IAD Framework to Reveal Power in Collaborative Governance – An Illustrative Application to Resource Industry Dominated Processes. World Development, 120, 159–168
Brunori, G., Branca, G., & Cembalo, L. (2020). Agricultural and Food Economics: The Challenge of Sustainability. Agricultural Economics, 8, 12-21.
Caselli, M., Fracasso, A., & Schiavo, S. (2021). Trade Policy and Firm Performance: Introduction to The Special Section. Economics & Politics, 38, 1–6.
Conti, V., Ruffo, S. S., & Vitabile, S. (2019). BIAM: a New Bio-inspired Analysis Methodology for Digital Ecosystems Based on a Scale-Free Architecture. Soft Computer, 23, 1133–1150.
Decimo, M., Quattrini, M., & Ricci, G. (2017). Evaluation of Microbial Consortia and Chemical Changes in Spontaneous Maize Bran Fermentation. AMB Express, 7, 205-212.
Dudukalov, E., Kuznetsova, I., Poltarykhin, A., Mandrik, N., Aleshko, R., & Poletaev S. (2021). Modeling Human Capital Dependence and Production with a High Level of Automation. International Review, (1-2), 69-79.
Dunets, A. N., Gerasymchuk, N. A., Kurikov, V. M., Noeva, E. E., Kuznetsova, M. Y., & Shichiyakh, R. A. (2020). Tourism Management in Border Destinations: Regional Aspects of Sustainable Development of Protected Natural Areas. Entrepreneurship and Sustainability Issues, 7(4), 3253-3268. https://doi.org/10.9770/jesi.2020.7.4(45)
Du, W., & Li, M. (2020). Assessing The Impact of Environmental Regulation on Pollution Abatement and Collaborative Emissions Reduction: Micro-evidence from Chinese Industrial Enterprises. Environmental Impact Assessment Review, 82, 106382
Farhad, K. K., Mehdi, A. T., Yeganeh, M. J., & Aliakbar, K. (2016). Calculating The Social Costs of Carbon Dioxide Emissions in Different Provinces of Iran. Journal of Energy Planning and Policy Research, 2(2), 77–110
Genys, D., & Krikštolaitis, R. (2020). Clusterization of Public Perception of Nuclear Energy in Relation to Changing Political Priorities. Insights into Regional Development, 2(4), 750-764.
Ho, S.H., Zhang, C., Tao, F., Zhang, C., & Chen, W.H. (2020). Microalgal Torrefaction for Solid Biofuel Production. Trends Biotechnol, 38(9), 1023–1033.
Iacondini, A., Mencherini, U., & Passarini, F. (2015). Feasibility of Industrial Symbiosis in Italy as an Opportunity for Economic Development: Critical Success Factor Analysis, Impact and Constrains of the Specific Italian Regulations. Waste Biomass Valor, 6, 865–874.
Kokkinos, K., Karayannis, V., & Lakioti, E. (2019). Exploring Social Determinants of Municipal Solid Waste Management: Survey Processing with Fuzzy Logic and Self-organized Maps Environmental Science and Pollution Research, 26, 35288–35304.
Leena, H. U., Premasudha, B. G., & Basavaraja, P.K. (2020). Data Optimisation and Partitioning in Private Cloud Using Dynamic Clusters for Agricultural Datasets. International Journal of Dynamics and Control, 8, 1027–1039.
Luk’yanova, M. T., & Kovshov, V. A. (2019). Modern State and Development Trends in Small Forms of Agribusiness in the Republic of Bashkortostan. Studies on Russian Economic Development, 30, 299–302.
Magdouli, S., Brar, S. K., & Blais, J. F. (2016). Co-Culture for Lipid Production: Advances and Challenges. Biomass Bioenerg, 92, 20–30.
Memarzadeh, M., Mahjouri, N., & Kerachian, R. (2013). Evaluating Sampling Locations in River Water Quality Monitoring Networks: Application of Dynamic Factor Analysis and Discrete Entropy Theory. Environmental Earth Sciences, 70, 2577–2585.
Mollard, A. (2001). Qualité et Développement Territorial: Une Grille d’analyse Théorique à Partir de la Rente. Économie rurale, 263, 16–34.
Moreno, S., Durán, C., & Galindo, D. (2020). Statistical and Spatial Analysis of Census Data for the Study of Family and Industrial Farming in Colombia. Applied Spatial Analysis and Policy, 13, 693–713.
Ostapets, M. M. (2019). Analysis of Approaches for Investment Project Valuation. Scientific Almanac of the France-Kazakhstan Association, 2, 104-107
Poghosyan, V. (2018). Philosophies of Social Behavior Research: Meta-Analytic Review. Wisdom, 11(2), 85-92. https://doi.org/10.24234/wisdom.v11i2.212.
Silva, S., Rodrigues, A.C., Ferraz, A., Alonso, J. (2017). An Integrated Approach for Efficient Energy Recovery Production from Livestock and Agro-Industrial Wastes. In: Singh L., & Kalia V. (eds). Waste Biomass Management – A Holistic Approach. Springer, Cham.
Rahman, P. A., & Shavier, G. D. K. N. F. (2018). Reliability Model of Disk Arrays RAID-5 with Data Striping. IOP Conference Series: Materials Science and Engineering, 327(2). https://doi.org/10.1088/1757-899X/327/2/022087
Taipova, E. (2020). Modeling of Forecast Performance Indicators of Organizations. Journal of the Knowledge Economy, 11, 57–69. https://doi.org/10.1007/s13132-018-0532-2
Toccaceli, D. (2006). Il Distretto Rurale Della Maremma: 1996–2006. Come si Forma un Distretto Rurale in Agriregionieuropa. Anno, 2(6), 54-56.
Toccaceli, D. (2015) Agricultural Districts in the Italian Regions: Looking Toward 2020. Agricultural Economics, 3, 12-21. https://doi.org/10.1186/s40100-014-0019-9
Vasconcelos, B., Teixeira, J. C., Dragone, G., & Teixeira, J. A. (2019). Oleaginous Yeasts for Sustainable Lipid Production—from Biodiesel to Surf Boards, a Wide Range of “Green” Applications. Applied Microbiology and Biotechnology, 103, 3651–3667.
Warr, P. G. (2001). Welfare Effects of an Export Tax: Thailand’s Rice Premium. American Journal of Agricultural Economics, 83(4), 903–920.
Zheng, C.H., Huang, H.L. (2016) Analysis of Technology Diffusion Among Agricultural Industry Clusters by Game Theory. In: Hung J., Yen N., Li KC. (eds) Frontier Computing. Lecture Notes in Electrical Engineering. Springer, Singapore.
DOI: 10.15408/etk.v20i2.22014
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