Agricultural economic security under the model of integrated agricultural industry development
Main Article Content
Keywords
integrated agricultural industry, agricultural economic security, risk spillover effect, vector autoregression model, blockchain, regulatory mechanisms, data security
Abstract
As globalization and technological advancements progress, the integrated agricultural industry development model has become crucial for agricultural modernization. This model fosters inter-industry integration, enhancing agricultural competitiveness and sustainability. However, it also introduces new challenges, such as increased risk spillover and outdated regulatory mechanisms. This study employs a vector autoregression (VAR) model and a directed weighted risk spillover network for a quantitative analysis of risk dynamics within agricultural markets, identifying pathways and influencers of risk transmission across industry segments. A blockchain-based regulatory framework is proposed to improve traceability, regulatory efficiency, and data security in agricultural product management. This includes the development of registration processes, smart contracts, consensus mechanisms, and data upload protocols. The research expands the theoretical base of agricultural economic security and offers practical policy guidance, with substantial academic and practical implications. It highlights how blockchain can address market trust issues, mitigate information asymmetry, and reduce potential risks, contributing to the healthier development of agricultural economy.
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Zhang, D., She, W., Qu, F., and He, C., 2023. Asymmetric risk connectedness between crude oil and agricultural commodity futures in China before and after the COVID-19 pandemic: evidence from high-frequency data. Energies 16(16): 5898. 10.3390/en16165898
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Bessonov, V., and Suglobov, A., 2019. Economic security of agricultural producers in the EAEU. Earth and Environmental Science (IOP Conference Series) 274(1): 12080. 10.1088/1755-1315/274/1/012080
Dhananjayan, P., Koirala, P., and Sharma, D., 2023. Advancing sustainable development in the Hindu-Kush-Himalaya region through certification strategies. Opportunities and Challenges in Sustainability 2(2): 93–103. 10.56578/ocs020204
Gao, X., Xiao, Y.Y., Li, P., Liu, X.Z., and Chen, J.L. 2023. Assessing the impact of rural population aging on China’s AGTFP: a mediation and threshold effect analysis. Opportunities and Challenges in Sustainability 2(4): 230–247. 10.56578/ocs020405
Jiuhardi, J., Hasid, Z., Darma, S., and Darma, D.C., 2022. Sustaining agricultural growth: traps of socio–demographics in emerging markets. Opportunities and Challenges in Sustainability 1(1): 13–28. 10.56578/ocs010103
Karanina, E., Selezneva, E., Karaulov, V., and Bahtimov, A., 2019. Methods of assessing the impact of the parameters of the consumer market development on the economic security of the region. Earth and Environmental Science (IOP Conference Series) 403(1): 12150. 10.1088/1755-1315/403/1/012150
Kashina, E., Yanovskaya, G., Fedotkina, E., Tesalovsky, A., Vetrova, E., Shaimerdenova, A., et al. 2022. Impact of digital farming on sustainable development and planning in agriculture and increasing the competitiveness of the agricultural business. International Journal of Sustainable Development and Planning 17(8): 2413–2420. 10.18280/ijsdp.170808
Kim, S., and Kim, S., 2024. Agricultural research and development center design with building integrated photovoltaics in Fiji. Energies 17(1): 207. 10.3390/en17010207
Li, Y., Chen, Y., Li, T., and Ren, X., 2021. A regulatable data privacy protection scheme for energy transactions based on consortium blockchain. Security and Communication Networks 2021: 4840253. 10.1155/2021/4840253
Li, H., Chen, Q., Liu, G., Lombardi, G.V., Su, M., and Yang, Z., 2023a. Uncovering the risk spillover of agricultural water scarcity by simultaneously considering water quality and quantity. Journal of Environmental Management 343: 118209. 10.1016/j.jenvman.2023.118209
Li, X., and Huang, D., 2020. Research on value integration mode of agricultural e-commerce industry chain based on internet of things and blockchain technology. Wireless Communications and Mobile Computing 2020: 8889148. 10.1155/2020/8889148
Li, H., Li, Y., and Guo, L., 2023b. Extreme risk spillover effect and dynamic linkages between uncertainty and commodity markets: a comparison between China and America. Resources Policy 85: 103839. 10.1016/j.resourpol.2023.103839
Lin, K.Y., and Hu, L., 2022. Supply and demand optimization of agricultural products in game theory: a state-of-the-art review. Journal of Engineering Management and Systems Engineering 1(2): 76–86. 10.56578/jemse010205
Mahler, R.L., 2020. The water nexus in southwestern Idaho, USA: development versus agriculture. International Journal of Environmental Impacts, 3(3): 248–259. 10.2495/EI-V3-N3-248-259
Miralles-Garcia, J.L., 2023. Challenges and opportunities in managing peri-urban agriculture: a case study of L’Horta de València, Spain. International Journal of Environmental Impacts 6(3): 89–99. 10.18280/ijei.060301
Omodero, C.O., 2021. Sustainable agriculture, food production and poverty lessening in Nigeria. International Journal of Sustainable Development and Planning, 16(1): 81–87. 10.18280/ijsdp.160108
Park, S., Kim, S.J., Park, E., Yu, H., Cha, S., Jo, H.W., et al. 2020. Development of decision-making tools for drought monitoring and early warning in Kyrgyzstan. In: 40th Asian conference on remote sensing, ACRS 2019: progress of remote sensing technology for smart future.
Puri, V., Solanki, V.K., and Aponte, G.J.R., 2023. Blockchain and federated learning based integrated approach for agricultural internet of things. In: The international conference on intelligent systems & networks. pp. 240–246. 10.1007/978-981-99-4725-6_30
Safitri, K.I., Abdoellah, O.S., Gunawan, B., Parikesit and Suparman, Y., 2022. Environmental certification schemes based on political ecology: case study on urban agricultural farmers in Bandung Metropolitan Area, Indonesia. Journal of Urban Development and Management 1(1): 67–75. 10.56578/judm010108
Sarkar, T., Salauddin, M., and Pati, S., 2022a. Expert knowledge-based system for shelf-life analysis of dairy cheese ball (Rasgulla). Food Analysis Methods 15: 1945–1960. 10.1007/s12161-022-02261-y
Sarkar, T., Salauddin, M., and Pati, S., 2022b. The fuzzy cognitive map–based shelf-life modelling for food storage. Food Analysis Methods 15: 579–597. 10.1007/s12161-021-02147-5
Shah, M.A., Shahnaz, T., Masoodi, J.H., Nazir, S., Qurashi, A., and Ahmed, G.H., 2023. Application of nanotechnology in the agricultural and food processing industries: a review. Sustainable Materials and Technologies 39: e00809. 10.1016/j.susmat.2023.e00809
Shchutskaya, A.V., and Kozhukhova, N.V., 2023. Food security of the Russian Federation and economic factors of agricultural development. In: International conference engineering innovations and sustainable development. pp. 10–17. 10.1007/978-3-031-38122-5_2
Song, X., Meng, Q., and Huang, Z., 2021. The construction of agricultural big data ecosystem based on ‘Internet +’. In: 2021 IEEE international conference on advances in electrical engineering and computer applications. pp. 277–282. 10.1109/AEECA52519.2021.9574432
Taneja, S., and Ozen, E., 2023. Impact of the European Green Deal (EDG) on the agricultural carbon (CO2) emission in Turkey. International Journal of Sustainable Development and Planning, 18(3): 715–727. 10.18280/ijsdp.180307
Vulevic, A., Castanho, R.A., Gómez, J.M.N., and Quinta-Nova, L., 2022. Tendencies in land use and land cover in Serbia towards sustainable development in 1990–2018. Acadlore Transactions on Geosciences 1(1): 43–52. 10.56578/atg010106
Wang, N., Hao, B.B., Zhang, S.Y., and He, B., 2023. Agricultural non-point source pollution in Guangdong Province of China from 1991 to 2021. Transactions of the Chinese Society of Agricultural Engineering 39(9): 190–200.
Xia, Q., Liao, M., Xie, X., Guo, B., Lu, X., and Qiu, H., 2023. Agricultural carbon emissions in Zhejiang Province, China (2001–2020): changing trends, influencing factors and has it achieved synergy with food security and economic development? Environmental Monitoring and Assessment 195(11): 1391. 10.1007/s10661-023-11998-w
Yu, H.J., Xu, D.M., Luo, N., Xing, B., and Sun, C.H., 2021. Design of the blockchain multi-chain traceability supervision model for coarse cereal supply chain. Transactions of the Chinese Society of Agricultural Engineering 37(20): 323–332. 10.11975/j.issn.1002-6819.2021.20.036
Zhang, G., Chen, X., Feng, B., Guo, X., Hao, X., Ren, H., ..., and Zhang, Y., 2022a. BCST-APTS: blockchain and CP-ABE empowered data supervision, sharing and privacy protection scheme for secure and trusted agricultural product traceability system. Security and Communication Networks 2022: 2958963. 10.1155/2022/2958963
Zhang, D., She, W., Qu, F., and He, C., 2023. Asymmetric risk connectedness between crude oil and agricultural commodity futures in China before and after the COVID-19 pandemic: evidence from high-frequency data. Energies 16(16): 5898. 10.3390/en16165898
Zhang, Y., Zhang, G., Feng, B., Chen, X., Wang, B., and Li, Q., 2022b. Flexible, safe, and trusted agricultural product blockchain traceability system. In: 2022 International conference on high performance big data and intelligent systems (HDIS). pp. 304–308. 10.1109/HDIS56859.2022.9991666
Zhao, J., Cui, L., Liu, W., and Zhang, Q., 2023. Extreme risk spillover effects of international oil prices on the Chinese stock market: a GARCH-EVT-Copula-CoVaR approach. Resources Policy 86: 104142. 10.1016/j.resourpol.2023.104142
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Published
Jul 27, 2024
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How to Cite
Chen, N., & Li, H. (2024). Agricultural economic security under the model of integrated agricultural industry development. Quality Assurance and Safety of Crops & Foods, 16(3), 25-41. https://doi.org/10.15586/qas.v16i3.1470
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Research Article
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
Chen, N., & Li, H. (2024). Agricultural economic security under the model of integrated agricultural industry development. Quality Assurance and Safety of Crops & Foods, 16(3), 25-41. https://doi.org/10.15586/qas.v16i3.1470