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Promoting the transformation of agricultural supply chain management under the development of digital economy in the Internet era

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Zhao, L., Chen, H., Wen, C., & Yu, J. (2024). Digital transformation of the agricultural industry: behavioral decision-making, influencing factors, and simulation practices in the yunnan highlands. Journal of Environmental Management, 358. Search in Google Scholar

Liu, Y., & Guo, X. (2018). Mechanism of cooperative performance improvement in agricultural product supply chain based on behavioral cognitive science. Neuroquantology, 16(5). Search in Google Scholar

Trivelli, L., Apicella, A., Chiarello, F., Rana, R., & Tarabella, A. (2019). From precision agriculture to industry 4.0: unveiling technological connections in the agrifood sector. British Food Journal. Search in Google Scholar

Galstyan, A. G., Aksyonova, L. M., Lisitsyn, A. B., Oganesyants, L. A., & Petrov, A. N. (2019). Modern approaches to storage and effective processing of agricultural products for obtaining high quality food products. Herald of the Russian Academy of Sciences(2), 89. Search in Google Scholar

Yu, X., Wang, Z., Wang, Y., & Zhang, C. (2021). Edge detection of agricultural products based on morphologically improved canny algorithm. Mathematical Problems in Engineering, 2021(3), 1-10. Search in Google Scholar

Prashar, D., Jha, N., Jha, S., Lee, Y., & Joshi, G. P. (2020). Blockchain-based traceability and visibility for agricultural products: a decentralized way of ensuring food safety in india. Sustainability, 12. Search in Google Scholar

Arora, C., Kamat, A., Shanker, S., & Barve, A. (2022). Integrating agriculture and industry 4.0 under ‘agri-food 4.0’ to analyze suitable technologies to overcome agronomical barriers. British food journal. Search in Google Scholar

Li, X., & 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. Search in Google Scholar

Wu, S., Wang, Q., Walt, W., & Lei, G. (2020). Prediction model of the logistics chain development of agricultural products in port city. Journal of Coastal Research, 103(sp1), 753. Search in Google Scholar

Yadav, V. S., Singh, A. R., Raut, R. D., Mangla, S. K., Luthra, S., & Kumar, A. (2022). Exploring the application of industry 4.0 technologies in the agricultural food supply chain: a systematic literature review. Computers & Industrial Engineering(Pt.2), 169. Search in Google Scholar

Fu, H., Li, J., Li, Y., Huang, S., & Sun, X. (2018). Risk transfer mechanism for agricultural products supply chain based on weather index insurance. Complexity, 2018, 1-17. Search in Google Scholar

Yan, B., Liu, G., Wu, X., & Wu, J. (2021). Decision-making on the supply chain of fresh agricultural products with two-period price and option contract. Asia-Pacific Journal of Operational Research, 38(01), 93-107. Search in Google Scholar

Sexton, R. J., & Xia, T. (2018). Increasing concentration in the agricultural supply chain: implications for market power and sector performance. Annual Review of Resource Economics, 10(1), annurev-resource-100517-023312. Search in Google Scholar

Wang, H., Ran, H., & Dang, X. (2022). Location optimization of fresh agricultural products cold chain distribution center under carbon emission constraints. Sustainability, 14. Search in Google Scholar

Zhang, H., Qiu, B., & Zhang, K. (2017). A new risk assessment model for agricultural products cold chain logistics. Industrial Management & Data Systems. Search in Google Scholar

Han, J. W., Zuo, M., Zhu, W. Y., Zuo, J. H., Lu, E. L., & Yang, X. T. (2021). A comprehensive review of cold chain logistics for fresh agricultural products: current status, challenges, and future trends. Trends in Food Science & Technology(109-), 109. Search in Google Scholar

Covaci, F. L., & Pascale Zaraté. (2019). Modelling decision making in digital supply chains: insights from the petroleum industry. Kybernetes, 49(2). Search in Google Scholar

Costa, F., Frecassetti, S., Rossini, M., & Portioli-Staudacher, A. (2023). Industry 4.0 digital technologies enhancing sustainability: applications and barriers from the agricultural industry in an emerging economy. Journal of Cleaner Production, 408. Search in Google Scholar

Xu, W., Zhong, Z., Proverbs, D., Xiong, S., & Zhang, Y. (2021). Enhancing the resilience of the management of water resources in the agricultural supply chain. Water(12). Search in Google Scholar

Chiarello, F., Rana, R., Trivelli, L., Apicella, A., Fantoni, G., & Tarabella, A. (2019). From precision agriculture to industry 4.0. British Food Journal, 121(8), 1730-1743. Search in Google Scholar

Li, Y., Zhou, S., Zhu, Q., Li, B., Wang, J., & Wang, C., et al. (2018). Quality control of the agricultural products supply chain based on “internet +”. Environmental Pollution. Search in Google Scholar

Li, Y., Li, N., & Li, Z. (2023). Evolution of carbon emissions in china’s digital economy: an empirical analysis from an entire industry chain perspective. Journal of cleaner production(Aug.15), 414. Search in Google Scholar

Jorge D.Laborda, Pablo Torrijos, José M.Puerta & José A. Gámez. (2024). Parallel structural learning of Bayesian networks: Iterative divide and conquer algorithm based on structural fusion.Knowledge-Based Systems111840-. Search in Google Scholar

Liu Yitian, Hu Kang, Zhou Ruifeng, Ai Xianfeng & Chen Yunqing. (2024). Data driven design optimisation: an empirical study of demand discovery combining theory of planned behaviour and Bayesian networks.International Journal of Production Research(13), 4696-4716. Search in Google Scholar

Anton Figuerola Wischke, José M. Merigó, Anna M. Gil Lafuente & Josefa Boria Reverter. (2024). A Bibliometric Review of the Ordered Weighted Averaging Operator.Mathematics(7). Search in Google Scholar

Janani K., Mohanrasu S.S., Lim Chee Peng, Manavalan Balachandran & Rakkiyappan R. (2023). Ensemble feature selection using Bonferroni, OWA and Induced OWA aggregation operators.Applied Soft Computing Journal. Search in Google Scholar

My Ha Dao, Quang Tuyen Le, Xiang Zhao, Chin Chun Ooi, Luu Trung Pham Duong & Nagarajan Raghavan. (2024). Modelling of aero-mechanical response of wind turbine blade with damages by computational fluid dynamics, finite element analysis and Bayesian network.Renewable Energy120580-. Search in Google Scholar

eISSN:
2444-8656
Lingua:
Inglese
Frequenza di pubblicazione:
Volume Open
Argomenti della rivista:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics