Application of Artificial Neural Networks for Optimizing Coordinated Development between Agriculture and Logistics in Zhejiang Province: A Case Study on Rural Revitalization Strategies

Document Type : Original Research

Authors
W. Wang 1
Y. Shan 1
J. Jing 2
1 School of Logistics and Supply Chain Management, Zhejiang Vocational and Technical College of Economics, Hangzhou, China, 3100182.
2 College of Foreign Languages, Xinjiang Agricultural University, Urumqi, China, 830091
10.22034/JAST.27.3.497
Abstract
This study applies Artificial Neural Networks (ANNs) to assess the impact of climate factors on the collaborative development of agriculture and logistics in Zhejiang, China. The ANN model investigates how average temperature and rainfall from 2017-2022 influence crop yield, water usage, energy demand, logistics efficiency, and economic growth at yearly and seasonal scales. By training the neural network using temperature and rainfall data obtained from ten weather stations, alongside output indicators sourced from statistical yearbooks, the ANN demonstrates exceptional precision, yielding an average R2 value of 0.9725 when compared to real-world outputs through linear regression analysis. Notably, the study reveals climate-induced variations in outputs, with peaks observed in crop yield, water consumption, energy usage, and economic growth during warmer summers that surpass historical norms by 1-2°C. Furthermore, the presence of subpar rainfall ranging from 20-30 mm also exerts an influence on these patterns. Seasonal forecasts underscore discernible reactions to climatic factors, especially during the spring and summer seasons. The findings underscore the intricate relationship between environmental and economic factors, indicating progress in agricultural practices, with vulnerability to short-term climate fluctuations. The study emphasizes the necessity of adapting supply management to address increased water demands and transitioning to clean energy sources due to rising energy consumption. Moreover, optimizing logistics requires strategic seasonal infrastructure planning.

Keywords

Subjects

[1] A.S. Singh, S.K. Parahoo, M. Ayyagari, T.D. Juwaheer, Conclusion: how could rural tourism provide better support for well-being and socioeconomic development?, Worldw. Hosp. Tour. Themes., 15 (2023) 84-93.
[2] N. Hariram, K. Mekha, V. Suganthan, K. Sudhakar, Sustainalism: An integrated socio-economic-environmental model to address sustainable development and sustainability, Sustain., 15 (2023) 10682.
[3] W. Bank, World development report 2018: Learning to realize education's promise, The World Bank 2017.
[4] M.H. Emon, M.N. Nipa, Exploring the Gender Dimension in Entrepreneurship Development: A Systematic Literature Review in the Context of Bangladesh, Westcliff Int. J. Appl. Res., 8 (2024) 34-49.
[5] M. Umar, M.M. Wilson, Inherent and adaptive resilience of logistics operations in food supply chains, J. Bus. Logist., 45 (2024) e12362.
[6] M. Metta, J. Dessein, G. Brunori, Between on-site and the clouds: Socio-cyber-physical assemblages in on-farm diversification, J. Rural Stud., 105 (2024) 103193.
[7] A. Glaros, R. Newell, A. Benyam, S. Pizzirani, L.L. Newman, Vertical agriculture’s potential implications for food system resilience: outcomes of focus groups in the Fraser Valley, British Columbia, Ecology and Society, 29 (2024).
[8] F.A. Kitole, F.Y. Tibamanya, J.K. Sesabo, Exploring the nexus between health status, technical efficiency, and welfare of small-scale cereal farmers in Tanzania: A stochastic frontier analysis, J. Agric. Food Res., (2024) 100996.
[9] R. Wazirali, E. Yaghoubi, M.S.S. Abujazar, R. Ahmad, A.H. Vakili, State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques, Electr. Power Syst. Res., 225 (2023) 109792.
[10] M. Kurucan, M. Özbaltan, Z. Yetgin, A. Alkaya, Applications of artificial neural network based battery management systems: A literature review, Renew. Sustain. Energy Rev., 192 (2024) 114262.
[11] E.S. Puchi-Cabrera, E. Rossi, G. Sansonetti, M. Sebastiani, E. Bemporad, Machine learning aided nanoindentation: A review of the current state and future perspectives, Curr. Opin. Solid State Mater. Sci., 27 (2023) 101091.
[12] K.B.W. Boo, A. El-Shafie, F. Othman, M.M.H. Khan, A.H. Birima, A.N. Ahmed, Groundwater Level Forecasting with Machine Learning Models: A Review, Water Res., (2024) 121249.
[13] G.U. Alaneme, K.A. Olonade, E. Esenogho, Critical review on the application of artificial intelligence techniques in the production of geopolymer-concrete, SN Appl. Sci., 5 (2023) 217.
[14] Y. Akkem, S.K. Biswas, A. Varanasi, Smart farming using artificial intelligence: A review, Eng. Appl. Artif. Intell., 120 (2023) 105899.
[15] I. Attri, L.K. Awasthi, T.P. Sharma, P. Rathee, A review of deep learning techniques used in agriculture, Ecol. Inform., (2023) 102217.
[16] O. Folorunso, O. Ojo, M. Busari, M. Adebayo, A. Joshua, D. Folorunso, C.O. Ugwunna, O. Olabanjo, O. Olabanjo, Exploring machine learning models for soil nutrient properties prediction: A systematic review, Big Data Cogn. Comput., 7 (2023) 113.
[17] N. Nandgude, T. Singh, S. Nandgude, M. Tiwari, Drought prediction: a comprehensive review of different drought prediction models and adopted technologies, Sustain., 15 (2023) 11684.
[18] X. Li, J. Jiang, J. Cifuentes-Faura, The impact of logistic environment and spatial spillover on agricultural economic growth: An empirical study based on east, central and west China, PLoS One, 18 (2023) e0287307.
[19] H. Ding, Y. Liu, Y. Zhang, S. Wang, Y. Guo, S. Zhou, C. Liu, Data-driven evaluation and optimization of the sustainable development of the logistics industry: Case study of the Yangtze River Delta in China, Environ. Sci. Pollut. Res. Int., 29 (2022) 68815-68829.
[20] S. Qi, Z. Huang, L. Ji, Sustainable Development Based on Green GDP Accounting and Cloud Computing: A Case Study of Zhejiang Province, Sci. Program., 2021 (2021) 1-8.
[21] Y. Qu, J. Li, S. Wang, Green total factor productivity measurement of industrial enterprises in Zhejiang Province, China: A DEA model with undesirable output approach, Energy Rep., 8 (2022) 307-317.
[22] Y. Xu, Logistic development along the Yangtze River economic belt, Contemporary Logistics in China: New Horizon and New Blueprint, (2016) 121-152.
[23] G. Liu, D.M. Doronzo, A novel approach to bridging physical, cultural, and socioeconomic indicators with spatial distributions of Agricultural Heritage Systems (AHS) in China, Sustain., 12 (2020) 6921.
[24] C. Nhemachena, L. Nhamo, G. Matchaya, C.R. Nhemachena, B. Muchara, S.T. Karuaihe, S. Mpandeli, Climate change impacts on water and agriculture sectors in Southern Africa: Threats and opportunities for sustainable development, Water, 12 (2020) 2673.
[25] A. Raihan, A review of the global climate change impacts, adaptation strategies, and mitigation options in the socio-economic and environmental sectors, J. of Environ. Sci. Econom., 2 (2023) 36-58.
[26] S. Singh, K.S. Babu, S. Singh, Machine learning approach for climate change impact assessment in agricultural production, Visualization techniques for climate change with machine learning and artificial intelligence, Elsevier (2023), pp. 317-335.
[27] Y. Dou, R.F.B. Da Silva, M. Batistella, S. Torres, E. Moran, J. Liu, Mapping crop producer perceptions: The role of global drivers on local agricultural land use in Brazil, Land Use Policy, 133 (2023) 106862.
[28] B. Wu, F. Chen, L. Li, L. Xu, Z. Liu, Y. Wu, Institutional investor ESG activism and exploratory green innovation: Unpacking the heterogeneous responses of family firms across intergenerational contexts, Br. Account. Rev., (2024) 101324.
[29] X. Li, Y. Sun, Application of RBF neural network optimal segmentation algorithm in credit rating, Neural Comput. Appl., 33 (2021) 8227-8235.
[30] A. Xu, K. Qiu, Y. Zhu, The measurements and decomposition of innovation inequality: Based on Industry− University− Research perspective, J. Bus. Res., 157 (2023) 113556.
[31] H. Guan, J. Huang, L. Li, X. Li, S. Miao, W. Su, Y. Ma, Q. Niu, H. Huang, Improved Gaussian mixture model to map the flooded crops of VV and VH polarization data, Remote Sens. Environ., 295 (2023) 113714.
[32] B. He, L. Yin, Prediction modelling of cold chain logistics demand based on data mining algorithm, Math. Probl. Eng., 2021 (2021) 1-9.
[33] X. Li, Y. Sun, Stock intelligent investment strategy based on support vector machine parameter optimization algorithm, Neural Comput. Appl., 32 (2020) 1765-1775.
[34] C. Jiang, Y. Wang, Z. Yang, Y. Zhao, Do adaptive policy adjustments deliver ecosystem-agriculture-economy co-benefits in land degradation neutrality efforts? Evidence from southeast coast of China, Environ. Monit. Assess., 195 (2023) 1215.
[35] Q. Li, J. Hu, B. Yu, Spatiotemporal patterns and influencing mechanism of urban residential energy consumption in China, Energies, 14 (2021) 3864.
[36] F. Hu, Q. Ma, H. Hu, K.H. Zhou, S. Wei, A study of the spatial network structure of ethnic regions in Northwest China based on multiple factor flows in the context of COVID-19: Evidence from Ningxia, Heliyon, 10 (2024).
[37] J. Luo, C. Zhao, Q. Chen, G. Li, Using deep belief network to construct the agricultural information system based on Internet of Things, J. Supercomput., 78 (2022) 379-405.
[38] B. Li, G. Li, J. Luo, Latent but not absent: the ‘long tail’nature of rural special education and its dynamic correction mechanism, PLoS One, 16 (2021) e0242023.
[39] C. Chen, J. Pan, The effect of the health poverty alleviation project on financial risk protection for rural residents: evidence from Chishui City, China, Int. j. equity health, 18 (2019) 1-16.
[40] J. Jia, L. Yin, C. Yan, Urban-rural logistics coupling coordinated development and urban-rural integrated development: Measurement, influencing factors, and countermeasures, Math. Probl. Eng., 2022 (2022).
[41] S. Zhang, C. Zhang, Z. Su, M. Zhu, H. Ren, New structural economic growth model and labor income share, J. Bus. Res., 160 (2023) 113644.
[42] J. Li, M. Ye, R. Pu, Y. Liu, Q. Guo, B. Feng, R. Huang, G. He, Spatiotemporal change patterns of coastlines in Zhejiang Province, China, over the last twenty-five years, Sustain., 10 (2018) 477.
[43] Q. Jiang, J. He, G. Ye, G. Christakos, Heavy metal contamination assessment of surface sediments of the East Zhejiang coastal area during 2012–2015, Ecotoxicol. Environ. Saf., 163 (2018) 444-455.
[44] J.-l. Cheng, S. Zhou, Y.-w. Zhu, Assessment and mapping of environmental quality in agricultural soils of Zhejiang Province, China, J. Environ. Sci., 19 (2007) 50-54.
[45] C. Zhu, Y. Lin, J. Zhang, M. Gan, H. Xu, W. Li, S. Yuan, K. Wang, Exploring the relationship between rural transition and agricultural eco-environment using a coupling analysis: A case study of Zhejiang Province, China, Ecol. Indic., 127 (2021) 107733.
[46] J. Tian, Y. Han, J. Shen, Y. Zhu, Leveraging sustainable development of agriculture with sustainable water management: The empirical investigation of “Five Water Cohabitation” of Zhejiang Province in China, Environ. Monit. Assess., 194 (2022) 124.
[47] Y. Wang, X. Wang, W. Chen, L. Qiu, B. Wang, W. Niu, Exploring the path of inter-provincial industrial transfer and carbon transfer in China via combination of multi-regional input–output and geographically weighted regression model, Ecol. Indic., 125 (2021) 107547.
[48] W. Yue, J. Gao, X. Yang, Estimation of gross domestic product using multi-sensor remote sensing data: A case study in Zhejiang province, East China, Remote Sens., 6 (2014) 7260-7275.
[49] C. Shi, Y. He, H. Li, How does ecological poverty alleviation contribute to improving residents' sustainable livelihoods?—Evidence from Zhejiang Province, China, Sustain. Prod. Consum., 41 (2023) 418-430.
[50] Z. Hu, G. Song, Z. Hu, B. Zhang, T. Lin, How to promote the balanced development of urban and rural China? Evidences from reallocating idle rural residential land of Zhejiang province, China, Plos one, 18 (2023) e0287820.
[51] Q. Gao, H. Chen, M. Zhao, M. Zeng, Research on the Impact and Spillover Effect of Green Agricultural Reform Policy Pilot on Governmental Environmental Protection Behaviors Based on Quasi-Natural Experiments of China’s Two Provinces from 2012 to 2020, Sustain., 15 (2023) 2665.
[52] X. Wu, B. Peng, Urban comprehensive carrying capacity analysis in Zhejiang Province of China from the perspective of production, living, and ecological spaces, Geo-Spat. Inf. Sci., (2024) 1-20.
[53] W. Wu, Y. Zhu, Y. Wang, Spatio-temporal pattern, evolution and influencing factors of forest carbon sinks in Zhejiang Province, China, Forests, 14 (2023) 445.
[54] F. Cao, H. Wang, C. Zhang, W. Kong, Social Vulnerability Evaluation of Natural Disasters and Its Spatiotemporal Evolution in Zhejiang Province, China, Sustain., 15 (2023) 6400.
[55] L. Jiang, X. Chen, W. Liang, B. Zhang, Alike but also different: a spatiotemporal analysis of the older populations in Zhejiang and Jilin provinces, China, BMC Public Health, 23 (2023) 1529.
[56] J. Huang, P. Shi, Regional rural and structural transformations and farmer's income in the past four decades in China, China Agric. Econ. Rev., 13 (2021) 278-301.
[57] S. Hu, Y. Yang, H. Zheng, C. Mi, T. Ma, R. Shi, A framework for assessing sustainable agriculture and rural development: A case study of the Beijing-Tianjin-Hebei region, China, Environ. Impact Assess. Rev., 97 (2022) 106861.
[58] I.H.V. Gue, A.T. Ubando, M.-L. Tseng, R.R. Tan, Artificial neural networks for sustainable development: a critical review, Clean Technol. Environ. Policy, 22 (2020) 1449-1465.
[59] Z.H. Munim, H.-J. Schramm, Forecasting container freight rates for major trade routes: a comparison of artificial neural networks and conventional models, Marit. Econ. Logist., 23 (2021) 310-327.
Journal of Agricultural Science and Technology
Volume 27, Issue 3
May and June 2025
Pages 497-511