A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 10 Issue 11
Nov.  2023

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2023  >  10(11): 2070-2080
Y. F. Wang, M. Z. Kang, Y. L. Liu, J. J. Li, K. Xue, X. J. Wang, J. Q. Du, Y. L. Tian, Q. H. Ni, and  F.-Y. Wang,  “Can digital intelligence and cyber-physical-social systems achieve global food security and sustainability?” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2070–2080, Nov. 2023. doi: 10.1109/JAS.2023.123951
Citation: Y. F. Wang, M. Z. Kang, Y. L. Liu, J. J. Li, K. Xue, X. J. Wang, J. Q. Du, Y. L. Tian, Q. H. Ni, and  F.-Y. Wang,  “Can digital intelligence and cyber-physical-social systems achieve global food security and sustainability?” IEEE/CAA J. Autom. Sinica, vol. 10, no. 11, pp. 2070–2080, Nov. 2023. doi: 10.1109/JAS.2023.123951
shu

Can Digital Intelligence and Cyber-Physical-Social Systems Achieve Global Food Security and Sustainability?

doi: 10.1109/JAS.2023.123951
Funds:  This work was supported in part by the National Key Research and Development Program of China (2021ZD0113704), the National Natural Science Foundation of China (62076239, 42041005, 62103411), and the Science and Technology Development Fund, Macau SAR (0050/2020/A1)
More Information
    Abstract
  • Plants sequester carbon through photosynthesis and provide primary productivity for the ecosystem. However, they also simultaneously consume water through transpiration, leading to a carbon-water balance relationship. Agricultural production can be regarded as a form of carbon sequestration behavior. From the perspective of the natural-social-economic complex ecosystem, excessive water usage in food production will aggravate regional water pressure for both domestic and industrial purposes. Hence, achieving a harmonious equilibrium between carbon and water resources during the food production process is a key scientific challenge for ensuring food security and sustainability.Digital intelligence (DI) and cyber-physical-social systems (CPSS) are emerging as the new research paradigms that are causing a substantial shift in the conventional thinking and methodologies across various scientific fields, including ecological science and sustainability studies. This paper outlines our recent efforts in using advanced technologies such as big data, artificial intelligence (AI), digital twins, metaverses, and parallel intelligence to model, analyze, and manage the intricate dynamics and equilibrium among plants, carbon, and water in arid and semi-arid ecosystems. It introduces the concept of the carbon-water balance and explores its management at three levels: the individual plant level, the community level, and the natural-social-economic complex ecosystem level. Additionally, we elucidate the significance of agricultural foundation models as fundamental technologies within this context. A case analysis of water usage shows that, given the limited availability of water resources in the context of the carbon-water balance, regional collaboration and optimized allocation have the potential to enhance the utilization efficiency of water resources in the river basin. A suggested approach is to consider the river basin as a unified entity and coordinate the relationship between the upstream, midstream and downstream areas. Furthermore, establishing mechanisms for water resource transfer and trade among different industries can be instrumental in maximizing the benefits derived from water resources. Finally, we envisage a future of agriculture characterized by the integration of digital, robotic and biological farming techniques. This vision aims to incorporate small tasks, big models, and deep intelligence into the regular ecological practices of intelligent agriculture.

     

    • FullText(HTML)
  • loading
    • References(68)
  • [1]
    L. A. Garibaldi, M. A. Aizen, A. M. Klein, S. A. Cunningham, and L. D. Harder, “Global growth and stability of agricultural yield decrease with pollinator dependence,” Proc. Natl. Acad. Sci. USA, vol. 108, no. 14, pp. 5909–5914, Apr. 2011. doi: 10.1073/pnas.1012431108
    [2]
    H. Sui, G. Song, W. Liu, Y. Zhang, R. Su, Q. Wang, G. Ren, and Y. Mi, “Spatiotemporal variation of cultivated land ecosystem stability in typical regions of Lower Liaohe Plain China based on stress-buffer-response,” Sci. Total Environ., vol. 858, p. 160213, Feb. 2023. doi: 10.1016/j.scitotenv.2022.160213
    [3]
    J. Rosa-Schleich, J. Loos, O. Mußhoff, and T. Tscharntke, “Ecological-economic trade-offs of diversified farming systems–A review,” Ecol. Econom., vol. 160, pp. 251–263, Jun. 2019. doi: 10.1016/j.ecolecon.2019.03.002
    [4]
    Q. Zhang, V. Achal, Y. Xu, and W.-N. Xiang, “Aquaculture wastewater quality improvement by water spinach (ipomoea aquatica forsskal) floating bed and ecological benefit assessment in ecological agriculture district,” Aquacult. Eng., vol. 60, pp. 48–55, May 2014. doi: 10.1016/j.aquaeng.2014.04.002
    [5]
    L. Levidow, D. Zaccaria, R. Maia, E. Vivas, M. Todorovic, and A. Scardigno, “Improving water-efficient irrigation: Prospects and difficulties of innovative practices,” Agric. Water Manage., vol. 146, pp. 84–94, Dec. 2014. doi: 10.1016/j.agwat.2014.07.012
    [6]
    T. Du, S. Kang, J. Zhang, and W. J. Davies, “Deficit irrigation and sustainable water-resource strategies in agriculture for China’s food security,” J. Exp. Bot., vol. 66, no. 8, pp. 2253–2269, Apr. 2015. doi: 10.1093/jxb/erv034
    [7]
    M. Flörke, C. Schneider, and R. I. McDonald, “Water competition between cities and agriculture driven by climate change and urban growth,” Nat. Sustain., vol. 1, no. 1, pp. 51–58, Jan. 2018. doi: 10.1038/s41893-017-0006-8
    [8]
    Y. Liu, J. Du, Y. Wang, X. Cui, J. Dong, Y. Hao, K. Xue, H. Duan, A. Xia, Y. Hu, Z. Dong, B. Wu, X. Zhao, and B. Fu, “Evenness is important in assessing progress towards sustainable development goals,” Nat. Sci. Rev., vol. 8, no. 8, p. nwaa238, Aug. 2021. doi: 10.1093/nsr/nwaa238
    [9]
    Y. Liu, J. Du, B. Ding, Y. Liu, W. Liu, A. Xia, R. Huo, Q. Ran, Y. Hao, X. Cui, and Y. Wang, “Water resource conservation promotes synergy between economy and environment in China’s northern drylands,” Front. Environ. Sci. Eng., vol. 16, no. 3, p. 28, Mar. 2022. doi: 10.1007/s11783-021-1462-y
    [10]
    J. Zhu, Y. Yang, Y. Liu, X. Cui, T. Li, Y. Jia, Y. Ning, J. Du, and Y. Wang, “Progress and water stress of sustainable development in Chinese northern drylands,” J. Cleaner Prod., vol. 399, p. 136611, May 2023. doi: 10.1016/j.jclepro.2023.136611
    [11]
    C. Li, T. T. Jiang, X. B. Luan, Y. L. Yin, P. T. Wu, Y. B. Wang, and S. K. Sun, “Determinants of agricultural water demand in China,” J. Cleaner Prod., vol. 288, p. 125508, Mar. 2021. doi: 10.1016/j.jclepro.2020.125508
    [12]
    R. Mega, F. Abe, J.-S. Kim, Y. Tsuboi, K. Tanaka, H. Kobayashi, Y. Sakata, K. Hanada, H. Tsujimoto, J. Kikuchi, S. R. Cutler, and M. Okamoto, “Tuning water-use efficiency and drought tolerance in wheat using abscisic acid receptors,” Nat. Plants, vol. 5, no. 2, pp. 153–159, Feb. 2019. doi: 10.1038/s41477-019-0361-8
    [13]
    X.-P. Deng, L. Shan, H. Zhang, and N. C. Turner, “Improving agricultural water use efficiency in arid and semiarid areas of China,” Agric. Water Manage., vol. 80, no. 1-3, pp. 23–40, Feb. 2006. doi: 10.1016/j.agwat.2005.07.021
    [14]
    S. Kang, X. Hao, T. Du, L. Tong, X. Su, H. Lu, X. Li, Z. Huo, S. Li, and R. Ding, “Improving agricultural water productivity to ensure food security in China under changing environment: From research to practice,” Agric. Water Manage., vol. 179, pp. 5–17, Jan. 2017. doi: 10.1016/j.agwat.2016.05.007
    [15]
    E. B. Alonso, L. Cockx, and J. Swinnen, “Culture and food security,” Global Food Secur., vol. 17, pp. 113–127, Jun. 2018. doi: 10.1016/j.gfs.2018.02.002
    [16]
    D. Seekell, J. Carr, J. Dell’Angelo, P. D’Odorico, M. Fader, J. Gephart, M. Kummu, N. Magliocca, M. Porkka, M. Puma, Z. Ratajczak, M. C. Rulli, S. Suweis, and A. Tavoni, “Resilience in the global food system,” Environ. Res. Lett., vol. 12, no. 2, p. 025010, Feb. 2017. doi: 10.1088/1748-9326/aa5730
    [17]
    H. El Bilali, C. Callenius, C. Strassner, and L. Probst, “Food and nutrition security and sustainability transitions in food systems,” Food Energy Secur., vol. 8, no. 2, p. e00154, May 2019. doi: 10.1002/fes3.154
    [18]
    H. El Bilali, “Research on agro-food sustainability transitions: Where are food security and nutrition?” Food Secur., vol. 11, no. 3, pp. 559–577, Jun. 2019. doi: 10.1007/s12571-019-00922-1
    [19]
    J. Pretty, “Agricultural sustainability: Concepts, principles and evidence,” Philos. Trans. R. Soc. B: Biol. Sci., vol. 363, no. 1491, pp. 447–465, Feb. 2008. doi: 10.1098/rstb.2007.2163
    [20]
    J. Pretty, “Intensification for redesigned and sustainable agricultural systems,” Science, vol. 362, no. 6417, p. eaav0294, Nov. 2018. doi: 10.1126/science.aav0294
    [21]
    H. C. J. Godfray and T. Garnett, “Food security and sustainable intensification,” Philos. Trans. R. Soc. B: Biol. Sci., vol. 369, no. 1639, p. 20120273, Apr. 2014. doi: 10.1098/rstb.2012.0273
    [22]
    B. Fu, X. Wu, Z. Wang, X. Wu, and S. Wang, “Coupling human and natural systems for sustainability: Experience from China’s Loess Plateau,” Earth Syst. Dyn., vol. 13, no. 2, pp. 795–808, Apr. 2022. doi: 10.5194/esd-13-795-2022
    [23]
    H. M. Leslie, X. Basurto, M. Nenadovic, L. Sievanen, K. C. Cavanaugh, J. J. Cota-Nieto, B. E. Erisman, E. Finkbeiner, G. Hinojosa-Arango, M. Moreno-Báez, S. Nagavarapu, S. M. W. Reddy, A. Sánchez-Rodríguez, K. Siegel, J. J. Ulibarria-Valenzuela, A. H. Weaver, and O. Aburto-Oropeza, “Operationalizing the social-ecological systems framework to assess sustainability,” Proc. Natl. Acad. Sci. USA, vol. 112, no. 19, pp. 5979–5984, May 2015. doi: 10.1073/pnas.1414640112
    [24]
    O. Friha, M. A. Ferrag, L. Shu, L. Maglaras, and X. C. Wang, “Internet of things for the future of smart agriculture: A comprehensive survey of emerging technologies,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 718–752, Apr. 2021. doi: 10.1109/JAS.2021.1003925
    [25]
    N. Zhang, X. Zhou, M. Kang, B.-G. Hu, E. Heuvelink, and L. F. M. Marcelis, “Machine learning versus crop growth models: An ally, not a rival,” AoB PLANTS, vol. 15, no. 2, p. plac061, Feb. 2023. doi: 10.1093/aobpla/plac061
    [26]
    W. Zhang, G. Cao, X. Li, H. Zhang, C. Wang, Q. Liu, X. Chen, Z. Cui, J. Shen, R. Jiang, G. Mi, Y. Miao, F. Zhang, and Z. Dou, “Closing yield gaps in China by empowering smallholder farmers,” Nature, vol. 537, no. 7622, pp. 671–674, Sept. 2016. doi: 10.1038/nature19368
    [27]
    M. Z. Kang, E. Heuvelink, S. M. P. Carvalho, and P. De Reffye, “A virtual plant that responds to the environment like a real one: The case for chrysanthemum,” New Phytol., vol. 195, no. 2, pp. 384–395, Jul. 2012. doi: 10.1111/j.1469-8137.2012.04177.x
    [28]
    F.-Y. Wang, “The emergence of intelligent enterprises: From CPS to CPSS,” IEEE Intell. Syst., vol. 25, no. 4, pp. 85–88, Jul.–Aug. 2010. doi: 10.1109/MIS.2010.104
    [29]
    M. Dong, R. Ranjan, A. Y. Zomaya, and M. Lin, “Guest editorial on advances in tools and techniques for enabling cyber-physical-social systems–Part I,” IEEE Trans. Comput. Soc. Syst., vol. 2, no. 3, pp. 38–40, Sept. 2015. doi: 10.1109/TCSS.2016.2527158
    [30]
    M. Dong, R. Ranjan, A. Y. Zomaya, and M. Lin, “Guest editorial on advances in tools and techniques for enabling cyber-physical-social systems–Part II,” IEEE Trans. Comput. Soc. Syst., vol. 2, no. 4, pp. 124–126, Dec. 2015. doi: 10.1109/TCSS.2016.2527159
    [31]
    J. J. Zhang, F.-Y. Wang, X. Wang, G. Xiong, F. Zhu, Y. Lv, J. Hou, S. Han, Y. Yuan, Q. Lu, and Y. Lee, “Cyber-physical-social systems: The state of the art and perspectives,” IEEE Trans. Comput. Soc. Syst., vol. 5, no. 3, pp. 829–840, Sept. 2018. doi: 10.1109/TCSS.2018.2861224
    [32]
    M. Kang, X.-R. Fan, J. Hua, H. Wang, X. Wang, and F.-Y. Wang, “Managing traditional solar greenhouse with CPSS: A just-for-fit philosophy,” IEEE Trans. Cybern., vol. 48, no. 12, pp. 3371–3380, Dec. 2018. doi: 10.1109/TCYB.2018.2858264
    [33]
    S. K. Lowder, J. Skoet, and T. Raney, “The number, size, and distribution of farms, smallholder farms, and family farms worldwide,” World Dev., vol. 87, pp. 16–29, Nov. 2016. doi: 10.1016/j.worlddev.2015.10.041
    [34]
    J. Hua, H. Wang, M. Kang, X. Wang, S. Guo, F. Chang, and F.-Y. Wang, “The design and implementation of a distributed agricultural service system for smallholder farmers in China,” Int. J. Agric. Sustainability, vol. 21, no. 1, p. 2221108, Jun. 2023. doi: 10.1080/14735903.2023.2221108
    [35]
    Y. Hu and U. Schmidhalter, “Opportunity and challenges of phenotyping plant salt tolerance,” Trends Plant Sci., vol. 28, no. 5, pp. 552–566, May 2023. doi: 10.1016/j.tplants.2022.12.010
    [36]
    G. Lee, O. Hossain, S. Jamalzadegan, Y. Liu, H. Wang, A. C. Saville, T. Shymanovich, R. Paul, D. Rotenberg, A. E. Whitfield, J. B. Ristaino, Y. Zhu, and Q. Wei, “Abaxial leaf surface-mounted multimodal wearable sensor for continuous plant physiology monitoring,” Sci. Adv., vol. 9, no. 15, p. eade2232, Apr. 2023. doi: 10.1126/sciadv.ade2232
    [37]
    H.-B. Qu and B.-G. Hu, “Variational learning for generalized associative functional networks in modeling dynamic process of plant growth,” Ecol. Inf., vol. 4, no. 3, pp. 163–176, Aug. 2009. doi: 10.1016/j.ecoinf.2009.06.004
    [38]
    D. P. Holzworth, N. I. Huth, P. G. deVoil, E. J. Zurcher, N. I. Herrmann, G. McLean, K. Chenu, E. J. van Oosterom, V. Snow, C. Murphy, A. D. Moore, H. Brown, J. P. M. Whish, S. Verrall, J. Fainges, L. W. Bell, A. S. Peake, P. L. Poulton, Z. Hochman, P. J. Thorburn, D. S. Gaydon, N. P. Dalgliesh, D. Rodriguez, H. Cox, S. Chapman, A. Doherty, E. Teixeira, J. Sharp, R. Cichota, I. Vogeler, F. Y. Li, E. Wang, G. L. Hammer, M. J. Robertson, J. P. Dimes, A. M. Whitbread, J. Hunt, H. van Rees, T. McClelland, P. S. Carberry, J. N. G. Hargreaves, N. MacLeod, C. McDonald, J. Harsdorf, S. Wedgwood, and B. A. Keating, “APSIM-Evolution towards a new generation of agricultural systems simulation,” Environ. Modell. Software, vol. 62, pp. 327–350, Dec. 2014. doi: 10.1016/j.envsoft.2014.07.009
    [39]
    J. Fu, Y. Hao, H. Li, J. C. Reif, S. Chen, C. Huang, G. Wang, X. Li, Y. Xu, and L. Li, “Integration of genomic selection with doubled-haploid evaluation in hybrid breeding: From GS 1.0 to GS 4.0 and beyond,” Mol. Plant, vol. 15, no. 4, pp. 577–580, Apr. 2022. doi: 10.1016/j.molp.2022.02.005
    [40]
    F.-Y. Wang, W. Ding, X. Wang, J. Garibaldi, S. Teng, R. Imre, and C. Olaverri-Monreal, “The DAO to DeSci: AI for free, fair, and responsibility sensitive sciences,” IEEE Intell. Syst., vol. 37, no. 2, pp. 16–22, Mar.–Apr. 2022. doi: 10.1109/MIS.2022.3167070
    [41]
    Y. Weng, X. Wang, J. Hua, H. Wang, M. Kang, and F.-Y. Wang, “Forecasting horticultural products price using ARIMA model and neural network based on a large-scale data set collected by web crawler,” IEEE Trans. Comput. Soc. Syst., vol. 6, no. 3, pp. 547–553, Jun. 2019. doi: 10.1109/TCSS.2019.2914499
    [42]
    M. Fan, M. Kang, X. Wang, J. Hua, C. He, and F.-Y. Wang, “Parallel crop planning based on price forecast,” Int. J. Intell. Syst., vol. 37, no. 8, pp. 4772–4793, Aug. 2022. doi: 10.1002/int.22739
    [43]
    C. B. Barrett, M. E. Bachke, M. F. Bellemare, H. C. Michelson, and S. Narayanan, “Smallholder participation in contract farming: Comparative evidence from five countries,” World Dev., vol. 40, no. 4, pp. 715–730, Apr. 2012. doi: 10.1016/j.worlddev.2011.09.006
    [44]
    Y. Yang, Y. Lu, and W. Yan, “A comprehensive review on knowledge graphs for complex diseases,” Briefings Bioinf., vol. 24, no. 1, p. bbac543, Jan. 2023. doi: 10.1093/bib/bbac543
    [45]
    G. Anacoreta, M. Medici, and M. Canavari, “Towards a phenotype classification of agricultural robots,” in Proc. European Conf. Agricultural Engineering AgEng2021, 2021, pp. 126–131.
    [46]
    N. Goel, S. Kaur, and Y. Kumar, “Chapter 23–Machine learning-based remote monitoring and predictive analytics system for crop and livestock,” in AI, Edge and IoT-based Smart Agriculture, A. Abraham, S. Dash, J. J. P. C. Rodrigues, B. Acharya, and S. K. Pani, Eds. London, UK: Elsevier, 2022, pp. 395–407.
    [47]
    D. A. Ayalew, D. Deumlich, and B. Šarapatka, “Agricultural landscape-scale C factor determination and erosion prediction for various crop rotations through a remote sensing and GIS approach,” Eur. J. Agron., vol. 123, p. 126203, Feb. 2021. doi: 10.1016/j.eja.2020.126203
    [48]
    H. Patel and B. Shrimali, “AgriOnBlock: Secured data harvesting for agriculture sector using blockchain technology,” ICT Express, vol. 9, no. 2, pp. 150–159, Apr. 2023. doi: 10.1016/j.icte.2021.07.003
    [49]
    A. O. Onojeghuo, G. A. Blackburn, J. Huang, D. Kindred, and W. Huang, “Applications of satellite ‘hyper-sensing’ in Chinese agriculture: Challenges and opportunities,” Int. J. Appl. Earth Obs. Geoinformation, vol. 64, pp. 62–86, Feb. 2018. doi: 10.1016/j.jag.2017.09.005
    [50]
    M. Kang, X. Wang, H. Wang, J. Hua, P. de Reffye, and F.-Y. Wang, “The development of AgriVerse: Past, present, and future,” IEEE Trans. Syst.,Man,Cybern.: Syst., vol. 53, no. 6, pp. 3718–3727, Jun. 2023. doi: 10.1109/TSMC.2022.3230830
    [51]
    X. J. Wang, M. Z. Kang, J. Hua, and P. de Reffye, “From stand to organ level–A trial of connecting DSSAT and GreenLab crop model through data,” Smart Agric., vol. 3, no. 2, pp. 77–87, Jun. 2021.
    [52]
    S. A. Osinga, D. Paudel, S. A. Mouzakitis, and I. N. Athanasiadis, “Big data in agriculture: Between opportunity and solution,” Agric. Syst., vol. 195, p. 103298, Jan. 2022. doi: 10.1016/j.agsy.2021.103298
    [53]
    X. Li, P. Ye, J. Li, Z. Liu, L. Cao, and F.-Y. Wang, “From features engineering to scenarios engineering for trustworthy AI: I&I, C&C, and V&V,” IEEE Intell. Syst., vol. 37, no. 4, pp. 18–26, Jul.-Aug. 2022. doi: 10.1109/MIS.2022.3197950
    [54]
    X. Wang, M. Kang, H. Sun, P. de Reffye, and F.-Y. Wang, “DeCASA in AgriVerse: Parallel agriculture for smart villages in metaverses,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 12, pp. 2055–2062, Dec. 2022. doi: 10.1109/JAS.2022.106103
    [55]
    L. Li, Y. Lin, N. Zheng, and F.-Y. Wang, “Parallel learning: A perspective and a framework,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 389–395, Jul. 2017. doi: 10.1109/JAS.2017.7510493
    [56]
    F.-Y. Wang, R. Qin, J. Li, X. Wang, H. Qi, X. Jia, and B. Hu, “Federated management: Toward federated services and federated security in federated ecology,” IEEE Trans. Comput. Soc. Syst., vol. 8, no. 6, pp. 1283–1290, Dec. 2021. doi: 10.1109/TCSS.2021.3125312
    [57]
    P. D’Odorico, K. F. Davis, L. Rosa, J. A. Carr, D. Chiarelli, J. Dell’angelo, J. Gephart, G. K. MacDonald, D. A. Seekell, S. Suweis, and M. C. Rulli, “The global food-energy-water nexus,” Rev. Geophys., vol. 56, no. 3, pp. 456–531, Sept. 2018. doi: 10.1029/2017RG000591
    [58]
    B. D. Richter, D. Bartak, P. Caldwell, K. F. Davis, P. Debaere, A. Y. Hoekstra, T. Li, L. Marston, R. McManamay, M. M. Mekonnen, B. L. Ruddell, R. R. Rushforth, and T. J. Troy, “Water scarcity and fish imperilment driven by beef production,” Nat. Sustain., vol. 3, no. 4, pp. 319–328, Mar. 2020. doi: 10.1038/s41893-020-0483-z
    [59]
    R. Zhou, J. Jin, C. Wu, L. Zhou, L. Zhang, Y. Cui, Y. Zhang, and Y. Zhou, “Connection number based model for coordination development evaluation of regional water resources, social economy and ecological environment complex system,” J. Environ. Manage., vol. 339, p. 117913, Aug. 2023. doi: 10.1016/j.jenvman.2023.117913
    [60]
    D. Di, Z. Wu, H. Wang, and F. Zhang, “Spatial pattern analysis on the functions of water resources economic-social-ecological complex system,” J. Cleaner Prod., vol. 336, p. 130323, Feb. 2022. doi: 10.1016/j.jclepro.2021.130323
    [61]
    T. Wang, S. Jian, J. Wang, and D. Yan, “Dynamic interaction of water-economic-social-ecological environment complex system under the framework of water resources carrying capacity,” J. Cleaner Prod., vol. 368, p. 133132, Sept. 2022. doi: 10.1016/j.jclepro.2022.133132
    [62]
    G. Cheng, X. Li, W. Zhao, Z. Xu, Q. Feng, S. Xiao, and H. Xiao, “Integrated study of the water-ecosystem-economy in the Heihe River Basin,” Nat. Sci. Rev., vol. 1, no. 3, pp. 413–428, Sept. 2014. doi: 10.1093/nsr/nwu017
    [63]
    X. Li, L. Zhang, Y. Zheng, D. Yang, F. Wu, Y. Tian, F. Han, B. Gao, H. Li, Y. Zhang, Y. Ge, G. Cheng, B. Fu, J. Xia, C. Song, and C. Zheng, “Novel hybrid coupling of ecohydrology and socioeconomy at river basin scale: A watershed system model for the Heihe River Basin,” Environ. Modell. Software, vol. 141, p. 105058, Jul. 2021. doi: 10.1016/j.envsoft.2021.105058
    [64]
    Y. Z. Wang, X. Guo, F. Zhang, H. Yin, P. Guo, W. Zhang, and Q. Li, “The spatially-distributed ANN-optimization approach for water-agriculture-ecology nexus management under uncertainties and risks,” Agric. Water Manage., vol. 271, p. 107780, Sept. 2022. doi: 10.1016/j.agwat.2022.107780
    [65]
    J. Qin, W. Duan, Y. Chen, V. A. Dukhovny, D. Sorokin, Y. Li, and X. Wang, “Comprehensive evaluation and sustainable development of water-energy-food-ecology systems in Central Asia,” Renewable Sustainable Energy Rev., vol. 157, p. 112061, Apr. 2022. doi: 10.1016/j.rser.2021.112061
    [66]
    D. Zhang and P. Guo, “Integrated agriculture water management optimization model for water saving potential analysis,” Agric. Water Manage., vol. 170, pp. 5–19, May 2016. doi: 10.1016/j.agwat.2015.11.004
    [67]
    J. Liu, H. Mooney, V. Hull, S. J. Davis, J. Gaskell, T. Hertel, J. Lubchenco, K. C. Seto, P. Gleick, C. Kremen, and S. Li, “Systems integration for global sustainability,” Science, vol. 347, no. 6225, p. e1258832, Feb. 2015. doi: 10.1126/science.1258832
    [68]
    X. Wang, J. Yang, Y. Wang, Q. Miao, F.-Y. Wang, A. Zhao, J.-L. Deng, L. Li, X. Na, and L. Vlacic, “Steps toward industry 5.0: Building “6S” parallel industries with cyber-physical-social intelligence,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 8, pp. 1692–1703, Aug. 2023. doi: 10.1109/JAS.2023.123753
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (457) PDF downloads(171) Cited by()

    Highlights

    • Advanced technologies of big data, artificial intelligence, digital twins, metaverses, and parallel intelligence are employed to achieve a harmonious equilibrium between carbon and water resources for ensuring food security and sustainability
    • A case analysis of water usage shows that, given the limited availability of water resources in the context of the carbon-water balance, regional collaboration and optimized allocation have the potential to enhance the utilization efficiency of water resources in the river basin
    • Envisioning the future of agriculture involves the integration of digital, robotic and biological farming techniques, incorporating small tasks, big models, and deep intelligence

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return