Open Access
Issue
SHS Web Conf.
Volume 216, 2025
International Conference on the Impact of Artificial Intelligence on Traditional Economic Sectors (ICIAITES 2025)
Article Number 01053
Number of page(s) 10
Section Intelligent Systems and Digital Transformation in Agricultural Economy and Sustainable Development
DOI https://doi.org/10.1051/shsconf/202521601053
Published online 23 May 2025
  1. R. Lal, Regenerative agriculture for food and climate. J. Soil Water Conserv. 75(5), 123A–124A (2020). https://doi.org/10.2489/jswc.2020.0620A [CrossRef] [Google Scholar]
  2. S. Thinkampheang, T. Nakashizuka, W. Suksavate, P. Kachina, S. Hermhuk, L. Asanok, D. Marod, Impacts of climate change on forest restoration dynamics in the lower montane forest of Doi Suthep-Pui National Park, Northern Thailand. Biodiversitas J. Biol. Divers. 25(12) (2024) [CrossRef] [Google Scholar]
  3. R. Amundson, L. Biardeau, Opinion: Soil carbon sequestration is an elusive climate mitigation tool. Proc. Natl. Acad. Sci. 118(28), e2109216118 (2021) [Google Scholar]
  4. L. Breiman, Random forests. Mach. Learn. 45(1), 5–32 (2001) [Google Scholar]
  5. Q. Chen, W. Zhou, W. Shi, Estimation of Soil Organic Carbon Density on the Qinghai-Tibet Plateau Using a Machine Learning Model Driven by Multisource Remote Sensing. Remote Sens. 16(16), 3006 (2024). https://doi.org/10.3390/rs16163006 [CrossRef] [Google Scholar]
  6. D.R. Cutler, et al., Advances in random forests for ecological applications: a review. Methods Ecol. Evol. 11(5), 492–502 (2020) [Google Scholar]
  7. A. Steiner, G. Aguilar, K. Bomba, J.P. Bonilla, A. Campbell, R. Echeverria, S. Zebiak, Actions to transform food systems under climate change (2020) [Google Scholar]
  8. N. Lehmann, R. Finger, T. Klein, P. Calanca, A. Walter, Adapting crop management practices to climate change: Modeling optimal solutions at the field scale. Agric. Syst. 117, 55–65 (2013) [CrossRef] [Google Scholar]
  9. S. James, A modelling study of the economic and environmental impacts of integrating forage and cash crops into a Canterbury dairy farm (LUDF), Master Thesis, Lincoln University, New Zealand, 2015 [Google Scholar]
  10. H.F. Han, L. Liu, H. Zheng, Z. Liu, H. Han, T. Ning, Comparative Assessment of Carbon Sequestration of Diverse Organic Waste for Sustainable Crop Production in China. Available at SSRN 4234249 (2022) [Google Scholar]
  11. R. Lal, Soil health and climate change: an overview, in Soil health and climate change, 3–24 (2011) [CrossRef] [Google Scholar]
  12. T.A. Ippolito, Advanced Analytics for Predicting Spatiotemporal Agricultural Outcomes, Ph.D. thesis, University of Colorado at Boulder (2023) [Google Scholar]
  13. A. Sakhaee, A. Gebauer, M. Ließ, A. Don, Spatial prediction of organic carbon in German agricultural topsoil using machine learning algorithms. Soil 8(2), 587–604 (2022) [CrossRef] [Google Scholar]
  14. A.A. Fenta, G. Hailu, Z. Hadush, Integrating climate-smart approaches across landscapes to improve productivity, climate resilience, and ecosystem health, in Climate-smart Agriculture: Enhancing resilient agricultural systems, landscapes and livelihoods in Ethiopia and beyond, 15–23 (2019) [Google Scholar]
  15. P. Mangal, A. Rajesh, R. Misra, Big data in climate change research: Opportunities and challenges, in 2020 International Conference on Intelligent Engineering and Management (ICIEM), 321–326 (2020) [CrossRef] [Google Scholar]
  16. Y. Kang, M. Ozdogan, X. Zhu, Z. Ye, C. Hain, M. Anderson, Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest. Environ. Res. Lett. 15(6), 064005 (2020) [CrossRef] [Google Scholar]
  17. E.K. Ruby, G. Amirthayogam, G. Sasi, T. Chitra, A. Choubey, S. Gopalakrishnan, Advanced Image Processing Techniques for Automated Detection of Healthy and Infected Leaves in Agricultural Systems. Mesopotamian J. Comput. Sci. 2024, 62–70 (2024) [Google Scholar]

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