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All issues Volume 678 (2025) E3S Web Conf., 678 (2025) 05002 References
Open Access
Issue
E3S Web Conf.
Volume 678, 2025
The 2nd International EcoHarmony Summit (IES 2025): Green Transitions and Innovations for a Sustainable Tomorrow
Article Number 05002
Number of page(s) 16
Section Sustainability Accounting and Green Finance
DOI https://doi.org/10.1051/e3sconf/202567805002
Published online 16 December 2025
  1. Dorley O.S., Ochuodho J.O., Auma E.O., Were J.O., Mutio J.M., Rice: Seed Systems, Production Characteristics, and Fungal Infections of Stored Grains in Major Production Zones of Liberia, Asian J. Agric. Rural Dev. 12, 297–305 (2022). https://doi.org/10.55493/5005.v12i4.4676 [Google Scholar]
  2. De Vos K., Janssens C., Jacobs L., Campforts B., Boere E., Kozicka M., et al., Rice availability and stability in Africa under future socio-economic development and climatic change, Nat. Food 4, (2023). https://doi.org/10.1038/s43016-023-00770-5 [Google Scholar]
  3. Zamanialaei M., McCarty J.L., Fain J.J., Hughes M.R., Understanding the perceived indicators of food sovereignty and food security for rice growers and rural organizations in Mazandaran Province, Iran, Agric. Food Secur. 11, (2022). https://doi.org/10.1186/s40066-022-00386-1 [Google Scholar]
  4. Khairulbahri M., Analyzing the impacts of climate change on rice supply in West Nusa Tenggara, Indonesia, Heliyon 7, e08515 (2021). https://doi.org/10.1016/j.heliyon.2021.e08515 [Google Scholar]
  5. Ansari A., Lin Y.P., Lur H.S., Evaluating and adapting climate change impacts on rice production in Indonesia: A case study of the Keduang Subwatershed, Central Java, Environ. 8, (2021). https://doi.org/10.3390/environments8110117 [Google Scholar]
  6. Jha R.K., Kalita P.K., Cooke R.A., Kumar P., Davidson P.C., Jat R., Predicting the Water Requirement for Rice Production as Affected by Projected Climate Change, Water 12, 3312 (2020). https://doi.org/10.3390/w12123312 [Google Scholar]
  7. Tang L., Risalat H., Cao R., Hu Q., Pan X., Hu Y., et al., Food security in China: A brief view of rice production, Foods 11, 3324 (2022). https://doi.org/10.3390/foods11213324 [Google Scholar]
  8. Jamaludin M., Fauzi T.H., Yuniarti Y., Mulyaningsih, Assessing the availability of rice by using system dynamics approach in West Java, Indonesia, Univers. J. Agric. Res. 9, 156–165 (2021). https://doi.org/10.13189/ujar.2021.090502 [Google Scholar]
  9. Tafakresnanto C., Kasno A., Susanti Z., Karmawati E., Adi S.H., Hidayanto M., et al., Utilising GIS-based spatial land resource analysis for subsidised fertiliser allocation in Jombang Regency, Indonesia, J. Water Land Dev. 2024, (2024). https://doi.org/10.24425/jwld.2024.151788 [Google Scholar]
  10. Sujarwo, Putra A.N., Setyawan R.A., Teixeira H.M., Khumairoh U., Forecasting rice status for a food crisis early warning system based on satellite imagery and cellular automata in Malang, Indonesia, Sustain. 14, (2022). https://doi.org/10.3390/su14158972 [Google Scholar]
  11. Taniushkina D., Lukashevich A., Shevchenko V., Belalov I.S., Sotiriadi N., Narozhnaia V., et al., Case study on climate change effects and food security in Southeast Asia, Sci. Rep. 14, (2024). https://doi.org/10.1038/s41598-024-65140-y [Google Scholar]
  12. Saen K., Factors influencing the practice of commercial rice production for food security in Vientiane capital, Laos, Asian J. Agric. Rural Dev. 13, 113–119 (2023). https://doi.org/10.55493/5005.v13i2.4777 [Google Scholar]
  13. Acierto R.A., Assessment of future risk of agricultural crop production under climate and social changes scenarios: A case of the Solo River Basin in Indonesia, J. Flood Risk Manag. 2025, 1–19 (2025). https://doi.org/10.1111/jfr3.13052 [Google Scholar]
  14. Kommey R., Fombad M., Strategies for knowledge sharing among rice farmers: A Ghanaian perspective, Electron. J. Knowl. Manag. 21, 114–129 (2023). https://doi.org/10.34190/ejkm.2L2.2803 [Google Scholar]
  15. Id E.M., Determinants of rice production and market supply: A study of Bench Sheko zone, PLoS One 2024, 1–13 (2024). https://doi.org/10.1371/journal.pone.0302115 [Google Scholar]
  16. Al A., Id M., Arafat S., Nihad I., Sarker M.R., Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh, PLoS One 2024, 1–28 (2024). https://doi.org/10.1371/journal.pone.0300648 [Google Scholar]
  17. Ay I., Mf N., Sa A., Isyanto A., Sustainable rice farming in Indonesia, Afr. J. Food Agric. Nutr. Dev. 24, 25409–25425 (2024). https://doi.org/10.18697/ajfand.127.23490 [Google Scholar]
  18. Rusyida W.Y., Teknik Peramalan: Metode ARIMA dan Holt Winter, Penerbit NEM (2022). https://books.google.co.id/books?id=z9-mEAAAQBAJ [Google Scholar]
  19. Li G.Z., Shao F.F., Zhang H., Zou C.P., Li H.H., Jin J., High mean water vapour pressure promotes the transmission of bacillary dysentery, PLoS One 2015, (2015). https://doi.org/10.1371/journal.pone.0124478 [Google Scholar]
  20. Liu G., Wiebe D.J., A time-series analysis of firearm purchasing after mass shooting events in the United States, JAMA Netw. Open 2, 1–15 (2019). https://doi.org/10.1001/jamanetworkopen.2019.1736 [Google Scholar]
  21. Miyama T., Matsui H., Azuma K., Minejima C., Itano Y., Time-series analysis of climate and air pollution factors associated with atmospheric nitrogen dioxide concentration in Japan, Int. J. Environ. Res. Public Health 17, 24, 9507 (2020). https://doi.org/10.3390/ijerph17249507 [Google Scholar]
  22. Wardani C., Jamhari J., Hardyastuti S., Suryantini A., Kinerja ketahanan beras di Indonesia: Komparasi Jawa dan luar Jawa periode 2005-2017, J. Ketahanan Nas. 25, 107–122 (2019). https://doi.org/10.22146/jkn.41770 [Google Scholar]
  23. Fidriyanto R., Ridwan R., Astuti W.D., Rohmatussolihat R., Sari N.F., Watman M., et al., In vitro ruminal fermentation and degradability of rice husk on rice bran substitution, Ann. Bogor 24, 50–58 (2020). https://doi.org/10.14203/ann.bogor.2020.v24.n1.50-58 [Google Scholar]
  24. Frandes M., Timar B., Timar R., Lungeanu D., Chaotic time series prediction for glucose dynamics in type 1 diabetes mellitus using regime-switching models, Sci. Rep. 7, 1–10 (2017). https://doi.org/10.1038/s41598-017-06478-4 [CrossRef] [Google Scholar]
  25. Wang K., Song W., Li J., Lu W., Yu J., Han X., The use of an autoregressive integrated moving average model for prediction of the incidence of dysentery in Jiangsu, China, Asia-Pac. J. Public Health 28, 336–346 (2016). https://doi.org/10.1177/1010539516645153 [Google Scholar]
  26. Le T.D., Gurney J.M., Nnamani N.S., Gross K.R., Chung K.K., Stockinger Z.T., et al., A 12-year analysis of nonbattle injury among US service members deployed to Iraq and Afghanistan, JAMA Surg. 153, 800–807 (2018). https://doi.org/10.1001/jamasurg.2018.1166 [Google Scholar]
  27. De Noordhout C.M., Devleesschauwer B., Haagsma J.A., Havelaar A.H., Bertrand S., Vandenberg O., et al., Burden of salmonellosis, campylobacteriosis and listeriosis: A time-series analysis, Belgium 2012-2020, Euro Surveill. 22, 30615 (2017). https://doi.org/10.2807/1560-7917.ES.2017.22.38.30615 [Google Scholar]
  28. Yashavanth B.S., Singh K.N., Paul A.K., An agricultural price forecasting model under nonstationarity using functional coefficient autoregression, J. Appl. Nat. Sci. 8, 50–54 (2016). https://doi.org/10.31018/jans.v8i1.745 [Google Scholar]
  29. Tang Y., Ma F., Zhang Y., Wei Y., Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model, Int. J. Financ. Econ. 27, 4770–4783 (2022). https://doi.org/10.1002/ijfe.2399 [Google Scholar]
  30. Hyndman R., Athanasopoulos G., Forecasting: Principles & Practice, OTexts (2014). [Google Scholar]
  31. Enario C.G.D., Licayan J.L., Mangubat M.J.E., Fabro D.L., Patalinghug K.M., Lumasag J.M., et al., ARIMA-based forecasting of rice area changes in Mindanao, Int. J. Res. Publ. Rev. 5, 666–673 (2024). [Google Scholar]
  32. Rosadi I., Fikry M., Aidilof H.A., Malikussaleh U., Pulo B., Performance evaluation of ARIMA model in forecasting rice production across Sumatera, Indonesia, Proc. ICoMDEN 2024, 1–9 (2024). [Google Scholar]
  33. Martanto R., Ngabekti S., Juhadi, Hamid N., Mahat H., Natsir N., et al., Determination of sustainable food land directions in Bantul Regency, Indonesia, based on food security level and land use conversion, Int. J. Sustain. Dev. Plan. 18, 161–169 (2023). https://doi.org/10.18280/ijsdp.180117 [Google Scholar]
  34. Kinose Y., Masutomi Y., Shiotsu F., Hayashi K., Ogawada D., Gomez-Garcia M., et al., Impact assessment of climate change on the major rice cultivar Ciherang in Indonesia, J. Agric. Meteorol. 76, 19–28 (2020). https://doi.org/10.2480/agrmet.D-19-00045 [Google Scholar]
  35. Schneider P., Asch F., Rice production and food security in Asian mega-deltas: A review on characteristics, vulnerabilities and agricultural adaptation options, J. Agron. Crop Sci. 206, 491–503 (2020). https://doi.org/10.1111/jac.12415 [Google Scholar]
  36. Indrianti M.A., Rukmana D., Demmallino E.B., Jamil M.H., Implementation of sustainable food and feed agricultural land regulation in Gorontalo District: A review, Adv. Anim. Vet. Sci. 12, 9–14 (2024). https://doi.org/10.17582/journal.aavs/2024/12.L9.14 [Google Scholar]
  37. Valverde-Arias O.R., Esteve P., Tarquis A.M., Garrido A., Remote sensing in an index-based insurance design for hedging economic impacts on rice cultivation, Nat. Hazards Earth Syst. Sci. 20, 345–362 (2020). https://doi.org/10.5194/nhess-20-345-2020 [Google Scholar]
  38. Dar M.H., Waza S.A., Shukla S., Zaidi N.W., Nayak S., Hossain M., et al., Drought-tolerant rice for ensuring food security in eastern India, Sustain. 12, 2214 (2020). https://doi.org/10.3390/su12062214 [Google Scholar]
  39. Rahman N.F., Malik A., Baten A., Kabir S., Rahman C., Ahmed R., et al., Sustaining rice productivity through weather-resilient agricultural practices, J. Sci. Food Agric. 103, 2303–2313 (2023). https://doi.org/10.1002/jsfa.13119 [Google Scholar]

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