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All issues Volume 344 (2025) EPJ Web Conf., 344 (2025) 01031 References
Open Access
Issue
EPJ Web Conf.
Volume 344, 2025
AI-Integrated Physics, Technology, and Engineering Conference (AIPTEC 2025)
Article Number 01031
Number of page(s) 9
Section AI-Integrated Physics, Technology, and Engineering
DOI https://doi.org/10.1051/epjconf/202534401031
Published online 22 December 2025
  1. V. Br. Sebayang, B. M. Sinaga, H. Harianto, and I. K. Kariyasa, The impact of domestic policy on farmers’ welfare and maize processing industry in Indonesia. International Journal of Economics and Financial Issues. 9, 3, 225–232 (2019). https://doi.org/10.32479/ijefi.7910 [Google Scholar]
  2. FAOSTAT,Value of Agricultural Production. Accessed: Dec. 21,2021. [Online]. Available:https://www.fao.org/faostat/en/#data/QV [Google Scholar]
  3. A. Mulyani and M. Sarwani, The Characteristic and Potential of Sub Optimal Land for Agricultural Development in Indonesia. Jurnal Sumberdaya Lahan. 7, 1, 46–57 (2013). [Google Scholar]
  4. Suhartono, A. Soegianto, and A. Amzeri, Mapping of land potentially for maize plant in madura island-indonesia using remote sensing data and geographic information systems (Gis). Ecology, Environment and Conservation. 26, 3, 145–155 (2020). https://doi.org/10.21107/amzeri.2020.1 [Google Scholar]
  5. A. Amzeri, Suhartono, S. Fatimah, G. Pawana, and K. P. W. Sukma, Combining ability analysis in maize diallel hybrid populations under optimum and drought stress conditions. SABRAO J Breed Genet. 56, 2, 476–492 (2024). https://doi.org/10.54910/sabrao2024.56.2.3 [Google Scholar]
  6. H. Tang, L. Zhang, X. Xie, Y. Wang, T. Wang, and C. Liu, Resilience of Maize to Environmental Stress: Insights into Drought and Heat Tolerance. Multidisciplinary Digital Publishing Institute (MDPI). 26, 11, 5274 (2025). https://doi.org/10.3390/ijms26115274 [Google Scholar]
  7. H. Tang, X. Xie, L. Zhang, and C. Liu, Assessing the Influence of Planting Dates on Sustainable Maize Production under Drought Stress Conditions. Sustainability. 16, 11, 4571 (2024). https://doi.org/10.3390/su16114571 [Google Scholar]
  8. M. Farid, Characterization of maize germplasm and promising genotypes selection based on morphological traits, heritability, and multivariate analysis. SABRAO J Breed Genet. 57, 5, 1809–1817 (2025). https://doi.org/10.54910/sabrao2025.57.5.3 [Google Scholar]
  9. A. Amzeri, D. Indradewa, B. Setiadi Daryono, and D. Rachmawati, Phenetic and Genetic Relationships among Madura Local Maize (Zea mays L.) Revealed by Morphological Characters and RAPD Markers. Biota : Jurnal Ilmiah Ilmu- Ilmu Hayati. 16, 2, 227–235 (2011). https://doi.org/10.24002/biota.v16i2.104 [Google Scholar]
  10. A. H. Syauqi, A. Amzeri, Seleksi Tanaman Jagung Toleran pada Cekaman Kekeringan. Rekayasa. 16, 1, 113–124 (2023). https://doi.org/10.21107/rekayasa.v16i1.20906 [Google Scholar]
  11. A. Amzeri, K. Badami, S. B. Santoso, and K. P. Sukma, Morphological and molecular characterization of maize lines tolerance to drought stress. Biodiversitas. 23, 11, 5844–5853 (2022). https://doi.org/10.13057/biodiv/d231138 [Google Scholar]
  12. A. Amzeri and K. Badami, Estimation of Combining Ability, Heritability and Genes action of Yield Components of Inbred Corn Lines in Diallel Crosses. International Conference on Social Science. 383, 1212–1216 (2019). https://doi.org/10.2991/icss-19.2019.145 [Google Scholar]
  13. A. Amzeri, F. Adiputra, and S. Khoiri, Selection of Maize Hybrids Resulting from Line × Tester Crossing Tolerant to Drought Stress. Journal of Global Innovations in Agricultural Sciences. 575– 584 (2024). https://doi.org/10.22194/jgias/24.1326 [Google Scholar]
  14. J. Crossa, From Genotype × Environment Interaction to Gene × Environment Interaction. Current genomics. 13, 3, 225-244 (2012). https://doi.org/10.2174/138920212800543066 [Google Scholar]
  15. S. Jubair, O. Tremblay-Savard, and M. Domaratzki, GxENet: Novel fully connected neural network based approaches to incorporate GxE for predicting wheat yield. Artificial Intelligence in Agriculture. 8, 60–76 (2023). https://doi.org/10.1016/j.aiia.2023.05.001 [Google Scholar]
  16. R. Herawati, A. P. Lestari, Nurmegawati, D. W. Ganefianti, and A. Romeida, Comparative study on the stability and adaptability of different models to develop a high-yield inbred line from landrace rice varieties. Annals of Agricultural Sciences. 66, 2, 184–192 (2021). https://doi.org/10.1016/j.aoas.2021.12.004 [Google Scholar]
  17. B. B. Daemo and Z. Ashango, Application of AMMI and GGE biplot for genotype by environment interaction and yield stability analysis in potato genotypes grown in Dawuro zone, Ethiopia. J Agric Food Res. 18, 101287 (2024). https://doi.org/10.1016/j.jafr.2024.101287 [Google Scholar]
  18. U. H. Masoodi et al., Analysis of yield stability and genotype–environment interaction for open- pollinated tomato varieties in the Kashmir Himalaya using the AMMI model. Sci Rep. 15, 1, (2025). https://doi.org/10.1038/s41598-025- 07621-2 [Google Scholar]
  19. A. T. Abebe et al., Genotype x environment interaction and yield stability of soybean (Glycine max l.) genotypes in multi-environment trials (METs) in Nigeria. Heliyon. 10, 19 (2024). https://doi.org/10.1016/j.heliyon.2024.e38097 [Google Scholar]
  20. C. Oroian et al., AMMI Analysis of Genotype × Environment Interaction on Sugar Beet (Beta vulgaris L.) Yield, Sugar Content and Production in Romania. Agronomy. 13, 10 (2023). https://doi.org/10.3390/agronomy13102549 [Google Scholar]
  21. A. Pour-Aboughadareh et al., Selection of High- Yielding and Stable Genotypes of Barley for the Cold Climate in Iran. Plants. 12, 13 (2023). https://doi.org/10.3390/plants12132410 [Google Scholar]
  22. A. Amzeri, B. S. Daryono, and M. Syafii, Genotype by environment and stability analyses of dryland maize hybrids. SABRAO J. Breed. Genet. 52, 4, 355-368 (2020). https://sabraojournal.org/wp- content/uploads/2021/01/SABRAO-J-BREED- Genet-524-355-368-AMZERI.pdf [Google Scholar]
  23. K. Bratković et al., Interpreting the Interaction of Genotype with Environmental Factors in Barley Using Partial Least Squares Regression Mode. Agronomy. 14, 1 (2024). https://doi.org/10.3390/agronomy14010194 [Google Scholar]
  24. S. S. Sahmat, M. Y. Rafii, Y. Oladosu, M. Jusoh, M. Hakiman, and H. Mohidin, Unravelling the dynamics of genotype and environment interactions on chilli (Capsicum annuum L.) yield-related attributes in soilless planting systems. Sci Rep. 14, 1 (2024). https://doi.org/10.1038/s41598-023-50381-0 [Google Scholar]
  25. G. Weldemichael Abrha, S. A. Kebede, L. T. Bedada, G. Berecha Yadessa, and A. Adugna Gutu, Genotype by Environment Interaction and Yield Stability of Coffee (Coffea arabica L.) Genotypes Evaluated in Western Ethiopia. Plant Prod Sci. 25, 4, 467–483 (2022). https://doi.org/10.1080/1343943X.2022.2136722 [Google Scholar]
  26. L. Alemayehu, M. Kebede, and E. Wada, AMMI analysis of elite bread wheat (Triticum aestivum L.) selections for genotype by environment interaction and stability of grain yield in Southern Ethiopia. PLoS One. 20, 1 (2025). https://doi.org/10.1371/journal.pone.0318559 [Google Scholar]
  27. M. Enyew, T. Feyissa, M. Geleta, K. Tesfaye, C. Hammenhag, and A. S. Carlsson, Genotype by environment interaction, correlation, AMMI, GGE biplot and cluster analysis for grain yield and other agronomic traits in sorghum (Sorghum bicolor L. Moench). PLoS One. 16, 10, 1–22 (2021). https://doi.org/10.1371/journal.pone.0258211 [Google Scholar]
  28. W. Yan, M. S. Kang, B. Ma, S. Woods, and P. L. Cornelius, GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 47, 2, 643–655 (2007). https://doi.org/10.2135/cropsci2006.06.0374 [Google Scholar]
  29. M. Ye, Z. Chen, B. Liu, and H. Yue, Stability Analysis of Agronomic Traits for Maize (Zea Mays) Genotypes Based on AMMI Model. Bangladesh J. Biot. 50, 2, 343–350 (2021). https://doi.org/https://doi.org/10.3329/bjb.v50i2.54091 [Google Scholar]
  30. G. B. Adu, B. Badu-Apraku, R. Akromah, and F. J. Awuku, Combining Abilities and Heterotic Patterns among Early Maturing Maize Inbred Lines under Optimal and Striga-Infested Environments. Genes (Basel). 13, 12 (2022). https://doi.org/10.3390/genes13122289 [Google Scholar]
  31. M. Azrai, Early-Yielding Hybrid Corn: Development Prospects in the Face of Climate Change. Iptek Tanaman Pangan. 8, 2, 90–96 (2013). [Google Scholar]

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