Identification of stable and high yielding early rice (Oryza sativa) lines across seasons using GGE Biplot analysis
121 / 46
Keywords:
Grain yield, GGE Biplot, Polygon, Rice, Stability analysisAbstract
The present study aimed to evaluate grain yield performance, stability, and adaptability of nine early-maturing rice (Oryza sativa L.) genotypes was conducted at Agricultural Research Station (Professor Jayashankar Telangana Agricultural University, Rajendranagar, Hyderabad) Kunaram, Peddapalli, Telangana during summer 2018–19, rainy (kharif) 2019, summer 2019–20, and rainy (kharif) 2020. The comprehensive analysis of variance unequivocally revealed statistically significant influences from lines, seasons, and their interaction, indicating a complex interplay that governs grain yield variability. The findings of GGE biplot methodology demonstrated that greatest variation of total variation was concentrated in environmental factors, subsequently in line impacts and their interactions influencing grain yield. More than 85% of the total variation was captured by the first two principal components, with PC1 and PC2 explaining 68.8% and 18.3% of the variation, respectively. In the GGE biplot genotype view, genotypes G1 and G5 were identified as high-yielding and stable, as indicated by their proximity to the biplot origin suggesting broad adaptability and minimal G×E interaction. The GGE biplot facilitated the identification of mega-environments through polygon, wherein the top-performing lines included G3, G5, and G1 in Mega Environment 1, while G7 stood out as the elite line in Mega Environment 2. G3 was closely aligned with the ideal line followed by G5 and G1 in biplot’s average-environment coordination (AEC) view, conclusively expressing remarkable yield and stability due to their extensive adaptability under the seasons tested suggests strong potential for inclusion in early rice varietal development and multi-environment breeding programmes.
Downloads
References
Aktar A, Hasan M J, Kulsumu, Rahman M H, Khatun M and Islam M R. 2015. GGE biplot analysis for yield stability in multi-environment trials of promising hybrid rice (Oryza sativa L.). Bangladesh Rice Journal 19(1): 1–8.
Atlin G N, McRae K B and Lu X. 2000. Genotype × Region interaction for two-row barley yield in Canada. Crop Science 40: 1–6. https://doi.org/10.2135/cropsci2000.4011
Balakrishnan D, Malathi S, Venkateswara Rao Y, Krishnam Raju A, Sukumar M, Kavitha B and Sarla N. 2020. Detecting CSSLs and yield QTLs with additive, epistatic and QTL × environment interaction effects from Oryza sativa × O. nivara IRGC81832cross. Scientific Reports 10: 7766. https://doi.org/10.1038/ s41598-020-64300-0
Bose L K, Jambhulkar N N, Pande K and Singh O N. 2014. Use of AMMI and other stability statistics in the simultaneous selection of rice genotypes for yield and stability under direct-seeded conditions. Chilean Journal of Agricultural Research 74(1): 3–9. https://dx.doi.org/10.4067/S0718-58392014000100001
Bueno C S and Lafarge T. 2017. Maturity groups and growing seasons as key sources of variation to consider within breeding programmes for high yielding rice in the tropics. Euphytica 213: 74. https://doi.org/10.1007/s10681-017-1862-z
Chandramohan Y, Krishna L, Srinivas B, Rukmini K, Sreedhar S, Prasad K S, Kishore N S, Rani C V D, Singh T V J and Jagadeeshwar R. 2023. Stability analysis of short duration rice genotypes in Telangana using AMMI and GGE bi-plot models. Environment Conservation Journal 24(1): 243–52. https://doi. org/10.36953/ECJ.11952311
Chen R, Wang G, Yu J, Lu Y, Tao T, Wang Z, Hua Y, Li N, Wang H, Gharib A, Zhou Y, Xu Y, Li P, Xu C and Yang Z. 2025. Yield, stability, and adaptability of hybrid Japonica rice varieties in the East Coast of China. Agronomy 15: 901.
De Los Reyes B G, Morsy M J, Gibbons, Varma T S N, Antoine W and Mc Grath J M. 2003. A snapshot of the low temperatures stress transcriptome of developing rice seedling (Oryza sativa L.) via ESTs from subtracted cDNA library. Theoretical and Applied Genetics 107(6): 1071–82. https://doi.org/10.1007/ s00122-003-1344-7
Department of Agriculture and Farmers Welfare. 2026. Unified Portal for Agricultural Statistics (UPAg): Rice Area, Production and Yield Statistics for 2024–25. Ministry of Agriculture and Farmers Welfare, Government of India, New Delhi. https:// upag.gov.in
El-Aty M S A, Abo-Youssef M I, Sorour F A, Salem M, Gomma M A, Ibrahim O M, Yaghoubi Khanghahi M, Al-Qahtani W H, Abdel-Maksoud M A and El-Tahan A M. 2024. Performance and stability for grain yield and its components of some rice cultivars under various environments. Agronomy 14: 2137. https://doi.org/10.3390/ agronomy14092137
Gauch H G and Zobel R W. 1996. AMMI analysis of yield trials. Genotype-by-Environment Interaction, pp. 85–122. Kang M S and Gauch H G (Eds). CRC Press, Boca Raton.
Gauch H G. 2006. Statistical analysis of yield trials by AMMI and GGE. Crop Science 46: 1488–1500.
Gupta D K, Pramanick S and Singh A K. 2025. Long-term impact of aerosols and climate variability on rice yields across agroclimatic zones in India. Earth Systems and Environment 9. https://doi.org/10.1007/s41748-025-00701-3
Hori K, Matsubara K and Yano M. 2016. Genetic control of flowering time in rice: Integration of Mendelian genetics and genomics. Theoretical and Applied Genetics 129(12): 2241–52.
Huang M, Qi-Yuan T, He-Jum A and Ying-Bin Z. 2017. Yield potential and stability in super hybrid rice and its production strategies. Journal of Integrative Agriculture 16(5): 1009–17. https://doi.org/10.1016/S2095-3119(16)61535-6
Huang X, Jang S, Kim B, Piao Z, Redona E and Koh H J. 2021. Evaluating genotype × environment interactions of yield traits and adaptability in rice cultivars grown under temperate, subtropical and tropical environments. Agriculture 11(6): 558. https:// doi.org/10.3390/agriculture11060558
Jain B T, Sarial A K and Kaushik P. 2019. Understanding G × E interaction of elite basmati rice (Oryza sativa L.) genotypes under north Indian condition using stability models. AppliedEcology and Environmental Research 17(3): 5863–85. https:// doi.org/10.15666/aeer/1703_58635885
Kang M S. 1993. Simultaneous selection for yield and stability in crop performance trials: Consequences for growers. Agronomy Journal 85(3): 754–57. https://doi.org/10.2134/agronj1993.00 021962008500030042x
Kesh H, Kharb R, Ram K, Munjal R, Kaushik P and Kumar D. 2021. Adaptability and AMMI biplot analysis for yield and agronomical traits in scented rice genotypes under diverse production environments. Indian Journal of Traditional Knowledge 20(2): 550–62.
Krishnamurthy S L, Sharma P C, Sharma D K, Singh Y P, Mishra V K, Burman D, Maji B, Mandal S, Sarangi S K, Gautam R K, Singh P K, Manohara K K, Marandi B C, Chattopadhyay K, Padmavathi G, Vanve P B, Patil K D, Thirumeni S, Verma O P, Khan A H, Tiwari S, Geetha S, Gill R, Yadav V K, Roy B, Prakash M, Anandan A, Bonifacio J, Ismail A M and Singh R K. 2021. Additive main effects and multiplicative interaction analyses of yield performance in rice genotypes for general and specific adaptation to salt stress in locations in India. Euphytica 217: 20. https://doi.org/10.1007/s10681-020-02730-7
Kumar B M D, Purushottam A P, Raghavendra P, Vittal T, Shubha K N and Madhuri R. 2020. Genotype environment interaction and stability for yield and its components in advanced breeding lines of red rice (Oryza Sativa L.). Bangladesh Journal of Botany 49(3): 425–35.
Liang Z, Menjivar J R, Zhang L, Zhang J and Shen X. 2024. Examining the effects of adopting early maturing crop varieties on agricultural productivity, climate change adaptation, and mitigation. International Journal of Low-Carbon Technologies 19: 1256–74. https://doi.org/10.1093/ijlct/ctad150
Parihar A K, Basandrai A K, Sirari A, Dinakaran D, Singh D, Kannan K, Kushawaha P S, Adinarayan M, Akram M, Latha T K S, Paranidharan V and Gupta S. 2017. Assessment of mungbean genotypes for durable resistance to Yellow Mosaic Disease: Genotype × Environment interaction. Plant Breeding 136(1): 94–100. https://doi.org/10.1111/pbr.12446
Poli Y, Balakrishnan D, Desiraju S, Panigrahy M, Voleti S R, Mangrauthia S K and Neelamraju S. 2018. Genotype × Environment interactions of Nagina22 rice mutants for yield traits under low phosphorus, water limited and normal irrigated conditions. Scientific Reports 8(1): 15530. https://doi. org/10.1038/s41598-018-33812-1
Ponnuswamy R, Rathore A, Vemula A, Das R R, Singh A K and Balakrishnan D. 2018. Analysis of multi-location data of hybrids rice trials reveals complex genotype by environment interaction. Cereal Research Communications 46(1): 146–57. https://doi.org/10.1556/0806.45.2017.065
Praveen K, Ajay B C, Gangadhara K, Kumar N, Choudhary R R, Mahatma M K, Singh S, Reddy K K, Bera S K, Sangh C H, Rani K, Chavada Z and Solanki K D. 2024. AMMI and GGE biplot analysis of genotype by environment interaction for yield and yield contributing traits in confectionery groundnut. Scientific Reports 14: 2943. https://doi.org/10.1038/s41598- 024-52938-z
Rahman A U, Akhtar M, Shah S M A, Shah M A, Arif M, Khan W, Rasheed S M, Davide L C and Khan S. 2025. Application of GGE biplot analysis for assessing stability in recombinant inbred lines (RILs) of rice (Oryza sativa L.). Indian Journal of Genetics and Plant Breeding 85(1): 237–42.
Sackey O K, Feng N, Mohammed Y Z, Dzou C F, Zheng D, Zhao L and Shen X. 2025. A comprehensive review on rice responses and tolerance to salt stress. Frontiers in Plant Science 16: 1561280. https://doi.10.3389/fpls.2025.1561280
Sato Y, Masuta Y, Saito K, Murayama S and Ozawa K. 2011. Enhanced chilling tolerance at the booting stage in rice by transgenic overexpression of the ascorbate peroxidase gene, OsAPXa. Plant Cell Reports 30(3): 399–406. https://doi. org/10.1007/s00299-010-0985-7
Seck F, Covarrubias-Pazaran G and Gueye T. 2023. Realized genetic gain in rice: Achievements from breeding programs. Rice 16: 61. https://doi.org/10.1186/s12284-023-00677-6
Senguttuvel P, Sravanraju N, Jaldhani V, Divya B, Beulah P, Nagaraju P, Manasa Y, Prasad A S H, Brajendra P, Gireesh C, Anantha M S, Suneetha K, Sundaram R M, Madhav M S, Tuti M D, Subbarao L V, Neeraja C N, Bhadana V P, Rao P R, Voleti S R and Subrahmanyam D. 2021. Evaluation of genotype by environment interaction and adaptability in lowland irrigated rice hybrids for grain yield under high temperature. Scientific Reports 11(1): 15825. https://doi.org/10.1038/s41598- 021-95264-4
Sharifi P, Aminpanah H, Erfani R, Mohaddesi A and Abbasian A. 2017. Evaluation of genotype × environment interaction in rice based on AMMI model in Iran. Rice Science 24(3): 173–80. https://doi.org/10.1016/j.rsci.2017.02.001
Suman K, Neeraja C N, Madhubabu P, Rathod S, Bej S, Jadhav K P, Kumar J A, Chaitanya U, Pawar S C, Rani S H, Subbarao L V and Voleti S R. 2021. Identification of promising RILS for high grain zinc through genotype × environment analysis and stable grain zinc QTL using SSRs and SNPs in rice (Oryza sativa L.). Frontiers in Plant Science 12: 587482. https://doi. org/10.3389/fpls.2021.587482
Yan W, Hunt L A, Sheng Q and Szlavnics Z. 2000. Cultivar evaluation and mega environment investigation based on the GGE biplot. Crop science 40(3): 597–605. https://doi. org/10.2135/cropsci2000.403597x
Yan W, Cornelius P L, Crossa J and Hunt L A. 2001. Two types of GGE biplots for analyzing multi-environment trial data. Crop Science 41: 656–63. https://doi.org/10.2135/ cropsci2001.413656x
Yan W and Kang M S. 2003. GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists and Agronomists, pp. 1–267. Yan W and Kang M S (Eds). CRC Press LLC, Boca Raton, Florida.
Yan W and Tinker N A. 2006. Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science 86: 623–45. https://doi.org/10.4141/P05-169
Zhou S, Zhu S, Cui S, Hou H, Wu H, Hao B, Cai L, Xu Z, Liu L, Jiang L, Wang H and Wan J. 2021. Transcriptional and post-transcriptional regulation of heading date in rice. New Phytol 230: 943–56.
Downloads
Submitted
Published
Issue
Section
License
Copyright (c) 2026 The Indian Journal of Agricultural Sciences

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The copyright of the articles published in The Indian Journal of Agricultural Sciences is vested with the Indian Council of Agricultural Research, which reserves the right to enter into any agreement with any organization in India or abroad, for reprography, photocopying, storage and dissemination of information. The Council has no objection to using the material, provided the information is not being utilized for commercial purposes and wherever the information is being used, proper credit is given to ICAR.