Stability analysis of short duration rice genotypes in Telangana using AMMI and GGE Bi-plot models

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Jan 15, 2023
Y. Chandramohan
L. Krishna B. Srinivas K. Rukmini S. Sreedhar
K. Shiva Prasad N. Sandhya Kishore Ch. V. Durga Rani T. V. J. Singh
R. Jagadeeshwar

Abstract

In order to identify stable short duration rice genotypes across different agro-climatic zones in Telangana state, Additive Main Effects and Multiplicative Interaction Models (AMMI) and GGE Bi-plot analyses was performed. Analysis of variance clearly revealed that genotypes contributed highest (34.57 %) followed by environments (32.31 %) and genotype environment interaction (17.10 %) in total sum of squares indicating very greater role played by genotypes, environments and their interactions in realizing final grain yield. AMMI analysis revealed that rice genotypes viz., KNM 2305 (G12), KNM 2307 (G16) and JGL 20776 (G9) were recorded higher mean grain yield with positive interactive principal component analysis 1 (IPCA1) scores whereas, KNM 2307 (G16) and RNR 23595 (G5) were plotted near to zero IPCA1 axis indicating relatively more stable performance across locations.  However, the GGE Bi-plot genotype view depicts that the genotypes viz., RDR 1188 (G6) and KNM 2305 (G12) were known as highly unsteady across locations. Among environments, Rudrur (E4), Kunaram (E2) and Rajendranagar (E5) locations were identified as relatively ideal to realize good yields whereas Jagtial (E1), Kampasagar (E3) and Warangal (E6) locations were poor and most discriminating.  Among the six locations, the performance of genotypes was relatively similar in Kunaram (E2), Kampasagar (E3) and Rudrur (E4), Warangal (E6) though they belong to different agro-climatic zones of Telangana state, whereas Jagtial (E1) location seems to be little divergent. Further, KNM 2305 (G12) and US 314 (G17) were performed better at Jagtial (E1) and Rajendranagar (E5) while MTU 1010 (G8) was found to have good performance in Rudrur (E4) and Warangal (E6) locations. The results conclude that KNM 2305 was high yielder but found to be unstable across locations whereas KNM 2307 (G16) and KPS 6251 (G15) were identified as good with reasonably higher grain yield and stable performance over locations.

How to Cite

Chandramohan, Y., Krishna, L., Srinivas, B., Rukmini, K., Sreedhar, S., Prasad, K. S., Kishore, N. S., Rani, C. V. D., Singh, T. V. J., & Jagadeeshwar, R. (2023). Stability analysis of short duration rice genotypes in Telangana using AMMI and GGE Bi-plot models. Environment Conservation Journal, 24(1), 243–252. https://doi.org/10.36953/ECJ.11952311

Downloads

Download data is not yet available.
Abstract 194 | PDF Downloads 240

##plugins.themes.bootstrap3.article.details##

Keywords

AMMI, GE interaction, GGE biplot, rice genotypes, stability

References
Amiri, R., Bahraminejad, S., Sasani, S., Jalali-Honarmand, S., & Fakhri, R. (2015). Bread wheat genetic variation for grain's protein, iron and zinc concentrations as uptake by their genetic ability. European Journal of Agronomy, 67, 20–26.
Anonymous, (2019). FAOSTAT. Food and Agriculture Organization of the United Nations, Rome, Italy. http:// www.fao.org/faostat/en/#data/QCL.
Anonymous, (2021). Telangana State Statistical Abstract. Telangana State Development Planning Society, Planning Department, Government of Telangana pp: 102.
Balakrishnan, D., Subrahmanyam, D., Badri, J., Raju, A. K., Rao, Y. V., Beerelli, K., Mesapogu, S., Surapaneni, M., Ponnuswamy, R., Padmavathi, G., Ravindra Babu, V., & Neelamraju, S. (2016). Genotype x Environment Interactions of Yield Traits in Backcross Introgression Lines Derived from Oryza sativa cv. Swarna/Oryza nivara. Frontiers in Plant Science, 7, 1530.
Chandra-mohan, Y., Krishna, L., Sreedhar, S., Satish-Chandra, B., Damodhar-Raju, Ch., Madhukar, P., Ramya, R., Virender-Jeet-Singh, T., & Ramana, M. V. (2021). Stability Analysis of Rice Hybrids for Grain Yield in Telangana through AMMI and GGE Bi-plot Model. International Journal of Bio-resource and Stress Management, 12(6), 687-695.
Cordero L, K. (2020). Temperate japonica rice (Oryza sativa L.) breeding: History, present and future challenges. Chilean journal of agricultural research, 80, 303-314.
Crossa, J., Gauch, H. G., & Zobel, R. W. (1990). Additive main effects and multiplicative interactions analysis of two international maize cultivar trials. Crop Science, 30, 493–500.
Das, S., Misra, R. C., Patnaik, M. C., & Das, S. R. (2010). G×E interaction, adaptability and yield stability of mid-early rice genotypes. Indian Journal of Agricultural Research, 44(2), 104–111.
Gauch, H. G., Piepho, H. P., & Annicchiarico, P. (2008). Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations. Crop Science, 48, 866–889.
Gauch, H. G., & Zobel, R. W. (1996). AMMI analysis of yield trials. In: Kang, M.S., Gauch, H.G. (Eds.), Genotype-by-environment interaction. Boca Raton, FL, CRC Press, Pp, 85–122.
GenStat, (2012). GenStat for Windows. Fifteenth Edition. VSN International Ltd., Oxford.
IRRI, (2014). Plant Breeding Tools (PB Tools), Version: 1.4, International Rice Research Institute, Los Banos.
Kempton, R. A., Fox, P. N., & Cerezo, M. (2012). Statistical Methods for Plant Variety Evaluation; Springer Science & Business Media: Berlin, Germany, ISBN 978-94-009-1503-9.
Mahalingam, L., Mahendran, S., Chandra-Babu, R., & Atlim, G. (2006). AMMI Analysis for Stability of Grain Yield in Rice (Oryza sativa L). International Journal of Botany, 2(2), 104–106.
Mary, A. I. A., Mallikarjuna-Swamy, B. P., Amparado, A. F., Gwen-Iris, L. D. E., Emily, C. A., & Reinke, R., (2019). Stability and GGE analysis of zinc-biofortified rice genotypes evaluated in diverse environments. Euphytica, 215, 1–17.
Mohammadi, M., Karimizadeh, R., Hosseinpour, T., Falahi, H. A., Khanzadeh, H., & Sabaghnia, N. (2012). Genotype × Environment interaction and stability analysis of seed yield of durum wheat genotypes in dryland conditions. Notulae Scientia Biologicae, 4, 57–64.
Nachit, M. M., Nachit, G., Ketata, H., Gauch, H. G., & Zobel, R. W. (1992). Use of AMMI and linear regression models to analyze genotype-environment interaction in durum wheat. Theoretical and Applied Genetics, 83, 597-601
Olivoto, T., Lucio, A. D. C., Da-Silva, J. A. G., Sari, B. G., & Diel, M. I. (2019). Mean Performance and Stability in Multi-Environment Trials II: Selection Based on Multiple Traits. Agronomy Journal, 111, 2961–2969.
Rakshit, S., Ganapathy, K. N., Gomashe, S. S., Rathore, A., Ghorade, R. B., Nagesh-Kumar, M. V., Ganesmurthy, K., Jain, S. K., Kamtar, M. Y., Sachan, J. S., Ambekar, S. S., Ranwa, B. R., Kanawade, D. G., Balusamy, M., Kadam, D., Sarkar, A., Tonapi, V. A., & Patil, J.V. (2012). GGE biplot analysis to evaluate genotype, environment and their
interactions in sorghum multi-location data. Euphytica, 185, 465–479.
Rao, P. J. M., Kishore, N. S., Sandeep, S., Neelima, G., Rao, P. M., Das, D. M., & Saritha, A. (2020). Evaluation of performance and yield stability analysis based on AMMI and GGE-biplot in promising Pigeonpea [Cajanus cajan (L.) Millspaugh] genotypes. Legume Research, LR- 4299, 1-7
Umma, K. M., Hasan, M., Jamil-Akter, A., Rahman, H., & Biswas, P. (2013). Genotype-environment interaction and stability analysis in hybrid rice: an application of additive main effects and multiplicative interaction. Bangladesh Journal of Botany, 42(1), 73–81.
Xing H., Jang, S., Kim, B., Piao, Z., Redona, E,. & Koh, H. J. (2021). Evaluating Genotype x Environment Interactions of Yield Traits and Adaptability in Rice Cultivars Grown under Temperate, Subtropical and Tropical Environments. Agriculture, 11, 558.
Xu, L., Yuan, S., & Man, J. (2020). Changes in rice yield and yield stability in China during the past six decades. Journal of the Science of Food and Agriculture, 100, 3560–3569.
Yan, W. (2016). Analysis and Handling of G x E in a Practical Breeding Program. Crop Science. 56, 2106–2118.
Yan, W., & Tinker, N. A. (2006). Bi-plot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science, 86(3), 623–645.
Yan, W., Hunt, L.A., Sheng, Q., & Szlavnics, Z. (2000). Cultivar Evaluation and Mega-Environment Investigation Based on the GGE Bi-plot. Crop Science, 40, 597–605.
Yan, W., & Kang, M.S. (2003). GGE Bi-plot Analysis: a Graphical Tool for Breeders, Geneticists and Agronomists. Boca Raton, FL: CRC Press.
Zewdu, Z., Abebe, T., Mitiku, T., Worede, F., Dessie, A., Berie A., & Atnaf, M. (2020). Performance evaluation and yield stability of upland rice (Oryza sativa L.) varieties in Ethiopia. Cogent Food & Agriculture, 6, 1–13.
Section
Research Articles