GENOTYPE-ENVIRONMENT (G×E) INTERACTION, STABILITY AND ADAPTABILITY STUDY ON GRAIN YIELD IN ADVANCED RICE LINES

Abstract

Stability and adaptability of a rice genotype is crucial to release it as a variety for commercial cultivation in a wide range of growing conditions. The present study was conducted during 2020-21 to assess the Genotype×Environment (G×E) interaction and to identify the stable rice lines for varietal development. Nine rice genotypes consisting advanced lines and released variety were investigated for stability in grain yield across three environments by Additive Main effects and Multiplicative Interaction (AMMI) and the Genotype Main Effect and Genotype by Environment interaction effects (GGE) analyses. AMMI and GGE analyses revealed significant G×E interactions indicating the variability among the genotypes and environments. As per AMMI1 and AMMI2 biplot models the genotypes BN-P-317 and BN-P-318 were identified as the best performer and suited for the environment Jamalpur. GGE biplot analysis showed that the genotypes BN-P-114, BN-P-115 and BN-P-317 were adapted to the environment Jamalpur, whereas BN-P-318 was more suitable for Nalitabari. According to GGE biplot-genotype view graph, the genotype BN-P-317 was identified as the ideal genotype for grain yield followed by BN-P-318. The GGE biplot- polygon view graph showed that the genotype BN-P-317 performed better in both the environments Jamalpur and Nalitabari. The genotypes BN-P-317 and BN-P-318 could be selected for further evaluation to release as variety.

References

Anandan, A., Eswaran, R., Sabesan, T. and Prakash, M., 2009. Additive main effects and multiplicative interactions analysis of yield performances in rice genotypes under coastal saline environments. Advances in Biological Research, 3(1-2), pp. 43-4.

Andiku, C., Lubadde, G., Aru, C., Ugen, M. and Ebiyau, J., 2020. Additive Main Effects and Multiplicative Interaction and Genotype Main Effect and Genotype by Environment Interaction Effects-Biplot Analysis of Sorghum Grain Yield in Uganda. Journal of Agricultural Science, 12(98), pp. 10-5539.

Asnake W., Henry, M., Temesgen, Z., Girma, T., 2013. Additive main effects and multiplicative interactions model (AMMI) and genotype main effect and genotype by environment interaction (GGE) biplot analysis of multi-environmental wheat variety trials. African Journal of Agricultural Research, 8(12), pp. 1033-1040.

Bartlett, F.C. (1932). Remembering; a study in experimental and social psychology. Cambridge [England]: The University Press.

Bose, K.L., Jambhulkar, N.N., Pande, K., 2014. Genotype by Environment interaction and stability analysis for rice genotypes under Boro condition. Genetika, 46 (2), pp. 521-528.

Cooper, M. and Somrith, B., 1997. Implications of genotype-by-environmental interactions for yield adaptation of rainfed lowland rice: influence of flowering date on yield variation. In Breeding strategies for rainfed lowland rice in drought-prone environments: proceedings of an International Workshop held at Ubon Ratchathani, Thailand, 5-8 November 1996. Canberra: Australian Centre for International Agricultural Research, 1997.

Crossa J., Gauch, H.G.J., and Zobel, R.W., 1990. Additive main effects and multiplicative interaction analysis of two international maize cultivar trials. Crop Science, 30 (3), pp. 493-500.

Crossa, J. and Cornelius, P.L., 1997. Sites regression and shifted multiplicative model clustering of cultivar trial sites under heterogeneity of error variances. Crop Science, 37(2), pp. 406-415.

Crossa, J., Cornelius, P.L. and Yan, W.J.C.S., 2002. Biplots of linear-bilinear models for studying crossover genotype × environment interaction. Crop Science, 42(2), pp. 619-633.

Ebdon, J.S., Gauch, H.G., 2002. Additive main effect and multiplicative interaction analysis of national turfgrass performance trials: II. Cultivar recommendations. Crop Science 42, (2), pp. 497-506.

Flores, F., Moreno, M.T. and Cubero, J.I., 1998. A comparison of univariate and multivariate methods to analyze G× E interaction. Field crops research, 56(3), pp. 271-286.

Gauch, H.G., & Zobel, R.W. (1996). AMMI analysis of yield trials. In M.S. Kang & H G. Gauch (Eds.), Genotype-by-Environment Interaction (pp. 85-122). Boca Raton CRE CRC.

Gauch, H.G., Piepho, H.P., & Annicchiarico, P.J.C.S., 2008. Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Science, 48(3), pp. 866-889.

Jadhav, S., Balakrishnan, D., Shankar, G., Beerelli, K., Chandu, G., Neelamraju, S., 2019. Genotype by environment (G×E) interaction study on yield traits in different maturity groups of rice. Journal of Crop Science and Biotechnology, 22 (5), pp. 425-449.

Katsura, K., Tsujimoto, Y., Oda, M., Matsushima, K.I., Inusah, B., Dogbe, W. and Sakagami, J.I., 2016. Genotype-by-environment interaction analysis of rice (Oryza spp.) yield in a floodplain ecosystem in West Africa. European Journal of Agronomy, 73 (2016), pp. 152-159.

Khumairoh, U., Lantinga, E.A., Schulte, R.P., Suprayogo, D. and Groot, J.C., 2018. Complex rice systems to improve rice yield and yield stability in the face of variable weather conditions. Scientific reports, 8(1), pp. 1-7.

Lubadde, G., Tongoona, P., Derera, J. and Sibiya, J., 2017. Analysis of genotype by environment interaction of improved pearl millet for grain yield and rust resistance. Journal of Agricultural Science, 9(2), p.188.

Malosetti, M., Ribaut, J.M. and van Eeuwijk, F.A., 2013. The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Frontiers in physiology, 4 (2013), p. 44.

Nyaligwa, L.M., Shimelis, H., Mwadzingeni, L. and Laing, M.D., 2018. Genotype-by-environment interaction analysis of maize hybrids for grain yield and maize streak virus severity in the mid-altitude agro-ecologies. Australian Journal of Crop Science, 12(8), pp. 1304-1312.

Ouk, M., Basnayake, J., Tsubo, M., Fukai, S., Fischer, K.S., Kang, S., Men, S., Thun, V. and Cooper, M., 2007. Genotype-by-environment interactions for grain yield associated with water availability at flowering in rainfed lowland rice. Field Crops Research, 101(2), pp. 145-154.

Renukadevi, P. and Subbalakshmi, B., 2006. Correlations and path coefficient analysis in chickpea. Legume Research-An International Journal, 29(3), pp. 201-204.

RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/.

Sabaghpour, S.H., Razavi, F., Fatemeh Danyali, S., Tobe, D. and Ebadi, A., 2012. Additive main effect and multiplicative interaction analysis for grain yield of chickpea (Cicer arietinum L.) in Iran. International Scholarly Research Notices, 2012.

Senguttuvel, P., Sravanraju, N., Jaldhani, V., Divya, B., Beulah, P., Nagaraju, P., Manasa, Y., Prasad, A.S., Brajendra, P., Gireesh, C. and Anantha, M.S., 2021. Evaluation of genotype by environment interaction and adaptability in lowland irrigated rice hybrids for grain yield under high temperature. Scientific Reports, 11(1), pp. 1-13.

Susanto, U., Rohaeni, W.R., Johnson, S.B. and Jamil, A., 2015. GGE biplot analysis for genotype x environment interaction on yield trait of high Fe content rice genotypes in Indonesian irrigated environments. AGRIVITA, Journal of Agricultural Science, 37(3), pp. 265-275.

Xu, F., Tang, F., Shao, Y., Chen, Y., Tong, C., Bao, J., 2014. Genotype × environment Interaction for agronomic traits of rice revealed by association mapping. Rice Science, 21(3), pp. 133- 141.

Yan, W. and Tinker, N.A., 2006. Biplot analysis of multi-environment trial data: Principles and applications. Canadian journal of plant science, 86(3), pp. 623-645.

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), pp. 597-605.

Zewdu, Z., Abebe, T., Mitiku, T., Worede, F., Dessie, A., Berie, A. and Atnaf, M., 2020. Performance evaluation and yield stability of upland rice (Oryza sativa L.) varieties in Ethiopia. Cogent Food & Agriculture, 6(1), p. 1842679.