GENOTYPE BY ENVIRONMENT INTERACTION AND YIELD STABILITY OF SOYBEAN GENOTYPES

Ayda Krisnawati, M. Muchlish Adie

Abstract


Soybean breeding program in Indonesia has been actively involved in improving the genetic yield potential to meet the needs of farmers in different parts of the country. The study aimed to determine the presence of soybean production mega-environments and to evaluate the yield performance and stability of 12 soybean genotypes. Soybean yield performances were evaluated in eight production centers in Indonesia during 2013 growing season. The experiment in each location was arranged in a randomized complete block design with four replications. Parameters observed included grain yield and yield components. The yield data were analyzed using GGE biplot and the yield components data were analyzed using analysis of variance. The results showed that the yield performances of soybean genotypes were highly influenced by genotype-environment interaction (GEI) effects. The yield components were significantly affected by GEI except per plant branch number. The partitioning of the G + GE sum of squares showed that PC1 and PC2 were significant components which accounted for 57.41% and 18.55% of G + GE sum of squares, respectively. Based on the GGE visual assessment, agro-ecology for soybean production in Indonesia was divided into at least three mega-environments. Genotypes 8 and 2 were the best yielding genotypes in the most discriminating environment, but adapted to specific environment, thus highly recommended for that specific location. Genotypes 9 and 10 were stable and had relatively high yield performances across environments. Those genotypes would be recommended to be proposed as new soybean varieties.


Keywords


environment; genotype; genotype-environment interaction; Glycine max; yields

Full Text:

PDF

References


Adie, M.M., Krisnawati, A. & Susanto, G. (2013) Genotype × environment interactions, yield potential and stability of black soybean {Glycine max ( L .) Merr .} promising lines. Berita Biologi. 12 (1), 79–86.

Asfaw, A. Fistum, A., Gurum, F. & Atnaf, M. (2009) AMMI and SREG GGE biplot analysis for matching varieties onto soybean production environments in Ethiopia. Scientific Research and Essay. 4 (11), 1322–1330.

Atnaf, M. (2013) GGE biplots to analyze soybean multi-environment yield trial data in north Western Ethiopia. Journal of Plant Breeding and Crop Science. [Online] 5 (12), 245–254. Available from: doi:10.5897/JPBCS13.0403.

Bhartiya, A. Aditya, J.P., Singh, K., Pushpendra, Purwar, J.P. & Agarwal, A. (2017) AMMI and GGE biplot analysis of multi environment yield trial of soybeanin North Western Himalayan state Uttarakhand of India. Legume Research - An International Journal. [Online] 40 (OF), 306–312. Available from: doi:10.18805/lr.v0iOF.3548.

Choi, D.-H. Ban, H.Y., Seo, B.S., Lee, K.J. & Lee, B.W. (2016) Phenology and seed yield performance of determinate soybean cultivars grown at elevated temperatures in a temperate region. Plos One. [Online] 11 (11), e0165977. Available from: doi:10.1371/journal.pone.0165977.

Cravero, V. Esposito, M.A., Anido F.L., Garcia, S.M. & Cointry, E. (2010) Identification of an ideal test environment for asparagus evaluation by GGE-biplot analysis. Australian Journal of Crop Science. 4 (4), 273–277.

Ding, M., Tier, B. & Yan, W. (2007) Application of the GGE biplot to evaluate genotype, environment and GxE interaction on P . radiata : a case study GGE biplot Application. AGBU, Joint venture of NSW D epartment of Primary Industries and the university of New England. [Online] 38 (1), 132–142. Available from: http://www.scionresearch.com/__data/assets/pdf_file/0007/5596/NZJFS_38_12008_Ding_et_al_132-142.pdf.

Eberhart, S.A. & Russell, W.A. (1966) stability parameters for comparing varieties 1. Crop Science. [Online] 6 (1), 36–40. Available from: doi:10.2135/cropsci1966.0011183X000600010011x.

El-Abady, M. El-Emam, A.A.M., Seadh, S.E. & Yousof, F.I. (2012) Soybean Seed Quality as Affected by Cultivars, Threshing Methods and Storage Periods.pdf. [Online] pp.115–125. Available from: doi:10.2923/rjss.2012.115.125.

Finlay, K.W. & Wilkinson, G.N. (1963) The analysis of adaptation in a plant-breeding programme. Australian Journal of Agricultural Research. [Online] 14 (6), 742–754. Available from: doi:10.1071/AR9630742.

Gauch, H.G. (1992) Statistical analysis of regional yield trials: AMMI analysis of factorial designs. [Online] Amsterdam, Netherland, Elsevier Science Publishers. Available from: http://www.cabdirect.org/.

Gauch, H.G. & Zobel, R.W. (1996) AMMI analysis of yield trials. In ‘Genotype by environment interaction’.(Eds MS Kang, HG Gauch) pp. 85–122. (January 1996), 85–122.

Gedif, M., Yigzaw, D. & Tsige, G. (2014) Genotype-environment interaction and correlation of some stability parameters of total starch yield in potato in Amhara region, Ethiopia. Journal of Plant Breeding and Crop Science. [Online] 6 (3), 31–40. Available from: doi:10.5897/JPBCS2013.0426.

Gurmu, F., Mohammed, H. & Alemaw, G. (2009) Genotype x environment interactions and stability of soybeanfor grain yield and nutrition quality. African Crop Science Journal. 17 (2), 87–99.

Jain, H. & Kharkwal, M. (2003) Plant breeding. New Delhi, Narosa Publishing House Pvt. Ltd.

Kumar, A., Kumar, S., Kapoor, C., Bhagawati, R., Pandey, A. & Pattnayak, A. (2014) GGE biplot analysis of genotype× environment interaction in soybean grown in NEH regions of India. Environment & Ecology. 32 (3A), 1047-1050 Available from: http://www.researchgate.net/profile/Avinash_Pandey3/publication/261475144_GGE_Biplot_Analysis_of_GenotypeEnvironment_Interaction_in_Soybean_Grown_in_NEH_Regions_of_India/links/541a72d80cf25ebee9889709.pdf.

Kuswantoro, H. (2016) Potential yield of acid-adaptive soybean promising lines in Ultisols of Tanah Laut Regency, South Kalimantan Province, Indonesia. Biotropia. [Online] 23 (1), 52–57. Available from: doi:10.11598/btb.2016.2.

Mulyani, A. Kuncoro, D., Nursyamsi, D. & Agus, F. (2016) Analisis konversi lahan sawah : penggunaan data spasial resolusi tinggi memperlihatkan laju konversi yang mengkhawatirkan. Jurnal Tanah dan Iklim. [Online] 40 (2), 121–133. Available from: doi:http://dx.doi.org/10.2017/jti.v40i2.5708.

Mulyani, Sukarman & Hidayat, A. (2009) Prospek perluasan areal tanaman kedelai di Indonesia. Sumberdaya Lahan. 3 (1), 27–38.

Obalum, E.S. Igwe, C.A., Obi, M.E. & Wakatsuki, T. (2011) Water use and grain yield response of rainfed soybean to tillage- mulch practices in southeastern Nigeria. Scientia Agricola. (October), 554–561.

Penalba, C.O., Bettolli, L.M. & Vargas, M.W. (2007) The impact of climate variability on soybean yields in Argentina. Multivariate regression. Meteorological Applications. [Online] 14 (January), 3–14. Available from: doi:10.1002/met.

Perkins, J.M. & Jinks, J.L. (1968) Environmental and genotype-environmental components of variability. 3. Multiple lines and crosses. Heredity. [Online] 23 (3), 339–356. Available from: doi:10.1038/hdy.1969.11.

Rakshit, S. Ganapathy, K.N., Gomashe, S.S., Rathore, A., Gorade, R.B., Kumar, M.V.N., Ganeshmurthy, 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. [Online] 185 (3), 465–479. Available from: doi:10.1007/s10681-012-0648-6.

Suwarto (2010) Genotype x Environment Interaction on Rice Fe Content. Gadjah Mada University.

Yan, W. Hunt, L.A., Sheng, Q. & Szlanics, Z. (2000) Cultivar Evaluation and Mega-Environment Investigation Based on the GGE Biplot. Crop Science. [Online] 40 (3), 597–605. Available from: doi:10.2135/cropsci2000.403597x.

Yan, W. Kang, M.S., Ma, B., Woods, S. & Cornellus, P.L. (2007) GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Science. [Online] 47 (2), 643–655. Available from: doi:10.2135/cropsci2006.06.0374.

Yan, W. (2001) GGEbiplot - A windows application for graphical analysis of multienvironment trial data and other types of two-way data. Agronomy Journal. [Online] 93 (5), 1111–1118. Available from: doi:10.2134/agronj2001.9351111x.

Yan, W. (2002) Singular-value partitioning in biplot analysis of multienvironment trial data. Agronomy Journal. [Online] 94 (5), 990–996. Available from: doi:10.2134/agronj2002.0990.

Yan, W. & Rajcan, I. (2003) Prediction of cultivar performance based on single- versus multiple-year tests in soybean. Crop Science. [Online] 43 (2), 549–555. Available from: doi:10.2135/cropsci2003.0549.

Yan, W. & Tinker, N.A. (2006) Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science. [Online] 86 (3), 623–645. Available from: doi:10.4141/P05-169.

Zanon, A.J., Streck, N.A. & Grassini, P. (2016) Climate and management factors influence soybean yield potential in a subtropical environment. Agronomy Journal. [Online] 108 (4), 1447–1454. Available from: doi:10.2134/agronj2015.0535.




DOI: http://dx.doi.org/10.21082/ijas.v19n1.2018.p25-32

Refbacks

  • There are currently no refbacks.




Copyright (c) 2018 Indonesian Journal of Agricultural Science

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Indonesian Journal of Agricultural Science (IJAS) by http://ejurnal.litbang.pertanian.go.id/index.php/ijas is licenced under a http://creativecommons.org/licenses/by-sa/4.0/ 

Publisher: Indonesian Agency for Agricultural Research and Development

Editorial Office:

Indonesian Institute for Agricultural Technology Transfer

Jalan Salak No. 22 Bogor-Indonesia

ISSN:1411-982X

E-ISSN:2354-8509

   

View Visitors Stats