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Assessing genotype-by-environment interactions and trait associations in forage sorghum using GGE biplot analysis

Published online by Cambridge University Press:  24 March 2015

C. ARUNA
Affiliation:
Directorate of Sorghum Research, Hyderabad, India
S. RAKSHIT
Affiliation:
Directorate of Sorghum Research, Hyderabad, India
P. K. SHROTRIA
Affiliation:
G.B. Pant University of Agriculture & Technology, Pantnagar, India
S. K. PAHUJA
Affiliation:
C.C.S. Haryana Agricultural University, Hisar, India
S. K. JAIN
Affiliation:
Sardarkrushinagar Dantiwada Agricultural University, Deesa, India
S. SIVA KUMAR
Affiliation:
Tamil Nadu Agricultural University, Coimbatore, India
N.D. MODI
Affiliation:
Navsari Agricultural University, Surat, India
D. T. DESHMUKH
Affiliation:
PDKV, Akola, India
R. KAPOOR
Affiliation:
Punjab Agricultural University, Ludhiana, India
J. V. PATIL
Affiliation:
Directorate of Sorghum Research, Hyderabad, India
Corresponding
E-mail address:

Summary

Forage sorghum is an important component of the fodder supply chain in the arid and semi-arid regions of the world because of its high productivity, ability to utilize water efficiently and adaptability to a wide range of climatic conditions. Identification of high-yielding stable genotypes (G) across environments (E) is challenging because of the complex G × E interactions (GEI). In the present study, the performance of 16 forage sorghum genotypes over seven locations across the rainy seasons of 2010 and 2011 was investigated using GGE biplot analysis. Analysis of variance revealed the existence of significant GEI for fodder yield and all eight associated phenotypic traits. Location accounted for a higher proportion of the variation (0·72–0·91), while genotype contributed only 0·06–0·21 of total variation in different traits. Genotype-by-location interactions contributed 0·02–0·13 of total variation. Promising genotypes for fodder yield and each of the associated traits could be identified effectively using a graphical biplot approach. The majority of test locations were highly correlated. A ‘Which-won-where’ study partitioned the test locations into two mega-environments (MEs): ME1 was represented by five locations with COFS 29 as the best genotype, while ME2 had two locations with S 541 as the best genotype. The existence of two MEs suggested a need for location-specific breeding. Genotype-by-trait biplots indicated that improvement for forage yield could be achieved through indirect selection for plant height, leaf number and early vigour.

Type
Crops and Soils Research Papers
Copyright
Copyright © Cambridge University Press 2015 

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References

Aruna, C., Shrotria, P. K., Pahuja, S. K., Umakanth, A. V., Venkatesh Bhat, B., Vishala Devender, A. & Patil, J. V. (2012). Fodder yield and quality in forage sorghum: scope for improvement through diverse male sterile cytoplasms. Crop & Pasture Science 63, 11141123.CrossRefGoogle Scholar
Baker, R. J. (1988). Tests for crossover genotype × environmental interactions. Canadian Journal of Plant Science 68, 405410.CrossRefGoogle Scholar
Casanoves, F., Macchiavelli, R. & Balzarini, M. (2005). Error variation in multi-environment peanut trials. Crop Science 45, 19271933.CrossRefGoogle Scholar
Cooper, M. & Byth, D. E. (1996). Understanding plant adaptation to achieve systematic applied crop improvement – a fundamental challenge. In Plant Adaptation and Crop Improvement (Eds Cooper, M. & Hammer, G. L.), pp. 467486. Wallingford, UK: CABI Publishing.Google Scholar
Cooper, M., Delacy, I. H. & Basford, K. E. (1996). Relationships among analytical methods used to analyse genotypic adaptation in multi-environment trials. In Plant Adaptation and Crop Improvement (Eds Cooper, M. & Hammer, G. L.), pp. 193224. Wallingford, UK: CAB International.Google Scholar
Dehghani, H., Ebadi, A., Yousefi, A. (2006). Biplot analysis of genotype by environment interaction for barley yield in Iran. Agronomy Journal 98, 388393.CrossRefGoogle Scholar
Dehghani, H., Omidi, H. & Sabaghnia, N. (2008). Graphic analysis of trait relations of rapeseed using the biplot method. Agronomy Journal 100, 14431449.CrossRefGoogle Scholar
Fan, X. M., Kang, M. S., Chen, H., Zhang, Y., Tan, J. & Xu, C. (2007). Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agronomy Journal 99, 220228.CrossRefGoogle Scholar
Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika 58, 453467.CrossRefGoogle Scholar
Gauch, H. G. (1992). AMMI analysis of yield traits. In Genotype by Environment Interaction (Eds Kang, M. S. & Gauch, H. G.), pp. 140. Boca Raton, FL, USA: CRC Press.Google Scholar
Gauch, H. G. (2006). Statistical analysis of yield trials by AMMI and GGE. Crop Science 37, 381385.Google Scholar
Gauch, H. G. & Zobel, R. W. (1997). Identifying mega-environment and targeting genotypes. Crop Science 37, 381385.CrossRefGoogle Scholar
Gauch, H. G., Piepho, H. P. & Annicchiarico, P. (2008). Statistical analysis of yield trials by AMMI and GGE: further considerations. Crop Science 48, 866889.CrossRefGoogle Scholar
Kaya, Y. M., Akcurra, M. & Taner, S. (2006). GGE-biplot analysis of multi-environment yield trials in bread wheat. Turkish Journal of Agriculture & Forestry 30, 325337.Google Scholar
Malla, S., Ibrahim, A. M. H., Little, R., Kalsbeck, S., Glover, K.D. & Ren, C. (2010). Comparison of shifted multiplicative model, rank correlation, and biplot analysis for clustering winter wheat production environments. Euphytica 174, 357370.CrossRefGoogle Scholar
Martin, J. S., Rubiales, D., Flores, F., Emeran, A. A., Shtaya, M. J. Y., Sillero, J. C., Allagui, M. B. & Prats, E. (2014). Adaptation of oat (Avena sativa) cultivars to autumn sowings in Mediterranean environments. Field Crops Research 156, 111122.CrossRefGoogle Scholar
Mohammadi, R. & Amri, A. (2013). Genotype × environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran. Euphytica 192, 227249.CrossRefGoogle Scholar
Mohammadi, R., Aghaee, M., Haghparast, R., Pourdad, S. S., Rostaii, M., Ansari, Y., Abdolahi, A. & Amri, A. (2009). Association among non-parametric measures of phenotypic stability in four annual crops. Middle Eastern and Russian Journal of Plant Science and Biotechnology 3 (Special Issue I), 2024.Google Scholar
Munawar, M., Hammad, G. & Shahbaz, M. (2013). Evaluation of maize (Zea mays L.) hybrids under different environments by GGE biplot analysis. American-Eurasian Journal of Agriculture & Environmental Science 13, 12521257.Google Scholar
Putto, W., Patanothai, A., Jogloy, S. & Hoogenboom, G (2008). Determination of mega-environments for peanut breeding using the CSM-CROPGRO-Peanut model. Crop Science 48, 973982.CrossRefGoogle Scholar
Rakshit, S., Ganapathy, K. N., Gomashe, S. S., Rathore, A., Ghorade, R. B., Kumar, M. V. N., 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, 465479.CrossRefGoogle Scholar
Rao, P. S., Reddy, P. S., Ratore, A., Reddy, B. V. S. & Panwar, S. (2011). Application of GGE biplot and AMMI model to evaluate sweet sorghum (Sorghum bicolor) hybrids for genotype × environment interaction and seasonal adaptation. Indian Journal of Agricultural Sciences 81, 438444.Google Scholar
Romagosa, I. & Fox, P. N. (1993). Genotype × environment interaction and adaptation. In Plant Breeding: Principles and Prospects (Eds Hayward, M. D., Bosemark, N. O. & Romagosa, I.), pp. 373390. London: Chapman & Hall.CrossRefGoogle Scholar
Roozeboom, K. L., Schapugh, T., Tuinstra, M. R., Vanderlip, R. L. & Milliken, G. A. (2008). Testing wheat in variable environments: genotype, environment, interaction effects, and grouping test locations. Crop Science 48, 317330.CrossRefGoogle Scholar
Sabaghnia, N., Dehghani, H. & Sabaghpour, S. H. (2008). Graphic analysis of genotype by environment interaction for lentil yield in Iran. Agronomy Journal 100, 760764.CrossRefGoogle Scholar
Saeed, M. & Francis, C. A. (1984). Association of weather variable with genotype×environment interactions in grain sorghum. Crop Science 24, 1316.CrossRefGoogle Scholar
Samonte, S. O. P. B., Wilson, L. T., McClung, A. M. & Medley, J. C. (2005). Targeting cultivars into rice growing environments using AMMI and SREG GGE biplot analyses. Crop Science 45, 24142424.CrossRefGoogle Scholar
Soto-Cerda, B., Westermeyer, F., Iniguez-Luy, F., Munoz, G., Montenegro, A. & Cloutier, S. (2014). Assessing the agronomic potential of linseed genotypes by multivariate analyses and association mapping of agronomic traits. Euphytica 196, 3549.CrossRefGoogle Scholar
Yan, W. (2001). GGE biplot-a Windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agronomy Journal 93, 11111118.CrossRefGoogle Scholar
Yan, W. (2002). Singular value partitioning for biplot analysis of multi-environment trial data. Agronomy for Sustainable Development 94, 990996.Google Scholar
Yan, W. & Holland, J. B. (2010). A heritability adjusted GGE biplot for test environment evaluation. Euphytica 171, 355369.CrossRefGoogle Scholar
Yan, W. & Kang, M. S. (2003). GGE Biplot Analysis: a Graphical Tool for Breeders, Geneticists, and Agronomists. Boca Raton, FL, USA: CRC Press.Google Scholar
Yan, W. & Rajcan, I. (2002). Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Science 42, 1120.CrossRefGoogle ScholarPubMed
Yan, W. & Tinker, N. A. (2005). An integrated biplot analysis system for displaying, interpreting and exploring genotype × environment interactions. Crop Science 45, 10041016.CrossRefGoogle Scholar
Yan, W. & Tinker, N. A. (2006). Biplot analysis of multi-environment trial data: principles and applications. Canadian Journal of Plant Science 86, 623645.CrossRefGoogle Scholar
Yan, W., Hunt, L. A., Sheng, Q. & Szlavnics, Z. (2000). Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Science 40, 597605.CrossRefGoogle Scholar
Yan, W., Kang, M. S., Ma, B. L., Woods, S. & Cornelius, P. L. (2007). GGE biplot vs AMMI analysis of genotype by environment data. Crop Science 47, 643653.CrossRefGoogle Scholar
Yan, W., Fregeau-Reid, J., Pageau, D., Martin, R., Mitchell-Fetch, J., Etieenne, M., Rowsell, J., Scott, P., Price, M., De Hann, B., Cumminskey, A., Lajeunesse, J., Durand, J. & Sparry, E. (2010). Identifying essential test location for oat breeding in eastern Canada. Crop Science 50, 504515.CrossRefGoogle Scholar
Yang, R. C., Crossa, J., Cornelius, P. L. & Burgueno, J. (2009). Biplot analysis of genotype×environment interaction: proceed with caution. Crop Science 49, 15641576.CrossRefGoogle Scholar
Zobel, R. W., Wright, M. J. & Gauch, H. G. (1988). Statistical analysis of a yield trial. Agronomy Journal 80, 388393.CrossRefGoogle Scholar
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