National Academy of Agricultural Sciences (NAAS)
|
PRINT ISSN : 2319-7692
Online ISSN : 2319-7706 Issues : 12 per year Publisher : Excellent Publishers Email : editorijcmas@gmail.com / submit@ijcmas.com Editor-in-chief: Dr.M.Prakash Index Copernicus ICV 2018: 95.39 NAAS RATING 2020: 5.38 |
Genotype +genotype x environment (GGE) biplot models is adopted to uncover GEI patterns and effectively identify high yielding genotypes with stable performance across environments. The components of GGE biplot analysis PCA 1 and PCA 2 together accounted for more than 95 percent of total variation which is more satisfactory to explain genotype + genotype x environment interaction for days to maturity, ear length, number of kernels per row,100 seed weight and grain yield per plant. Heypool was identified as the winning inbred for ear length and 100 seed weight in all the seasons; grain yield per plant in kharif and rabi and number of kernels per row in kharif and summer. For number of kernels per row, DFTY was found more responsive in kharif and summer. Summer season was found to have more discriminating ability for days to maturity, number of kernels per row, 100 seed weight and grain yield, while rabi had the discriminating power for ear length. Kharif season was found to be more representativeness for ear length, number of kernels per row, 100 seed weight and grain yield per plant and rabi for days to maturity. The inbreds viz., DFTY, PDM 1452, Heypool and PDM 1474 with high mean and stability for ear length, number of kernels per row and 100 seed weight in were close the ideal genotype. Inbreds viz; DFTY, PDM 1452 and PDM 1474 with moderate stability in performance over seasons may be recommended for improving parental lines and also developing high yielding hybrids in maize.
Badu-Apraku B, Obeng-Antwi KM, Osuman SG, Ado N, Coulibaly C, Yallou MS, Abdulai GA, Boakyewaa A and Didjeira. (2011) Performance of extra-early maize cultivars based on GGE biplot and AMMI analysis. Journal of Agricultural Science. 150: 1-11. https://doi.org/10.1017/S0021859611000761
Bernal EF and Villardon PG. (2016). GGE Biplot GUI: Interactive GGE Biplots in R.
Dimitrios B, Christos G, Jesus R and Eva B. (2008). Separation of cotton cultivar testing sites based on representativeness and discriminating ability using GGE biplots. Agronomy Journal. 100: 1230–1236.
Kaya Y, Akcura M and Tanner S. (2006) GGE biplot analysis of multi-environment yield trials in bread wheat. Turkish Journal of Agricultural Forum. 30: 325-337.
Kuchanur PH, Salimath PM, Wali MC and Hiremath C. (2015). GGE biplot analysis for grain yield of single cross maize hybrids under stress and non-stress conditions. Indian Journal Genetics. 75(4): 514-517. https://doi.org/10.5958/0975-6906.2015.00082.6
Mitrovic B, Stanisavljevi D, Treski S, Stojakovic M, Ivanovic M, Bekavac G and Rajkovic M. (2012). Evaluation of experimental Maize hybrids tested in Multi-location trials using AMMI and GGE biplot analysis. Turkish Journal of Field Crops. 17(1): 35-40.
Panse VG and Sukhatme PV. (1985). Statistical methods for Agricultural workers. Published by ICAR, New Delhi.
R Core Team. (2020) R: A language and environment for statistical computing (V., Austria: R Foundation for Statistical Computing).
Stojakovic M, Mitrovic B, Zoric M, Ivanovic M, Stanisavljevic D, Nastasic A, and Dodig. (2015) Grouping pattern of maize test locations and its impact on hybrid zoning. Euphytica. 204(2): 419-431. https://doi.org/10.1007/s10681-015-1358-7
Yan W and Rajcan I. (2002). Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Science. 42: 11-20. https://doi.org/10.2135/cropsci2002.1100
Yan W and Tinker, NA. (2006). Biplot analysis of multi environment trial data; principles and application. Canadian Journal of Plant Science. 86: 623-645. https://doi.org/10.4141/P05-169
Yan W, and Hunt LA. (2002). Biplot analysis of diallel data. Crop Science. 42:21–30. http://dx.doi.org/10.2135/cropsci2002.0021
Yan W, Hunt LA, Sheng Q and Szlavnics Z. (2000). Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Science. 40(3): 597-605. https://doi.org/10.2135/cropsci2000.403597x
Yan W, Kang YS, Woods BMS and Cornelius PL. (2007). GGE biplot vs AMMI analysis of genotype by environment data. Crop Science. 47: 643-653. http://dx.doi.org/10.2135/cropsci2006.06.0374
Yan W, Sheng YGH and Hunt LA. (2001). GGE biplot-an ideal method for graphical analysis of genotype by environment interaction. ActaAgronomicaSinica. 27: 21-28.
Yan W. (2014). Crop variety trials: Data management and analysis. Wiley-Blackwell, Hoboken, New Jersey, USA. pp: 349. https://doi.org/10.1002/9781118688571
Yan, W and Kang MS. (2003). GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton, FL. 271. https://doi.org/10.1201/9781420040371![]() |
![]() |
![]() |
![]() |
![]() |