Dissecting genotype × environment interaction in advanced varietal lines of finger millet (Eleusine coracana (L.)) evaluated for seed and fodder yield across twenty environments
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Abstract
The identification of superior and stable genotypes in any crop for commercial cultivation in farmers’ fields is constrained majorly by the existence of genotype × environment interaction (GEI). The current study aimed to assess the patterns of GEI governing seed and fodder yield, identify stable and high seed and fodder yielding genotypes, besides deciphering the correlation among the them in finger millet genotypes evaluated across twenty environments (ten locations-two years combination) in India. The results revealed that the variance due to genotype, environment and GEI were highly significant (P < 0.001) for seed and fodder yield. The AMMI8 model was adequate to explain the detected variation of seed and fodder yield attributable to GEI. For obvious reasons, the check GPU 67 exhibited relatively higher mean seed and fodder yield and also showed excellent stability across all the environments based on AMMI- and BLUP-model indices. In this study, the seed and fodder yielding ability of the genotypes VR1101 and WN559 was comparable to the checks and had broad adaptation across the test environments. The most representative and discriminative environments for seed and fodder yield were E1 and E9. Seed and fodder yield revealed highly significant positive correlations indicating the possibility of effective selection for these two traits simultaneously. The identified stable and high seed and fodder yielding genotypes VR1101 and WN559 are not just worthy genetic resources, and can be recommended for commercial cultivation after further yield trials. Consequently, the genotype VR1101 is approved for commercial cultivation across South Indian states.
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References
Adugna A., Tesso T., Degu E., Tadesse T., Merga F. and Legesse F. 2011. Genotype-by-environment interaction and yield stability analysis in finger millet (Eleucine coracana L. Gaertn) in Ethiopia. American Journal of Plant Science 2:408–415 doi: 10.4236/ajps.2011.23046
Ajay B. C., Aravind J. and Abdul Fiyaz R. 2018. Ammistability: additive main effects and multiplicative interaction model stability parameters. https://CRAN.R-project.org/package=ammistability
ASSOCHAM. 2021. The Knowledge Paper on ‘Millets 2021: Status & Way Forward’. The Associated Chambers of Commerce and Industry of India (ASSOCHAM) associated with Indian Institute of Millet Research And Nutria- Hub. New Delhi: ASSOCHAM.
Badu-Apraku B., Oyekunle M., Obeng-Antwi K., Osuman A., Ado S., Coulibay N., Yallou N., Abdulai CG., Boakyewaa M. and Didjeira G. A. 2012. Performance of extra-early maize cultivars based on GGE-biplot and AMMI analysis. The journal of agricultural science 150:473–483.
Bartlett M. S. 1937. Properties of sufficiency and statistical tests. Proceedings of the Royal Society of London. Series A-Mathematical and Physical Sciences 160 (901):268-82.
Birhanu M., Tesfay M., Nigus C. and Wolday K. 2016. Stability analysis of finger millet genotypes in moisture stressed areas of Northern Ethiopia. Journal of natural sciences 6:2016.
Cairns J. E., Hellin J., Sonder J., Araus J. L., MacRobert J. F., Thierfelder C. and Prasanna B. M. 2013. Adapting maize production to climate change in sub-Saharan Africa. Food Security 5:345–360
Dagnachew L., Masresha F., de Villiers S. and Tesfaye K. 2014. Additive main effects and multiplicative interactions (AMMI) and genotype by environment interaction (GGE) biplot analyses aid selection of high yielding and adapted finger millet varieties. Journal of Applied Biosciences 76:6291. doi: 10.4314/jab.v76i1
de Resende M. D. V. 2004. Optimal statistical methods in the analysis of field experiments.
de Resende M. D. V. 2016. Software Selegen-REML/BLUP: a useful tool for plant breeding. Crop Breeding and Applied Biotechnology 16:330–339 doi: 10.1590/1984-70332016v16n4a49
Eberhart and Russell W. 1966. Stability parameters for comparing varieties 1. Crop Science 6(1):36–40.
Finlay K. and Wilkinson G. 1963. The analysis of adaptation in a plant-breeding programme. Australian journal of agricultural research 14(6):742–54.
Gauch H. 1992. Jr Statistical analysis of regional yield trials: AMMI analysis of factorial designs: Elsevier Science Publishers.
Gauch H. G. 1988. Model selection and validation for yield trials with interaction. Biometrics 44:705–715.
Gauch H. G. and Zobel R. W. 1996. AMMI analysis of yield trials. In: Gauch HG (ed) Kang MS. Genotype-by-environment interaction, CRC Press, Boca Raton, 85–122.
Hatakeyama M., Aluri S. and Balachadran M. T. 2018. Multiple hybrid de novo genome assembly of finger millet, an orphan allotetraploid crop. DNA Research 25:39–47. https://doi.org/10.1093/dnares/dsx036
Hittalmani S., Mahesh H. and Shirke M. D. 2017. Genome and transcriptome sequence of finger millet (Eleusine coracana (L.) Gaertn.) provides insights into drought tolerance and nutraceutical properties. BMC Genomics 18:465
Jamshidmoghaddam M. and Pourdad S. S. 2013. Genotype × environment interactions for seed yield in rainfed winter safflower (Carthamus tinctorius L.) multi-environment trials in Iran. Euphytica 190(3):357–69
Lakew T., Dessie A., Tariku S. and Abebe D. 2017. Evaluation of performance and yield stability analysis based on AMMI and GGE models in introduced upland rice genotypes tested across Northwest Ethiopia. International Journal of Research Studies in Agricultural Sciences 3:17–24. doi: 10.20431/2454-6224.0302003.
Mahmodi N., Yaghotipoor A. and Farshadfar E. 2011. AMMI stability value and simultaneous estimation of yield and yield stability in bread wheat ('Triticum aestivum'L.). Australian Journal of Crop Science 5(13):1837-44.
Mahmodi N., Yaghotipoor A. and Farshadfar E. 2011. AMMI stability value and simultaneous estimation of yield and yield stability in bread wheat (’Triticum aestivum’ L.). Australian Journal of Crop Science 5 (13):1837.
Mohammadi R., Abdulahi A. and Haghparast Armion M. 2007. Interpreting genotype- environment interactions for durum wheat grain yields using non-parametric methods. Euphytica 157:239–251 23.
Mohammadi R. and Amri A. 2008. Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica 159:419–432.
Molla F., Alemayehu A. and Belete K. 2013. AMMI analysis of yield performance and stability of finger millet genotypes across different environments. World Journal of Agricultural Sciences 9:231–237.
Nagaraja T. E., Bhat S. and Nandini C. 2022. Current Scenario of Crop Improvement of Finger Millet [Eleusine coracana (L.) in India: A Review. Agricultural Reviews DOI: 10.18805/ag.R-2545
Oduori C. O. A. 2005. The importance and research status of finger millet in Africa presented at The McKnight Foundation Collaborative Crop Research Program Workshop on Tef and Finger Millet: comparative genomics of the chloridoid cereals at the Biosciences for East and Central Africa (BECA) ILRI, Nairobi, Kenya.
Olivoto T. and Lúcio A. D. 2020. metan: An R package for multi‐environment trial analysis. Methods in Ecology and Evolution 11(6):783-9
Phillips S. M. 1974. Eleusine. In: Polhill RM (ed) Flora of Tropical East Africa. Crown Agents for Overseas Governments and Administrations, 260–267.
R Core Team. 2020. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria.
Resende M. D., Furlani-Júnior E. N., Moraes M. L. and Fazuoli L. C. 2001. Estimates of genetic parameters and prediction of genotypic values in coffee breeding by the REML/BLUP procedure. Bragantia 60:185-93.
Setimela P. S., Magorokosho C., Lunduka R., Gasura E., Makumbi D., Tarekegne A., Cairns J. E., Ndhlela T., Erenstein O. and Mwangi W. 2017. On-farm yield gains with stress-tolerant maize in eastern and southern Africa. Agronomy Journal 109:406–417.
Seyoum A., Semahegn Z., Nega A. and Gebreyohannes A. 2019. AMMI and GGE Analysis of G × E and yield stability of finger millet [Eleusine coracana (L.) Gaertn] genotypes in Ethiopia. Int. J. Trend Res., 6:379–386.
Tukamuhabwa P., Asiimwe M., Nabasirye M., Kabayi P. and Maphosa M. 2012. Genotype by Environment Interaction of Advanced Generation. African crop science journal 20:107–115.
Upadhyaya H. D., Gowda C. L. L. and Reddy V. G. 2007. Morphological diversity in finger millet germplasm introduced from southern and eastern Africa. Journal of SAT Agricultural Research http://ejournal.icrisat.org/
Wei T., Simko V. R. and Levy M. 2017. package “corrplot”: Visualization of a Correlation Matrix Version 0.84. 2021 Apr.
Yan W. 2001. GGE biplot—A Windows application for graphical analysis of multienvironment trial data and other types of two-way data. Agronomy Journal 93(5):1111–8.
Yan W., Hunt L., Sheng Q. and Szlavnics Z. 2000. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci., 40(3):597–605.
Yan W. and Kang M. S. 2002. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists: CRC press.
Yan W., Kang M. S., Ma B., Woods S. and Cornelius P. L. 2007. GGE (genotype and genotype by environment interaction) biplot vs. AMMI (additive main effects and multiplicative interaction) analysis of genotype-by-environment data. Crop Sci., 47:643–653.
Yan W. and Rajcan I. 2002. Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Sci., 42, 11–20.
Yan W. and Tinker N. A. 2006. Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Sci., 86(3):623–45.