Stability analysis of advanced breeding clones of lemongrass for citral content, herb and oil yield using AMMI, GGE biplot, and YREM
Stability analysis of lemongrass for citral, herb and oil yield
DOI:
https://doi.org/10.25081/josac.2025.v34.i1.9779Keywords:
Clones, GGE bi-plot, AMMI, stability value, stability indexAbstract
The study analysed lemongrass using additive main effects and multiplicative interaction (AMMI), genotype +genotype-by-environment (GGE), and genotype-environment interaction biplot analysis. Stable lemongrass genotypes were identified using AMMI stability value (ASV), stability index, and yield relative to environment maximum (YREM) computation. To investigate the current study's stability and adaption patterns, a set of six advanced breeding clones and two control varieties, Krishna and CIM-Shikhar, were tested in triplicate over four years using a randomized complete block design (RCBD). The genotype x environment linear (G x E) component and genotype variance analysis were significant for herbage, oil, and citral content, respectively. The G x E interaction was found to be 19.12% (citral), 31.92% (herb), and 4.34% (oil) in the AMMI analysis of variance. Trait variation was found to be a stable factor in the performance of many genotypes; no genotype demonstrated high levels of stability across multiple characteristics. Stable clones with optimal performance were identified as clones 8 and 3 for citral content, clone 5 for herb yield, and clone 1 for oil yield, as indicated by the biplot of the mean yield and AMMI stability value. Clone 8 was found to be a stable clone for citral content, with a unity YREM based on estimations. Nevertheless, clones 7 and 6 were steady performers for both oil content and herb production, respectively. No clones demonstrated unity YREM for either characteristic.
Downloads
References
Acosta-Pech R, Crossa J, de Los G, Campos, Teysse`dre S, Claustres B, Pe´rez-Elizalde S & Pe´rez-Rodrı´guez P 2017 Genomic models with genotype x environment interaction for predicting hybrid performance: an application in maize hybrids. Theor. Appl. Genet. 130: 1431–1440. https:// doi.org/10.1007/s00122-017-2898-0
Alidu H, Gloria B A, Samuel S B, Roger A L K, Amegbor I K, Abdulai M S, Obeng & Antwi K 2017 Analysis of genotype by environment interaction for grain yield of inter mediate maturing drought tolerant top-cross maize hybrids under rain-fed conditions. Cogent Food & Agri. 3:1–13. https://doi.org/10.1080/23311932.2017.1333243
Annicchiarico P, Bellah F, & Chiari T 2006 Repeatable genotype × location interaction and its exploitation by conventional and GIS-based cultivar recommendation for durum wheat in Algeria. European J Agron. 24: 70 – 81.
Anputhas M, Samita S & Abeysiriwardena D S D Z 2011 Stability and adaptability analysis of rice cultivars using environment-centered yield in two-way ANOVA model. Communic. Biometry Crop Sci. 6: 80–86.
Ashwini K V R, Ramesh S & Sunitha N C 2021 Comparative BLUP, YREM-based performance and AMMI model-based stability of horse gram [Macrotyloma uniflorum (Lam.) Verdc.] genotypes differing in growth habit”. Genet. Resour. Crop Evol. 6: 457 - 467.
Berhanu B D, Derbew B Y, Tewodros M B & Wosene G A 2023 AMMI and GGE Biplot Analyses for Mega Environment Identification and Selection of Some High-Yielding Cassava Genotypes for Multiple Environments. Int. J. Agron. 13: 2023. https://doi.org/10.1155/2023/6759698
Bhagwat G V, Joseph J & Antony R 2018 Stability of advanced generation of inter varietal crosses in black gram (Vigna mungo L.) through AMMI analysis. Electron. J. Plant Breed. 9: 465–475.
Bhan M K, Pal S, Rao B L, Dhar A K & Kang M S 2005 GGE Biplot Analysis of Oil Yield in Lemongrass (Cymbopogon spp.). J. New Seeds 7: http://www.haworthpress.com/web/JNS
Bishaw Z & Van Gastel A J G 2009 Variety release and policy options. In: Ceccarelli S, Guimaraes EP, Weltzien E (eds) Plant breeding and farmer participation, vol 21. FAO, Rome, 565–587.
Clevenger J F 1928 Apparatus for the Determination of Volatile Oil. J. Am. Pharm. Assoc. 17: 345-349
Crossa J, Gauch H G &. Zobel R W 1990 Additive main effect and multiplicative interaction analysis of two international maize cultivar trials. Crop Sci. 30: 493–500.
Eberhart S A & Russell W A 1966 Stability parameters for comparing varieties. Crop Sci. 6: 36–40.
Fekadu W, Mekbib F, Lakew B & Bettina I G H 2023 Genotype × environment interaction and yield stability in barley (Hordeum vulgare L.) genotypes in the central highland of Ethiopia. JCSB 26: 119–133. https://doi.org/10.1007/s12892-022-00166-0
Freeman G H 1973 Statistical methods for the analysis of genotype-environment interactions. Heredity 3l 339–354.
Gauch H G & Zobel R W 1997 Identifying mega-environments and targeting genotypes. Crop Sci. 37: 311–326
Gauch JR H G& Zobel R W 1988 Predictive and postdictive success of statistical analyses of yield trials. Theor. Appl. Genet. 76, 1 - 10.
Gauch H G 1992 Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs 278.
Gauch JR H G 1988 Model selection and validation for yield trials with interaction. Biometrics. 44: 705 - 715. http://dx.doi.org/10.2307/2531585.
Gauch JR H G 2013 A simple protocol for AMMI analysis of yield trials. Crop Sci. 53: 1860 - 1869.
Gezahagn K, Walelign W, Habte J & Fekede F 2023 GGE biplot analysis of genotype by environment interaction and grain yield stability of oat (Avena sativa L.) in Ethiopia. AGE 6 https://doi.org/10.1002/agg2.20410
Gupta P, Dhawan S S & Lal R K 2015 Adaptability and stability-based differentiation and selection in aromatic grasses (Cymbopogon species) germplasm. Ind Crops Prod. 78: 1–8. doi: 10.1016/j.indcrop.2015.10.018
Hassani M, Bahram H, Ali D & Piergiorgio S 2018 Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris L.). Euphytica 214:79 https://doi.org/10.1007/s10681-018-2160-0
Ill´es A, Mousavi S M N, Bojtor C & Nagy J 2020 The plant nutrition impact on the quality and quantity parameters of maize hybrids grain yield based on different statistical methods. Cereal Res. Commun. https://doi.org/10.1007/s42976-020-00074-5
Inabangan-Asilo M A, Mallikarjuna S B P, Amparado A F, Descalsota-Empleo G I L, Arocena E C & Reinke R 2019 Stability and G * E analysis of zinc-biofortified rice genotypes evaluated in diverse environments Euphytica 215, https://doi.org/10.1007/s10681-019-2384-7
Kang M S 1993 Simultaneous selection for yield and stability in crop performance trials: consequences for growers. J. Agron. 85: 754–757
Kavya T & Rangaiah S 2019 Stability of selected high-yielding genotypes across environments represented by dates of sowing in black gram [Vigna mungo (L.) Hepper]. MJAS 53:19 - 25.
Khan M M H, Rafii M Y, Ramlee S I, Jusoh M & Mamun M A 2021 AMMI and GGE biplot analysis for yield performance and stability assessment of selected Bambara groundnut (Vigna subterranea L. Verdc.) genotypes under the multi‑environmental trials (METs). Sci. Rep. 11: 22791 https://doi.org/10.1038/s41598-021-01411-2
Kirankumar R, Ramesh S, Chandana B R, Basanagouda G, Gazala P, Siddu C B & Kalpana M P 2023 AMMI Model and YREM - Based Grain Yield Stability of Horse Gram [Macrotyloma uniflorum (Lam.) Verdc.] YMV Disease Resistant Genotypes. Mysore J Agri. Sci. 57: 136-146.
Kumar A, Jnanesha A C, Kumar, & Lal R K 2022 GGE biplot vs. AMMI analysis of genotype-by-environment data on essential oil yield in lemongrass [Cymbopogon flexuosus (nees ex. Steud) wats.] grown in semi-arid tropical regions of southern India under different agro-climatic conditions. Biochem. Syst. Ecol. 103: 104439 https://doi.org/10.1016/j.bse.2022.104439
Kumar A, Jnanesha A C, Chanotiya C S & Lal R K 2023 Climate-smart lemongrass (Cymbopogon khasianus (Hack.) Stapf ex Bor) yields quality essential oils consistently across cuttings and years in semi-arid, tropical southern India. Biochem. Syst. Ecol. 110: 104716 https://doi.org/10.1016/j.bse.2023.104716
Lal R K 2012 Stability for oil yield and variety recommendations’ using AMMI (additive main effects and multiplicative interactions) model in lemongrass (Cymbopogon species). Ind Crop Prod. 40: 296-301. https://doi.org/10.1016/j.indcrop.2012.03.022
Lal R K, Chanotiya C S, Pankhuri G, Sougata S, Smita S, Ranjana M, Shubham S & Pramod K C 2017 Phenotypic stability, genotype × environmental interactions, and cultivar recommendations for essential oil yield in khus aromatic grass (Chrysopogon zizanioides (L.) Roberty). Ind Crop Prod.111, 871-877.
Lal R K, Chanotiya C S, Pankhuri G, Anand M, Subham S, Anju Y & Deepa B 2022 Genetic variability and stability pattern in vetiver (Chrysopogon zizanioides (L.) Roberty). Acta Ecol. Sin. 42 3, 233-242
Lal R K, Chanotiya C S, Singh V R & Kumar A 2023 Genotype-environment Interaction and genotype selection for yield stability in the commercially important patchouli (Pogostemon cablin (Blanco) Benth) crop. Ind. Crop Prod. 205: 117400 https://doi.org/10.1016/j.indcrop.2023.117400
Mousavi S M N and Nagy J 2020 Evaluation of plant characteristics related to grain yield of FAO410 and FAO340 hybrids using regression models. Cereal Res. Commun. 49: 161–169.
Mukuze C P, Tukamuhabwa M, Maphosa S, Dari T, Obua H, Kongai & Rubaihayo P 2020. Evaluation of the performance of advanced generation soybean [Glycine max (L.) Merr.] genotypes using GGE biplot. J. Plant Breed. Crop Sci. 12: 246-257, DOI: 10.5897/JPBCS2020.0905
Oladosu Y, Mohd Y, Rafii N A, Usman M, Gous M & Ghazali H A R 2017 Genotype × Environment interaction and stability analyses of yield and yield components of established and mutant rice genotypes tested in multiple locations in Malaysia. Acta Agric. Scand. - B Soil Plant.67: 590-606, DOI: 10.1080/09064710.2017.1321138
Purchase J L, Hatting H & Van Deventer C S 2000 Genotype × environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. Stability analysis of yield performance. S. Afr. J. Plant Soil. 17: 101–107.
Shivakumar M S, Sunitha N C, Akshitha H J, Saji K V & Sasikumar B 2024 Predictive power of YREMs and BLUPs for selecting superior genotypes in perennial crops: A black pepper case study, Journal of Appl. Res. on Medic. and Arom. Plants, 41(100555): 1-8 https://doi.org/10.1016/j.jarmap.2024.100555Shukla G K, 1972 Some statistical aspects of partitioning genotypes–environmental components of variability. Heredity 29: 237–245.
Singh S K, Singh I P, Singh B B & Singh O 2009 Stability analysis in mungbean [Vigna radiata (L.) Wilczek]. Legume Res. 32 (2): 108–112.
Singh V, Yadav R K, Yadav R, Malik R S, Yadav N R & Singh J 2013 Stability analysis in mungbean [Vigna radiata (L) wilczek] for nutritional quality and seed yield. Leg Res. 36 (1): 56–61.
Spoorthi V, Ramesh S, Sunitha N C &. Vaijayanthi P V 2021 Are genotype’s single-year YREMs and BLUPs good predictors of their performance in future years? An empirical analysis in dolichos bean [Lablab purpureus (L.) Sweet]. Genet. Resour. Crop Evol. 68 (4): 1401 - 1409.
Sunita M, Neelav S & Mohan L 2020 GxE interaction of 72 accessions with three-year evaluation of Cymbopogon winterianus Jowitt. using regression coefficient and Additive Main effects and Multiplicative Interaction model (AMMI). Ind. Crop Prod. 146: 112169
Temesgen T, Keneni G, Sefera T & Jarso M 2015 Yield stability and relationships among stability parameters in faba bean (Vicia faba L.) genotypes. e Crop Journal 3: 258–268.
Tena E, Goshu F, Mohamad H, Tesfa M, Tesfaye D and Seife A 2019 Genotype × environment interaction by AMMI and GGE-biplot analysis for sugar yield in three crop cycles of sugarcane (Saccharum officinirum L.) clones in Ethiopia. Cogent food agric. 5, 1651925.
Usha Rani G, Satyanarayana Rao V, Ahmad M L & Narasimha Rao K L 2017 Assessment of genotype-environment interaction using additive main effects and multiplicative interaction model (AMMI) in Maize (Zea mays L.) hybrids. Elect. J. Plant Breed. 8 (4): 1223–1228.
Vaijayanthi P V, Ramesh S, Chandrashekhar A, Keerthi C M, Marappa N, Mahadevan P and Chandrakant 2017 Yield stability analysis of dolichos bean genotypes using AMMI model and GGL Biplot. Int. J. Agric. Sci. 9 (47): 4800 - 4805.
Vimala Y, Lavania U C, Singh M, Lavania S, Srivastava S and Basu S 2022 Realization of Lodging Tolerance in the Aromatic Grass Cymbopogon khasianus Through Ploidy Intervention. Front. Plant Sci. 13:908659. doi: 10.3389/fpls.2022.908659
Wrike G 1962 vbereine method zur Er fassuny der okologischen Streubreite in Feldversuchen. Z. Pflanzenz¨vchtg 47: 92–96.
Xu Y 2016 Envirotyping for deciphering environmental impacts on crop plants Theor. Appl. Genet. 129:653–673. https://doi.org/10.1007/s00122-016-2691-5
Yan W 1999 Methodology of cultivar evaluation based on yield trial data-with special reference to winter wheat in Ontario”. Doctoral dissertation, University of Guelph, Ontario, Canada.
Yan W 2001 GGE biplot- a window application for graphical analysis of multi-environment trial data and other types of two-way data. Agronomy Journal 93: 1111–1118
Yan W & Hunt L. A 2002 Biplot analysis of multi-environment trial data”. P. 289-303. In M.S. Kang (ed.) Quantitative genetics, genomics and plant breeding. CABI Publishing, Wallingford, Oxon, U.K
Yan W, Hunt L A, Sheng Q & Szlavnics Z 2000 Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci. 40: 597–605.
Yan W & Tinker N A 2006 Biplot analysis of multi environment trial data: principles and application. Can. J. Plant Sci. 86: 623–645.
Yan W, Kang M S, Ma M, Woods S & Cornelious P L 2007 GGE Biplot vs AMMI analysis of genotype-by-environment data. Crop Sci. 47: 641–653.
Zobel R W, Wright M J & Gauch Jr H G 1988 Statistical analysis of a yield trial. Agron J 80(3):388-93.
Zoric´ M, Gunjac´a J & S ˇ imic´ D 2017 Genotypic and environmental variability of yield from seven different crops in Croatian official variety trials and comparison with on farm trends. J. Agric. Sci. 155:804–811. https://doi.org/10.1017/S0021859616000903
Published
How to Cite
Issue
Section
Copyright (c) 2025 Channayya Hiremath, K V Ashwini, K Baskaran

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

.