GGE-BIPLOT Analysis of Durum Wheat Yield Trials

Durum wheat (Triticum durum L.) is a vital crop in the rain-fed areas of Turkey. In this study, the grain yield of twenty durum wheat genotypes (sixteen advanced lines with four checks) tested across 8 rain-fed environments during the 2008-2009 cropping season was evaluated using GGE (Genotype (G) Main Effect + Genotype by Environment Interaction (GEI)) Biplot Approach. Environment (E) captured most (83 %) of the grain yield (GY) variation, while the portions attributed to G and GEI were only 6 and 11 %, respectively. In addition, most of the testing Es were highly correlated. According to GGE-Biplot analysis, ‘Which won where’ pattern partitioned the testing Es into three mega-environments (ME): the first ME with six Es with G13 (the highest yielder) as the winning genotype; the second ME encompassed one environment (E1, Konya) with G4 (the lowest yielder) as the winning genotype, and the last ME represented by one location (E6, Altintas) with G10 (the higher yielder) as the winning genotype. GGE-Biplot analysis showed that although the Durum Wheat Yield Trials were conducted in many environments, outcomes alike can be obtained from one or two representatives of each ME. On the other hand, no correlation of these MEs with their geographic location was observed. In conclusion, the presence of cross-over GEI underscores that efforts should be given to identify specifically adapted genotypes rather than broadly adapted ones tested on multi-environment trials (METs).

PDF

___

Akcura M, Taner S, Kaya Y. 2011. Evaluation of bread wheat genotypes under irrigated multi-environment conditions using GGE biplot analyses. Zemdirbyste-Agri, 98(1): 35-40.
Casanoves F, Macchiavelli R, Balzarini M. 2005. Error variation in multi-environment peanut trials. Crop Sci, 45: 1927-1933.
Crossa J. 1990. Statistical analyses of multi-location trials. Adv Agron, 44: 55-85.
Dehghani H, Ebadi A, Yousefi. A. 2006. Biplot analysis of genotype by environment interaction for barley yield in Iran. Agron J, 98: 388-393.
Dehghani H, Omidi H, Sabaghnia. N. 2008. Graphic analysis of trait relations of rapeseed using the biplot method. Agron J, 100: 1443-1449.
Fan XM, Kang MS, Chen H, Zhang Y, Tan J, Xu C. 2007. Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China Agron J, 99: 220-228.
Gabriel KR. 1971. The biplot graphic display of matrices with application to principal component analysis. Biometrika, 58: 453-467.
Gauch HG. 1992. AMMI analysis of yield trials. In: Kang MS, Gauch HG, editors. Genotype-by-environment interaction. CRC Press, Boca Raton, US, pp: 40.
Gauch HG, Zobel RW. 1997. Identifying mega-environment and targeting genotypes. Crop Sci, 37: 381-385.
Ilker E, Tonk FA, Caylak O, Tosun M, Ozmen I. 2009. Assessment of genotype x environment interactions for grain yield in maize hybrids using AMMI and GGE biplot analyses. Turk J Field Crops, 14(2): 123-135.
Kaya Y, Akcura M, Taner S. 2006. GGE-biplot analysis of multi-environment yield trials in bread wheat. Turk J Agric For, 30: 325-337.
Kendal E. 2019. Comparing durum wheat cultivars by genotype × yield × trait and genotype × trait biplot method. Chil J Agr Res, 79(4): 512-522.
Li H, He Z. 2021. Warming climate challenges breeding. Nat Plants, 7: 1164-1165.
Mohammadi R, Aghaee M, Haghparast R, Pourdad SS, Rostaii M, Ansari Y, Abdolahi A, Amri. A. 2009. Association among non-parametric measures of phenotypic stability in four annual crops. Middle East-Russian J Plant Sci Biotec, 3: 20-24.
Mohammadi R, Sadeghzadeh B, Poursiahbidi, MM, Ahmadi, MM. 2021. Integrating univariate and multivariate statistical models to investigate genotype × environment interaction in durum wheat. Ann Appl Biol, 178: 450-465.
Nehe A, Akin B, Sanal T, Evlice AK, Ünsal R, Dinçer N, Demir L, Geren H, Sevim I, Orhan Ş, Yaktubay S, Ezici A, Guzman C, Morgounov A. 2019. Genotype x environment interaction and genetic gain for grain yield and grain quality traits in Turkish spring wheat released between 1964 and 2010. PLoS ONE, 14(7): e0219432.
Plavsin I, Gunjaca J, Simek R, Novoselovic D. 2021. Capturing GEI patterns for quality traits in biparental wheat populations. Agronomy, 11(6): 1022.
Putto W, Patanothai A, Jogloy S, Hoogenboom S. 2008. Determination of mega-environments for peanut breeding using the CSM-CROPGRO-Peanut model. Crop Sci, 48: 973-982.
Rakshit S, Ganapathy KN, Gomashe SS, Rathore A, Ghorade RB, Kumar MVN, Ganesmurthy K, Jain SK, Kamtar MY, Sachan JS, Ambekar SS, Ranwa BR, Kanawade DG, Balusamy M, Kadam D, Sarkar A, Tonapi VA, Patil JV. 2012. GGE biplot analysis to evaluate genotype, environment and their interactions in sorghum multi-location data. Euphytica, 185: 465-479.
Rao PS, Reddy PS, Ratore A, Reddy BVS, Panwar S. 2011. Application GGE biplot and AMMI model to evaluate sweet sorghum (Sorghum bicolor) hybrids for genotype x environment interaction and seasonal adaptation. Indian J Agric Sci, 81: 438-444.
Roozeboom K, Schapugh T, Tuinstra MR, Vanderlip R, Milliken GA. 2008. Testing wheat in variable environments: genotype, environment, interaction effects, and grouping test locations. Crop Sci, 48: 317-330.
Sabaghnia N, Dehghani H, Sabaghpour SH. 2008. Graphic analysis of genotype by environment interaction for lentil yield in Iran. Agron J, 100: 760-764.
Samonte SOPB, Wilson LT, Mcclung AM, Medley JC. 2005. Targeting cultivars onto rice growing environments using AMMI and SREG GGE biplot analysis. Crop Sci, 45: 2414-2424.
Scapim CA, Oliveira VR, Braccini A, Cruz CD, Andrade CAB, Vidigal MCG. 2000. Yield stability in maize (Zea mays L.) and correlations among the parameters of the Eberhart and Russell, Lin and Binns and Huehn models. Genet Mol Biol, 23: 387-393.
Tekdal S, Kendal E, Aktaş H, Karaman M, Doğan H, Bayram S, Düzgün M, Efe A. 2017. Biplot analiz yöntemi ile bazı makarnalık buğday hatlarının verim ve kalite özelliklerinin değerlendirilmesi. TARM Derg, 26: 68-73.
Yan W. 2001. GGEbiplot–A Windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agron J, 93: 1111-1118.
Yan W. 2002. Singular value partitioning for biplot analysis of multi-environment trial data. Agron J, 4: 990-996.
Yan W. 2019. LG biplot: a graphical method for mega-environment investigation using existing crop variety trial data. Sci Rep, 9: 7130.
Yan W, Hunt LA. 2002. Biplot analysis of diallel data. Crop Sci, 42: 21-30.
Yan W, Kang MS. 2003. GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton, US, pp: 224.
Yan W, Rajcan I. 2002. Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Sci, 42: 11-20.
Yan W, Tinker NA. 2006. Biplot analysis of multi-environment trial data: principles and applications. Canadian J Plant Sci, 86: 623-645.
Yan W, Hunt LA, Sheng Q, Szlavnics Z. 2000. Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Sci, 40: 597-605.
Yan W, Kang MS, Ma BL, Woods S, Cornelius PL. 2007. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci, 47: 643-653.
Yan W, Fregeau-Reid J, Pageau D, Martin R, Mitchell-Fetch J, Etieenne M, Rowsell J, Scott P, Price M, De Hann B, Cummiskey A, Lajeunesse J, Durand J, Sparry E. 2010. Identifying essential test location for oat breeding in eastern Canada. Crop Sci, 50: 504-515.
Yan W, Mitchell-Fetch J, Beattie A, Nilsen KT, Pageau D, DeHaan B, Hayes M, Mountain N, Cummiskey A, MacEachern D. 2021. Oat mega-environments in Canada. Crop Sci, 61: 1141-1153.