Elucidation of GxE interactions by AMMI, BLUP and non parametric measures of Wheat genotypes evaluated in NWPZ
DOI:
https://doi.org/10.5958/2582-2683.2023.00013.8Keywords:
AMMI, BLUP, Biplot analysis, Non parametric composite measuresAbstract
Significant variations due to environments (38.3%), GxE interactions (32.1%), and genotypes (10.9%) were observed by AMMI analysis. Absolute IPCA-1 scores pointed for G13, G2, G11 as per IPCA-2, G1, G3, G8 genotypes would be of choice. ASV1 and ASV measures recommended G13, G2, G11 genotypes. Based on 97.8 per cent of GxE interactions sum of squares MASV1 identified G13, G3, G7 whereas MASV settled for G13, G1, and G7 genotypes. BLUP-based measures HMGV, RPGV, HMRPGV identified G9, G6, G13 genotypes for this mega wheat producing zone of the country. Non parametric composite measure NPi (1) observed suitability of G3, G13, G12 whereas NPi(2), NPi(3), NPi(4) identified G3, G10, G12 genotypes. Biplot analysis observed 65.4% of the total variation in the considered measures accounted by PC1 and PC2 with respective contributions of 35.4% & 29.9% respectively. Si1, Si2, Si4, Si3, NPi(1), Si6, ASV, ASV1, IPC7 accounted more in PC1 whereas for second component RPGV HMRPGV, HMGV, BLAvg, BLGM, NPi (4) were major contributors. Cluster of IPC2, NPi(2), NPi(3), NPi(4) placed adjacent to BLUP based measures in the same quadrant. ASV, ASV1, MASV, MASV1, clustered with Si1, Si2, Si3, Si4, Si5, Si6, Si7 BLStd and NPi(1), BLCV, IPC7 observed in different quadrant of biplot analysis. Strong positive Spearmen rank correlations of ASV & ASV1 seen of moderate to strong direct nature with measures exception of NPi(2), NPi(3), NPi(4). MASV, MASV1 expressed moderate to strong positive correlation values Si1, Si2, Si3, Si4, Si5, Si6, Si7, NPi(1), NPi(2), NPi(3), NPi(4). Similar type of relationships were expressed by BLUP based measures.
Downloads
References
Ahakpaz F., Abdi H., Neyestani E., Hesami A., Mohammadi B., Nader Mahmoudi K., Abedi-Asl G., Jazayeri Noshabadi M.R., Ahakpaz F. and Alipour H. (2021). Genotype-by environment interaction analysis for grain yield of barley genotypes under dry land conditions and the role of monthly rainfall. Agricultural Water Management, 245:10665.
Anuradha N., Patro T.S.S.K., Singamsetti A., Sandhya Rani Y., Triveni U., Nirmala Kumari A., Govanakoppa N., Lakshmi Pathy T. and Tonapi V.A. (2022). Comparative Study of AMMI- and BLUP-Based Simultaneous Selection for Grain Yield and Stability of Finger Millet [Eleusine coracana (L.) Gaertn.] Genotypes. Front. Plant Science, 12:786839. doi: 10.3389/fpls.2021.786839
Bocianowski J., Tratwal A. and Nowosad K. (2021). Genotype by environment interaction for main winter triticale varieties characteristics at two levels of technology using additive main effects and multiplicative interaction model. Euphytica, 217:26.
George N. and Lundy M. (2019). Quantifying genotype x Environment effects in long-term common wheat yield trials from an agroecologically diverse production region. Crop Science, 59:1960-1972.
Gerrano A.S., Rensburg W.S.J.V., Mathew I., Shayanowako A.I.T., Bairu M.W., Venter S.L., Swart W., Mofokeng A., Mellem J. and Labuschagne M. (2020). Genotype and genotype x environment interaction effects on the grain yield performance of cowpea genotypes in dry land farming system in South Africa. Euphytica, 216:80.
Gonçalves G. de M. C., Gomes R. L. F., Lopes . C. de A. and Vieira P. Fe. de M. J. (2020). Adaptability and yield stability of soybean genotypes by REML/BLUP and GGE Biplot. Crop Breeding and Applied Biotechnology, 20: e282920217.
Pour Aboughadareh, A., Ali B., Ali K. S., Mehdi J., Akbar M., Ahmad G., Kamal S.H., Hassan Z., Poodineh Omid and Masoome, K. (2022). Dissection of genotype by environment interaction and yield stability analysis in barley using AMMI model and stability statistics. Bulletin of National Research Centre, 46:19.
Pour-Aboughadareh, A., Yousefian M., Moradkhani H., Poczai P. and Siddique K.H. (2019). STABILITYSOFT: A new online program to calculate parametric and non-parametric stability statistics for crop traits. Applications in Plant Sciences, 7: e1211.
Mehraban R. A., Hossein-Pour T., Koohkan E., Ghasemi S., Moradkhani H. and Siddique K.H. (2019). Integrating different stability models to investigate genotype ×
environment interactions and identify stable and high yielding barley genotypes. Euphytica, 215:63.
Silva E. M. da, Nunes E. W. L. P., Costa J. M. da, Ricarte A. de O., Nunes G. H. de S. and Aragão Fernando Antonio Souza de. (2019). Genotype x environment interaction, adaptability and stability of ‘Piel de Sapo’ melon hybrids through mixed models Crop Breeding and Applied Biotechnology, 19: 402-411.
Sousa, A.M.C.B., Silva V.B., Lopes A.C.A., Ferreira-Gomes R.L. and Carvalho L.C.B. (2020). Prediction of grain yield, adaptability, and stability in landrace varieties of lima bean (Phaseolus lunatus L.) Crop Breeding and Applied Biotechnology, 20: e295120115.
Vaezi, B., A. Pour-Aboughadareh, A. Mehraban, T. Hossein-Pour, Mohammadi R. Armion, M. and Dorri, M. (2018). The use of parametric and non-parametric measures for selecting stable and adapted barley lines. Archives of Agronomy and Soil Science, 64: 597-611.
Downloads
Published
Issue
Section
How to Cite
