Multi-locational trials are critical for establishing stable and adaptable genotypes across different geographic areas prior to considering commercial release. The stability and adaptation of 20 tropical maize hybrids in environments infected with Northern leaf blight disease were assessed using 12 parametric and 14 nonparametric parameters across five environments. The purpose of this research is to estimate the genotype-environment interaction (GEI) for grain yield in selected maize genotypes and to identify associated stability factors to aid in the rationalization of stability analysis in Multi-Environment Trial (MET) data used in breeding programs. Except for De Kroon and Van der Laan (1981), both the combined ANOVA and nonparametric tests of GEI showed significant differences across hybrids, as well as significant crossover and non-crossover interactions. This suggests differential genotypes responses to the test environments. Spearman correlation analysis revealed significant differences between many nonparametric and parametric parameters, indicating that the two may be utilized interchangeably. Additionally, the correlation matrix and principal component analysis results from parametric and nonparametric parameters demonstrated their potential to assess the responses of maize genotypes to changing environments. G13 and G20 appeared most phenotypically stable with associated high mean yield based on the high values expressed by most parametric and nonparametric parameters.
| Published in | American Journal of BioScience (Volume 9, Issue 6) |
| DOI | 10.11648/j.ajbio.20210906.15 |
| Page(s) | 199-209 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2021. Published by Science Publishing Group |
Maize, Parametric, Non-Parametric, Genotypes, Environment, Multi-Environment Trial, Spearman Correlation, Principal Component Analysis
| [1] | FAO (Food and Agriculture Organization) (2019). Maize. Retrieved July 20, 2020, from httpJ/www.fao.org/ag/AGS/agse/activity. |
| [2] | Ohunakin, AO., Odiyi, AC. & Akinyele, BO. Genetic variance components and GGE interaction of tropical maize genotypes under Northern leaf blight disease infection. CEREAL RESEARCH COMMUNICATIONS (2020). https://doi.org/10.1007/s42976-020-00100-6. |
| [3] | Yue, GL, Roozeboom KL, Schapaugh WT, and Liang GH (1997) Evaluation of soybean cultivars using parametric and nonparametric stability estimates. Plant Breeding 116: 271-275. |
| [4] | Ebadi Segherloo A, Sabaghpour SH, Dehghani H, and Kamrani M (2008) Nonparametric measures of phenotypic stability in chickpea genotypes (Cicer arietinum L.). Euphytica 162: 221-229. |
| [5] | Zali H, Farshadfar E, and Sabaghpour SH (2011) Nonparametric analysis of phenotypic Stability in chickpea (Cicer arietinum L.) genotypes in Iran. Crop Breed 1: 89-100. |
| [6] | Lin CS, Binns MR, Lefkovitch LP (1986) Stability analysis: Where do we stand? Crop Sci 26: 894- 900. |
| [7] | Lin CS and Binns MR (1988a) A superiority measure of cultivar performance for cultivar x location data. CJPS 68: 193-198. |
| [8] | Pinthus MJ (1973) Estimate of genotypic value: A proposed method. Euphytica 22, 121-123. |
| [9] | Francis TR and Kannenberg LW (1978) Yield stability studies in short-season maize. 1. A descriptive method for grouping genotypes. CJPS 58: 1029-1034. |
| [10] | Becker HC and Léon J (1988) Stability analysis in plant breeding. Plant Breed 101: 1-23. |
| [11] | Shukla GK (1972) Some statistical aspects of partitioning genotype-environmental components of variability. Heredity 29: 237-245. |
| [12] | Finlay K. W. and Wilkinson G. N. (1963). The analysis of adaptation in a plant-breeding programs. AJARs 14: 742-754. |
| [13] | Eberhart S. A. and Russel W. A. (1966) Stability parameters for comparing varieties. Crop Sci 6: 36-40. |
| [14] | Perkins J. M. and Jinks J. L. (1968). Environmental and genotype-environmental components of variability: III. Multiple lines and crosses. Heredity 23: 339-356. |
| [15] | Wricke G (1962) Über eine Methode zür Erfassung der Okologischen Streubreite in Feldresuchen. Z. Pflanzenzuchtg 47: 92-96. |
| [16] | Lin CS and Binns MR (1988b) A method of analyzing cultivar × location × year experiments: A new stability parameter. TAG 76: 425- 430. |
| [17] | Holhs T (1995) Analysis of genotype environment interactions. SAJS 91: 121-124. |
| [18] | Gauch HG (1988) Model selection and validation for yield trials with interaction. Biometrics 44: 705-715. |
| [19] | Crossa J. (1990) Statistical analysis of multi-location trials. Adv in Agron. 44: 55-85. |
| [20] | Zobel RW, Wright MJ, and Gauch HG (1988) Statistical analysis of a yield trial. Agron J 80: 388-393. |
| [21] | Gauch HG, Zobel RW (1996) AMMI analysis of yield trials. In: Kang MS, Gauch HG (eds) Genotype by environment interaction. CRCP Boca Raton, FL. |
| [22] | Nassar R., Huehn M. (1987). Studies on estimation of phenotypic stability: Tests of significance for nonparametric measures of phenotypic stability. Biometrics 43: 45-53. |
| [23] | Fox PN, Skovmand B, Thompson BK, Braun HJ, and Cormier R (1990) Yield and adaptation of hexaploid spring triticale Euphytica 47: 57- 64. |
| [24] | Kang MS and Pham HN (1991) Simultaneous selection for high yielding and stable crop genotypes. Agron J 83: 161-165. |
| [25] | Kang MS (1993) Simultaneous selection for yield and stability in crop performance trials: Consequences for growers. Agron J 85: 754-757. |
| [26] | Thennarasu K (1995). On certain nonparametric procedures for studying genotype-environment interactions and yield stability. Ph.D. Thesis. P. J. School, IARI, New Delhi. |
| [27] | Ohunakin A. O., Odiyi A. C. and Akinyele B. O. (2019) Comparison of rank sum and Area under Disease Progress Curve (AUDPC) as determinant for relative resistance status of maize populations to Northern leaf blight disease of maize. Adv Plants Agric Res. 2019; 9 (3): 395‒400. DOI: 10.15406/apar.2019.09.00455. |
| [28] | Kubinger KD (1986) A note on nonparametric tests for the interaction on two-way layouts. Biometric J 28: 67-72. |
| [29] | Hildebrand H (1980) Asymptotosch verteilungsfreie rangtests in linearen modellen. Med, Inform. Stak 17: 344-349. |
| [30] | De Kroon J, and Van der Laan P (1981) Distribution-free test procedures in two-way layouts: A Concept of rank-interaction. Stat. Neeri 35: 189-213. |
| [31] | SAS Institute Inc. (2016). Statistical Analysis Software (SAS) user’s guide. SAS Institute, Inc., Cary, NC, USA. |
| [32] | Akbarpour, Omid, Dehghani, Hamid, Sorkhi-Lalelo, Bezad and Kang Manjit. (2016). A SAS macro for computing statistical tests for two-way table and stability indices of nonparametric method from genotype-by-environment interaction. Acta Scientiarum. Agronomy. 38. 35. 10.4025. |
| [33] | GEA-R 4.1: Pacheco, Ángela; Vargas, Mateo; Alvarado, Gregorio; Rodríguez, Francisco; Crossa, José; Burgueño, Juan, 2015, "GEA-R (Genotype x Environment Analysis with R for Windows) Version 4.1", https://hdl.handle.net/11529/10203, CIMMYT Research Data & Software Repository Network, V16. |
| [34] | STABILITYSOFT (2019). Pour-Aboughadareh, A, Yousefian M, Moradkhani M, Poczai P, and Siddique KHM. (2019): A new online program to calculate parametric and nonparametric stability statistics for crop traits. Applications in Plant Sciences 7 (1): e1211. doi: 10.1002/aps3.1211. |
| [35] | 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. South African Journal of Plant and Soil, 17 (3), 101-107. |
| [36] | Plaisted, R. L., & Peterson, L. C. (1959). A technique for evaluating the ability of selections to yield consistently in different locations or seasons. American Potato Journal, 36 (11), 381-385. |
| [37] | Huehn M (1990) Non-parametric measures of phenotypic stability. Part I: Theory. Euphytica 47: 189-194. |
| [38] | Kang, M. S. (1988). A rank-sum method for selecting high yielding, stable corn genotypes. Cereal Research Communications, 16, 113-115. |
| [39] | Ketata, H. (1988). Genotype× environment interaction. Proceedings of Biometrical Techniques for Cereal Breeders. ICARDA, Aleppo, Syria, 16-32. |
| [40] | Steel, R. G., & Torrie, J. H. (1980). Principles and Procedures of Statistics McGraw-Hill Book Co. Inc., New York, 481. |
| [41] | Badu-Apraku, B., Fakorede, B., Akinwale, R., Annor, B., Adewale, S., Toyinbo, J., & Akintibu, S. (2020). Application of the GGE Biplot as a Statistical Tool in the Breeding and Testing of Early and Extra-Early Maturing Maize in Sub-Saharan Africa. Crop Breeding, Genetics and Genomics, 2 (3). |
| [42] | Mohammadi, R, and Amri, A. (2008) Comparison of parametric and nonparametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica, 159 (3), 419-432. |
| [43] | Alwala, S, Kwolek, T, McPherson, M, Pellow, J, and Meyer, D. (2010). A comprehensive comparison between Eberhart and Russell joint regression and GGE biplot analyses to identify stable and high yielding maize hybrids. Field crops research, 119 (2-3), 225-230. |
| [44] | Piepho, H. P. (2000). A mixed-model approach to mapping quantitative trait loci in barley on the basis of multiple environment data. Genetics, 156 (4), 2043-2050. |
| [45] | Robert N (2002) Comparison of stability statistics for yield and quality traits in bread wheat. Euphytica 128: 333–341. |
| [46] | Baker, RJ (1988). Tests for crossover genotype-environmental interactions. Canadian Journal of Plant Science 68: 405-410. |
| [47] | Sabaghnia N, Dehghani H, Sabaghpour SH (2006) Nonparametric methods for interpreting genotype × environment interaction of Lentil genotypes. Crop Sci 46: 1100-1106. |
| [48] | Solomon KF, Smith HA, Malan E, and DuToit WJ (2007). Comparison study using rank based nonparametric stability statistics of durum wheat. WJAS 3: 444-450. |
| [49] | Mevlüt Akçura and Yuksel Kaya (2008). Nonparametric stability methods for interpreting genotype by environment interaction of bread wheat genotypes (Triticum aestivum L.). Genetics and Molecular Biology. 31. 10.1590/S1415-47572008000500018. |
| [50] | Mohammadi, R, Armion, M, Kahrizi, D, and Amri A (2012) Efficiency of screening techniques for evaluating durum wheat genotypes under mild drought conditions. International Journal of Plant Production, 4 (1), 11-24. |
| [51] | Scapim CA, Oliveira VR, Braccini AL, Cruz CD, Andrade CAB, and Gonçalves-Vidigal MC (2000) Yield stability in maize (Zea mays L.) and correlations among the parameters of the Eberhart & Russel, Lin & Binns and Huehn models. Genet Mol Biol 23: 387-393. |
| [52] | Yaksel K, Seyfi T, and Sait C (2003) Nonparametric stability analysis of yield performances in Oat (Avena sativa L.) genotypes across environments. AJPS 2: 286-289. |
APA Style
Akinlolu O. Ohunakin, Odiyi Alex C., Akinyele Benjamin O., Fayeun Lawrence Stephen, Alake Gideon Collins. (2021). Parametric and Non-parametric Procedures for Identifying Stable and Adapted Tropical Maize Genotypes in NLB Disease Infested Environments. American Journal of BioScience, 9(6), 199-209. https://doi.org/10.11648/j.ajbio.20210906.15
ACS Style
Akinlolu O. Ohunakin; Odiyi Alex C.; Akinyele Benjamin O.; Fayeun Lawrence Stephen; Alake Gideon Collins. Parametric and Non-parametric Procedures for Identifying Stable and Adapted Tropical Maize Genotypes in NLB Disease Infested Environments. Am. J. BioScience 2021, 9(6), 199-209. doi: 10.11648/j.ajbio.20210906.15
AMA Style
Akinlolu O. Ohunakin, Odiyi Alex C., Akinyele Benjamin O., Fayeun Lawrence Stephen, Alake Gideon Collins. Parametric and Non-parametric Procedures for Identifying Stable and Adapted Tropical Maize Genotypes in NLB Disease Infested Environments. Am J BioScience. 2021;9(6):199-209. doi: 10.11648/j.ajbio.20210906.15
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Download@article{10.11648/j.ajbio.20210906.15,
author = {Akinlolu O. Ohunakin and Odiyi Alex C. and Akinyele Benjamin O. and Fayeun Lawrence Stephen and Alake Gideon Collins},
title = {Parametric and Non-parametric Procedures for Identifying Stable and Adapted Tropical Maize Genotypes in NLB Disease Infested Environments},
journal = {American Journal of BioScience},
volume = {9},
number = {6},
pages = {199-209},
doi = {10.11648/j.ajbio.20210906.15},
url = {https://doi.org/10.11648/j.ajbio.20210906.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajbio.20210906.15},
abstract = {Multi-locational trials are critical for establishing stable and adaptable genotypes across different geographic areas prior to considering commercial release. The stability and adaptation of 20 tropical maize hybrids in environments infected with Northern leaf blight disease were assessed using 12 parametric and 14 nonparametric parameters across five environments. The purpose of this research is to estimate the genotype-environment interaction (GEI) for grain yield in selected maize genotypes and to identify associated stability factors to aid in the rationalization of stability analysis in Multi-Environment Trial (MET) data used in breeding programs. Except for De Kroon and Van der Laan (1981), both the combined ANOVA and nonparametric tests of GEI showed significant differences across hybrids, as well as significant crossover and non-crossover interactions. This suggests differential genotypes responses to the test environments. Spearman correlation analysis revealed significant differences between many nonparametric and parametric parameters, indicating that the two may be utilized interchangeably. Additionally, the correlation matrix and principal component analysis results from parametric and nonparametric parameters demonstrated their potential to assess the responses of maize genotypes to changing environments. G13 and G20 appeared most phenotypically stable with associated high mean yield based on the high values expressed by most parametric and nonparametric parameters.},
year = {2021}
}
TY - JOUR T1 - Parametric and Non-parametric Procedures for Identifying Stable and Adapted Tropical Maize Genotypes in NLB Disease Infested Environments AU - Akinlolu O. Ohunakin AU - Odiyi Alex C. AU - Akinyele Benjamin O. AU - Fayeun Lawrence Stephen AU - Alake Gideon Collins Y1 - 2021/12/29 PY - 2021 N1 - https://doi.org/10.11648/j.ajbio.20210906.15 DO - 10.11648/j.ajbio.20210906.15 T2 - American Journal of BioScience JF - American Journal of BioScience JO - American Journal of BioScience SP - 199 EP - 209 PB - Science Publishing Group SN - 2330-0167 UR - https://doi.org/10.11648/j.ajbio.20210906.15 AB - Multi-locational trials are critical for establishing stable and adaptable genotypes across different geographic areas prior to considering commercial release. The stability and adaptation of 20 tropical maize hybrids in environments infected with Northern leaf blight disease were assessed using 12 parametric and 14 nonparametric parameters across five environments. The purpose of this research is to estimate the genotype-environment interaction (GEI) for grain yield in selected maize genotypes and to identify associated stability factors to aid in the rationalization of stability analysis in Multi-Environment Trial (MET) data used in breeding programs. Except for De Kroon and Van der Laan (1981), both the combined ANOVA and nonparametric tests of GEI showed significant differences across hybrids, as well as significant crossover and non-crossover interactions. This suggests differential genotypes responses to the test environments. Spearman correlation analysis revealed significant differences between many nonparametric and parametric parameters, indicating that the two may be utilized interchangeably. Additionally, the correlation matrix and principal component analysis results from parametric and nonparametric parameters demonstrated their potential to assess the responses of maize genotypes to changing environments. G13 and G20 appeared most phenotypically stable with associated high mean yield based on the high values expressed by most parametric and nonparametric parameters. VL - 9 IS - 6 ER -
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