Research Article

Non-parametric Stability Analysis in Durum Wheat (Triticum durum Desf.) Genotypes Growing under Semi-arid Conditions

Ali Guendouz and Radia Boucherb

  • Page No:  247 - 251
  • Published online: 31 Mar 2022
  • DOI : HTTPS://DOI.ORG/10.23910/1.2022.2492

  • Abstract
  •  guendouz.ali@gmail.com

Field experiments were carried out in National Institute of Agronomic Research of Algeria INRAA- station of Setif, Algeria where grain yield of 10 durum wheat genotypes tested during 4 cropping seasons from 2009 to 2013 and sowing in the same period in December .The objectives were to determine stable genotypes based on non-parametric stability indices. The results of the Thennarasu (NP) as following: Si(1) (varied from 2 to 5), Si(2) (varied from 3 to 17), Si(3) (varied from 1.2 to 9.27) and Si(6) (varied from 0.67 to 2.55) based on these methods the genotype Mexicali75is considered to be most stable and had highest grain yield. Based on NP1, NP3 and NP4 the genotype Mexicali75is considered to be most stable and had highest grain yield, the genotype Hoggar had low values for the index NP2 we can considered more stable but with moderate grain yield. In addition, the results of the methods of Huehn demonstrate that the genotype Mexicali75 with the lowest values was identified as desirable. The results of Spearman’s rank correlation coefficients between mean yield and the non-parametric stability statistics are shown in Table 4. The mean grain yield as well as negatively and significantly correlated with NP2, NP3 and NP4  and insignificant negative correlation with NP1. In conclusion, according to results of these different non-parametric stability measurements, genotype Mexicali75 is recommended for commercial release as a favorable durum wheat genotype for the semi-arid condition.

Keywords :   Algeria, durum wheat, non-parametric, semi-arid, stability analysis

  • INTRODUCTION

    Durum wheat is grown under varying agro climatic situations. It is an important crop grown worldwide for food (Beres et al., 2020). Algeria with its topographical and bioclimatic characteristics which make it possible to show a diversity of landscapes and cropping systems, cereal growing is the predominant speculation of agriculture. It extends over an annual area of ​​about 3.6 million hectares compared to the useful agricultural area (UAA) (MADR, 2012). Yield stability is an important criterion for the development of cultivars intended for environments with variable rainfall. Many methods of stability analysis are proposed in the literature such as parametric and non-parametric stability indices (Benmahammad et al., 2010; Rose et al., 2008). The genotype-environment interaction reduces association between phenotypic and genotypic values and leads to bias in the estimates of gene effects and combining ability for various characters sensitive to environmental fluctuations (Wardofa et al., 2019). Highlighting a genotype-environment interaction (GEI) makes it difficult to identify superior genotypes for a range of environments and leads us to assess genotypes in many environments to estimate their true genetic potential (Yaghotipour and Farshadfar, 2007). The importance of G×E interactions in national cultivar evaluation and breeding programs have been demonstrated in almost all major crops, including wheat genotypes (Frih et al., 2021). Various statistical methods (parametric and non-parametric) have been proposed to study Genotype×environment interactions (Mohammadi and Amri, 2008; Mohammadi et al., 2010). Parametric approaches are: (1) univariate analysis (regression analysis and stability variance analysis) (Bashir et al., 2020) and (2) multivariate analysis (principal component analysis, factor analysis, canonical component analysis, cluster analysis and biplot analysis) (Chahal and Gosal, 2002).  The other approach is to use nonparametric techniques, and several procedures have been proposed based on comparing ranks of genotypes in each environment, with genotypes with similar ranking across environments being considered stable (Sbaghnia, 2016; Huehn, 1996; Fox et al., 1990). in plant breeding research and in view of the increased use of nonparametric stability measures, it is very important to study the effect of the correction on these statistics. In addition, a nonparametric superiority measure for general adaptability has been suggested based on stratified ranking of the cultivars in each separate environment (Smutna et al., 2021), with the proportion of sites at which a specific cultivar occurred in the top third of the ranks (the TOP value), the middle third of the ranks (the MID value) and the lower third of the ranks (the LOW value) being calculated, a genotype with a high TOP value (i.e., occurring principally in the top third of the ranks) being considered as a widely adapted genotype (Fox et al., 1990). based on the classification of genotypes in each environment, Nassar and Huehn (1987) assume four types of nonparametric statistics of phenotypic stability (Si(1), Si(2), Si(3) and Si(6)), they define stable genotypes such as those which have not changed their positions in relation to others in all the environments evaluated. Thennarasu (1995) proposed non-parametric statistics NPi(1), NPi(2), NPi(3) and NPi(4) based on ranks of adjusted means of the genotypes in each environment and defined stable genotypes using Nassar and Huehn (1987)’s definition. The objectives of this study were to identify durum wheat genotypes that have both high grain yield and stable performance across different years for semi-arid areas of Algeria and study the relationships between different nonparametric stability statistics.


  • MATERIALS AND METHODS

    2.1.  Plant material

    This study was carried out with 10 durum wheat (Table 1) during 4 cropping seasons from 2009 to 2013 and sowing in the same period in December.


    2.2.  Study area

    Field experiments were carried out in  the National Institute of Agronomic Research of Algeria INRAA- station of  Setif, Algeria (5°37’E, 36°15’N, 981 m above mean sea level). Genotypes were grown in randomized block design with four replicates. Plots were 2.5×6 m2 rows with 0.20 m row spacing and sowing density was adjusted to 300 g m-2.

    2.2.  Methods and data collection

    2.2.1.  Non-parametric measures

    Huehn (1990) proposed four non-parametric stability statistics that combine mean yield and stability (Si(1), Si(2), Si(3) and Si(6)). For a two-way data set with ‘‘i’’ genotypes and ‘‘m’’ environments, we denote rij as the rank of the ith genotype in the jth environment, and : as the mean rank across all environments for the ith genotype. Other nonparametric stability measures that proposed by Thennarasu (NPi(1), NPi(2), NPi(3) and NPi(4))  described in detail by Thennarasu (1995) and Mohammadi et al. (2007).

    2.2.2.  Statistical analysis

    The data were analyzed by using the software (STABILITYSOFT) developed by Pour-Aboughadareh et al. (2019).  


  • RESULTS AND DISCUSSION

    3.1.  Analysis of  VA riance (ANOVA)

    The main effect of years (Y) was high significant (p<0.001), the main effect of genotype (G) and GY interaction was only significant at p<0.05 (Table 2). Based on the grain yield ranking the genotypes Altar84, Mexicali75 and Sooty, respectively, were the highest yielding cultivars.


    3.2.  Non-parametric measures of stability

    According to the Si(1) (varied from 2 to 5), Si(2) (varied from 3 to 17), Si(3) (varied from 1.2 to 9.27) and Si(6) (varied from 0.67 to 2.55) the genotype Mexicali75 with the lowest value were identified as desirable (Table 3). In addition, and based on the some indices the genotype Bousselem with highest values indicating lower stability. According to Thennarasu’s (1995) stability statistics (NP1, NP2, NP3 and NP4) (Table 4), genotypes with minimum values are considered more stable. Based on NP1, NP3 and NP4 the genotype Mexicali75is considered to be most stable and had highest grain yield, the genotype Hoggar had low values for the index NP2 we can considered more stable but with moderate grain yield. However, based on NP1 and NP3 methods the genotype Altar85 was unstable. According to the other two methods NP2 and NP3 the genotype Oued Zenati was unstable.


    3.3.  Interrelationship among non-parametric methods and grain yield

    The results of Spearman’s rank correlation coefficients between mean yield and the non-parametric stability statistics are shown in Table 4. The mean grain yield as well as negatively and significantly correlated with NP2, NP3 and NP4 (r=-0.76, r=-0.69 and r=-0.60) and insignificant negative correlation with NP1. Similarly, Segherloo et al. (2008) found a highly significant correlation between mean grain yield and Huehn-rank. Mahtabi et al. (2013) found significantly and negatively correlated between mean yield and Si(3), Si(6), NP2 and NP4 (Table 3). In addition, significant negative correlation between grain yield and Si(6), and insignificant negative correlation registered between grain yield and Si(3). Our results are in agreement with the results of Mohammadi and Amri (2008).  


  • CONCLUSION

    Based on the methods of Huehn the genotypes Mexicali75 with the lowest values were identified as desirable and the genotype Bousselem with highest values indicating lower stability. Thennarasu Stability statistics demonstrate that the genotypes with minimum values are considered stable. The genotype Mexicali75is considered to be most stable and had highest grain yield Based on NP1, NP3 and NP4, the genotype Hoggar had low values for the index NP2 we can considered more stable but with moderate GY.  


  • Reference
  • Bashir, M., Muhammad, Y., Abid, M., Ahsan, M., Khan, Q., 2020. Adaptability trials of sesame germplasm against Macrophomina phaseolina by using AMMI biplot analysis in Pakistan. International Journal of Agriculture and Biology 23, 851–856.

    Benmahammed, A., Nouar, H., Haddad, L., Laala, Z., Oulmi, A., Bouzerzour, H., 2010. Analyse de la stabilité des performances de  rendement  du  blé  dur  (Triticum  durum  Desf.)  sous  conditions  semi-arides.  Biotechnologien, Agronomie, Societe et Environnement 1(14), 177-186.

    Beres, B.L., Rahmani, E., Clarke, J.M., Grassini, P., Posniak, C.J., Geddes, C.M., Porker, K.D., May, W.E., Ranson, J.K.A., 2020. Systematic review of durum wheat: Enhancing production systems by exploring genotype, environment, and management (G×E×M) synergies. Frontiers in Plant Science 11, 568657.

    Chahal, G.S., Gosal, S.S., 2002. Principles and Procedures of plant breeding. Alpha Science International Ltd., Pang Bourne, India, 149 p.

    Frih, B., Oulmi,  A., Guendouz, A., 2021. Study of the Drought Tolerance of Certain of Durum Wheat (Triticum durum Desf.) Genotypes Growing under Semi-arid Conditions in Algeria. International Journal of Bio-resource and Stress Management 12(2), 137–141.

    Fox, P.N., Skovmand, B., Thompson, B.K., Braun, H.J., Cormier, R., 1990. Yield and adaptation of hexaploid spring triticale. Euphytica 47, 57–64.

    Huehn, M., 1996. Non-parametric analysis of genotype× environment interactions by ranks. In: Kang, M.S., Gauch, H.G. (Eds), Genotype by environment interaction. CRC Press, Boca Raton, pp 213–228.

    Mahtabi, E., Farshadfar, E., Jowkar, M.M., 2013. Non parametric estimation of phenotypic stability in Chickpea (Cicer arietinum L.). International Journal of Agriculture and Crop Sciences 5, 888–895.

    MADR, Ministere  de  l’Agriculture  et  du  Developpement  Rural. 2012. Statistiques  agricoles,  superficies  et  productions, Direction des Statistiques Agricoles et des Enquetes Economiques, Serie B. Avable on http://madrp.gov.dz/ . Accessed on December 15th , 2021.

    Mohammadi, R., 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.

    Mohammadi, R., Mozaffar Roostaei, M., Yousef, A., Mostafa, A., Amri, A., 2010. Relationships of phenotypic stability measures for genotypes of three cereal crops. Canadian Journal of  Plant Science 90, 819–830.

    Nassar, R., Huhn, M., 1987. Studies on estimation of phenotypic stability: Tests of significance for non-parametric measures of phenotypic stability. Biometrics 43, 45–53.

    Pour-Aboughadareh, A., Yousefian, M., Moradkhani, H., Poczai, P., Siddique, K.H.M., 2019.STABILITYSOFT: A new online program to calculate parametric and non-parametric stability statistics for crop traits. Applications in Plant Sciences 7(1), e1211.

    Rose,  L.W., Das,  M.K., Taliaferro, C.M.A., 2008.  Comparison  of  dry  matter  yield  stability  assessment  methods  for  small numbers of genotypes of Bermuda grass. Euphytica 164, 19–25.

    Sabaghnia, N., 2016. Nonparametric statistical methods for analysis of genotype×environment interactions in plant pathology. Australasian Plant Pathology 45(6), 571–580.

    Segherloo, A.E., Sabaghpour, S.H., Dehghani, H., Kamrani, M., 2008. Nonparametric measures of phenotypic stability in chickpea genotypes (Cicer arietinum L.). Euphytica162, 221–229.

    Smutna, P., Mylonas, I., Tokatlidis, I.S., 2021. The Use of Stability Statistics to Analyze Genotype×environments interaction in rainfed wheat under diverse agroecosystems. International Journal of Plant Production 15, 261–271.

    Wardofa, G., Mohammed, H., Asnake, D., Alemu, T., 2019. Genotype×environment interaction and yield stability of bread wheat genotypes in Central Ethiopia. Journal of Plant Breeding and Genetics 7(2), 87–94.

    Yaghotipoor, A., Farshadfar, E., 2007. Non-parametric estimation and component analysis of phenotypic stability in chickpea (Cicer arietinum L.). Pakistan  Journal of  Biological Sciences 10, 2646–264.


Cite

1.
Guendouz A, Boucherb R. Non-parametric Stability Analysis in Durum Wheat (Triticum durum Desf.) Genotypes Growing under Semi-arid Conditions IJBSM [Internet]. 31Mar.2022[cited 8Feb.2022];13(1):247-251. Available from: http://www.pphouse.org/ijbsm-article-details.php?article=1584

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