This study was carried out to determine the ranks of 9 bread wheat (Triticum aestivum L.) genotypes across eleven environments in Central Anatolia, Turkey, in the 2000-2002 growing seasons. Experimental layout was a randomized complete block design with four replications. Analysis of Non parametric stability revealed that genotypes 4 and 8 were most stable and well adapted across eleven environments. In addition, it was concluded that plots obtained by both mean yield (kg ha-1) vs. S1 (1) and mean yield (kg ha-1) vs. S2
(2) values could be enhanced visual efficiency of selection based on genotype x environment interaction

S. TANER

Y. KAYA

Journal of Central European Agriculture

English

This study was carried out to determine the ranks of 9 bread wheat (Triticum aestivum L.) genotypes across eleven environments in Central Anatolia, Turkey, in the 2000-2002 growing seasons. Experimental layout was a randomized complete block design with four replications. Analysis of Non parametric stability revealed that genotypes 4 and 8 were most stable and well adapted across eleven environments. In addition, it was concluded that plots obtained by both mean yield (kg ha-1) vs. S1 (1) and mean yield (kg ha-1) vs. S2 (2) values could be enhanced visual efficiency of selection based on genotype x environment interaction

Y KAYA

S TANER

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ORIGINAL ARTICLE Manuscript received: 24. February, 2003. Accepted for publication: 3. March, 2003.   ESTIMATING  GENOTYPIC  RANKS  BY  NONPARAMETRIC  STABILITY  ANALYSIS  IN  BREAD  WHEAT  (TRITICUM  AESTIVUM  L.)  KAYA* Y., TANER S.     ABSTRACT This study was carried out to determine the ranks of 9 bread wheat (Triticum aestivum L.) genotypes across eleven environments in Central Anatolia, Turkey, in the 2000-2002 growing seasons. Experimental layout was a randomized complete block design with four replications. Analysis of Non parametric stability revealed that genotypes 4 and 8 were most stable and well adapted across eleven environments. In addition, it was concluded that plots obtained by both mean yield (kg ha-1) vs. S1(1) and mean yield (kg ha-1) vs. S2(2) values could be enhanced visual efficiency of selection based on genotype x environment interaction.  KEY WORDS: Bread Wheat (Triticum aestivum L.), yield, nonparametric stability analysis KAYA Y., TANER S. Journal of Central European Agriculture, Volume 4 (2002) No. 1 48INTRODUCTION Parametric methods for estimating genotype x environment interactions and phenotypic stability are widely used in plant breeding and production. The proper use of these parametric measures requires some statistical assumptions, however, and the estimates can be unduly influenced by one or two outliers in small samples. Several nonparametric methods proposed by Huhn (1979) are based on the ranks of genotypes in each environment and use the idea of homeostasis as a measure of the stability. Genotypes with similar rankings across environments are classified as stable. The statistical properties and significance for measures of nonparametric stability analysis (NPSA) were given by Nassar and Huhn (1987). Nonparametric measures for stability based on ranks provide a viable alternative to existing parametric measures based on absolute data. For many applications, including selection in breeding and testing programs, the rank orders of the genotypes are the most essential information. Stability measures based on ranks require no statistical assumptions about the distribution of the phenotypic values. They are easy to use and interpret and, compared with parametric measures, are less sensitive to errors of measurement. Furthermore, addition and deletion of one or a few observations is not as likely to cause great variation in the estimates as would be the case for parametric stability measures (Nassar and Huhn, 1987; Lu, 1995). Fox et. al (1990) suggest a nonparametric superiority measure for general adaptability. They used stratified ranking of the cultivars. Ranking was done at each location separately and the number of sites at which the cultivar occurred in the top, middle, and bottom third of the ranks was computed. A genotype that occurred mostly in the top third was considered as a widely adapted cultivar. Kang and Pham’s (1991) rank-sum is another non-parametric stability statistics where both yield and Shukla’s (1972) stability variance are used as selection criteria. This statistics assigns a weight of one to both yield and stability and enables the identification of high-yielding and stable genotype. The genotype with the highest yield is given a rank of 1 and a genotype with the lowest stability variance is assigned a rank of 1. All genotypes are ranked in this manner. The ranks by yield and by   stability variance are added for each genotype. The genotype with the lowest rank-sum is the most desirable one. The objectives of this study were to (i) interpret ranks obtained by NPSA of 9 bread wheat genotypes over eleven environments, (ii) visually assess how to vary rank measures vs. yield performances across eleven environments based on the plot, and (iii) determine promising genotypes with high yielding and stability.  MATERIALS AND METHODS This study was carried out across eleven environments, including six rain-fed environments undertaken in Karaman-Kazimkarabekir, Konya-Center, Konya-Cumra and Konya-Obruk, and also three irrigated environments conducted in Konya-Cumra, Konya-Center, and Aksaray-Kocas during the 2000-2002 growing seasons. Of 9 bread wheat genotypes used, 7 were from the National Bread Wheat Improvement Program, Turkey, and 2 from the International Winter Wheat Improvement Program based on a joint project between Turkey, CIMMYT and ICARDA (Table 1). Experimental layout was a randomized complete block design with four replications. Sowing was done by an experimental drill in 1.2 m x 7 m plots, consisting of six rows with 18 cm left between the rows. Seeding rate was 450 seeds m-2 for irrigated and 550 seeds m-2 for rain-fed environments. Fertilizer application was 27 kg N ha-1 and 69 kg P2O5 ha-1 at planting and 40 kg N ha-1 at the stem elongation stage. Harvesting was done in 1.2 m x 5 m plots by experimental combine. Details of code, growing season, date of planting, date of harvesting and status of rainfall and/or irrigation for eleven environments are given in Table 2. Yield (kg ha-1) was obtained by converting the grain yields obtained from plots to hectares.  SAS software (1996) was used to perform analysis of NPSA on the mean values of yield (kg ha-1) obtained over environments. PROC MEAN of SAS was run to calculate adjusted means of genotypes across environments. PROC RANK of SAS was ranked genotypes based on corrected means of genotypes within environment. Rank measures and adjusted means of yield were used to depict plot by SAS PLOT procedure (Lu, 1995). While ranking ESTIMATING GENOTYPIC RANKS BY NONPARAMETRIC STABILITY ANALYSIS IN BREAD WHEAT (TRITICUM AESTIVUM L.) Journal of Central European Agriculture, Volume 4 (2002) No. 1 49 genotypes within environment, adjusted values of yield were used, instead of raw data of yield obtained from trials. The genotype with the highest adjusted yield was given a rank of 9 and a genotype with the lowest adjusted yield was assigned a rank of 1 (Table 3). All genotypes were ranked, judging from this case. A genotype is stable over environments if its ranks are similar over environments; i.e. maximum stability occurs with equal ranks over environments.  Table 1: Code, Origin, Pedigree and Selection History of Genotypes Genotypic Code Pedigree and Selection History of Genotypes Origin 1 ATLAS 66//HYS/7C BDKE 900096 -2F5 BD-OBD RBWYT1 2 BOLAL 2973/THUNDERBIRD BDKE 900003 -1F5 BD-OBD RBWYT 3 LND/SWO791O95A/4/YM/TOB//MCD/3/LIRA ICWH90-0217 -7F5 BD-OBD RBWYT 4 KS2142/4/KRC66/3/TT-50-18/P101//11-50-18/VGDWVF BDKE 910010 -1F5 BD-OBD RBWYT 5 UNKNOWN XXX RBWYT 6 63-122-66-2/NO//LOV2F1/3/F1KVZ/HYS/4/TJB916.46/CB306//2*MHB/3/BUC ‘S’ YA 20682 RBWYT 7 KKZ/AU//GRK79 BDKE 890017 -2F5 BD-OBD  RBWYT 8 VEE/TSI//GRK79/3/NS55.03/5/0126.15/COFN/3/N10B/P14//P101/4/KRC66 TCI 932322 -OSE-OYC-2YC-OYC  TCI2 9 JI5418/MARAS TCI 922142  -OSE-OYC-3YC-OYC TCI 1 Regional Bread Wheat Yield Trial-Turkey; 2 TURKEY/CIMMYT/ICARDA International Winter Wheat Improvement Program   Table 2: Code, growing season, date of planting, date of harvesting, status of rainfall + irrigation for each environment Environment Code Growing season Date of planting Date of harvesting Rainfall + (irrigation) (mm) Karaman-Kazimkarabekir 1* 2000-01 05.11.00 16.07.01 255 Konya-Cumra 2* 2000-01 28.10.00 15.07.01 240 Konya-Center 3* 2000-01 21.10.00 10.07.01 210 Konya-Cumra 4** 2000-01 27.10.00 24.07.01 240+100 Konya-Center 5** 2000-01 22.10.00 23.07.01 210+100 Aksaray-Kocas 6** 2000-01 08.11.00 25.07.01 265+100 Konya-Center 7* 2001-02 25.10.01 15.07.02 384 Konya-Cumra 8* 2001-02 22.10.01 12.07.02 376 Konya-Obruk 9* 2001-02 19.10.01 08.07.02 358 Konya-Center 10** 2001-02 24.10.01 16.07.02 384+100 Konya-Cumra 11** 2001-02 26.10.01 13.07.02 376+100 *,** rain-fed and irrigated, respectively  RESULTS AND DISCUSSION Ranks of 9 bread wheat genotypes based on corrected yield (kg ha-1) within each environment are given in Table 3. Genotypic ranks within environment revealed that genotype 8 invaded top of ranking, with yield ranks of 4, 7, 3, 6, 2, 7, 9, 8, 2, 4   and 8 across eleven environments, respectively, prior to genotype 7 (Table 3). However, genotype 4 occupied bottom of the ranking, with yield ranks of 1, 2, 9, 3, 5, 5, 5, 4, 5, 2 and 7 over eleven environments. KAYA Y., TANER S. Journal of Central European Agriculture, Volume 4 (2002) No. 1 50Table 3: Ranking 9 bread wheat genotypes based on corrected yield (kg ha-1) within environment Env Gen Yield  (kg ha-1) Corrected Yield (kg ha-1) Rank Env Gen Yield  (kg ha-1) Corrected Yield  (kg ha-1) Rank 1 1 1 1 1 1 1 1 1  2 2 2 2 2 2 2 2 2  3 3 3 3 3 3 3 3 3  4 4 4 4 4 4 4 4 4  5 5 5 5 5 5 5 5 5  6 6 6 6 6 1 2 3 4 5 6 7 8 9  1 2 3 4 5 6 7 8 9  1 2 3 4 5 6 7 8 9  1 2 3 4 5 6 7 8 9  1 2 3 4 5 6 7 8 9  1 2 3 4 5 1499 1408 1398 1276 1177 1474 1372 1520 1608  2352 3002 2761 2691 2913 3382 2778 3152 2963  3271 3126 3652 3962 3389 2452 3081 3188 3532  3262 4552 4048 4097 4269 4313 3894 4465 3941  6630 5581 6321 6463 6435 5693 5638 5791 6937  4212 3292 3298 3692 3565 1568 1252 1527 1149 1214 1659 1629 1332 1400  2420 2846 2890 2564 2950 3567 3035 2964 2755  3339 2970 3781 3835 3426 2637 3338 3001 3324  3330 4396 4177 3970 4306 4498 4151 4277 3733  6698 5425 6450 6336 6472 5878 5895 5603 6729  4280 3136 3427 3565 3602 7 3 6 1 2 9 8 4 5  1 4 5 2 6 9 8 7 3  6 2 8 9 7 1 5 3 4  1 8 5 3 7 9 4 6 2  8 1 6 5 7 3 4 2 9  9 2 3 5 6 6 6 6 6  7 7 7 7 7 7 7 7 7  8 8 8 8 8 8 8 8 8  9 9 9 9 9 9 9 9 9  10 10 10 10 10 10 10 10 10  11 11 11 11 11 11 11 11 11  6 7 8 9  1 2 3 4 5 6 7 8 9  1 2 3 4 5 6 7 8 9  1 2 3 4 5 6 7 8 9  1 2 3 4 5 6 7 8 9  1 2 3 4 5 6 7 8 9 3267 3587 3889 2651  4575 4967 4505 4915 4782 4275 4182 5170 5162  2172 3235 2402 2502 2262 1965 2185 3045 2695  1272 1701 1697 1812 1015 1705 1652 1441 2074  7201 6922 5745 6762 7600 6857 6585 6990 7962  3442 4570 3392 3862 2827 3217 2855 4057 3402 3452 3844 3701 2443  4643 4811 4634 4788 4819 4460 4439 4982 4954  2240 3079 2531 2375 2299 2150 2442 2857 2487  1340 1545 1826 1685 1052 1890 1909 1253 1866  7269 6766 5874 6635 7637 7042 6842 6802 7754  3510 4414 3521 3735 2864 3402 3112 3869 3194 4 8 7 1  4 6 3 5 7 2 1 9 8  2 9 7 4 3 1 5 8 6  3 4 6 5 1 8 9 2 7  7 3 1 2 8 6 5 4 9  5 9 6 7 1 4 2 8 3  ESTIMATING GENOTYPIC RANKS BY NONPARAMETRIC STABILITY ANALYSIS IN BREAD WHEAT (TRITICUM AESTIVUM L.) Journal of Central European Agriculture, Volume 4 (2002) No. 1                   51 Two rank stability measures from Nassar and Huhn (1987) were expressed as S1(1) and  S2(2). The S1(1) statistic measures the mean absolute rank difference of a genotype over environments. For a genotype with maximum stability, S1(1) = 0. S2(2) gives the variance among the ranks over environments. Zero variance is indication of maximum stability. The exact variance and expectation of S1(1) and  S2(2) were given by Huhn (1990a). The parameters S1(1) and  S2(2) are measurements of the stability alone. They are strongly intercorrelated with each other even in the case of using the uncorrelated yield data. If one adjusts the uncorrected yield data by genotypic effects; i.e. using the corrected values, then non parametric measures S1(1) and  S2(2) are nearly perfectly correlated between each other. The two stability rank orders of the genotypes obtained by using the uncorrected yield data and by using the corrected values are often considerably different. The correlations are medium or low (Hunh, 1990b).  For several reasons for practical applications, S1(1) against S2(2) parameter can be preferred. This stability parameter, S1(1), is very easy to compute and allows a clear and relevant interpretation (mean absolute rank difference between the environments). Furthermore, an efficient test of significance is available (Huhn, 1990a). For each genotype, Z1(1) and Z2(2) values were calculated based on the ranks of the corrected data and summed over genotypes to obtain Z values (Table 4). It is seen that Z1(1) sum = 8.032 and Z2(2) sum = 7.564. Since both of these statistics were less than the critical value X2 0.05, 9 = 16.919, no significant differences in rank stability were found among the nine genotypes grown in eleven environments. On inspecting the individual Z values, it was found that no genotypes were significantly unstable relative to others, because they showed small Z values, compared with the critical value X2 0.01, 1 = 6.63. It was used that the significance level P = 0.01 corresponds to a comparison-wise error rate of about 0.05 (Lu, 1995).    Table 4: Estimation and test of nonparametric stability measures for 9 bread wheat genotypes across environments Genotype Mean yield (kg ha-1) Mean rank S1(1)¥ Z1(1) ¥ S2(2) ¥ Z2(2) ¥ 1 3626 4.818 3.381 0.783 7.963 0.429 2 3850 4.636 3.381 0.783 8.454 0.815 3 3565 5.090 2.290 2.017 4.090 1.692 4 3821 4.363 2.690 0.330 5.454 0.374 5 3657 5.000 3.018 0.013 7.200 0.072 6 3509 5.090 3.616 3.267 10.490 3.731 7 3437 5.363 3.054 0.037 6.854 0.009 8 3882 5.454 3.018 0.013 6.472 0.009 9 3902 5.181 3.381 0.783 7.963 0.429 Sum    8.032  7.564  Test statistics  E(S1(1)) E(S2(2)) Var(S1(1)) Var(S2(2)) X2 Z1, Z2§ X2 sum§  2.962 6.666 0.223 3.919 7.689 16.919 Grand mean = 3694 kg ha-1 ¥ S1(1) statistic measures the mean absolute rank difference of a genotype over environments, and S2(2) is the common variance of the ranks; the Z-statistics are measures of stability; § X2 Z1, Z2: chi-square for Z1(1), Z2(2); X2 sum: chi-square for sum of Z1(1), Z2(2)  Figures 1 and 2 represent plots portrayed by mean yield (kg ha-1) vs. S1(1) and S2(2) values. Mean S1(1) and S2(2) values and grand mean yield divide both figures into four sections; section 1 refers that genotypes have high yield and small S1(1) and S2(2) values, section 2 signs that genotypes posses high yield and large S1(1) and S2(2) values, section 3 presents that genotypes exist low yield and large S1(1) and S2(2) values, and section 4 exhibits that genotypes are of low yield and small S1(1) and S2(2) values. According to these configurations, genotypes interesting in section 1 can be considered as stable. Section 1, both figures, contains that genotypes 4 and 8 are most stable, and well adapted to all environments, that is, those have general adaptable ability. Genotypes 2 and 9 appear in section 2, where KAYA Y., TANER S. Journal of Central European Agriculture, Volume 4 (2002) No. 1 52describes genotypes with increasing sensitivity to environmental change, and greater specificity of adaptability to high-yielding environments. Section 3 referring poorly adapted genotypes to all environments captures genotypes 1 and 6 in figure 1, while genotypes 1, 5 and 6 in figure 2. Besides, Section 4 in figure 1 includes genotypes 3, 5 and 7 that response greater resistance to environmental fluctuation, and therefore increasing specificity of adaptability to low-yielding environments. However, genotypes 3 and 7 appear in the corresponding section of the figure 2, except that genotype 5 has exhibited tendency to section 4 in figure 1, while to section 3 in the figure 2. Nassar and Huhn (1987) suggest that S1(1) statistic measure should be utilized in any case that a genotype represents unfair fluctuations among sections, regarding S1(1) and S2(2) values.  Prior to selection, it is quite crucial to be aware of genotypes ranking in each environment and figures 1 and 2 provided by mean yield (kg ha-1) vs. S1(1) and mean yield (kg ha-1) vs. S2(2) values are of great accordance. To illustrate, genotypes 4 and 8 are most stable and well adapted across environments, as presented in Figure 1 and 2. Genotype 8 has the highest mean rank, while genotype 4 the lowest. Rather, genotype 8 with regard to genotype 4 may be selected on account of the fact that genotype 8 has revealed higher mean yield across environments than genotype 4.  Figure 1: Plot of S1(1) vs. mean yield (kg/ha) for 9 bread wheat genotypes over environments   Figure 2: Plot of S2(2) vs. mean yield (kg/ha) for 9 bread wheat genotypes over environments  ESTIMATING GENOTYPIC RANKS BY NONPARAMETRIC STABILITY ANALYSIS IN BREAD WHEAT (TRITICUM AESTIVUM L.) Journal of Central European Agriculture, Volume 4 (2002) No. 1 53 CONCLUSION Nonparametric measures for stability based on ranks provide a useful alternative to parametric measures currently used which are based on absolute data. Moreover, nonparametric vs. parametric stability statistics exist some advantages (see more details, Huhn, 1990b). As a consequence, for an estimation of the non parametric stability statistics of genotypes    grown in different environments, use of non parametric statistics S1(1) and S2(2) values, together with ranks, can be recommend to breeders and agronomists who make selection based upon genotype x environment interaction. In addition, plots provided by mean yield (kg ha-1) against S1(1) and mean yield (kg ha-1) against S2(2) values are likely to enhance visual efficiency of selection.   REFERENCES [1] Fox, P.N, B. Skovmand, B.K. Thompson, H.J. Braun and R. Cormier, 1990. Yield and adaptation of hexaploid spring triticale. Euphytica, 47: 57-64.  [2] Huhn, M., 1979. Beitrage zur Erfassung der phanotypischen stabilitat. I. Vorschlag einiger auf Ranginformationnen beruhenden stabilitatsparameter. EDV in Medizin und Biologie, 10: 112-117 (in German).  [3] Huhn, M., 1990a. Nonparametric measures of phenotypic stability: I. Theory. Euphytica, 47: 189-194.  [4] Huhn, M., 1990b. Nonparametric measures of phenotypic stability: II. Applications. Euphytica, 47: 195-201.    [5] Kang, M.S and H.N. Pham, 1991. Simultaneous selection for yielding and stable crop genotypes. Agronomy Journal, 83: 161-165. [6] Nassar, R and M. Huhn, 1987. Studies on estimation of phenotypic stability: Tests of significance for nonparametric measures of phenotypic stability. Biometrics, 43: 45-53.   [7] Lu, H.S., 1995. PC-SAS Program for estimating Huhn’s nonparametric stability statistics. Agronomy Journal, 87: 888-891.  [8] SAS Institute., 1996. SAS/STAT user’s guide, second edition. SAS Institute Inc., Cary. NC.     [9] Shukla, G.K., 1972. Some statistical aspects of partitioning genotype-environmental components of the variability. Heredity, 29: 237-245.               Yuksel Kaya, yuksel_k@yahoo.com, * correspondence author,  Seyfi Taner,  Bahri Dagdas International Agricultural Research Institute,  P.O. Box: 125, Konya, Turkey    KAYA Y., TANER S. Journal of Central European Agriculture, Volume 4 (2002) No. 1 54                     

ESTIMATING GENOTYPIC RANKS BY NONPARAMETRIC STABILITY ANALYSIS IN BREAD WHEAT (TRITICUM AESTIVUM L.)

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ESTIMATING GENOTYPIC RANKS BY NONPARAMETRIC STABILITY ANALYSIS IN BREAD WHEAT (TRITICUM AESTIVUM L.)

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