ASSOCIATION BETWEEN PARAMETRIC AND NONPARAMETRIC MEAURES OF PHENOTYPIC STABILITY IN RICE GENOTYPES Rice (Oryza sativa L Diyala Agricultural Sciences Journal, 2( 2 ) 20 – 33 ,2010 Kadhem et al. 20 ASSOCIATION BETWEEN PARAMETRIC AND NONPARAMETRIC MEAURES OF PHENOTYPIC STABILITY IN RICE GENOTYPES (Oryza sativa L.) Fawzi A. Kadhem* Ibrahim S. Al-Nedawi* Sabah D. Al-Atabe** Fadil Y. Baktash* * College of Agriculture ** Ministry of Agriculture ABSTRACT Evaluation of performance stability and high yield is essential for yield trials in different environments. The mostly used, classical parametric approaches for an analysis of genotype x environment interaction are based on several assumptions: normality of the distribution, homogeneity of variances, additively. If some of mentioned assumptions are not fulfilled, the validity of these methods may be questionable. By use of nonparametric methods, which are simple and easy for analysis, all of the mentioned assumptions are avoided. In this paper we used five of parametric and 11 of nonparametric techniques for analysis of genotype x environment interaction for grain yield of 7 rice (Oriza sativa L.) genotypes through three locations in two years (2005, and 2006). The objectives of this study were to study the interrelationship among various parametric and nonparametric phenotypic stability statistics, and to evaluate the similarity between these methods, and to determine the most suitable methods for assessing the rice genotypes yield stability. Values of the stability measures shown that genotypes with the highest grain yield in the majority of cases were not the most stable. The results of Spearman’s rank correlation indicates that the nonparametric measure Si (1), Si (2), Si (3), Si (6), NP(1), NP(2), NP(3), NP(4) and parametric measures bi, S 2 d, S 2 i, and Wi were positively related with each others and negatively correlated with mean yield and only the rank-sum and modified rank-sum showed a positive correlation with mean yield. The Principle Component Analysis (PCA) showed four distinct groups: group1 consist of Si (1), Si (2), Si (3) ,Si (6), NP(1), NP(2), NP(3),NP(4),bi,,S 2 d,S 2i, and Wi ; group 2 consist of RS, RS1, and RS2; group 3 consist of mean yield (Y); and group 4 consist of CV. ـــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــ Received for publication June 28 , 2010 . Accepted for publication August 10 , 2010 . Diyala Agricultural Sciences Journal, 2( 2 ) 20 – 33 ,2010 Kadhem et al. 21 In conclusion, the modified rank-sum methods (Rs 1, Rs 2) which use the nonparametric measures Si (1) and Si (2) with the rank mean yield of genotypes, seems to be useful under conditions where the basic assumptions of parametric stability are not met, and for simultaneously selection for high yield and stability. INTRODUCTION The success of crop improvement activities largely depends on the identification of superior varieties for mass production. A genotype can be considered superior if it has potential for high yield under favorable environment, and at the same time has a great deal of phenotypic stability. The genotype by environment interaction (GEI) is a major problem in the study of quantitative traits because it complicates the interpretation of genetic experiments and makes predictions difficult. It is a particular problem in plant breeding where genotypes have to be selected in one environment and used in another (Kearsey and Pooni, 1996; Giaufert et al, 2000; Farshadfar and Sutka, 2003). Genotype-by-environment interactions are important sources of variation in any crop, and the term stability is sometimes used to characterize a genotype, which shows a relatively constant yield, independent of changing environmental conditions. On the basis of this idea, genotypes with a minimal variance for yield across different environments are considered stable. This idea of stability may be considered as a biological or static concept of stability (Becker and Leon, 1988). This concept of stability is not acceptable to most breeders and agronomists, who prefer genotypes with high mean yields and the potential to respond to agronomic inputs or better environmental conditions (Becker, 1981). The high yield performance of released varieties is one of the most important targets of breeders; therefore, they prefer a dynamic concept of stability (Becker and Leon, 1988). There are two major approaches to studying GEI and determine the adaptation of genotypes (Huehn, 1996; Truberg and Huehn, 2000). First, is the parametric approach which based on statistical assumptions about distribution of genotype, environment, and GEI effects. Second, is the nonparametric or analytical clustering, which makes no specific assumptions when relating to environment and phenotypic relative to biotic and abiotic environmental factors. Parametric methods for estimating phenotypic stability are widely used in plant breeding and they were mostly related to the variance components and related statistics. Lin et al. (1986) identified three concepts of parametric Diyala Agricultural Sciences Journal, 2( 2 ) 20 – 33 ,2010 Kadhem et al. 22 stability. Type 1, A genotype is considered to be stable if its among- environment variance is small. Becker and Léon, (1988) called this stability a static, or a biological concept of stability. Parameters used to describe this type of stability are coefficient of variability (CVi) used by Francis and Kannenburg (1978) for each genotype and the genotypic variances across environments (Si2),and the coefficient of determination (r2). Type 2, a genotype is considered to be stable if its response to environments is parallel to the mean response of all genotypes in the trial. Becker and Léon, (1988) called this stability the dynamic or agronomic concept of stability. Parameters used to describe this type of stability are regression coefficient bi(Finlay and Wilkinon, 1963), Wricke’s (Wricke, 1962) ecovalence (Wi) and Shukla’s stability variance σ2i (Shukla, 1972). Type 3, A genotype is considered to be stable if the residual MS from the regression model on the environmental index is small. Type 3 is also part of the dynamic or agronomic stability concept according to Becker and Léon (1988). Parameters used to describe this type of stability are the methods of Eberhart and Russell (1966) and Perkins and Jinks (1968). These stability estimates have good properties under certain statistics assumptions, based on the normal distribution of error and GEI effects, but may not perform well if these assumptions are violated by factors such as the presence of outliers (Akura and Kaya, 2008). Nonparametric stability measures based on ranks provide a viable alternative to present parametric measures based on absolute data (Nassar and Huehn, 1987). For many applications, including selection in breeding programs, the rank order of genotypes are the most essential data. There is ample justification for the use of nonparametric measures of crop varieties in the assessment of yield stability. According to Huehn (1990) nonparametric procedures have the following advantages over parametric stability methods: (i) they reduce the bias cause of outliers, (ii) No assumptions are needed about the distribution of observed values. (iii)They are easy to use and interpret. (iv) Addition or deletion of one or more genotypes does not cause much variation in results. It is a known fact that the nonparametric are less powerful than their parametric counterpart. The studies conducting against this background by Raiger (1997) and Raiger and Prabhakaran (2000) have shown that when a number of genotypes is fairly large, the power efficiency of the nonparametric measures will be quite close to those of parametric measures. So in situation, which are commonly encountered, i.e. those involving good number of genotypes being performance tested in a set of environments whose number is neither too small nor too large, the risk of selecting inferior Diyala Agricultural Sciences Journal, 2( 2 ) 20 – 33 ,2010 Kadhem et al. 23 genotypes from use of nonparametric measures is minimal (Rao and Prabhakaran, 2000). There are an increasing number of stability measures for genotypes grown in different environments. It is therefore, useful to study the statistical relations between the parametric and nonparametric stability statistics to find the best and appropriate parameters for testing genotypes in breeding programs. One approach is to calculate the rank correlation coefficient (Spearman’s correlation) between different stability parameters on the basis of empirical data sets. Another approach is using the principle component analysis to study the relationship between stability statistics (Piepho and Lotito,1992). The objectives of this study were to study the interrelationship among various parametric and nonparametric phenotypic stability statistics, and to evaluate the similarity between these methods, and to determine the most suitable methods for assessing the rice genotypes yield stability. MATERIALS AND METHODS Yield performance of six rice genotypes from various genetics background (Table 1) were evaluated at three locations (Diyala, Kut, and Najaf Governorate) in mid-region of Iraq during the 2005 and 2006 growing seasons. For both growing seasons the sowing dates were 15th of June for all locations. Table 1. Names and code names of 7 rice genotypes grown in 6 environments. Code Genotype Code Genotype Code Genotype G1 Anbar 33 G4 CNTLR G7 Mishkhab-1 G2 Yasamin G5 A nbar hybrid G3 Sumood G6 Program-4 Experimental layout was a randomized complete block design with four replicates in each location. The experimental unit was 5 x 5 m, and seeding rate was 120 kg ha-1. Fertilizers applications was 100 kg ha-1of triple super phosphate (46% P2O5)and 340 kg ha-1Urea (46% N) added as followed; 100 kg/ha at seedling stage, 140 kg/ha at tillering stage, and 100 kg ha-1at flowering stage (Jadoa, 1999). Harvesting was done to 2 x 2 m from each plot, and grain yield was obtained by converting plot yield (at 14% moisture content) to seed yield per hectare. Diyala Agricultural Sciences Journal, 2( 2 ) 20 – 33 ,2010 Kadhem et al. 24 Statistical analysis The statistical procedures adopted for the stability analysis of the genotypes were listed in Table (2). Table 2. The parametric and nonparametric statistical procedures that used in this study and their references. ________________________________________________________________________ Parametric statistics Symbol Reference Coffiecient of Variation CV Francis and kannenberg (1978) Regression coefficient bi Finlay and Wilkinson (1963) Deviationfrom regression S2d Eberhart and Russel (1966) Ecovaliance Wi Wricke (1962) Genotypic variance S2i Shukla (1972) Nonparametric statistics Rank-sum Rs Kang (1988) Huehn and Nassar Si (1), Si (2), Si (3), Si (6) Huehn (1979), Nassar and Huehn (1987) Thennarasu NP NP(1), NP(2), NP(3), NP(4) Thennarasu (1995) Modified rank-sum Rs 1, Rs 2 Yue at el.(1997)¥ ¥ The modified rank-sum method of which both yield (in rank) and first two Huehn (1979) nonparametric stability statistics (in rank) are combined. The stability statistics were compared using spearman’s rank correlation coefficient. Spearman’s rank correlation coefficient, as calculated from the rank of parametric and nonparametric stability statistics results in measuring the linear relationship between these methods. Principle component analysis (PCA) method was used for stratifying phenotypic stability methods and genotypes. The combined experimental yield data were statistically analyzed using Genstat version 12 (2009), for plots and correlation matrix Minitab V.15 were used. MS-EXCEL (2003) used to calculate parametric and nonparametric stability measures with spreadsheet formula commands. RESULTS AND DISCUSSION The results of eleven different nonparametric stability statistics and genotypes mean yield and their ranks are presented in Table 3 and 4 respectively; and the results of the parametric statistics and their ranks are presented in Table 5 and 6 respectively. Evaluation of the genotypes based on the 11 different nonparametric measurements and genotype mean yield and the significant test statistics of Si(1) and Si(2) are discussed in details in the previous paper (Kadhem et al, 2010). Evaluation of the genotypes based on the different parametric measurements and genotype mean yield are discussed in Atabe (2008). Diyala Agricultural Sciences Journal, 2( 2 ) 20 – 33 ,2010 Kadhem et al. 25 The results of the Spearman’s rank correlations between mean yield and each pair of parametric and nonparametric stability methods are presented in Table 7. Correlation between mean yield and rank-sum and modified rank- sum were modernity positive, but it was negatively correlated with the other stability parameters. The non-significant and negative correlation between yield and stability parameters suggest that, stability parameters provide information that can not be gleaned from average yield alone (29). The nonparametric stability parameters Si (1), Si (2), Si (3), Si (6) and NP(1), NP(2), NP(3), NP(4) were highly positively correlated between others and with the parametric parameters bi, S 2 d, S 2 i, and Wi , and negatively with CV and moderate non-significant positive correlation with the ranks-sum method. Scapim et al (30) and Kang and Pham (31), Mut et al, (32) also reported significant positive correlation between Si (1), Si (2), Si (3), Si (6), NP(2), and NP(4). That would suggest the possibility of using only one of them to select stable genotypes in breeding programs. Table 3. Mean yield values (Y) and nonparametric stability parameters for grain yield of 7 rice genotypes evaluated in 6 environments. Genotyp‡ Y◊ Si (1)© Si (2)© Si (3)© Si (6)© NP(1)¥ NP(2) ¥ NP(3) ¥ NP(4) ¥ RS § RS 1 † RS 2† G1 4.08 1.8 2.17 3.97 2.83 1.17 0.47 0.54 0.48 4 6 6 G2 4.55 3.33 7.9 11.2 11.2 2.17 2.17 1.92 0.82 7 7 7 G3 3.7 2.47 4.57 7.03 5.48 1.5 0.3 0.43 0.47 9 9 9 G4 3.83 1.33 1.87 3.2 2.15 0.67 0.17 0.30 0.30 6 5 6 G5 4.15 3.47 8.8 12.2 11 2.67 1.07 0.96 1.09 9 9 9 G6 3.63 1.4 1.5 2.9 1.67 0.83 0.14 0.20 0.29 9 8 7 G7 2.48 2.93 5.87 8.8 8 2 0.29 0.32 1.12 12 12 12 ◊ Mean grain yield (t. ha-1), © Huehn (1990) parameters, ¥ Thennarasu (1995) parameters ‡ Genotype codes (see Table 1). § RS is the rank-sum of Kang (1988). † RS 1and RS 2 the modified rank-sum (Yue et al, 1997) Table 4. Ranks of mean yield and nonparametric stability parameters for grain yield of 7 rice genotypes evaluated in 6 environments. Genotype Y Si (1) Si (2) Si (3) Si (6) NP(1) NP(2) NP(3) NP(4) RS RS 1 RS 2 G1 3 3 3 3 3 3 5 5 4 1 2 2 G2 1 6 6 6 7 6 7 7 5 3 3 4 G3 5 4 4 4 4 4 4 4 3 6 6 6 G4 4 1 2 2 2 1 2 2 2 2 1 2 G5 2 7 7 7 6 7 6 6 6 6 6 6 G6 6 2 1 1 1 2 1 1 1 6 4 4 G7 7 5 5 5 5 5 3 3 7 7 7 7 Diyala Agricultural Sciences Journal, 2( 2 ) 20 – 33 ,2010 Kadhem et al. 26 Table 5. Mean yield values (Y) and parametric stability parameters for grain yield of 7 rice genotypes evaluated in 6 environments. Genotyp‡ Y◊ CV bi S 2 d Wi Si 2 G1 4.08 11.17 1.05 0.077 0.08 0.01 G2 4.55 14.93 1.56 0.215 0.49 0.24 G3 3.7 14.62 1.30 0.058 0.14 0.07 G4 3.83 12.93 1.17 0.036 0.06 0.03 G5 4.15 6.30 0.32 0.255 0.66 0.33 G6 3.63 13.42 1.13 0.071 0.09 0.04 G7 2.48 10.64 0.47 0.158 0.41 0.20 Table 6. Ranks of mean yield and parametric stability parameters for grain yield of 7 rice genotypes evaluated in 6 environments. Genotyp‡ Y◊ CV bi S 2 d Wi Si 2 G1 3 3 1 4 1 1 G2 1 7 6 6 6 6 G3 5 6 4 2 4 4 G4 4 4 3 1 2 2 G5 2 1 7 7 7 7 G6 6 5 2 3 3 3 G7 7 2 5 5 5 5 To understand the relationships among the rank-based statistics, principal component analysis (PCA) was performed on the rank correlation matrix (Table 7). PCA is a multivariate statistical technique which can be used for simplification and dimensionality reduction in a data set by retaining those characteristics that contribute most to its variation (Rao, 1964). In this regard lower-order principal components are retained and higher order ones are ignored. The results indicate that the loadings of the first two PCAs which explained 86.6% (65.5% and 21.1% by PCA1 and PCA2 respectively) of the variation of original variables. PCA1 is primarily stability and PCA2 is mostly yield. The relationships among the different parametric and nonparametric stability statistics are graphically displayed in a biplot of PCA1 versus PCA2 in Fig. 1, where both axes were considered simultaneously. Four groups in Fig. 1 can be defined as; Group 1: Si (1), Si (2), Si (3) ,Si (6), NP(1), NP(2), NP(3),NP(4),bi,,S 2 d,S 2i, and Wi Group 2: RS, RS 1 and RS 2 Group 3 Mean yield: (Y) Group 4 :, CV Diyala Agricultural Sciences Journal, 2( 2 ) 20 – 33 ,2010 Kadhem et al. 27 Group 1 that included the nonparametric statistics Si (1), Si (2), Si (3) ,Si (6), NP(1), NP(2), NP(3), and NP(4), in addition to the parametric statistics bi, S 2 d, S 2i, and Wi. These measures were positively linearly correlated with each other and with RS, RS 1 and RS 2 but a negative correlated with mean yield (Table 7). Group 1 statistics provide a measure of stability in the static sense. Static stability is analogous to the biological concept of homeostasis: a stable genotype tend to maintain a constant yield across environments (Becker and Leon, 1988; Lin et al, 1986). Since a genotype showing a constant performance in all environments does not necessarily respond to improved growing conditions with increased yield. Nassar and Huehn (1987) reported that their test statistics Si (1), Si (2), Si (3) ,and Si (6)) associate with static (biological) concept of stability. Therefore, group1 stability parameters represent a static concept of stability and could be used as compromise methods that select genotypes with moderate yield and high stability. Therefore, stable genotypes according to these methods are adapted for those regions where growing conditions are unfavorable. Group 2, which contains rank-sum RS and modified RS 1 and RS 2 were found to be positively and significantly correlated (p<0.01) to each other while moderately positively correlated with mean yield (Table7). This group consists of statistics that were influenced simultaneously by both mean yield and stability. Yue et al., (1996) and Yue et al., (1997) have reported that the rank-sum is related to high yield performance. Therefore rank-sum stability statistics RS 1 and RS 2 are related to dynamic concept of stability. Becker and Leon (1988) suggested that a dynamic concept of stability does not require the genotypic response to environmental conditions to be equal for all genotypes. Group 3 contains only the mean yield. Group 4 contain only the coefficient of variation (CV) which showed no or a week negative correlation with all measures. This stability measure considered as static sense of stability, and that agreed with the classification of Lin et al (1986) of parametric stability measures. The nonparametric stability methods Si (1), Si (2), Si (3) ,Si (6), NP(1), NP(2), NP(3), and NP(4) were positively and significantly correlated , indicating that these statistics were similar under different environmental conditions (Table 7). As a result, only one of these statistics would be sufficient to select stable genotypes in a breeding program. Scapim et al., (2000) found significantly positive correlations between the nonparametric measures, and negative correlation with yield in maize. Flores et al., (1998) also reported high positive rank correlation between the nonparametric in fababean and peas. Piepho and Lotito (1992) have reported that generally, the results for the large data sets are more constant than Diyala Agricultural Sciences Journal, 2( 2 ) 20 – 33 ,2010 Kadhem et al. for the small data sets and they found strong positive linear relation between nonparametric in sugar beet. In conclusion, several of parametric and nonparametric statistics that have been employed in this study quantified stability of genotypes with respect to yield, stability, and both of them. The results obtained indicates that the rank-sum and modified rank sum were the best stability measures that the plant breeder should considered because both yield and stability were considered simultaneously to exploit the useful effect of GEI and to make selection of the genotypes more precise and refined. Similar conclusions were drawn from Elsahooki (1996) who stated that either to do the analysis into two steps; first to identify high yielding genotypes through ANOVA then second, apply the appropriate, stability measure. Mohammadi and Amri (2008) ; Segherloo et al. (2008) stated that the stability values do not provide enough information for reaching definitive conclusion, and both stability and yield should be considered simultaneously. Figure 3. Principle component analysis (PCA1 and PCA2) plot of the rank of stability yield as estimated parametric and nonparametric measures based on yield data from 7 rice genotypes grown in 6 environments showing the interrelationship between these parameters. 28 Diyala Agricultural Sciences Journal, 2( 2 ) 20 – 33 ,2010 Kadhem et al. Table 7. Spearman’s correlation coefficient between ranks of parametric and nonparametric stability measures for grain yield of rice genotypes. Y Si(1) Si(2) Si(3) Si(6) NP(1) NP(2) NP(3) NP(4) RS RS1 RS2 CV bi S2d Wi Si(1) -0.429 Si(2) -0.500 0.964 Si(3) -0.500 0.964 1.000 Si(6) -0.536 0.929 0.964 0.964 NP(1) -0.429 1.000 0.964 0.964 0.929 NP(2) -0.821 0.786 0.821 0.821 0.857 0.786 NP(3) -0.821 0.786 0.821 0.821 0.857 0.786 1.000 NP(4) -0.179 0.821 0.857 0.857 0.821 0.821 0.607 0.607 RS 0.553 0.391 0.260 0.260 0.163 0.391 -0.228 -0.228 0.260 RS1 0.408 0.612 0.510 0.510 0.408 0.612 0.068 0.068 0.544 0.917 RS2 0.349 0.660 0.582 0.582 0.504 0.660 0.116 0.116 0.582 0.909 0.982 CV -0.143 -0.214 -0.250 -0.250 -0.036 -0.214 0.036 0.036 -0.500 -0.195 -0.306 -0.233 bi -0.357 0.857 0.893 0.893 0.857 0.857 0.571 0.571 0.679 0.456 0.544 0.660 -0.143 S2d -0.464 0.893 0.821 0.821 0.786 0.893 0.714 0.714 0.786 0.228 0.408 0.427 -0.357 0.679 Wi -0.286 0.893 0.857 0.857 0.821 0.893 0.536 0.536 0.643 0.586 0.646 0.737 -0.107 0.964 0.750 Si2 -0.286 0.893 0.857 0.857 0.821 0.893 0.536 0.536 0.643 0.586 0.646 0.737 -0.107 0.964 0.750 1.00 29 Diyala Agricultural Sciences Journal, 2( 2 ) 20 – 33 ,2010 Kadhem et al. 30 REFERENCES مج الوطني لتطوير زراعة الرز في المنطقة البرنا. نصائح في زراعة الرز وارشادات. 1999. جدوع ، خضير عباس .6نشرة رقم . وزارة الزراعة . الشلبية Akura, M. and Y. 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Plant Breeding. 116: 271-275. العالقة بين معاييرالثبات المظهري المعيارية وغير المعيارية لتراكيب وراثية من الرز (Oryza sativa L.) * فاضل يونس بكتاش** صباح درع العتابي* ابراهيم سعيد النداوي* فوزي عبد الحسين كاظم جامعة بغداد /آلية الزراعة* اعة وزارة الزر** خالصةال من الضروري في تجارب مقارنة التراكيب الوراثية في بيئات مختلفة التركيز على مبدأين البيئي هي -وان اكثرالطرق شيوعا في دراسة التداخل الوراثي. اساسين هما االنتاجية و الثباتية ية متعددة منها التوزيع الطرق المعلمية في قياس الثباتية واتي تعتمد على فرضيات احصائ واذا لم يتم تحقيق هذه الفرضيات اعاله . الطبيعي ، والتأثير االضافي ، وتجانس تباينات البيئات في البيانات المأخوذة من التجارب المقامة في مواقع وسنوات مختلفة او اإلخالل بإحداها فان نتائج لذا فان استعمال الطرق غير . ة للجدلمعاييرالثبات المعلمية المستحصلة تكون غيردقيقة ومثير المعلمية هي الوسيلة االكثر ضمانا لعدم اعتمادها على اية فرضية احصائية باالضافة الى سهولة طريقة غير معلمية لقياس الثباتية 11 طرق معلمية و 5في هذه الدراسة استخدمت . حسابها بذور لسبعة تراكيب وراثية من الرز البيئي لمعدل حاصل ال-لدراسة وتحليل التداخل الوراثي وتهدف الدراسة الى ). 2006 و 2005(مزروعة في ثالث محافظات وسط العراق ولمدة سنتين دراسة العالقة بين مختلف معايير الثبات المظهري المعلمية والغير معلمية واوجه الشبه . تحقق اهداف مربي النباتواالختالف بينهما وعالقتهما بالحاصل وإختيار الطريقة األمثل التي ان قيم الثبات المظهري المستحصلة من الطرق المعلمية وغير المعلمية تؤكد بان التراكيب الوراثية ذات المتوسط االنتاج العالي ليست بالضرورة ان تكون ثابتة الحاصل عند تغير البيئة في تشير الى وجود ) سبيرمان(وان النتيجة المستحصلة من معامل االرتباط للرتب . معظم الحاالت Siعالقة موجبة بين معايير الثبات الغير معلمية (1), Si (2), Si (3), Si (6), NP(1), NP(2), NP(3), NP(4) وبين معايير الثبات المعلميةbi, S 2 d, S 2 i, و Wi وعالقة سالبة بين المعايير اعاله قة موجبة بين معاير الثبات وكذلك فان النتائج تشير فقط الى وجود عال. ومتوسط الحاصل RS, RSالمظهري 1, RS وان نتائج تحليل المكون االساسي تشير الى وجود . ومتوسط الحاصل2 Diyala Agricultural Sciences Journal, 2( 2 ) 20 – 33 ,2010 Kadhem et al. 33 Siتضم المجموعة االولى المعايير . اربعة مجاميع اساسية مميزة (1), Si (2), Si (3), Si (6), NP(1), NP(2), NP(3), NP(4) و bi, S 2 d, S 2 i, و Wi ،الثانية وتضم المجموعة RS, RS 1, RS ، وتضم 2 وتوصي . CVالمجموعة الثالثة متوسط الحاصل، وتضم المجموعة الرابعة معامل االختالف RS هذه الدراسة الى استعمال طريقة قياس الثبات المظهري الغير معلمية 1, RS والتي تستعمل 2 Siجمع رتب المقياس غير المعياري (1), Si صل في حالة عدم التحقق مع رتب متوسط الحا (2) من فرضيات الطرق المعلمية ولتحقيق رغبات مربي النبات بانتخاب التراكيب الوراثية التي تحقق .االنتاجية العالية مع ثباتية واستقرار في االنتاج في مختلف البيئات