Correlation Among Some Statistical Measures of Phenotypic Stability in Maize (Zea mays L.)

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1 International Journal of Applied Agricultural Research ISSN Volume 5 Number 4 (2010) pp Research India Publications Correlation Among Some Statistical Measures of Phenotypic Stability in Maize (Zea mays L.) Solomon Admassu 1, Mandefro Nigussie 2 and Habtamu Zelleke 3 1 Ethiopian Institute of Agricultural Research, Awassa National Maize Research Project, Awassa, Ethiopia 2 Ethiopian Institute of Agricultural Research, Melkasa Research Center, Nazareth, Ethiopia 3 Faculty of Agriculture, Department of Plant Science, University of Haramaya, Dire Dawa, Ethiopia Abstract Fifteen maize genotypes were tested at nine different locations in 2005 main cropping season under rain fed condition to determine stability of grain yield in maize genotypes, to determine the level of association among the stability parameters, and evaluate the type and extent of interrelationship of agronomic traits to yield. The experiment was conducted using randomized complete block design with three replications. Standard analysis of variance test showed the main effect for genotype, location and genotype x location were highly significant (p<0.01) for grain yield. Several methods of statistical analysis were conducted to determine yield stability and the level of association among the parameters was assessed using Spearman's rank correlation. Shukla's stability variance with environmental covariance (S i 2 ), mean square for deviation from regression and Additive Main Effects and Multiplicative Interaction (AMMI) Stability Value (ASV) were equivalent to rank genotypes for their stability. Shukla's stability variance with no environmental covariance (δ 2 i) was also equivalent to Wricke s ecovalence (W i ). The direct contribution of lodging per cent to grain yield was highest (5.456) followed by plant height (1.261), stand count at harvest (0.446), number of cobs harvested (0.176) and number of cobs diseased (0.067). Keywords: Maize, Stability, Zea mays, Path coefficient. Introduction Genotype x environment interaction (GEI) and its effect on the predictability of future

2 550 Solomon Admassu et al genotype performance is the essence of the concept of trait stability. The presence of large GEI effects in a trial decreases the predictive value of a main effect model. The main difficulty for breeder is in demonstrating the superiority of any cultivar (Comstock and Moll, 1963). Eberhart and Russell (1966) stated that while stratification of environments has been used effectively to reduce G x E interactions, it was better to select stable genotypes that interact less with the environments in which they are grown. Statistical methods for determining stability and adaptation of crop cultivars in diverse environments (locations and years) are usually used to assist plant breeders in selecting superior genotypes. There are several concepts of stability and statistics that can be used to estimate the stability of genotype performance. The concepts, formulas, and relationships among many stability statistics have been well reviewed (Lin et al., 1986; Becker and Leon, 1988). One of the earliest stability methods was developed by Plaisted and Peterson (1959) who estimated a mean variance component for pair wise G x E interaction. Wricke (1962) proposed the concept of ecovalence, which is the contribution of a genotype to the G x E sum of squares; the G x E interaction for a genotype, squared and summed across all environments, is the stability measure for that genotype. Shukla (1972) proposed a stability statistic, which is based on the partitioning of the GEI sum of squares into components attributable to individual genotypes. Other methods of assessing stability use regression. Joint regression was first used by Yates and Cochran (1938) who suggested that the environment in which a trial is conducted can be described by the average performance of the genotypes in the particular trial. In 1963, Finlay and Wilkinson used joint regression of mean individual yield on a logarithmic scale to improve linearity on the mean yield of all cultivars for each environment. Eberhart and Russell (1966) proposed regression of mean yield on an environmental index. They also summed the squared deviations from regression to obtain another estimate of stability. Although regression is widely applied, the fact that the mean of all the cultivars in each environment is taken as a measure of the environmental index and is used as an independent variable in the regression may be considered a serious limitation to this procedure because there cannot be independence among the variables, especially when the number of cultivars is less than 15 (Becker and Leon, 1988; Crossa, 1990). Furthermore, the variation of the estimates of the regression coefficient is usually so small that classification of the genotypes for stability and adaptability is difficult (Scapim et al., 2000). Francis and Kannenberg (1978) noted the biological and statistical limitations of the Eberhart and Russell (1966) regression method, and the cumbersome nature of the intermediate calculations required by Shukla s (1972) stability variance approach, and proposed the genotype grouping technique as an easier method of measuring stability. Their technique classifies genotypes on the basis of mean yields and coefficients of variation across environments. Several workers (Kang and Miller, 1984; Pham and Kange, 1988; Negeve and Bouwkamp, 1993; Scapim et al., 2000 and Alberts, 2004) have compared methods of stability analysis to study the usefulness of the different procedures for their specific data.

3 Correlation among Some Statistical Measures of Phenotypic Stability in Maize 551 The level of association among the adaptability or stability estimates of different models is indicative of whether one or more estimates should be obtained for reliable predictions of cultivar behavior, and also helps the breeder to choose the best adjusted and most informative stability parameter(s) to fit his concept of stability (Duarte and Zimmermann, 1995). Maize has been shown to be very sensitive to environmental variation. In spite of the presence of large G x E interactions in maize, most of the stability methods mentioned above have not been well studied with this crop in Ethiopia. The objectives of our study were to determine stability of grain yield in maize genotypes, to determine the level of association among the stability parameters, and evaluate the type and extent of interrelationship of agronomic traits to yield. Materials and Methods Fifteen maize genotypes were evaluated at nine locations in 2005 main cropping season under rainfed condition. Randomized complete block design (RCBD) with three replications was used. Each plot had four rows of 5.1 meter length with spacing of 75 cm between rows and 30 cm between plants. Two seeds were planted per hill and then thinned to one plant per hill to have a final plant density of about 44,444 plants per hectare. To reduce border effects, data were recorded from the two central rows of each plot. Other management practices were done as recommended for each location. Fifteen maize genotypes of diverse origin were included in the study. The genotypes include top crosses, single crosses, three-way crosses, and synthetics (Table 1). Table 1: Description of maize genotypes used for the study. Genotype Status Source Texture Type Year of release BH-541 TWC BNMR D Normal 2002 BH-660 TWC BNMR F Normal 1993 BH-670 TWC BNMR SD Normal 2002 BHQP-542 TWC BNMR D QPM 2002 BHQP-543 TWC BNMR D QPM On pipeline FH x F-7215 x b TWC BNMR D Normal On pipeline BH-544 TWC BNMR D Normal On pipeline BH-540 SC BNMR D Normal 1995 Ambo synthetic-1 Syn AMR SF Normal 2005 Ambo synthetic-5 Syn AMR SF Normal On pipeline AMH-800 TC AMR SF Normal 2005 SC-715 TWC Syngenta D Normal 2005 PHB-3253 TWC Pioneer D Normal H83 TWC Pioneer D Normal 2001 ESE-203 SC ESE D Normal 2005 SF = semi flint; D = dent; SD = semi-dent; F = flint; TWC = three-way cross; SC = single cross hybrid; TC = top cross; Syn = synthetic; ESE = Ethiopian Seed Enterprise; QPM = quality protein maize; BNMR = Bako National Maize Research and AMR = Ambo Maize Research.

4 552 Solomon Admassu et al The locations where the experiment was conducted were different in soil type, altitude and mean annual rainfall and considered as individual environment (Table 2). Analysis of variance for each environment was done for grain yield and other traits, using the SAS computer program (SAS Ins., 2001). Bartlett s test was used to assess homogeneity of error variances prior to combine analysis over environments. Mixed model was used which partitions the total variance into its component parts. Environments were considered as random factors while the effect of genotypes was regarded as fixed. Table 2: Description of the test locations. Mean of 10 years. Location Altitude Rainfall Soil type Alemaya Fluvisol Areka Nitosol Arsi-Negele Andosol Awada Nitosol Awassa Andosol Bako Nitosol Goffa Acrisol Hirna Fluvisol Jinka Nitosol The statistical procedures proposed by Eberhart and Russell (1966), Wricke (1962), Shukla (1972), Francis and Kannenberg (1978), Lin and Binns (1988) and Purchase (1997) were used for estimating stability parameters of genotypes and were analyzed using Agrobase TM Since AMMI model does not make provision for a quantitative stability measure, AMMI stability value (ASV) (Purchase, 1997) measure is essential in order to quantify and rank genotypes according to their yield stability: AMMI Stability Value (ASV) = IPCA sumofsquares ( IPCA1score) IPCA2sumofsquares + [IPCA2score] Since the Interaction Principal Component Axis 1 (IPCA1) score contributes more to G x E sum of squares, it has to be weighted by the proportional difference between IPCA1 and IPCA2 scores to compensate for the relative contribution of IPCA1 and IPCA2 to total G x E sum of squares. To statistically compare between the above stability analysis procedures, Spearman s coefficient of rank correlation (r s ) (Steel and Torrie, 1980) was calculated instead of ordinary coefficients of correlation, for the stability parameters cannot be assumed to be normally distributed. After the test of homogeneity of error variance-covariance matrix of the maize genotypes, the calculations related to path analysis were done with three blocks and genotypes. The path coefficient analysis was performed using phenotypic correlations to assess direct

5 Correlation among Some Statistical Measures of Phenotypic Stability in Maize 553 and indirect effect of yield components on grain yield following the method of Dewey and Lu (1959). Results and Discussion The AMMI analysis of variance for grain yield of 15 genotypes at nine locations indicated the presence of G x E interactions (Table 3). The presence of significant G x E interaction showed the inconsistency in performance of maize genotypes across environments and indicated the need to develop cultivars that are adapted to specific environmental conditions and the need to identify cultivars that are exceptional in their stability across environments. Table 3: Additive main effects and multiplicative interaction analysis of variance for grain yield of genotypes across environments. Source df SS MS % Explained Environments ** Reps within Env NS 4.40 Genotype ** Genotype x Env ** IPCA ** IPCA ** IPCA NS 8.34 IPCA NS 7.99 IPCA NS 4.81 IPCA NS 4.02 IPCA NS 2.86 IPCA NS 1.72 Residual NS, ** non-significant and significant at P 0.01 level, respectively. Grand mean = 7.25 t ha -1 R-squared = C.V. = 13.5% The statistics varied considerably in their ability to classify cultivars as stable or unstable (Table 4). These differences may be caused by variation in the precision of the estimation and/or the significance tests associated with different statistics (Sneller et al., 1997). The testes of stability were performed primarily to determine whether there was genetic variation among the cultivars in the data sets for stability as estimated by the different statistics. The results indicate that all statistics could detect differences for yield stability among maize genotypes. The mean CV analysis introduced by Francis and Kannenberg (1978) was designed to aid in studies on the physiological basis of yield stability. High yield and small variation group of genotypes appear the most desirable using this approach. Based on this definition, 30H83, BH-544, and ESE-203 fall into the high yield and low variation group and can be considered the most stable (Table 4). Genotypes BHQP-542, PHB-3253, and BHQP-543 were the most undesirable ones having high CV and low yield.

6 554 Solomon Admassu et al Table 4: Mean yield (t ha -1 ), estimates of stability parameters and their ranking order for 15 genotypes evaluated at nine locations. Genotype Mean CV δ 2 i, W i P i S 2 di b i ASV yield BH I 169.1** ** ** BH I 204.4** ** * ** BH I 103.5** 768.5** * 46.39* BHQP IV 115.6** 852.5** ** 83.74** BHQP IV ** FH X F-7215 X144-7-b 8.26 I 109.4** 816.9** ** BH II 84.4* 636.3* * 51.74* BH III ** Ambo Synth III ** Ambo Synth III 82.3* 621.2* ** 38.18* AMH III ** SC III 208.2** ** ** ** PHB IV 134.9** 986.9** ** ** H II ESE II 81.8* 618.1* ** 48.93* *, ** significant at P 0.05 and 0.01, respectively. CV = Francis and Kannenberg's (1978) coefficient of variability (I = high yield and high CV; II = high yield and low CV; III = low yield and low CV and IV = low yield and high CV); P i = Lin and Binns's (1988) cultivar performance measure; S 2 i = Shukla's (1972) stability variance with environmental covariance; δ 2 i = Shukla's (1972) stability variance with no covariance; W i = Wricke's (1962) ecovalence; S 2 di = Eberhart and Russells' (1966) deviation from regression parameters; b i = coefficient of regression and ASV = Purchase s (1997) AMMI Stability Value Cultivar performance measure (P i ) of Lin and Binns (1988) characterizes the genotypes by associating stability and productivity, and defines a superior genotype with a performance near the maximum in various environments. This implies that a stable genotype is one that performs in tandem with the environment. From this analysis, the most stable genotypes that ranked first for P i and for mean yield was 30H83 followed by FH x F-7215 x b which ranked 2 nd for P i and 3 rd for mean yield and BH-541 which was 3 rd for P i and 2 nd for mean yield (Table 4). This procedure appears to be considerably more of a genotype performance measure, rather than a stability measure over sites. Stable genotypes based on other procedures, BH- 540, BHQP-543, AMH-800 and Ambo Synth-1 were unstable based on this procedure because they were low yielders (Table 4). Genotypes with low Wricke s ecovalence (W i ) have smaller fluctuations across environments and therefore are stable. The most stable genotypes according to the ecovalence method of Wricke (1962) were 30H83, Ambo Synth-1, BH-540, BHQP- 543 and AMH-800. The most stable genotypes as indicated by Shukla s stability parameter were 30H83, Ambo Synth-1, BH-540, AMH-800 and BHQP-543 (Table 4).

7 Correlation among Some Statistical Measures of Phenotypic Stability in Maize 555 The hybrids with poor stability according this procedure were SC-715, BH-660 and BH-541. Based on these stability parameter, a genotype is said to be stable when its contribution to G x E is small. Eberhart and Russell s joint regression analysis model divides the genotype x environment interaction into linear and nonlinear. These estimates of the linear and non-linear parameters provide an adequate account of the dynamic response of genotypes to changing environment and are used with mean performance to assess the potentialities of different genotypes (Kenga et al., 2003). All genotypes showed non-significant difference from unity (b i = 1). However, all genotypes except 30H83, AMH-800, BH-540, Ambo Synth-1 and BHQP-543 showed significant deviation from regression value from 0 (Table 4) and could be considered unstable. However, Eberhart and Russell (1966) described a desirable genotype as one with high mean yield, b i = 1.0 and S 2 di = 0. Considering this definition 30H83 can be considered the most desirable among 15 genotypes. This hybrid has high determination coefficient (r 2 = 91.21) and its response is highly predictable. It could be considered an ideal genotype, since it maintained good performance with stability. According to ASV ranking, BHQP-543, BH-540, Ambo Synth-1, AMH-800 and 30H83 were the most stable genotypes (Table 4). The most unstable were SC-715, PHB-3253, BH-544 and BH-660. According to the Eberhart and Russell s mean square for deviation from regression, Shukla s stability variance, Wricke ecovalence and ASV, the most stable genotypes were 30H83, AMH-800, Ambo synthetic-1, BH-540 and BHQP-543 (Table 4). Rank Correlation of Stability Parameters Spearman s coefficient of rank correlation (Steel and Torrie, 1980) was then determined for each of the possible pair wise comparisons of the ranks of the different stability statistics (Table 5). Lin and Binns s (P i ) method showed the greatest deviation from all other methods, having negative rank correlation coefficients compared to all other procedures (Table 5). It was negatively and significantly correlated with mean yield and b i. This indicates high yielding genotypes tended to have lower P i value, which is in harmony with the findings by Lin and Binns (1988), Scapim et al. (2000) and Alberts (2004). Regression coefficient was positively and significantly rank correlated with mean yield and CV. This parameter also showed a great deviation from other measures in assessing yield stability. The Eberhart and Russell s mean square for deviation from regression (S 2 di) procedure showed highly significant correspondence (P 0.01) with the procedures of CV (0.73 ** ), δ 2 i (0.97 ** ), S 2 i (1.00**), W i (0.97 ** ), and ASV (0.91 ** ). A high rank correlation among S 2 di, δ 2 i and S 2 i will be expected when the data do not fit the linear model, or the data fit the linear model but b i about the same i.e homogenous (Table 4). The ranking order of δ 2 i and S 2 i will be primarily determined by S di 2 (Becker, 1981). If the linear fit is good and all b i are heterogeneous, then the correlation between δ 2 i and S 2 i with S 2 di will be low. Shukla s (1972) stability variance with environmental covariance (S 2 i) was highly rank correlated with δ 2 i (0.97**), ASV (1.00**), W i (0.97**), and CV (0.61*). Rank

8 556 Solomon Admassu et al correlation of 1.00 was noted with S 2 di and ASV (Table 5). Lin et al. (1986) did not consider S 2 i; however, the S 2 i and S 2 di are equivalent (Pham and Kang, 1988). Therefore, their rank correlation will be expected to be high regardless of the covariate used to obtain S 2 i. Table 5: The Spearman's rank correlation for all estimates of stability parameters. Yield P i CV b i S 2 di W i S 2 i δ 2 i, ASV Yield P i ** CV b i 0.58 * * 0.57 * S 2 di ** 0.29 W i ** ** 2 S i * ** 0.97 ** δ 2 i, * ** 1.00 ** 0.97 ** ASV * ** 0.84 ** 1.00 ** 0.84 ** *, ** = Significant at P 0.05 and 0.01, respectively. P i = Lin and Binns's (1988) cultivar performance measure; CV = Francis and Kannenberg's (1978) Coefficient of variability; b i = coefficient of regression, S 2 di = Eberhart and Russells' (1966) deviation from regression; W i = Wricke's (1962) ecovalence; S 2 i = Shukla's stability variance with environmental covariance, δ 2 i = Shukla's (1972) stability variance with no covariance; ASV = Purchase s (1997) AMMI Stability Value. The Wricke s procedure of stability statistics showed the highest positive significant correlation (P 0.01) with δ 2 i (1.00 ** ), S 2 i (r = 0.97 ** ), CV (0.65 ** ) and ASV (0.84 ** ). A rank correlation coefficient of 1.00 was found between Shukla s (δ 2 i) and Wricke s procedures (Table 5). This indicated that these two procedures were equivalent for ranking purposes in the absence of environmental covariance. Shukla s stability variance is a linear combination of deviation mean squares, in other words the ecovalence of Wricke. This equivalency for ranking was reported by (Becker and Léon, 1988; Purchase, 1997; Annicchiarico, 2002; Alberts, 2004). The high rank correlation between δ 2 i and S 2 i statistics indicated that the standard covariate of environmental index did not significantly change the relative ranking of genotypes after heterogeneity due to covariate had been removed (Table 5). The magnitude of the ecovalence generally depends chiefly on the magnitude of the deviation mean square, for the component which is due to linear regression is usually small. Thus ecovalence and mean squares for deviations from regression are expected to be highly correlated (Becker, 1981). Purchase s AMMI stability value was positively and significantly correlated with CV (r = 0.61*), S 2 di (r = 0.91 ** ), δ 2 i (r = 0.84**), S 2 i (r = 1.00 ** ) and W i (r = 0.84 ** ) but it did not correspond with mean yield and P i. Shukla's stability variance with environmental covariance is equivalent to mean square for deviation from regression and ASV as a measure of stability according to

9 Correlation among Some Statistical Measures of Phenotypic Stability in Maize 557 the agronomic concept of a stable genotype (one with a yield which is predictable from the level of the productivity of the environment). The ecovalence, Shukla's stability variance (δ 2 i, S 2 i), ASV and mean square for deviation from regression indicates the phenotypic stability according to the agronomic concept of stability and should be as small as possible in a genotype. The coefficient of regression measures the response of the genotype based on the biological concept of stability. Its desired value depends upon the special situation and the breeder's objectives. The use of different concepts of stability will lead to different ranking of genotypes, for the parameters belonging to the two different concepts are not correlated with each other. Path Coefficient Analysis The path coefficient analysis appeared to provide a clue to the contribution of various components of yield to overall grain yield in the genotypes under study. It provides an effective way of finding out direct and indirect sources of correlation. The direct contribution of lodging per cent to grain yield was highest (5.456) followed by plant height (1.261), stand count at harvest (0.446), number of cobs harvested (0.176) and number of cobs diseased (0.067) (Table 6); whereas, ear height had maximum negative direct effect (-0.922) on grain yield. Number of cobs diseased had the highest indirect effect (3.001) via lodging per cent. Ear height and thousand seed weight had appreciable positive indirect effect (1.746) and (1.528) respectively via per cent lodging (Table 6). Table 6: Path analysis showing direct (Bold diagonal) and indirect effect of traits on maize grain yield. CDI CHA PER TSW STHA EHE PHE CDI CHA PER TSW STHA EHE PHE CDI = number of cobs diseased; CHA = number of cobs harvested; PER = lodging per cent; TSW = thousand seed weight; STHA = stand at harvest; EHE = ear height and PHE = plant height Ear height had highest positive indirect effect of and via per cent lodging and plant height respectively. Plant height had positive indirect effect of via per cent lodging. Plant height, ear height, per cent lodging, number of cobs diseased and number of cobs harvested appeared to contribute to the grain yield. Therefore, indirect selection for higher grain yield may be effective for improving these characters, as had been shown by Chowdhry et al. (2000) in studies on wheat crop.

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