Important morphological markers for improvement of yield in bread wheat.
Improving grain yield through breeding methods and improving yield components can be efficient methods in breeding programs; therefore, the relationship between yield components with yield plays an important role in this context. Path coefficients analysis is a method for the separation of correlation coefficients to their direct and indirect effects through other traits and it can provide useful information about affectability form of traits to each other and relationships between them. Contribution of each yield component can be affected by other components indirectly while justifying grain yield . Khan et al  in a study on the relationships between yield and its components in wheat showed that yield is highly correlated plant height, number of leaves and straw weight. Ibrahim  while examining the correlation between some traits of bread wheat showed that there is high correlation between grain yield and grain number per spike, 1000 grain weight and harvest index. Gupta and Chaturvedi  in reviewing spring wheat showed that the harvest index, plant height, maturity date, biological yield and flowering time had a direct effect on grain yield. Moghaddam et al.  in their study showed that the number of grains per spike and 1000 grain weight was highly correlated with grain yield and had large and significant direct effects on this trait. Mollasadeghi  in their study showed that biological yield was highly correlated with grain yield. Some researchers have reported positive correlation between grain yield and spike number per square meter, number of grains per spike and grain weight. Although, there was positive relation between yield and some of its components, negative correlations exist between some of yield components that make possible selection for all components simultaneously as a factor in increasing wheat may not be beneficial . Mondal et al.  based on path coefficients in path analysis in Indian bread wheat genotypes in 1999 concluded that the number of grains per spike, 1000 grain weight and number of tillers per plant had a direct effect on grain yield, while the height and maturity date had a negative direct effect on grain yield. Bakhit et al.  in Egypt while evaluating the correlation and path analysis in durum wheat plants showed the number of spike per plant had the highest direct effect on grain yield. Dokuyuca and Akaya  while the path analysis of wheat genotypes for yield and its components reported that the number of spikes per square meter and grain weight per spike had a positive and direct effect on grain yield and grain number per spike had indirect and positive effect on yield of grain weight. Hoxha and Sulovary  via study of relationships between production through some quantity characteristics of hard wheat by path analysis showed that plant height, growth period and mean spike weight had direct effect on grain yield and characters such as leaf area, fertile tillers spike length, spikelet number, harvest index and leaf angle had indirect effect on yield. Rashidi et al  in a study which carry on the yield correlation with its components by path analysis in local spring wheat of East Azerbaijan concluded that the number of fertile tillers and plant height are the main components of grain yield and number of fertile tiller and plant height are the main components of straw yield, and straw yield is the main component of the harvest index. So to increase any of these dependent traits, we can benefit from effective traits and characteristics associated with it.
This research was done to determine some of important morphological traits emphasizing breeding of wheat yield.
Materials and methods
An experiment was conducted in Research Farm of Islamic Azad University, Ardabil branch in 20082009 agricultural year. This farm is located in Hassan Barugh village lands (5 km West of Ardabil) with latitude and longitude, respectively, 38.15 North 48.2 East and 1350 m above sea level. Twelve wheat genotypes (Gascogne, Sabalan, 4057, Ruzi-84, Gobustan, Saratovskaya-29, MV17/Zrn, Sardari, 4061, 4041, Sissons and Toos) planted as a completely randomized block design with three replications. Each experimental plot included three rows 20 cm apart from each other and was three meters in length. Two times fall irrigation and three times spring irrigation were done. Some of vegetative traits and yield related characteristics measured or calculated. Analysis of variance was conducted and the simple correlation coefficients among all traits were determined and those traits that most influence on grain yield had been determined using stepwise regression. Finally, based on path coefficients on the basis of simple correlation, form of effectiveness of traits and grain yield components were determined and the causal reasons were presented. Under-study traits in this research included plant height, fertile tillers number, length of main spike, length of secondary spikes, main spike weight, secondary spike weight, grains number per main spike, number of grains per secondary spikes, grain weight per main spike, grain weight per secondary spikes, 1000 grain weight per main spike, 1000 grain weight per secondary spikes, biological yield, straw yield, harvest index and grain yield. Statistical analysis was performed, including analysis of variance, simple correlation coefficients, stepwise regression analysis and path analysis. For this purpose computer software's SPSS-16 and MSTAT-C, Minitab-15 was used.
Results and discussions
Results of variance analysis of twelve bread wheat genotypes (Table 1) indicated there is significant difference between genotypes in terms of plant height, main spike length, main spike weight, number of grain per main spike, number of grain per secondary spikes, grain weight per main spike, one-thousand grain weight for main spike, one-thousand grain weight for secondary spikes, biological yield and straw yield at probability level of 5 and 1 percent. Also there was no significant difference among the genotypes for other traits. Results of simple correlation (Table 2) showed that the relationship between grain yield and main spike length, grain weight per main spike, one-thousand grain weight per main spike, one-thousand grain weight per secondary spike and harvest index was significantly positive. The relationship between grain weight per main spike with main spike weight, secondary spike weight, number of grain per main spike and number of grain per secondary spikes was significant and positive, but it was negative and significant with plant height. The relationship between number of grain per main spike with number of grain secondary spike and weight of grain per main spike was significantly positive, but it was negative and significant with one-thousand grain weight per main spike and one-thousand grain weight per secondary spikes. Correlation between biological yield with straw yield was significant and positive and correlation between straw yield and harvest index was significant and negative.
In order to remove effect of non-effective characteristics in regression model on grain yield, stepwise regression was used. In stepwise regression analysis, grain yield as dependent variable (Y) and other traits as independent variables were considered. Results of stepwise regression (Table 3) showed that the biological yield, straw yield, main spike weight and number of grain per secondary spike with R square of 99%, had justified the maximum of yield changes. Considering that the biological yield was X1, straw yield X2, main spike weight X3 and number of grain per secondary spikes X4, therefore the following equation can be obtained:
Y = 0.171 + 0.931**X1 - 6.675**X2 + 0.153**X3 + 0.06 X4
Existence of significant R square in a successful regression equation indicates the effectiveness of these traits to increase grain yield. Above equation showed that the biological yield and main spike weight and number of grain per secondary spikes had most positive influence on grain yield and also the straw yield had the most negative influence on grain yield.
Path analysis is very important in determining relationship between economic performance and important traits. Calculation of correlation coefficients does not determine nature of relationship, but it is possible to identify direct and indirect effects on yield traits using path analysis. For this purpose, breeders use this analytic method as a tool to determine important traits affect on yield . In order to understand the cause and effect relationships between the dependent variable of grain yield and the variables that had significant impact on economic performance, the path analysis were used. Results (Table 4) showed that biological yield, main spike weight and number of grain per secondary spikes on grain yield with 2.143, 0.129 and 0.019 respectively, had direct and positive effect. Most of the positive direct effect was related to biological yield (+2.143) and the straw yield had the most negative direct effect (-2.205). Although the direct effect of straw yield was negative, positive indirect effects through other traits which more related to the biological yield (+1.926) led to increasing yield. Residual effects (0.09) indicated that in addition to the above variables, there are also other factors to justifying grain yield changes. Figure 1 shows diagram of path coefficients of independent traits on grain yield as dependent variable.
[FIGURE 1 OMITTED]
Mobasser et al.  were done path analysis for grain yield in barley and suggested that number of grain per spike with direct effect of 1.36, is the most important component affecting yield. Also direct effect of number of spike per unit area and grain weight was positive. Soghi et al.  by examining relationship between yields with yield components of 19 advanced wheat lines in Gorgan showed that direct effect of one-thousand grain weight was little, but the direct effect of number of grain per spike was high. Also indirect effects of number of grain per spike increased by one-thousand grain weight and grain weight per spike. Mohammady  investigated yield relationship with its components for 600 local genotypes of bread wheat in Iran and showed that the most direct effect on grain yield is related to one-hundred grain weight. Tarrynegad  reported that direct effect of plant height on grain yield under irrigated conditions is negative but negligible (-0.051). Whereas traits of number of grain per main spike and one-thousand grain weight per main spike have positive direct effect on grain yield, selection process for each of these traits would increase yield. Moghaddam et al. (quoted as Tarrynegad, 1998) reported that direct effect of three main components of the yield in local masses of southeastern of Iran on grain yield were positive. Basirat  suggested that the highest direct effect on grain yield is related to number of grain per spike. Ahmadzadeh  reported that the direct effect of one-thousand grain weight and spike length on yield was positive.
It can be concluded that biological yield and the main spike weight can be a criterion to select high-yielding wheat genotypes. Of course in the selection programs it can utilize traits like number of spike per square meter and grain weight per spike to improve grain yield of wheat.
[1.] Heidari, B., G. Saeidi and B.E. Sayed-Tabatabaei, 2008. Factor analysis for quantitative traits and path analysis for grain yield in wheat. J. Sci. & Technol. Agric. & Nature. Resour., 42: 135-144.
[2.] Khan, H.A., S.H. Mohammad and S. Mohammad, 1999. Character association and path coefficient analysis of grain yield component in wheat. Crop Research-Hisar, 17(2): 229-233.
[3.] Ibrahim, K., 1994. Association and path coefficient analysis of some traits in bread wheat. Annals of Agricultural science Moshtohor, 32(3): 1189-1198.
[4.] Gupta, R.R. and B.K. Chaturvedi, 1995. Selection parameters for some grain and quality attributes in spring wheat. Agricultural Science Digest Karnal, 15(4): 186-190.
[5.] Moghaddam, M., B. Ehdaei and J.G. Waines, 1997. Genetic variation and interrelationships of agronomic characters in landraces of bread wheat form southeastern Iran. Euphytica, 95: 361-365.
[6.] Mollasadeghi, V., 2010. Effect of potassium humate on yield and yield components of wheat genotypes under end seasonal drought stress condition. MSc. thesis in Plant Breeding. Islamic Azad University, Ardabil branch.
[7.] Gebeyhoue, G.D., R. Kontt and R.J. Baker, 1982. Relationship among duration of vegetative and grain filling phases, yield components and grain yield in durum wheat cultivars. Cr. P. sci. 22: 278-290.
[8.] Mondal, A.B., D.P. Sadhu and K.K. Sarkar, 1997. Correlation and path analysis in bread wheat. Enviroment and Ecology, 15(3): 537-539.
[9.] Bakhit, B.R., M.G. Mossad, M.A. EL-Morshidy and A.M. Tamam, 1989. Correlation under normal field and aphid infestation condition and path analysis in durum wheat. Assiut-Journal of Agricultural sciences, 20(3): 207-220.
[10.] Dokuyuca, T. and A. Akaya, 1999. Path coefficient analysis and correlation of grain yield and yield components of wheat genotypes. Rachis, 18(2): 17-20.
[11.] Hoxha, S. and H. Sulovari, 1999. The relation of the production with some quantitative characteristics hard wheat. Literature up data on wheat barley and Triticale. CIMMYT, 5(6).
[12.] Rashidi, V., M. Moghaddam and N. Khodabandeh, 1998. Study on yield correlation with its components by path analysis in spring local wheats of Azerbaijan e Sharghi. Abstract proceeding of the 5th Iranian Crop Science and Crop Breeding Congress, pp: 107.
[13.] Mobasser, S., G.H. Noormohammadi, A. Kashani and M. Moghadam, 2000. Casuality analysis for grain yield in barley. Iranian journal of crop sciences, 2(1): 15-220.
[14.] Soghi, H.A., M. Kalate Arabi and S.A.M. Abrudi, 2006. Stability analysis of grain yield and study on relationship between traits in advanced bread wheat lines in Gorgan. Pajuhesh va Sazandegi in Agronomy and Horticulture, 70: 56-62.
[15.] Mohammadi, M., 2000. Study of yield relationship and yield components in 600 native genotypes of bread wheat in Iran by statistical multivariable methods. MSc. thesis in Plant Breeding. Faculty of Agriculture. University of Tehran.
[16.] Tarrynegad, A., 1998. Evaluation of lines response obtained from native masses of fall wheat to drought stress and irrigation conditions. MSc. thesis in Plant Breeding. Faculty of Agriculture. University of Tabriz.
[17.] Basirat, M., 1994. Selection for drought tolerance in wheat. Keynote proceeding of the First Iranian Crop Science and Crop Breeding Congress, pp: 43-62.
[18.] Ahmadzadeh, A., 1998. Evaluation of lines derived from local spring wheat masses of Azerbaijan e Sharghi in terms of drought. MSc. thesis in Plant Breeding. Islamic Azad University, Ardabil branch.
Vahid Mollasadeghi, Reza Shahryari
Islamic Azad University, Ardabil branch, Iran
Vahid Mollasadeghi, Reza Shahryari Important Morphological Markers for Improvement of Yield in Bread Wheat
Vahid Mollasadeghi, Islamic Azad University, Ardabil branch, Iran
Table 1: Variance analysis of the measured traits in bread wheat genotypes S.O.V df Mean of Squares Plant Fertile Length Length of height tillers of main secondary number spike spikes Replication 2 516.38 ** 467.6 ** 0.189 0.169 Genotype 11 297.8 ** 98.5 0.382 ** 0.346 Error 22 20.98 53.34 0.171 0.45 C. V (%) 6.25 35.92 5.37 10.16 S.O.V df Mean of Squares Main Secondary Grains Number of spike spike number grains per weight weight per main secondary spike spikes Replication 2 0.364 ** 0.147 13.272 3.591 Genotype 11 0.216 ** 0.188 93.473 ** 37.69 ** Error 22 0.05 0.178 10.35 9.88 C. V (%) 13.63 23.70 11.14 15.65 S.O.V df Mean of Squares Grain Grain 1000 grain 1000 grain weight per weight per weight per weight per main spike secondary main spike secondary spikes spikes Replication 2 14.943 * 468.38 ** 55.479 ** 131.78 ** Genotype 11 11.796 ** 51.273 152.34 ** 63.265 ** Error 22 2.713 41.79 8.429 10.273 C. V (%) 10.64 30.47 5.22 8.09 S.O.V df Mean of Squares Biological Straw Harvest Grain yield yield index yield Replication 2 12.411 * 0.169 * 88.568 0.633 Genotype 11 10.817 * 0.208 * 57.601 0.302 Error 22 2.726 0.05 67.39 0.395 C. V (%) 20.02 35.66 17.73 16.63 * and ** Significantly at p < 0.05 and < 0.01, respectively. Table 2: Simple correlation coefficients among under-study traits PH (1) FTN (2) LMS (3) LSS (4) MSW (5) FTN 0.469 1 LMS -0.393 -0.422 1 LSS -0.217 -0.372 0.721 ** 1 MSW -0.596 * -0.370 0.069 0.032 1 SSW -0.552 -0.564 0.322 0.480 0.678 * GNMS -0.623 * -0.409 0.043 0.29 0.823 ** NGSS -0.492 -0.438 0.317 0.360 0.655 * GWMS 0.660 * 0.401 0.015 0.071 0.926 ** GWSS10 0.143 0.784 ** -0.267 -0.107 0.168 1000GWMS 0.349 0.432 -0.080 -0.031 -0.593 * 1000GWSS 0.078 0.233 -0.087 -0.008 -0.264 BY -0.086 0.066 0.294 -0.263 0.371 SY 0.125 0.236 0.160 -0.297 0.226 HI -0.582 * -0.420 0.114 0.244 0.102 GY -0.497 -0.381 0.687 * 0.057 0.372 SSW (6) GNMS (7) NGSS (8) GWMS (9) GWSS (10) FTN LMS LSS MSW SSW 1 GNMS 0.381 1 NGSS 0.439 0.864 ** 1 GWMS 0.600 * 0.892 ** 0.686 * 1 GWSS10 -0.092 0.092 0.112 0.138 1 1000GWMS -0.187 -0.883 ** -0.891 ** -0.609 * 0.031 1000GWSS 0.156 -0.653 * -0.730 ** -0.268 0.025 BY -0.069 0.272 0.239 0.120 0.169 SY -0.248 0.251 0.305 0.023 0.358 HI 0.512 -0.02 -0.110 0.224 -0.409 GY 0.448 0.780 ** -0.141 0.264 -0.396 1000GWMS (11) 1000GWSS1 (12) BY (13) SY (14) FTN LMS LSS MSW SSW GNMS NGSS GWMS GWSS10 1000GWMS 1 1000GWSS 0.901 ** 1 BY -0.344 -0.404 1 SY -0.419 -0.515 0.899 ** 1 HI 0.297 0.515 -0.552 -0.799 ** GY 0.846 ** 0.697 * 0.191 -0.255 HI (15) GY (16) FTN LMS LSS MSW SSW GNMS NGSS GWMS GWSS10 1000GWMS 1000GWSS BY SY HI 1 GY 0.584 * 1 * and ** Significantly at p < 0.05 and < 0.01, respectively 1, 2, 16 abbreviation marks for respectively plant height, Fertile tillers number, Length of main spike, Length of secondary spikes, Main spike weight, spike Secondary spike weight, Grains number per main spike, Number of grains per secondary spikes, Grain weight per main spike, Grain weight per secondary spikes, 1000 grain weight per main spike, 1000 grain weight per secondary spikes, Biological yield, Straw yield, Harvest index and grain yield. Table 3: Regression coefficients of standard component and R square of traits related to grain yield in 12 bread wheat genotypes Standardized Stage Model Coefficients t Beta 1 GY = f(BY) 2.143 41.909 2 GY = f(BY & SY) -2.205 -44.058 3 GY = f(BY & SY &MSW) 0.129 4.524 4 GY = f(BY & SY & MSW & NGSS) 0.019 -2.63 Stage sig Tolerance 1 0 0.126 2 0 0.132 3 0.003 0.379 4 0.034 0.425 Table 4: Direct and indirect effects of dependent traits on grain yield on the basis of phenotypical correlation coefficients Traits Direct Indirect effect effect Biological Straw yield yield Biological yield 2.143 - -1.983 Straw yield -2.205 1.926 - Main spike weight 0.129 0.795 -0.499 Number of grains per secondary spikes 0.019 0.362 -0.79 Traits Total corre- Main Number of lation spike grains per weight secondary spikes Biological yield 0.026 0.003 0.191 Straw yield 0.016 0.006 -0.255 Main spike weight - 0.003 0.372 Number of grains per secondary spikes 0.012 - -0.396 R-Sq(adj) = 0.99
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|Title Annotation:||Original Article|
|Author:||Mollasadeghi, Vahid; Shahryari, Reza|
|Publication:||Advances in Environmental Biology|
|Date:||Feb 1, 2011|
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