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Character expression and yield potential difference of 10 genotypes of cowpea (Vigna unguiculata L. walp)

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https://www.eduzhai.net International Journal of Plant Research 2014, 4(3): 63-71 DOI: 10.5923/j.plant.20140403.01 Character Expression and Differences in Yield Potential of Ten Genotypes of Cowpea (Vigna unguiculata L. Walp) Ajayi A. T.1,*, Adekola M. O.1, Taiwo B. H.2, Azuh V. O.3 1Department of Plant Science and Biotechnology, Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria 2Department of Agriculture, University of Reading, Berkshire, United Kingdom 3Department of Botany, University of Ibadan, Ibadan, Nigeria Abstract Cowpea, an important grain legume in the tropics and sub-tropics; serves as a source of protein in the diets of the people with tremendous ability to fix atmospheric nitrogen for soil improvement. Ten (10) genotypes of cowpea were grown during the rainy season of 2013 to study the interrelationship among quantitative traits. Estimates of phenotypic and genotypic coefficients of variation, broad sense heritability, and genetic advance as percent of mean and correlations were performed on 20 quantitative traits. The experiment was laid out in a randomized complete block design (RCBD) with three replications at the Research Field of the Department of Plant Science and Biotechnology, Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria. Genotypes differed significantly at (P ≤ 0.01) for all traits studied which showed the existence of sufficient genetic variability among the tested genotypes. High broad sense heritability values for all traits studied except for plant height (moderate) shows that these traits are less influenced by environmental effects; which make them effectively transmitted to the progeny. The high positive genotypic and phenotypic correlations between numbers of pods per plant, number of seeds per pod, number of seeds per plant and seed weight indicates that selection for these will result in increase in yield. Keywords Broad sense heritability, Genetic advance as percent of mean, Cowpea, Genotypic coefficient of variation, Phenotypic coefficient of variation 1. Introduction Cowpea, a grain legume, very high in protein content (20 – 25%), has been an important crop grown in the tropical and sub-tropical regions of the world; where it serves as a source of protein for both urban and rural populations ([1]; [2]). It is widely distributed in sub-Saharan Africa, Europe and United States of America [3]. It has a very good ability to improve soils as a consequence of its potential to fix atmospheric nitrogen ([4]; [5]; [2]). Within two decades, IITA made significant advances on cowpea seeds improvement, hence production in Nigeria showed a significant improvement of about 44% and 40% increase in area planted and yield respectively from 1961 to 1995 ([6]; [7]), in fact over 850,000 tons of cowpea is produced annually in Nigeria [8]. Still the production and release of improved cowpea varieties have been slow especially in Nigeria, especially in the humid regions while highest yields have been obtained in drier regions of the country [9]. However, the claim by [10] that selection of appropriate genotype for environmental stress * Corresponding author: ajayi.abiola.toyin@hotmail.com (Ajayi A. T.) Published online at https://www.eduzhai.net Copyright © 2014 Scientific & Academic Publishing. All Rights Reserved was limited by inadequate screening techniques and the lack of genotypes showing clear differences in environmental stress was also true for cowpea in Nigeria and still applicable today. Before embarking on any form of crop improvement programme, in any crop species, a thorough knowledge concerning the amount of genetic variability existing in such crop species for various traits is essential [11]. Assessment of the variations in yield determining quantitative traits of the crop is a pre-requisite in breeding to improve yield [12]. In crop species, phenotypes are controlled mainly by genetic make-up of such crops coupled with the kinds of environment where they are being grown as well as the interactions between the genotypes and the environments. It is necessary therefore, to partition the observed phenotypic variability into heritable and non-heritable components with suitable parameters such as phenotypic and genotypic co-efficient of variation, heritability and genetic advance. Thus, knowledge of genetic variability, heritability and genetic advance in cowpea is essential for a breeder in selecting best genotypes for improvement. Estimates of genetic parameters provide an indication of the relative importance of the various types of genes effects, affecting the total variation of a plant character. In fact, genotypic and phenotypic coefficients of variation and heritability 64 Ajayi A. T. et al.: Character Expression and Differences in Yield Potential of Ten Genotypes of Cowpea (Vigna unguiculata L. Walp) accompanied with genetic advance are very important parameters in improving traits [13]. The importance of selecting and evaluating varieties for quantitative traits and yield capacity in any breeding programme has been previously pointed out by [14] and assessment of genetic diversity in cowpea genotypes would facilitate development of cultivars for adaptation to specific production constraints. Several workers ([15]; [16]; [17]; [18]; [19]; [20]; [21]) have calculated Genotypic and phenotypic components of variance, heritability and genetic advance for different yield characters in cowpea and other crops and have revealed that selection was effective for a population with broad genetic variability and character with high heritability. Yield, being a complex character and controlled by a large number of contributing characters and their interactions, which in turn are governed by a few numbers of genes, also is influenced to a great extent by environment. Correlation of characters also helps to simultaneously select for more than one character of importance at a time. Still, the total correlation is insufficient to explain the true association between characters, as the yield is dependent on many components characters. It would be more desirable to consider the relative magnitude of various characters in order to have a clearer picture of yield components for effective selection programme [11]. The objectives of the present research were to study the level of genetic variability for yield and it components and correlation of characters in 10 genotypes of cowpea from IITA. 2. Materials and Methods The experiment was conducted at the Research Field of the Department of Plant Science and Biotechnology, Adekunle Ajasin University, Akungba-Akoko (latitude 7.20 N, longitude 5.440 E, Altitude 423M above sea level), Ondo State Nigeria, between April and July, 2013. 10 cowpea genotypes used in this study were collected from International Institute of Tropical Agriculture (IITA) Ibadan. The collected accessions are: IT98K-205-8, IT98K-555-1, TVu-4886, TVu-4866, IT89KD-288, TVu-9225, TVu-11986, TVu-9256, TVu-9252 and TVu-11979; coded as G1, G2, G3, G4, G5, G6, G7, G8, G9 and G10 respectively for easy statistical analysis and presentation of data. The 10 cowpea genotypes were evaluated in a field laid out in a Randomized Complete Block Design with three (3) replications. Each replicate consisted of 10 row plots of 10 genotypes, each of which was regarded as a treatment, with 1m distance between treatments. Each treatment consisted of 30 plants, and the total number of plants on the field was 900. Each treatment was sown at a spacing of 30cm between plants and 50cm between rows. Weeding of the experimental field was done as when required to keep the weed pressure low. Sniper and Cypermethrin (cyperforce) were used at different growth stages to control insect pests at appropriate time. 2.1. Data Collection and Analysis Data on emergence percentage (10 days after planting), morphological and agronomical characters (at eight weeks after planting and at maturity respectively) were collected from 10 randomly selected plants and their means were recorded for all observations according to International Board for Plant Genetic Resources (IBPGR), cowpea descriptors [22]. Analysis of variance was estimated according to the procedure of [23]. Genotypic and phenotypic coefficients were estimated according to [24] and [25] as: VG = MSG – MSE/r……………….eqn. 1 VP = VG + MSE…………………...eqn. 2 Where, MSG, MSE and r, are: mean square genotypes, mean square error and number of replicates respectively. The phenotypic coefficient of variation (PCV %) and Genotypic coefficient of variation (GCV %) were estimated by method of [26] and [14] as: PCV = √Vp X 100/ ͞X........................eqn. 3 GCV = √Vg X 100/ ͞X…………….....eqn. 4 Where, VP, VG and ͞X are phenotypic variance, genotypic variance and grand mean respectively for traits under consideration, and were classified according to [27] as follows: 0 – 10% = low; 10 – 20% = moderate; 20% = high. Broad sense heritability (H2B) was expressed as the percentage of the ratio of VG to VP as described by Allard et al. [28] as: h2B =Vg/Vp x 100…………………eqn. 5 and was categorized according [29] as follows: 0 – 30% = low; 30 – 60% = moderate; 60% = high. Genetic advance (GA) was estimated by the method given by [30] as: GA = h2B x K x √Vp……………eqn. 6 Where, K is a constant (2.06) at 50% selection pressure; √Vp is standard deviation of VP and h2B is the heritability ratio. GA was also calculated as percentage of the mean (GAM) according to the formula of [14] and categorized as 0 – 10% = low; 10 – 20% = moderate; 20% = high. Genotypic and phenotypic correlations were estimated according to [31]. VXY (G) = COV XY (G) / √VX (G) x VY (G)………. eqn. 7 VXY (P) = COV XY (P) / √VX (P) x VY (P) …………..eqn. 8 Where COVXY (G) is genotypic covariance between x and y; COV XY (P) is phenotypic covariance between x and y; VX (G) and VX (P) are genotypic and phenotypic variances of character x; VY (G) and VY (P) are genotypic and phenotypic variances of character y. Test of significance of correlation was tested by the International Journal of Plant Research 2014, 4(3): 63-71 65 following statistics: t = r√ n – 2/√ 1 – r2….…….…..…..eqn. 9 Where, r and n are correlation coefficients and number of observations respectively. The ‘t’ table was entered with (n – 2) degree of freedom. Twenty quantitative (20) traits were measured using the International Board for Plant Genetic Resources (IBPGR) Cowpea Descriptors, which are: Emergence percentage (%), plant height (cm), number of leaves per plant, number of nodes on the main stem, number of main branches, terminal leaflet length (cm), terminal leaflet width (cm), number of peduncle per plant, peduncle length (cm), number of days to flowering, number of pods per peduncle, number of pods per plant, pod length (cm), pod weight (g), pod wall thickness, number of seeds per pod, number of seeds per plant, seed length (mm), seed width (mm) and 100-seed weight (g). 3. Results Analysis of variance showed that the mean squares for all traits (Table 2) indicated the existence of high significant differences among the genotypes. Wide range significant variations were observed in emergence percentage (22% to 90%), plant height (12.17cm to 26.26cm), number of leaves per plant (12 to 43), and number of nodes on main stem (4 to 10), number of peduncle per plant (11 to 18), peduncle length (13.58cm to 39.52cm), number of days to flowering (43days to 88 days), number of pods per peduncle (0 to 4), number of pods per plant (0 to 67), pod length (0.00cm to 19.34cm), number of seeds per pod (0 to 19), number of seeds per plant (0 to 1052), seed length (0.00cm to 8.42cm), seed width (0.00cm to 5.27cm) and 100-seed weight (0.00g to 16.53g). The range of variations among the genotypes for other traits was low (Table 1). Phenotypic variance was higher than genotypic variance in all the traits studied, similarly the phenotypic coefficients of variation (PCV) was higher than genotypic coefficient of variation (GCV) in all the traits studied. PCV and GCV values ranged from 6% and 5% respectively for terminal leaflet length to 56% and 55% respectively for number of seeds per plant (Table 2). The values of PCV and GCV were low for terminal leaflet length; number of main branches and terminal leaflet width had moderate PCV and GCV while all other traits had high PCV and GCV values (Table 2). Heritability values for the studied traits ranged from 54% for plant height to 99% in pod length, pod wall thickness, seed length and seed weight. All traits had high heritability estimates (65% to 99%) except for plant height (54%) which was moderate (Table 2). Low genetic advance as per cent of means (GAM) was recorded for terminal leaflet length (9%), and moderate GAM was recorded for terminal leaflet width (20%) while all other measured traits had high genetic advance as percent of means (24% to 110%) (Table 2). The estimates of genotypic and phenotypic correlations between all combinations of the 19 pair of traits among the studied traits showed plant height to be highly significant and positively correlated with number of leaves per plant (0.76 and 0.52), number of nodes on main stem (0.66 and 0.44), number of main branches (0.69 and 0.41), terminal leaflet width (0.51 and 0.30) and number of days to flowering (0.52 and 0.38). This trait was also found to be positive and highly correlated with terminal leaflet length (0.26) only at the genotypic level. Plant height was also found to be highly significant and negatively correlated with peduncle length (-0.72 and -0.51), number of pods per peduncle (-0.45 and -0.33), number of pods per plant (-0.33 and -0.23), number of seeds per plant (-0.64 and -0.45), and seed weight (-0.78 and -0.56). Number of leaves per plant was positive and highly correlated with number of nodes on main stem (0.95 and 0.79), number of main branches (0.87 and 0.67), terminal leaflet length (0.35 and 0.27) and number of days to flowering (0.45 and 0.42). It was also found to be highly significant and negatively correlated with number of pods per plant (-0.61 and -0.55), number of seeds per plant (-0.83 and -0.77) and seed weight (-0.75 and -0.70). Number of nodes on main stem was positive and highly correlated with number of main branches (0.86 and 0.63), terminal leaflet length (0.47 and 0.35) and number of days to flowering (0.41 and 0.36). Number of nodes was also found to be highly significant and negatively correlated with number of pods per plant (-0.65 and -0.56), seeds per plant (-0.62 and -0.55) and seed weight (-0.64 and -0.57). Number of main branches was positive and highly correlated with terminal leaflet length (0.41 and 0.27), number of days to flowering (0.57 and 0.46), and highly correlated with number of peduncle per plant (0.32 and 0.24) only at the genotypic level. It was also found to be highly significant and negatively correlated with number of pods per plant (-0.51 and -0.39), seeds per plant (-0.55 and -0.44) and seed weight (-0.56 and -0.46). Terminal leaflet width was found to be positive and highly correlated with peduncle length (0.39 and 0.30) and number of seeds per plant (0.55 and 0.51) but was found to be negative and highly correlated with number of days to flowering (-0.45 and -0.35). Peduncle length was positive and highly correlated with number of seeds per pod (0.76 and 0.72) and seed weight (0.77 and 0.74), but was highly significant and negatively correlated with number of days to flowering (-0.88 and -0.83). Number of days to flowering was highly significant and negatively correlated with number of pods per peduncle (-0.79 and -0.78), number of pods per plant (-0.81 and -0.77), pod length (-0.84 and -0.83), pod weight (-0.85 and -0.75), pod wall thickness (-0.83 and -0.81), number of seeds per pod (-0.89 and -0.79), number of seeds per plant (-0.67 and -0.65), seed length (-0.79 and -0.78), seed width (-0.64 and -0.63) and seed weight (-0.68 and -0.67). Many traits were found to be highly correlated among the tested genotypes (Table 3). 66 Table 1. Mean Values of 20 Quantitative Traits among the tested Cowpea Genotypes Ajayi A. T. et al.: Character Expression and Differences in Yield Potential of Ten Genotypes of Cowpea (Vigna unguiculata L. Walp) Trait G1 G2 T1 21.67±6.67a 46.67±3.33ab T2 12.17±0.79a 15.68±0.88ab T3 14.53±2.35a 20.40±1.20ab T4 6.89±0.43ab 6.72±0.40ab T5 3.37±0.12a 5.00±0.31bc T6 9.07±0.75a 9.73±0.33a T7 4.19±0.21a 5.43±0.35abc T8 11.77±1.13a 16.73±1.99b T9 39.22±1.11c 37.91±1.07c T10 44.33±0.88a 47.47±1.09a T11 3.80±0.11d 3.87±0.06d T12 45.87±5.07cd 64.53±7.12e T13 15.65±0.43de 15.28±0.07d T14 2.26±0.08d 2.30±0.01d T15 0.96±0.01ef 1.12±0.01h T16 15.33±1.00cd 11.87±0.52b T17 690.40±34.18bcd 762.65±77.69cd T18 8.34±0.13f 8.42±0.11f T19 4.69±0.02f 5.27±0.05h T20 13.40±0.21d 14.77±0.19e G3 58.33±1.67b 19.49±1.20abc 35.00±1.10cde 9.61±0.40cd 5.13±0.17bc 10.79±0.34a 5.55±0.47abc 14.6±0.83a 29.81±0.20b 46.27±0.87a 2.13±0.07b 31.2±1.17bc 13.57±0.16c 1.59±0.06bc 0.77±0.01c 10.67±0.07b 332.65±10.62ab 6.37±0.06c 2.85±0.08c 13.2±0.12d G4 85.00±5.00d 22.47±0.20bc 35.53±0.76de 8.69±0.11d 5.13±0.07bc 10.05±0.30a 5.35±0.14abc 13.73±0.18ab 30.56±0.21b 47.20±0.41a 2.20±0.11b 30.40±2.02b 12.54±0.19b 1.31±0.03b 0.69±0.00b 10.87±0.18b 330.61±24.14ab 6.38±0.07c 2.81±0.04c 11.50±0.26c G5 68.33±8.82c 18.25±0.54abc 14.93±1.33a 5.43±0.17ab 4.33±0.27ab 10.05±0.65a 5.44±0.31abc 11.13±0.33a 33.70±1.21bc 62.67±2.56b 3.07±0.07c 34.20±2.11bc 16.57±0.13f 1.72±0.13c 0.95±0.00ef 13.87±0.13c 473.65±32.71bc 8.41±0.08f 4.99±0.06g 16.53±0.18f G6 75.00±0.00c 17.65±0.98abc 25.60±2.10bc 6.33±0.37bc 4.47±0.24abc 10.83±0.64a 5.67±0.31bc 17±1.42b 38.57±0.65c 44.93±0.94a 4.00±0.12d 67.87±3.88e 16.17±0.09ef 2.21±0.05d 0.93±0.02de 15.87±0.24d 744.89±26.06cd 5.73±0.03b 2.54±0.02b 10.47±0.12b G7 90.00±0.0d 22.41±1.36bc 11.93±0.69a 4.47±0.17a 4.00±0.12ab 9.54±0.52a 6.43±0.05c 11.93±0.07a 39.52±2.38c 46.4±1.30a 4.40±0.12e 52.6±1.25de 17.65±0.08g 2.73±0.07e 1.05±0.03g 17.87±0.07e 939.67±19.93d 7.59±0.08d 4.16±0.03d 10.33±0.08b G8 75.00±5.00c 20.73±2.32abc 27±2.50bcd 6.20±0.20bc 5.00±0.12bc 9.63±0.51a 6.25±0.40c 17.53±1.58b 36.72±0.68c 42.93±0.47a 3.80±0.12d 66.40±4.54e 15.81±0.18def 2.24±0.12d 0.88±0.02d 15.87±0.29d 1051.59±60.23d 7.93±0.08e 4.28±0.02de 11.27±0.14c G9 78.33±11.67d 26.61±3.49c 42.8±4.22e 9.00±0.64d 5.73±0.47c 9.77±0.32a 4.68±0.25ab 15.73±0.50ab 13.58±2.39a 87.87±0.59c 0.00±0.00a 0.00±0.00a 0.00±0.00a 0.00±0.00a 0.00±0.00a 0.00±0.00a 0.00±0.00a 0.00±0.00a 0.00±0.00a 0.00±0.00a G10 84.13±6.71d 25.35±3.98c 20.93±3.48ab 6.40±0.61bc 4.67±0.57bc 10.19±0.22a 6.39±0.47c 12.07±0.53a 33.84±0.91bc 46.33±1.83a 4.47±0.07e 52.47±1.27de 19.34±0.12h 2.89±0.05e 0.93±0.01fg 19.53±0.24f 1024.64±23.56d 7.54±0.07d 4.46±0.12e 10.30±0.17b Means followed by the same alphabet within a row are not significantly different from one another at P ≤ 0.05 using Duncan Multiple Range Test (DMRT). Values are means of measurements ± Standard error (S.E). T1: Emergence percentage; T2: Plant height (cm); T3: number of leaves per plant; T4: number of nodes on the main stem; T5: number of main branches; T6: terminal leaflet length (cm); T7: terminal leaflet width (cm); T8: number of peduncle per plant; T9: peduncle length (cm); T10: number of days to flowering; T11: number of pods per peduncle; T12: number of pods per plant; T13: pod length (cm); T14: pod weight (g); T15: pod wall thickness (mm); T16: number of seeds per pod; T17: number of seeds per plant; T18: seed length (mm); T19: seed width (mm); T20: 100-seed weight (g). Table 2. Estimates of Means, Mean Squares, Variance Components, Heritability and Genetic Advance as Percent of Mean among the tested Cowpea Genotypes International Journal of Plant Research 2014, 4(3): 63-71 TRAITS T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 T20 MEAN 68.67 20.09 24.87 6.29 4.67 9.95 5.54 12.66 33.34 51.64 3.18 44.57 14.26 1.93 0.83 13.17 635.07 6.66 3.58 11.18 MS 1360.74** 58.13** 316.95** 7.06** 1.50** 0.95** 1.55** 76.05** 181.14** 576.63** 5.81** 1343.05** 86.17** 2.12** 0.31** 88.66** 412001.59** 19.17** 7.60** 59.21** б‾2Ph 531.48 27.84 114.99 2.72 0.63 0.40 0.68 27.62 63.69 195.74 1.98 473.81 28.80 0.82 0.11 30.18 141392.28 6.42 2.55 19.80 б‾2g 414.63 15.15 100.98 2.17 0.43 0.27 0.44 24.22 58.72 190.44 1.93 434.62 28.69 0.65 0.10 29.24 135304.65 6.37 2.52 19.71 PCV 33.57 26.26 43.12 26.17 17.16 6.37 14.87 41.52 23.94 27.28 43.98 48.84 37.64 46.92 38.85 41.71 56.25 38.05 49.48 39.81 GCV 29.65 19.37 40.41 23.11 14.11 5.23 11.95 38.88 22.98 26.73 43.61 46.77 37.57 41.77 38.67 41.06 55.02 37.89 44.34 39.73 h2B % 78.01 54.40 87.82 79.78 67.57 68.00 65.00 87.67 92.2 97.28 98.37 91.73 99.62 79.27 99.36 96.89 95.69 99.16 98.82 99.58 GAM 53.96 29.44 78.00 43.09 24.10 8.93 19.97 74.99 45.56 54.30 98.12 92.29 77.24 76.62 79.52 83.70 110.87 77.73 81.76 81.66 T1: Emergence percentage; T2: Plant height (cm); T3: number of leaves per plant; T4: number of nodes on the main stem; T5: number of main branches; T6: terminal leaflet length (cm); T7: terminal leaflet width (cm); T8: number of peduncle per plant; T9: peduncle length (cm); T10: number of days to flowering; T11: number of pods per peduncle; T12: number of pods per plant; T13: pod length (cm); T14: pod weight (g); T15: pod wall thickness (mm); T16: number of seeds per pod; T17: number of seeds per plant; T18: seed length (mm); T19: seed width (mm); T20: 100-seed weight (g). MS: mean squares; б‾2Ph: phenotypic variance; б‾2g: genotypic variance; PCV: phenotypic coefficients of variation; GCV: genotypic coefficients of variation; h2B: broad sense heritability; GAM: genetic advance as percent of mean 67 68 Table 3. Genotypic and Phenotypic Correlations between Traits (genotypic above and phenotypic below in each row) among the tested Cowpea Genotypes Ajayi A. T. et al.: Character Expression and Differences in Yield Potential of Ten Genotypes of Cowpea (Vigna unguiculata L. Walp) Traits T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 T20 T2 1.00 0.76** 0.66** 0.69** 0.26** 0.51** -0.01 -0.72** 0.52** -0.45** -0.51** -0.45** -0.38** -0.62** -0.33** -0.64** -0.68** -0.63** -0.78** 1.00 0.52** 0.44** 0.41** 0.15 0.30** -0.00 -0.51** 0.38** -0.33** -0.35** -0.32** -0.25** -0.45** -0.23** -0.45** -0.49** -0.45** -0.56** T3 1.00 0.95** 0.87** 0.35** -0.04 0.24* -0.79** 0.45** -0.78** -0.61** -0.77** -0.76** -0.81** -0.73** -0.59** -0.83** -0.91** -0.75** 1.00 0.79** 0.67** 0.27** -0.03 0.21 -0.71** 0.42** -0.73** -0.55** -0.76** -0.63** -0.76** -0.68** -0.53** -0.77** -0.84** -0.70** T4 1.00 0.86** 0.47** -0.20 1.00 0.63** 0.35** -0.15 0.20 -0.81** 0.41** -0.80** -0.65** -0.71** -0.75** -0.80** -0.68** -0.62** -0.77** -0.87** -0.64** 0.17 -0.69** 0.36** -0.71** -0.56** -0.64** -0.60** -0.71** -0.61** -0.55** -0.69** -0.77** -0.57** T5 1.00 0.41** -0.00 0.32** -0.83** 0.57** -0.79** -0.51** -0.75** -0.77** -0.76** -0.81** -0.55** -0.72** -0.69** -0.56** 1.00 0.27** 0.00 0.24* -0.65** 0.46** -0.64** -0.39** -0.61** -0.57** -0.62** -0.65** -0.44** -0.59** -0.56** -0.46** T6 1.00 0.35** 0.22 -0.14 -0.07 -0.14 -0.00 0.05 -0.09 -0.05 -0.02 -0.05 -0.22* -0.35** 0.03 1.00 0.23* 0.17 -0.12 -0.06 -0.11 -0.00 0.04 -0.07 -0.04 -0.01 -0.04 -0.18 -0.28** 0.02 T7 1.00 -0.06 0.39** -0.45** 0.57** 0.56** 0.59** 0.65** 0.46** 0.64** 0.65** 0.33** 0.30** 0.15 1.00 -0.04 0.30** -0.35** 0.46** 0.43** 0.47** 0.46** 0.37** 0.51** 0.51** 0.26** 0.24* 0.13 T8 1.00 -0.12 0.07 -0.15 0.04 -0.20 -0.17 -0.14 -0.23* -0.11 -0.21 -0.22 -0.15 1.00 -0.11 0.06 -0.14 0.03 -0.19 -0.14 -0.13 -0.21 -0.10 -0.14 -0.21 -0.14 T9 1.00 -0.88** 0.95** 0.91** 0.92** 0.95** 0.96** 0.91** 0.76** 0.91** 0.83** 0.77** 1.00 -0.83** 0.91** 0.84** 0.89** 0.81** 0.92** 0.86** 0.72** 0.87** 0.79** 0.74** T10 1.00 -0.79** -0.81** -0.84** -0.85** -0.83** -0.89** -0.67** -0.79** -0.64** -0.68** 1.00 -0.78** -0.77** -0.83** -0.75** -0.81** -0.79** -0.65** -0.78** -0.63** -0.67** T11 1.00 0.91** 0.89** 0.97** 0.94** 0.96** 0.87** 0.81** 0.79** 0.61** 1.00 0.87** 0.92** 0.98** 0.93** 0.94** 0.85** 0.80** 0.72** 0.60** T12 1.00 0.81** 0.91** 0.85** 0.82** 0.84** 0.72** 0.68** 0.55** 1.00 0.77** 0.77** 0.81** 0.77** 0.79** 0.69** 0.65** 0.52** T13 1.00 0.93** 0.90** 0.97** 0.76** 0.82** 0.47** 0.79** 1.00 0.94** 0.92** 0.95** 0.74** 0.89** 0.43** 0.78** T14 1.00 0.83** 0.97** 0.91** 0.83** 0.81** 0.59** 1.00 0.90** 0.94** 0.79** 0.75** 0.73** 0.59** T15 1.00 0.89** 0.73** 0.93** 0.90** 0.83** 1.00 0.87** 0.72** 0.92** 0.89** 0.82** T16 1.00 0.84** 0.78** 0.71** 0.60** 1.00 0.81** 0.80** 0.75** 0.61** T17 1.00 0.64** 0.66** 0.37** 1.00 0.62** 0.64** 0.36** T18 1.00 0.91** 0.84** 1.00 0.91** 0.84** T19 1.00 0.95** 1.00 0.94** T20 1.00 1.00 **Significant at 1% level, * Significant at 5% level T2: Plant height (cm); T3: number of leaves per plant; T4: number of nodes on the main stem; T5: number of main branches; T6: terminal leaflet length (cm); T7: terminal leaflet width (cm); T8: number of peduncle per plant; T9: peduncle length (cm); T10: number of days to flowering; T11: number of pods per peduncle; T12: number of pods per plant; T13: pod length (cm); T14: pod weight (g); T15: pod wall thickness (mm); T16: number of seeds per pod; T17: number of seeds per plant; T18: seed length (mm); T19: seed width (mm); T20: 100-seed weight (g). International Journal of Plant Research 2014, 4(3): 63-71 69 4. Discussions To conquer the problem of low productivity in cowpea, it is pertinent to identify high yielding genotypes among with resistance to major biotic and abiotic constraints among available germplasm collection. Identification of these better genotypes, their introduction in breeding programmes coupled with establishment of suitable selection criteria will be helpful for successful varietal improvement programme. Analysis of variability among genotypes for various traits and association of traits in relation to yield contributing factors of the crop would be of great importance for successful breeding programme. For this research, analysis of variance revealed high significant differences for all traits studied, which indicated the existence of sufficient genetic variability among the tested genotypes. These patterns of high level of variability have been observed by many researches on cowpea ([12]; [21]) and on many other crops [32]. The high genotypic coefficients of variations (GCV) and phenotypic coefficients of variations (PCV) associated with traits like emergence percentage, number of leaves per plant, number of nodes on main stem, number days to flowering, peduncle length, number of peduncles per plant, number of pods per peduncle, number of pods per plant, pod length, pod weight, pod wall thickness, number of seeds per pod, number of seeds per plant, seed length, seed width and seed weight implied that these traits are governed by genetic factors; and existence of greater magnitude of genetic variability in the listed traits as a consequence and possibility of their improvement through selection. The results agree with several workers [33]; [34]; [35]). The moderate GVCs and PVCs experienced in traits like terminal leaflet width, number of main branches and plant height were in line with findings of [35], and show the presence of moderate variability among the studied genetic stock [11], and indicate that selection for these traits is less effective compared to those traits with high GVCs and PVCs. High broad sense heritability values for all traits studied except for plant height are in accordance with results of many researchers on cowpea ([18]; [19]; [36]; [37]) and these shows that these traits are less influenced by environmental effects, which makes them effectively transmitted to the progeny [21]. There is an influence of fixable additive gene effects for inheritance of these traits ([38]; [39]) and therefore, selection for these traits may lead to fast genetic improvement. Similar results of high heritability estimates have been reported for days to flowering, pod length, pod weight, number of pods per plant, number of seeds per pod and 100-seed weight in cowpea ([40]; [41]). For heritability estimates to be reliable, it must be accompanied by high genetic advance ([32]; [21]) for a reliable index for selection of traits [18]. High heritability and high genetic advance as percent of mean (GAM) was observed for almost all studied traits except for terminal leaflet length and terminal leaflet width, observations which indicate additive gene effects, and suggest that effective progress in improvement through selection could be achieved for yield. These results are in agreement with the findings of [40]; [21] on cowpea and several workers on other crops ([42]; [11]; [32]). The estimates of genotypic and phenotypic correlations among the measured characters obtained in this study would be of interest for breeding objectives. The degree of correlation among traits is an important factor in economic and complex character as yield [43]. Correlations are measures of the intensity of association between traits, whereby selection for a trait results in progress for all traits that are positively correlated and retrogress for traits that are negatively correlated [43]. The high negative significance genotypic and phenotypic correlations between plant heights, number of pods per plant, pod length, seeds per pod and seed weight imply that plant with higher height may have fewer numbers of pods and seeds. This contradicts the results of [44]. The high significance genotypic and phenotypic correlations between number of days to flowering and number of pods per plant, pod length, seeds per pod, and seeds per plant and seed weight indicate that genotypes which flower earlier produce more pods and seeds per plant. This agrees with the work of [45] and [44]. The high positive genotypic and phenotypic correlations between numbers of pods per plant, number of seeds per pod, number of seeds per plant and seed weight indicates that selection for these will result in increase in yield. Based on phenotypic and genotypic association between yield and yield contributing traits, it is suggested that selection should be made for the characters, which are having positive significant association to improve the seed yield per plant in cowpea. The results from this research have shown that enough variability and genetic heritability exist in the studied traits among the tested cowpea genotypes. High mean values for yield and its component traits, high genotypic coefficients of variation, high heritability and genetic advance coupled with high genotypic and phenotypic positive significant associations among yield and yield contributing traits warrant effective selection for further improvement. 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