DIVERSITY AND INTERRELATIONSHIP OF SOME AGRONOMIC AND NUTRITIVE CHARACTERS IN SORGHUM [Sorghum bicolor (L.) Moench] OF ADAMAWA STATE

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1 Nigerian Journal of Agriculture, Food and Environment. 6(1&2):24-28 DIVERSITY AND INTERRELATIONSHIP OF SOME AGRONOMIC AND NUTRITIVE CHARACTERS IN SORGHUM [Sorghum bicolor (L.) Moench] OF ADAMAWA STATE ABSTRACT Bello, D. Department of Crop Production And Horticulture, Federal University of Technology Yola, Nigeria. Two years trial was carried out on the experimental field of the Department of Crop Production and Horticulture, Federal University of Technology Yola in the cropping season of 2008 and 2009, to examine the degree of variability among eight sorghum genotypes collected within Adamawa state and also to examine the degree of relationship among some agronomic and nutritive characters in the collected cultivars. Seeds of the different cultivars were planted in a Randomized Compete Block Design (RCBD) with three (3) replications in each year. The following parameters were measured viz: plant height, number of leaves, length of internodes, flag leaf length, days to 50% anthesis, yield per plant, protein, carbohydrates, ash, fat, and fiber. These parameters were subjected to analysis of variance. Results revealed high degree of variability among these genotypes for all the parameters mentioned above, also high degree of relationship was observed between the agronomic and nutritive characters measured. Keywords: Sorghum, variability, correlation, agronomic and nutritive characters. INTRODUCTION Archaeological evidences by Murdock, (1959) and Doggett (1988) suggest that early domestication of Sorghum [Sorghum bicolor (L.) Moench] (2n =20), occurred 5,000 years ago or even earlier in east central Africa near Ethiopia or Sudan because of the great diversity or type growing in that region. Soon after the initial domestication, sorghum spread to west and east Africa. Further, human and natural selection, supplemented by introgression with local, wild and weedy taxa, led to the cultivated races of sorghum, that are included under sorghum bicolor Spp. Sorghum is the fourth in importance among the world s leading cereals being cultivated over an area of about 43.7 million hectares with a global production record of about 62.8millions tones in 2001, (FAO, 2003), out of this Africa produced about 20,000 millions tones with Nigeria (which is the third world producing country) accounting for 7, millions tones. Sorghum is a staple food for about 300 million people who live in the dry tropics and temperate regions. In India, the rainy season sorghum grain is used mostly as animal feed while the post rainy season sorghum grains is primarily for human consumption; it is a valuable source of fodder and fuel. The use of malted sorghum in the preparation of diverse drinks is widespread particularly in the sub-humid and humid zones of Africa, where brown grain cultivars are commonly cultivated for this purpose. It is used in the pharmaceutical industries. The sorghum stover (straw) is used as animal feed and for fencing purpose as well as fuel for cooking (FAO 2003). It is essential to improve yield, quality, nutritive and economic values. Therefore, genetic resources need to be identified and used to enhance important yield and nutritive characters, such characters usually under the control of polygene whose individual effects are small and cumulatively expression is profoundly influenced by environment. Variability among such characters is of great importance and an understanding of their interrelationship is a primary objective where ever useful application of genetic principles is a goal. MATERIALS AND METHODS Eight local Sorghum cultivars, designated as Ex-Bole Ex-Gulak Ex-Geriyo, Ex-Wagga, Ex-Hong, Girei, Mbamba, and Ex-Numan, collected within Adamawa State were used for this work. Treatment and experimental design evaluation: Each cultivar was sown on two 5m ridges, spaced 0.75m apart at an intra row spacing of 0.30m apart, The layout of the plots was a randomized complete block design. Each cultivar was in three replicates Two to three (2-3) seeds were sown per hill. These plants were later thinned to 1 plant per hill given rise to about 16 plants per ridge (44,444 plants/ha). Manual weeding using hoe was carried out and NPK fertilizer was applied at the rate of 60kg N/ha, 30kg/ha of phosphorus (P 2 Q 5 ) and 30kg/ha potassium (K 2 O) in two split doses at 3 and 6 weeks after sowing for better results. In an event of pest disease outbreak, appropriate measures were taken. NJAFE VOL. 6 NOS.1 & 2,

2 Nigerian Journal of Agriculture, Food and Environment. 6(1&2):24-26 Data collection: At maturity five (5) plants from each cultivar in the middle rows (with least border effects) were sampled for measurement on the following characters. i. Days to 50% anthesis: number of days from sowing to the date when about 50% of the plants on a plot flowered was recorded. ii. Plant height: the height of each of the sampled plants was measured to the nearest centimeter from ground level to the tip of the panicle and their mean recorded. iii. Flag leaf length: the length of leaf of each sampled plant was measured from the base of the flag leave to the tip and their mean recorded. iv. Flag leaf width: the width of flag leaves of the plants were measured at the widest point and their mean were recorded. v. Panicle length: the length of the panicle of the sampled plants were measured from the collar to the tip of panicle and the mean recorded. vi. The number of leaves per plants: the number of leaves was counted for each of the sampled plants and their mean recorded. vii. Yield per plant: average of the 5 sample plants were taken and recorded per each replication. viii. The nutritional contents were analysed for protein, carbohydrate, ash, fat and fibre, at the laboratories of the Department of Food Science Technology, Federal Polytechnic, Mubi Adamawa State, Nigeria. Data analysis: Data collected were subjected to analysis of variance using the PROC GLM procedures of the SAS Statistical Software Package for windows, (SAS, 1999). Mean separation was done using DMRT ranking of the SAS Package. Phenotypic and genotypic correlation: The two variance and covariance matrices necessary for calculating genotypic and phenotypic correlation coefficient, were obtained from the mean squares and mean cross products of the genotypes, phenotypes and error for the different characters measured in the replicated trial pooled over the two cropping seasons, was used in calculating the genotypic and phenotypic correlation between pairs of characters; as described by Chhidda (2003). The genotypic correlation coefficient (rg) was computed as follows:- δ 2 g 1 g 2 rg 12 = δ 2 g 1 x δ 2 g 2 and phenotypic correlation coefficient (rph) rph = δ 2 ph 12 δ 2 ph 1 x δ 2 g 2 Where; δ 2 g 1 g 2 = genotypic covariance for characters 1 & 2 δ 2 g 1 = genetic variance for character 1 δ 2 g 2 = genetic variance for character 2 rg 12 = covariance coefficient for character 1& 2 rph = phenotypic correlation coefficient for characters 1 & 2 δ 2 ph 1 =phenotypic variance for character 1 δ 2 ph 2 = phenotypic variance for character 2 δ 2 ph 12 = phenotypic co-variance for characters 1 & 2 RESULTS AND DISCUSSIONS The mean square values obtained from the analysis of variance (Table 1) suggest that difference exist between the sorghum cultivars (genotypes) for most characters, indicating that they are highly variable. It is most likely therefore that the cultivars will respond to selection. Similar observations were made by Bello et al. (2007). The cultivars exhibited variability irrespective of environment. The non-significant mean square values observed for days to 50% shows that the cultivars are genetically uniform with regards to his characters, selecting this character will therefore show no impact on genetic improvement. A comparative performance of the eight sorghum cultivars and their measured characters (Table 2), provides a clear indications of the superiority of some of the cultivars over others, good breeding potential exist for cultivars such as Ex-Numan, Ex-Wagga, Ex-Geriyo and Ex-Gulak which performed very well in yield per plant and nutritive characters, depending on the breeding objectives, there is a wide range of cultivars to chose from. For instance if the breeding objectives is to produce high yielding and early maturing variety then hybridization between Ex-Numan and Ex-Hong, that is highest yielding cultivars per plant and the earliest maturing cultivars as well as nutritive characters such as carbohydrate content would be promising. High heritability estimates were NJAFE VOL. 6 NOS.1 & 2,

3 Nigerian Journal of Agriculture, Food and Environment. 6(1&2):24-26 recorded (Table 3) for carbohydrate, Ash, days to 50%, number of leaves and flag leaf length which indicates the possibility of their gene transfer to the recipient parent during hybridization (Doggett, 1988). Correlation is important in plant breeding in that it is a measure of the degree of association, genetic or nongenetic between two or more characters. Where genetic association exists, selection for the charter will lead to changes in other characters, from the results of this study (Table 4), there is more genotypic association between the different pairs of characters than the phenotypic correlation coefficient, indicating that the characters are more related genotypically than phenotypically. From the breeders point of view the most important association are the relationships between yield component, yield per plant as well as the nutritive characters. Since yield is a complex character highly influenced by environment fluctuations, direct selection for yield per se would be difficult. It is therefore desirable to select for one or more characters indirectly, which will improve grain yield. Several authors such as Eckebil et al. (1977), Totok (1997), Sadia et al. (2001), Aba et al, (2001) and Bello et al. (2007) reported strong genotypic association for components of yield, which included plant height, grain weight per panicle and 1000 seed weight with grain yield. In this study, consistent and significant positive correlations were obtained between yield and plant height, yield and panicle length. This indicates that selecting for these two characters would increases grain yield indirectly in this sorghum population. Similar conclusions were also made by Bello et al. (2001), when phenotypic correlation was considered for these characters. On the other hand, negative association between characters would lead to opposite effect on the pair of characters. The negative correlation between plant height and panicle length, days to 50% and between days to 50% and panicle length indicates that selection for any of these characters would affect the other character in the opposite direction. Finally, there were also strong positive correlations between some of the yield components and nutritive characters, indicating that whichever character is selected for, would lead to a positive response in the other. Similar results were reported by Goud and Vasudero (1976), Martin (1978), Sadia et al. (2001), on Sorghum and Muldoon et al. (1984) in maize. CONCLUSION From the study it was concluded that EX-Numan has the highest performances in terms of plant height, length of internode and yield per plant. While EX- Bole, EX- Gulak, EX- Geriyo, EX- Wagga, EX- Mbamba and EX- Numan were excellent for the nutritive characters such as protein, carbohydrate, ash, fat and fibre contents. REFERENCES Aba, D.A. Nwasike, A and Zaria, A.A. (2001). Correlation and pah analysis for some characters contributing ot grasin yield in sorghum. Polymath Journal 1(1): Bello, D., Kadams, A.M., Simon, S.Y. and Mashi, D.S. (2007). Studies on genetic variability in cultivated sorghum of Adamawa state Nigeria. American Eurasian Journal of agriculture and environmental science (Pakistan). 3: Bello, D., Kadams, A.M., Simon, S.Y. (2001). Correction and path coefficient analysis of grain yield and its components in sorghum. Nigeria Journal of Tropical Agriculture. 3: 4-9. Chhidda, S. (2003). Modern Techniques of Raising Field Crops. Vijay primilani for oxford and IBH pub. Co; PVT Ltd. pp Doggett H. (1988). Sorghum. Longman Group UK, Ltd pp.512. Eckebil, J. P., Ross, M. W., Gardner, C. O. and Maranville, J. W. (1977). Heritability estimates, genetic correlation and predicted gains from S 1. progeny test in three grain sorghum population. Crop Science journal 17: Food and agricultural organization FAO (2003). Production Year Book. Goud, J. V. and Vasudero, R.J. (1976). Inheritance of Height in Sorghum American Society of Agronomy Journal 24: Martin J.H. (1978). Plant Characteristics and Yield in Grain Sorghum. American Society of Agronomy Journal. 20: Muldoon, J.F., Daynard, T.B., Vanduinea, B. and Toolenaar, M. (1984). Comparison among rates of appearance of leaf tips, collars and lead area in maize (zea mays) Maydica 29: Murdock, G.P. (1959). Africa, its peoples and their culture history. MC. Graw-Hill, New York Sadia, A. Asghar, A. Qamar, I.A. Arshad, M. and Salim, S. (2001). Correlation of economically important traits in sorghum bicolor varieties. Journal of biological science 1(5): Statistical Analysis System (SAS) Institute, (1999). SAS/STAT Users Guide for personal computers. Version 8 SAS Inst. Cary NC: pp 378. Totok, A.D.H. (1997). Estimating heritability values and genetic correlation, in several agronomic characters. American Journal of Botany. 87(3):P.145. NJAFE VOL. 6 NOS.1 & 2,

4 Nigerian Journal of Agriculture, Food and Environment. 6(1& 2):24-28 Table 1: Pooled (over years) mean square values for agronomic and nutritive characters in sorghum Source of variation degree of freedom Plant height Number of leaves Length of internodes lag leaf length Days to 50% Yield per plant Protein Carbohydrate Ash Fat fibre Years * * * Rep(Years) Cultivars * 28.98** 13.92** ** ** 0.91** 85.53** 5.14** 3.68** 39.87** Cultivar * 56.24* 45.38** 40.02* 34.54** 41.33* * 66.23* 42.51* 57.21* x Year Error *, ** Significant at 1% and 5% probability levels respectively Table 2: Pooled (over years) mean performance values for agronomic and nutritive characters in sorghum S/N Cultivars Plant Number Length of Flag leaf Days to 50% Yield per Protein % Carbohy- Ash % Fat % Fibre % height of leaves internode length plant (g) drate % 1 Ex-Bole d 11.6 e 19.1 c 9.95 d a 3.35 d 11.3 ab 70.5 a 1.25 g 3.05 f 1.95 f 2 Ex-Gulak c 14.0 d 17.1 d a a 2.96 d 10.5 c 65.5 b 1.50 f 4.66 b 1.96 f 3 Ex-Geriyo b 17.9 bc 23.3 a b a 4.60 b 11.6 a 62.4 cd 2.10 e 1.40 g 3.50 e 4 Ex-Wagga e 16.3 c 19.0 c 8.20 d a 0.96 f 11.1 b 62.6 c 3.20 c 3.96 c 6.66 c 5 Ex-Hong f 16.8 bc 19.0 c 7.23 e a 5.90 a 10.1 d 60.7 d 3.03 d 3.46 e 5.10 d 6 Ex-Girei h 18.5 b 19.1 c 6.90 e a 1.96 e 10.1 d 54.0 e 5.30 a 5.10 c 7.50 b 7 Ex-Mbamba g 11.2 e 21.1 b 9.10 c a 0.46 g 11.1 b 66.4 b 4.33 b 3.80 d 13.5 a 8 Ex-Numan a 20.3 a 23.1 a 8.10 e a 3.93 c 11.2 b 70.9 a 3.20 c 3.43 e 5.16 d Mean with the same letter are not significantly different (P 0.05) Duncan Multiple Rate Test (DMRT). NJAFE VOL. 6 NOS.1 & 2,

5 Nigerian Journal of Agriculture, Food and Environment. 6(1& 2):24-28 Table 3: Estimate of Variance Components and Heritability for eleven characters in sorghum pooled over two years S/N Traits δ 2 p SE δ 2 g SE Heritability (%) Plant height Number of leaves Length of internodes Flag leaf length Days to 50% Yield per plant Protein Carbohydrate Ash Fat Fibre Table 4: Genotypic upper right and Phenotypic lower left correlation coefficients for the eleven characters studied in sorghum pooled over two years Characters Plant height Number of leaves Length of internode Flag leaf length Days to 50% Yield per plant Plant height -0.64** 0.61** 0.36 ns 0.53** -0.63** 0.76** 0.81** -0.73** -0.8 ns ns Number of leaves -0.86** ns 0.29* -0.84** 0.99** -0.94** -0.91** 0.99** -0.41* -0.17* Length of internode ns 0.23* -0.42* 0.59* ns 0.79** 0.69* ns -0.17* 0.24* Flag leaf length ns ns 0.13 ns ns 0.29* -0.29* -0.20* 0.25* ns -0.47* Days to 50% 0.41* -0.51** 0.14 ns 0.25* -0.84** 0.81** 0.76** -0.83** 0.34* 0.17* Yield per plant -0.84** 0.99** 0.20* ns ns -0.94** -0.90** 0.99** -0.44* -0.21* Protein 0.62 ns -0.80** -0.21* ns 0.61 ns -0.82** 0.94** -0.95** 0.16* 0.14 ns Carbohydrate 0.66* -0.78** -0.17* ns 0.65 ns -0.80** 0.74** -0.92** 0.25* 0.78 ns Ash -0.85** 0.99** 0.19* 0.10 ns ns 0.99** -0.84** -0.81** ns ns Fat 0.17* -0.19* ns -0.44* -0.32* -0.18* 0.91 ns 0.13 ns ns 0.29* Fibre 0.22* -0.17* ns -0.57* -0.41* -0.19* ns ns ns 0.79 ** *, ** Significant at 1% and 5% probability levels respectively ns= not significant Protein Carbohy drate Ash Fat Fibre NJAFE VOL. 6 NOS.1 & 2,