The present study was aimed to determine the variations in nutritional qualities of 15 mutant lines and two landraces of ginger (Zingiber officinale). Fifteen (15) gamma (γ)-ray induced mutants lines and two landraces of ginger were planted in 2017 early cropping season in the Teaching and Research Farm, Department of Crop Science, Faculty of Agriculture, Forestry and Wildlife Resources Management, University of Calabar, Calabar, Nigeria. To evaluate the nutritional qualities of these seventeen ginger genotypes at maturity, proximate analysis was carried out in the Biochemistry Laboratory of the National Root Crop Research Institute Umudike, Abia State, Nigeria. Using standard and official protocols of the Association of Official Analytical Chemists (AOAC). Results showed that the ginger lines varied significantly (P < 0.01) in all their proximate attributes. The moisture content ranged from 10.13% (UG1) to 12.95% (UG2). Mean dry matter was 88.89%; UG1 and UG2 had the highest (89.89%) and lowest (87.05%) dry matter content, respectively. Mean crude protein was 7.74%; UG2-9-01 and UG2-11-03 had the highest (8.25%) and lowest (7.29%) crude protein respectively. UG1-5-38 and UG1-5-22 had the highest (8.12%) and lowest (6.41%) crude fibre content respectively. The oleoresin content ranged from (6.25%) in UG2-9-01 to (9.09%) in UG1-11-07. UG1-5-04 and UG1-5-22 had the highest (2.88%) and lowest (2.22%) ash content respectively. UG2-9-01 had the highest carbohydrate content of (65.10%). While UG1-5-52 had the lowest (61.27%) The result showed that the ginger lines used in this study had high mean carbohydrate (62.85%) and protein (7.74%) contents as such can be used as supplementary sources of these nutrients for human and livestock. UG1-7-24, UG1-11-07 and UG1-5-18 with high oleoresin contents of 9.11%, 9.09% and 9.05% respectively are recommended to ginger breeders as useful genotypes for improving other ginger lines through micropropagation techniques especially when breeding for oleoresin quality, which is an important quality of ginger. In conclusion, further evaluation and testing of these ginger lines is recommended.
A field study was set to highlight the relationships and contributions of yield and yieldrelated traits to the choice of a superior cowpea variety. Five cowpea varieties, Sampea-7 (IAR 48), Sampea-8 (IAR 452-1), Sampea-10 (IAR 499-35), Sampea-11 (IAR 288) and Sampea-12 (IAR 391) were evaluated under normal growing conditions during the 2011/2012 growing season at the University of Calabar Teaching and Research Farm. The analysis of variance (ANOVA) for a randomized complete block design (RCBD) with three replications did show significant (P = .05) varietal differences for days to 50% flowering (50% FLW), days to 75% maturity (75% MTY), number of pods per plant (NPP), seed size (SDS), total plant biomass (TOB) and grain yield (GRY). The number of branches (NBR), pod length (PDL) and harvest index (HI) were not significantly different. The GRY had a positive correlation with all other yield-related traits except for the flowering traits and breeding for the former traits will be an indirect way to select for high grain yields. However, based on the weighted combined contributions of all the traits, the superiority of the varieties, Sampea-7 and Sampea-8, which were significantly different, followed an order different from their average grain yield order. Apparently, this re-ordered result highlights that the choice of a high performing cowpea variety could not be viewed as a function of high grain yield but a collective contribution of all other yield-related traits. These findings suggest that placing huge emphasis only on the economic yield (in this case, grain yield) as the main selection index could possibly fault the breeding and evaluation of superior cowpea varieties.
Multi-location trials were conducted in 2016 and 2017 at Calabar, Ikom and Ogoja in Cross River State, Nigeria, to determine the yield stability of 17 ginger genotypes (G1-G17) using genotype and genotype by environment (GGE) biplot model. The location and year combination gave six environments (E1-E6). The experiment was laid out in split plots using a randomized complete block design with three replications. Yield related traits like rhizome fingers, rhizome length, and rhizome yield were determined. E3 (i.e. Ikom in 2016) was ranked as the ideal environment for ginger production in Cross River State. While G5 (UGI-5-04) was classified as the ideal genotype for rhizome yield in Calabar, Ikom and Ogoja. Ikom in 2016 (E3) and 2017 (E4) were identified as megaenvironments for UG1-13-02, UG1-5-04, UG1-5-18, UG1-5-35, UG1-5-38, UG2-11-03 and UG2-9-01 while Ogoja in 2016 (E5) and 2017 (E6) were identified as mega-environments for UG1-2-35, UG1-5-48, UG1-5-52 and UG1-7-24 ginger mutants. Ikom is recommended as a suitable environment for the cultivation of generally adapted ginger genotypes namely, UG1-5-04, UG1-5-38, UG1-13-02 and UG2-9-01). Ogoja was suitable for specific adaptation of UG1-7-24 and UG1-5-48 ginger mutants. These mutants are recommended for consideration in subsequent ginger breeding and improvement programmes. Contribution/ OriginalityThe research explored that the Ikom region has an ideal environment for the cultivation of ginger in Cross River State. Different varieties of ginger mutants were identified as ideal genotypes in other regions like Ogoja and Calabar.
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