Genetic Diversity of Cassava (Manihot esculenta Crantz) Genotypes in Ethiopia

Mulualem, Tewodros; WeldeMichael, Getachew; Benti, Tadesse; Walle, Tesfaye

doi:10.15580/gjas.2013.3.061013658

Cited by 11 publications

(4 citation statements)

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“…Genotypic variance accounted for a large proportion of the observed phenotypic variance for HI and plant height indicating inherent genetic variation for these traits [38]. On the contrary, Tewodros et al [39] found lower genotypic variances for these traits indicating strong environmental influences. Though estimates of genetic components indicated large genotypic variance ( 2 ) for a number of traits, their error variances were higher than the phenotypic variances in most cases implying strong environmental influence.…”

Section: Discussionmentioning

confidence: 99%

AMMI Stability Analysis and Estimation of Genetic Parameters for Growth and Yield Components in Cassava in the Forest and Guinea Savannah Ecologies of Ghana

Adjebeng‐Danquah

Manu-Aduening

Gracen

et al. 2017

International Journal of Agronomy

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Twenty cassava genotypes were arranged in a randomised complete block design with three replications and assessed for growth and yield stability using the additive main effect and multiplicative interaction (AMMI) analysis. Highly significant (P<0.001) effects of genotype, environment, and genotype ⁎ environment interaction were observed for all traits studied. The AMMI analysis of variance indicated that genotype accounted for 51% of the total sum of squares for height at first branching followed by environment (33%) and interaction (15%). For fresh root yield, environment effects accounted for 37% of the total sum of squares, whilst genotype and interaction accounted for 32% and 29%, respectively. Genotypic variances for harvest index (HI), plant height, storage root yield, and dry matter content contributed a greater proportion of the phenotypic variance indicating stronger genetic control. This suggests better chance of progress in the genetic improvement of these traits. Genotype MM96/1751 combined high yield with stability according to the yield stability index ranking across environments. On the other hand genotypes UCC 2001/449 and 96/1708 though high yielding were unstable according to AMMI stability value scores. However they can be tested further in more environments to ascertain their specific adaptability for release to farmers for cultivation to boost cassava production and ensure food security.

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Section: Discussionmentioning

confidence: 99%

AMMI Stability Analysis and Estimation of Genetic Parameters for Growth and Yield Components in Cassava in the Forest and Guinea Savannah Ecologies of Ghana

Adjebeng‐Danquah

Manu-Aduening

Gracen

et al. 2017

International Journal of Agronomy

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“…In Ethiopia, root crops such as potatoes, sweet potatoes, taro, and cassava covered <2% of the country's total cropping area, and accounted for 23.4, 38.4, and 17.7% of the overall production of root crops, respectively [CSA (Central Statistical Agency), 2018]. Cassava is among the most widely cultivated crops in some districts of Wolaita zone, southern Ethiopia (Kebede et al, 2012;Mulualem et al, 2013;Mulualem and Dagne, 2015;Laekemariam, 2016;Sarka, 2017;Tadesse et al, 2017;Legesse, 2018). It was the first root and tuber crop produced as a food and revenue-generating crop, followed by taro and sweet potato in this zone [Offa District Agricultural and Natural Resource Development Office (ODANRDO), 2018].…”

Section: Introductionmentioning

confidence: 99%

Cassava Production Efficiency in Southern Ethiopia: The Parametric Model Analysis

Tafesse¹,

Mena²,

Belay³

et al. 2021

Front. Sustain. Food Syst.

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Due to capital constraints and land scarcity in developing countries, introducing new technology to boost productivity is difficult. As a result, working to improve cassava production efficiency is the best option available. Cassava is increasingly being used as a food source as well as an industrial raw material in the production of economic goods. This study estimates cassava production efficiency and investigates the causes of inefficiency in southern Ethiopia. Cross-sectional data from 158 households were collected using a systematic questionnaire. The Cobb-Douglas (CDs) stochastic frontier production model was used to calculate production efficiency levels. The computed mean result showed technical efficiency (TE), allocative efficiency (AE), and economic efficiency (EE) levels of 74, 90, and 66%, respectively. This demonstrated that existing farm resources could increase average production efficiency by 26, 10, and 34%, respectively. The study found that land size, urea fertilizer application, and cassava planting cut all had a positive and significant effect on cassava production. It was discovered that TE was more important than AE as a source of benefit for EE. Inefficiency effects modeled using the two-limit Tobit model revealed that household head age, level of education, cassava variety, extension contact, rural credit, off-farm activities involvement to generate income, and farm size were the most important factors for improving TE, AE, and EE efficiencies. As a result, policymakers in government should consider these factors when addressing inefficiencies in cassava production. It is especially important to provide appropriate agricultural knowledge through short-term training, to provide farmers with access to formal education, to access improved cassava varieties, and to support agricultural extension services.

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“…Clusters IV and V, which are isolated and contain a single genotype each, suggest that genotypes within a cluster probably don't differ much from one another when considering the total number of characters evaluated. As a result, it would be preferable to attempt crossings between cultivars from distant clusters in order to obtain highly heterotic crossovers that are likely to generate a diverse range of segregants for selection [11,12].…”

Section: Grouping Of Genotypes Into Various Clustersmentioning

confidence: 99%

Assessment of Breeding Potential of Taro (Colocasia esculenta L.) Genotypes Using D2 Analysis under Hill Zone of Karnataka

Devaraju,

Latha, G. K.,

Sangeeta

et al. 2024

JABB

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Using Mahalanobis D2 statistics, the genetic diversity of 20 genotypes of taro for 21 characteristics was assessed as part of a breeding work to increase production potential. Five clusters were formed from the genotypes, indicating a significant level of genetic variation in the material being studied. With eight genotypes, Cluster I was the largest, followed by Cluster II with seven, and Cluster III with three. Each of the solitary clusters IV and V has a single genotype. The clusters that exhibit the greatest number of genotypes are indicative of limited genetic diversity. The intra-cluster distance was highest in cluster III and lowest in clusters IV and V. Clusters I and V have the highest inter-cluster distance, followed by II and V. These clusters could potentially be used as prospective genotypes for a hybridization programme. Tuber yield/plot (13%) showed the largest contribution to the overall divergence, followed by Corm yield/plant (12.20%) and Tuber yield/plant (11.43%). The finding that clusters V and III showed high cluster means for plant height, number of leaves per plant, leaf breadth, leaf area, leaf area index, tuber yield per plant, tuber yield per plot, and estimated tuber yield is noteworthy. These clusters can be effectively used in hybridization programmes to obtain desired transgressive segregants.

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Genetic Diversity of Cassava (Manihot esculenta Crantz) Genotypes in Ethiopia

Cited by 11 publications

References 4 publications

AMMI Stability Analysis and Estimation of Genetic Parameters for Growth and Yield Components in Cassava in the Forest and Guinea Savannah Ecologies of Ghana

AMMI Stability Analysis and Estimation of Genetic Parameters for Growth and Yield Components in Cassava in the Forest and Guinea Savannah Ecologies of Ghana

Cassava Production Efficiency in Southern Ethiopia: The Parametric Model Analysis

Assessment of Breeding Potential of Taro (Colocasia esculenta L.) Genotypes Using D2 Analysis under Hill Zone of Karnataka

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