Genotype environment interaction on biomass production in sorghum (Sorghum bicolor L. Moench) in North-Western Nigeria

Abubakar, L.; Bubuche, T. S.

doi:10.5897/ajar2013.6868

Cited by 11 publications

(9 citation statements)

References 15 publications

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“…Genetic variability can be inferred by phenotypic expression although the consequences of phenotypic variations depend largely on environmental changes and are further complicated by the fact that genotypes do not respond similarly to environmental changes [14]. In this study phenotypic variability among the forage and grain sorghum genotypes was observed for several traits and results of the mean sum of squares for genotypes, locations, seasons and their interactions are presented in Table 2.…”

Section: Phenotypic Variabilitymentioning

confidence: 93%

“…The genotype by season (G×S) interaction effects were significantly different (P < 0.001) for all traits in both grain and forage sorghums except days to 50% flowering in the grain types and leaf stem ratio among the forage sorghums. The wider utilization of quantitative traits that exhibited significant G×L effects is limited possibly because significant G×E for a trait reduces the usefulness of the genotype over all locations for selecting and advancing superior genotypes to the next stage of selection [14].…”

Section: Phenotypic Variabilitymentioning

confidence: 99%

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Phenotypic Diversity of Selected Dual Purpose Forage and Grain Sorghum Genotypes

Chikuta

Odong

Kabi

et al. 2015

AJEA

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Section: Phenotypic Variabilitymentioning

confidence: 93%

Section: Phenotypic Variabilitymentioning

confidence: 99%

Phenotypic Diversity of Selected Dual Purpose Forage and Grain Sorghum Genotypes

Chikuta

Odong

Kabi

et al. 2015

AJEA

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“…It occupied third position in terms of production in Africa after wheat and maize and fifth in the world after wheat, maize, rice and barley (FAO, 2017). Moreover, it is widely used as a source of nutrition, fodder, biofuel, fiber and confection (Abubakar and Bubuche, 2013). It is able to grow under severe stress conditions.…”

Section: Introductionmentioning

confidence: 99%

“…It is able to grow under severe stress conditions. Sorghum can be cultivated successfully on almost all soils and in the temperature range of 16-40°C (Abubakar and Bubuche, 2013). Ethiopia is the third largest sorghum producer in Africa next to Nigeria and Sudan (FAOSTAT, 2012).…”

Section: Introductionmentioning

confidence: 99%

Additive Main Effect and Multiplicative Interactions (AMMI) and Regression Analysis in Sorghum [<i>Sorghum bicolor</i> (L). Moench] Varieties

Chala¹,

Tesso²,

Lule³

et al. 2019

J. App. Bioscience.

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Background and justification: Ethiopia is the third largest sorghum producer in Africa next to Nigeria and Sudan. Shortage of widely adapted and stable high yielding variety is one of the major bottlenecks for production and productivity of sorghum in the country. Grain yield performance is not the only parameter for selection as a genotype with the highest grain yield would not necessarily mean stable and adaptable across location and years. Eberhart and Russell (1996) and AMMI model could be the preferable tools to identify stable, high yielding and adaptable genotype (s) for wider or specific environments. Objectives: To identify stable high yielding sorghum varieties that could be adapted for wider and/or specific environments and make recommendations for further demonstration and production in the test environments and similar agro ecologies. Material and methods: A total of 21 released sorghum varieties and a local check were evaluated at three locations in western Ethiopia (Bako, Gute, Biloboshe) and two locations in eastern Ethiopia (Mechara and Mieso) in 2017 main cropping seasons. The trial was arranged in a randomized complete block design (RCBD) in three replications. Summary of results and application of the study: The combined analysis of variance revealed highly significant effect of environment and genotype by environment interactions for grain yield. This indicated that the tested varieties showed inconsistent grain yield performance across locations. Birmash variety gave the highest grain yield with average yield of 3.5 ton ha-1 but specifically adapted to Gute, Biloboshe and Mechara. Baji was the second high yielding variety with mean grain yield of 3.3 ton ha-1 and relatively with wider adaptability. The first two IPCAs accounted for a total of 88.64% of the interaction sum square. In general, deviation from regression coefficient, AMMI stability value and genotype selection index revealed that Baji, Birmash, Emahoy, IS9302 and Gambella-1107 were relatively stable varieties with optimum grain yield and therefore recommended for further demonstration and popularization in the test locations and areas with similar agro-ecologies.

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“…Similar findings from Chikuta et al (2015) and Ayana et al (2000) indicate that gradient of rainfall, temperature and growing sites are important for genotype variation. The phenotypic expression can infer genetic variability and consequences of phenotypic variation due to changes in the environment (Abubakar and Bubuche, 2013). Contrary, Seetharam and Ganesamurthy (2013) found that a narrow difference between the phenotypic and genetic variation are an indication of little environmental influence.…”

Section: Variability and Heritability Of The Charactersmentioning

confidence: 99%

Identification of important morphological traits in Mozambican sorghum [Sorghum bicolor (L.) Moench] germplasm using multivariate analysis

Mulima¹,

Sibiya²,

Musvosvi³

et al. 2018

Afr. J. Agric. Res.

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Classification of sorghum [Sorghum bicolor (L) Moench] breeding material based on multiple crucial characters is important towards the possible formation of homogeneous groups of genotypes and groups that can be exploited in the identification of parents for use in a breeding program. The objective was to determine the morphological characters that distinguish desirable breeding material and group the genotypes of sorghum according to similarity. Principal component analysis (PCA) and cluster analysis were used to establish the relationships among germplasm and the Shannon Diversity index was used to quantify the level of diversity. The experiment involving 26 sorghum genotypes was conducted at Sussundenga Research Station across two seasons and laid out in a 13 × 2 alpha lattice design with four replications. Cluster analysis grouped genotypes into four clusters based on 15 evaluated traits. Five principal components cumulatively accounting for 58.5% of the total variation were estimated from the PCA analysis. The results showed that genotypes 150B, IS 14257R, LARSVYT 46B, TX 631B, TX 630B and 8601B were the early maturity while for late maturity genotypes were MA 6B, A 6352R, ICSA 19B and MZ 30R. The genotypes IS 7179R, SPL 9B, A 6353R, SPL 38B, SDS 6013R and MZ 2R showed a potential for grain yield improvement. Other genotypes presented potential for drought tolerance and birds attack. The multivariate analyses clearly showed the grouping of the genotypes according to the characters outlined in the study. These results have implications in selecting parents for use in sorghum breeding program.

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Genotype environment interaction on biomass production in sorghum (Sorghum bicolor L. Moench) in North-Western Nigeria

Cited by 11 publications

References 15 publications

Phenotypic Diversity of Selected Dual Purpose Forage and Grain Sorghum Genotypes

Phenotypic Diversity of Selected Dual Purpose Forage and Grain Sorghum Genotypes

Additive Main Effect and Multiplicative Interactions (AMMI) and Regression Analysis in Sorghum [<i>Sorghum bicolor</i> (L). Moench] Varieties

Identification of important morphological traits in Mozambican sorghum [Sorghum bicolor (L.) Moench] germplasm using multivariate analysis

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