Genotype by Environment Interaction for Protein Content of Malt Barley Genotypes Using the Additive Main Effect and Multiplicative Interaction Effect Model (AMMI) and Genotype Plus Genotype by Environment Interaction (GGE) Biplot

Mehari, Muez; Alamerew, Sentayehu; Lakew, Berhane

doi:10.3923/ajar.2015.305.314

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…This means that Tengeru was able to provide much information about differences among genotypes than other locations. In line with explanation by Mehari et al (2015), Tengeru was more strongly correlated to Mlangarini as the angle between them was small, while the angle between both Tangeru and Mlangarini with Selian was bigger and hence not closely related to them. Therefore, the biplot identified Tengeru to be the test environment that is close to an ideal environment which is eventually defined to be both discriminating and representative (Yan et al, 2007).…”

Section: Test Environment Evaluationsupporting

confidence: 78%

Evaluation of maize accessions for diversity, yield and tolerance to maize lethal necrosis disease in Tanzania

Kabululu¹

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Maize (Zea mays L.) is among the most important crops in Tanzania with still low average yield of 1.2 metric tonnes per hectare as compared with potential yields of 4 to 5 metric tonnes per hectare. Low yield is due to a number of factors including pests and diseases. Recently, East Africa has been hard hit by a new deadly disease of maize called Maize Lethal Necrosis Disease (MLND). The disease started in Kenya in 2011 and later spread to other countries including Tanzania and it continues to spread fast in other countries. It is caused by a combination of two viruses i.e., Maize chlorotic mottle virus (MCMV) and any other Potyvirus, with Sugarcane mosaic virus (SCMV) reported for East Africa. The aim of this study was to evaluate genetic diversities of maize accessions (focusing more on landraces) with respect to their MLND resistance and optimum yield in Tanzania. Fifty one maize landraces from National Plant Genetic Resources Centre (NPGRC) in Tanzania, thirty four commercial varieties as checks for yield and thirteen elite lines from CIMMYT Kenya as checks for resistance against MLND were used. Three field experiments were conducted at Tengeru, Mlangarini and Selian in Tanzania for genetic diversity study and MLND evaluation, one field experiment was conducted at Naivasha Kenya for MLND evaluation. Molecular study was conducted at Nelson Mandela African Institution of Science and Technology in Tanzania. Significant (p < 0.05) variations were observed among the tested accessions and an OPV Situka 1 and a hybrid DH 04 were the best yielding varieties across Tengeru, Mlangarini and Selian with 116.01g and 115.90g per plant respectively. Landraces TZA 2793 and TZA 5170 were among the highest yielding accessions with 100.46g and 99.80g per plant respectively. The allele distribution and frequency associated with quantitative trait loci for SCMV resistance were detected with landraces and TZA 2793 as well as TZA 3544 expressed low MLND progression across Mlangarini and Naivasha. The results of significant genetic diversity and response against MLND of maize landraces tested in this study calls for further investigation to ascertain their utilization in breeding and crop improvement.

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Section: Test Environment Evaluationsupporting

confidence: 78%

Evaluation of maize accessions for diversity, yield and tolerance to maize lethal necrosis disease in Tanzania

Kabululu¹

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“…This means that Tengeru was able to provide much information about differences among genotypes than other locations. In relation to the concept explained by Mehari et al (2015), Tengeru was more strongly correlated to Mlangarini as the angle between them was small (Figure 3), while the angle between both Tangeru and Mlangarini with Selian was bigger and hence not closely related to them. Therefore, the biplot (Figure 3) identified Tengeru to be the test environment that is close to an ideal environment which is eventually defined to be both discriminating and representative (Yan et al, 2007).…”

Section: Gge Biplot Analysismentioning

confidence: 94%

Evaluation of agronomic performance of local and improved maize varieties in Tanzania

Kabululu

Feyissa

Ndakidemi

2017

IJARe

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<span>Fifty Tanzanian local maize cultivars, seven popularly grown commercial varieties in Tanzania and eleven elite lines from CIMMYT, Nairobi, Kenya were evaluated for agronomic performance. The genotypes were subjected to randomized complete block design at three sites in 2015, both conducted at Arusha region, in Tanzania. The analysis identified highly significant variances among genotypes evaluated and their interactions with environments. The GGE biplot analyses identified the winning genotypes on mean yield and stability. An open pollinated variety (OPV) Situka 1 and an hybrid DH 04 had generally the best performance in terms of grain yield and stability across all three locations. A local cultivar TZA 2793 emerged to be the promising landrace with overall appealing yield and stability performance. The obtained information through this current study may be a good source of new allelic diversity that could be used for developing different important elite maize materials.</span>

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“…However, due to low productivity, poor malt quality, and very limited expansion of malt barley production, the net import bill for malt barley jumped from US$240 thousand in 1997 to US$40 Abiotic and biotic stresses, weak technology generation and transfer, limited availability and access to inputs, sub-optimal application of fertilizers, and poor access to markets due to poor linkages between the farmer producers and the brewing industry are the main constraints responsible for low productivity, poor malt quality and limited expansion of malt barley production in Ethiopia. Among abiotic stresses, it is a well-established fact that the physical environment strongly affects the genetic potential and quality of malt barley (Ajith, 2009;Muhe, 2011;EIAR, 2015;Mehari, Alamerew, & Lakew, 2015;Mekonnen, 2013;Rashid, Abate, Lemma, Warner, Kasa, & Minot, 2015). In our study, the physical environment includes the soil type, soil depth, soil pH, soil texture, soil drainage, land slope, altitude, length of growing period, and climate (rainfall and temperature).…”

Section: Introductionmentioning

confidence: 99%

Integrated Multi-criteria Land Suitability Evaluation and Mapping for Scaling Malt Barley Varieties in Rain-Fed Production Areas of Ethiopia

Molla¹,

Nigussie²,

Bishaw³

et al. 2020

JAS

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Information on variety specific land suitability analysis was not available in Ethiopia. Therefore, integrated multi-criteria land suitability analysis and mapping for contrasting malt barley varieties was carried out to identify where and how much potentially suitable land exists in the country. The main factors considered for analysis include rainfall and temperature during the growing period, length of growing period, digital elevation models, (altitude and slope data) and soil characteristics (types, pH, depth, texture and drainage). The malt barley varieties included are late maturing Bekoji-1, EH1847 and Holker; and early maturing Grace, IBON 174/03 and Sabini. For classification of the data layers according to the degree of suitability for each variety, various reports and other relevant information were reviewed and used in defining the limits of the suitability ranges of malt barley varieties. The overall suitability was computed by multiplying the selected criteria weight by the assigned sub-criteria score and summing these values in the ArcGIS Model Builder. The analysis showing the extent and patterns of suitable land area available for the selected malt barley varieties are presented in the form of tabular data and maps. Highly suitable areas for these varieties include: 125,332 ha for Bekoji-1; 124,004 ha for EH1847; 775,312 ha for Grace; 125,356 ha for Holker; 1,677,388 ha for IBON 174/03; and 307,952 ha for Sabini. The results suggest that current improved malt barley varieties can be targeted for scaling out in the identified land suitability classes in the highlands of Ethiopia. Results also suggest that future research and development works should give priority for developing early maturing, acidic and waterlogging soil tolerant malt barley varieties. The results can be useful for policy and decision making to ensure land resources are used in the most productive and sustainable ways and solve the mismatches between current land use and land suitability for malt barley varieties in the country.

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Genotype by Environment Interaction for Protein Content of Malt Barley Genotypes Using the Additive Main Effect and Multiplicative Interaction Effect Model (AMMI) and Genotype Plus Genotype by Environment Interaction (GGE) Biplot

Cited by 4 publications

References 12 publications

Evaluation of maize accessions for diversity, yield and tolerance to maize lethal necrosis disease in Tanzania

Evaluation of maize accessions for diversity, yield and tolerance to maize lethal necrosis disease in Tanzania

Evaluation of agronomic performance of local and improved maize varieties in Tanzania

Integrated Multi-criteria Land Suitability Evaluation and Mapping for Scaling Malt Barley Varieties in Rain-Fed Production Areas of Ethiopia

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