Interpreting genotype×environment interactions for grain yield of rainfed durum wheat in Iran

Mohammadi, Reza; Farshadfar, Ezatollah; Amri, Ahmed

doi:10.1016/j.cj.2015.08.003

Cited by 46 publications

(40 citation statements)

References 28 publications

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“…This means that regardless of the climatic and soil conditions in a given growing season, the amount of grain yield variation explained by cultivar is similar. Similar results have been obtained for other 250 A. Derejko, M. Studnicki plant species, for example, in the studies of Temesgeri et al (2015) with faba beans (Vicia faba L.) and Mohammadi et al (2015) with durum wheat. The contribution of cultivar in these studies ranged from 2.5% to 5.3%.…”

Section: Discussionsupporting

confidence: 85%

Effect of cultivar, crop management, location and growing season on the grain yield of triticale

Derejko

Studnicki

2019

Biometrical Letters

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Triticale (Triticosecale Wittmack) is obtained through the crossing of wheat (Triticum ssp.) and rye (Secale cereale L.) and is characterized by high yield potential, good health and grain value, and high tolerance to biotic and abiotic stress. Poland is a very important region for progress in triticale breeding, since it is home to most cultivars, and numerous genetic studies on triticale have been carried out. Despite the tremendous interest in triticale among both breeders and researchers, there are no studies assessing the adaptation of cultivars to environmental conditions across growing seasons. This study was conducted to investigate the influence of cultivar, management, location and growing season on grain yield. At the same time, this approach provides a new way to determine whether there is any dependency between the eight seasons, and to find the cause of the yield response to environmental conditions in a given growing season.

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Section: Discussionsupporting

confidence: 85%

Effect of cultivar, crop management, location and growing season on the grain yield of triticale

Derejko

Studnicki

2019

Biometrical Letters

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“…The environments being assessed are grouped into different clusters with the same genotype reaction. Environment grouping using GGE biplot analysis has been reported for different crops such as wheat (Yan and Tinker, 2006;Kaya et al, 2006 andBavandpori et al, 2015), durum wheat (Mohammadi et al, 2010;2012 andLetta et al, 2008), barley (Mohammadi et al, 2009), soybean (Yan and Rajcan, 2002), rice (Samonte et al, 2005) and maize (Meseka et al, 2008 andChoukan, 2011).…”

Section: Introductionmentioning

confidence: 99%

Assessment of yield stability of maize inbred lines in multi-environment trials

Miah¹,

Salahuddin

Uddin³

2016

Bangladesh J. Sci. Ind. Res.

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Stable performance of maize hybrids at a specific growing region is critical for obtaining high and stable yield. The objectives of this study were to assess grain yield stability of fourteen maize inbred lines from five different diverse regions of Bangladesh during 2010 -2011 growing season (rabi) using genotype main effect plus genotype by environment interaction (GGE) biplot and to identify maize inbred lines that have both high mean yield and stable yield performance across test environments of Bangladesh. The GGE biplot method was effective in recognition that the inbreds G2 and G13 to be the most desirable ones for growing in Jessore (E1), G6 and G8 in Gazipur (E2), G3 in Rahmatpur (E3) and G1, G12, G14 and G9 in Debiganj (E5) region. The inbreds G5 and G7 had average grain yield and high stability, whereas the inbred line G10 had high grain yield and high stability. Besides, the inbred line G13 was the least stable across different environments. This technique can serve as a useful tool for recommendation of maize inbreds for specific growing regions taking into account the specificities of hybrids and growing conditions.

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“…, Mohammadi et al. ). Besides, the seed starch QTL detected in this study exhibited PVE in the range of 5.6–11.3%.…”

Section: Discussionmentioning

confidence: 99%

Mapping quantitative trait loci controlling soybean seed starch content in an interspecific cross of ‘Williams 82’ (Glycine max) and ‘PI 366121’ (Glycine soja)

et al. 2017

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Seed starch content (SSC) greatly affects the taste, flavour and processing properties of soy foods. The objective in this study was to identify quantitative trait loci (QTL) for SSC in soybean. A total in 169 recombinant inbred lines (RILs) derived from a cross in ‘Williams 82’ and ‘PI 366121’ were grown for three consecutive years. The SSC of the RILs displayed continuous variation with transgressive segregation and hence amenable for QTL mapping. Nine significant QTL exhibiting 5.6–11.3% of the total phenotypic variation (PVE) were identified. The QTL qSTR06_2 showed highest PVE (9.1–11.3%) at LOD values of 4.25–5.39. No stable QTL over 3 years were identified, indicating strong environmental influence on SSC. The QTL qSTR11_1 and qSTR20_1 were found to colocalize with some of the previously reported QTL for sucrose content in soybean, implying the interrelationship between starch and sucrose biosynthesis. As the carbohydrate components may affect key constituents such as oil and protein in soybean seed, findings of the study may be useful in breeding soybeans with improved seed composition.

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Interpreting genotype×environment interactions for grain yield of rainfed durum wheat in Iran

Cited by 46 publications

References 28 publications

Effect of cultivar, crop management, location and growing season on the grain yield of triticale

Effect of cultivar, crop management, location and growing season on the grain yield of triticale

Assessment of yield stability of maize inbred lines in multi-environment trials

Mapping quantitative trait loci controlling soybean seed starch content in an interspecific cross of ‘Williams 82’ (Glycine max) and ‘PI 366121’ (Glycine soja)

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