Genetic analysis for phenological and physiological traits in wheat (<i>Triticum aestivum</i>L.) under heat stress environment

Kumar, Rajendra; Prasad, Birendra; Singh, M. P.; Verma, Ajay; Tyagi, Bhuvdeva Singh

doi:10.5958/j.0976-058x.48.1.011

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…Kumar et al (2014) evaluated the 50 diverse wheat genotypes and exhibited highly significant differences at genotypic level for all the traits studied, under normal and heat-stress environments. All the traits expressed significant interaction with environments, indicating that all traits respond to high temperature in different ways in different genotypes.…”

Section: Resultsmentioning

confidence: 99%

Genetic analysis of yield and heat stress related traits in wheat (Triticum aestivum L. em. Thell) using microsatellite markers

Gupta

Chawla

Goel

et al. 2015

JANS

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Microsatellite markers were used for genetic analysis of terminal heat tolerance in F 2 (PBW373 × WH1081) population of wheat (Triticum aestivum L. em. Thell). Two parents were evaluated in field under normal sown and late sown conditions. For genotyping DNA from both parents PBW373 and WH1081 was amplified using 200 SSRs. Only 22 SSRs produced polymorphic bands, of size between 100 to 300 bp and an average of 1.45 alleles. The single marker analysis identified 19 markers indicating the putative QTLs for yield, its components and heat stress related physiological traits. The number of markers on these 16 linkage groups varied from one to four. On A genome 13 QTLs on B genome 5 QTLs and on D genome 9 QTLs were identified, respectively. The A, B and D genomes had 1360.3 cM, 272.4 cM and 919.5 cM of linkage coverage with average interval distances of 104.63 cM, 54.48 cM and 102.16 cM/Marker. A total of nine QTLs were resolved following composite interval mapping, one QTL was detected at a LOD score equal to threshold value of 2.5 while eight at LOD scores above the threshold value. All the nine QTLs were shown to be on definitive location on chromosome 3A (QDh.CCSHAU-3A, QDa.CCSHAU-3A and QPm.CCSHAU-3A), chromosome (QBm.CCSHAU-5A, QCtd.CCSHAU-5A and QCl.fl.CCSHAU-5A), chromosome6A (QPh.CCSHAU-6A) and chromosome3B (QTgw.CCSHAU and QMts.CCSHAU-3B). Use of these markers save times, resources and energy that are needed not only for raising large segregating populations for sveral generations, but also for estimating the parameters used for selection.

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Section: Resultsmentioning

confidence: 99%

Genetic analysis of yield and heat stress related traits in wheat (Triticum aestivum L. em. Thell) using microsatellite markers

Gupta

Chawla

Goel

et al. 2015

JANS

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“…All datasets were standardized, using linear min-max normalization (Equations (1) and (2)) according to (Iyengar and Sudarshan) [16] [17]. When the indicators are related positively with the vulnerability, the normalized value of the indicator is computed as follow.…”

Section: Methods and Datamentioning

confidence: 99%

Social Vulnerability Assessment to Flood in Medina Gounass Dakar

Sané¹,

Gaye²,

Diakhaté³

et al. 2015

JGIS

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This paper is about the assessment of social vulnerability (SV) as a critical component of comprehensive disaster risk assessment. This study was conducted in Medina Gounass Dakar, Senegal, to bring out evidence that flooding was a threat to human security. The aim of this present study is to assess the social vulnerability to flood in Medina Gounass. Survey was carried out using structured questionnaires drawn on one hundred randomly selected households. For vulnerability assessment, the Methods for the Improvement of Vulnerability Assessment in Europe (MOVE) framework and Arc GIS are used to characterize vulnerability through three key factors, namely, 1) exposure, 2) susceptibility, and 3) lack of resilience. As a result, Medina Gounass inhabitants have a particular relationship with the place they have been living for decades. Although facing diseases and many challenges in their everyday life, people actually resist the government's relocation projects because of their symbolic relationship with the area.

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“…Two types of functional relationships are possible: vulnerability increases with increase (decrease) in the value of the indicator. All datasets were normalised, using linear min-max normalization (Equations (3) and (4)) according to (Iyengar and Sudarshan) [12] [13].…”

Section: Computation Of Flood Vulnerability Index (Fvi)mentioning

confidence: 99%

Quantitative Assessment of Vulnerability to Flood Hazards in Downstream Area of Mono Basin, South-Eastern Togo: Yoto District

Kissi¹,

Abbey²,

Agboka³

et al. 2015

JGIS

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The population located along Mono River in the Yoto district faces great challenges in terms of repeated flood disasters in recent years. This paper aims at assessing the conditions which influence flood damage in the study area by using indicators to compute a Flood Vulnerability Index (FVI). The study relies on Turner et al.'s vulnerability framework and distinguishes three main components (exposure, susceptibility and resilience) that allow a more in-depth analysis and interpretation of local indicators. As a result flood disaster in the study area is not only due to the extreme variability in terms of flood magnitude and frequency in the Mono River, but also to the interaction between human and the environment. The lack of vegetation along the river bank, the closeness of households' farmlands to the river body, the type of construction and the position of settlements, the household size, the low level education of household head, the lack of diversification of livelihood strategies, the lack of adequate flood warning system, the lack of willingness and ability to take responsive actions coupled with inadequate emergency services, are identified as main determinants increasing communities' vulnerability to flood disaster. Furthermore, the computation of Flood Vulnerability Index (FVI) offers easy comparison of communities' vulnerability to flood disaster and pinpoints the most vulnerable communities. At the end of the study, flood exposure, susceptibility, resilience and vulnerability maps were generated.

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Genetic analysis for phenological and physiological traits in wheat (Triticum aestivumL.) under heat stress environment

Cited by 5 publications

References 6 publications

Genetic analysis of yield and heat stress related traits in wheat (Triticum aestivum L. em. Thell) using microsatellite markers

Genetic analysis of yield and heat stress related traits in wheat (Triticum aestivum L. em. Thell) using microsatellite markers

Social Vulnerability Assessment to Flood in Medina Gounass Dakar

Quantitative Assessment of Vulnerability to Flood Hazards in Downstream Area of Mono Basin, South-Eastern Togo: Yoto District

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