Browsing by Author "Maya Kumari"

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Association of staygreen trait with canopy temperature depression and yield traits under terminal heat stress in wheat (Triticum aestivum L.)
(Kluwer Academic Publishers, 2013) Maya Kumari; R.N. Pudake; V.P. Singh; Arun K. Joshi
The presence or absence of the staygreen trait was screened for 3 consecutive years in 963 wheat lines from various sources, including Indian and CIMMYT germplasm. Staygreen was assessed at the late dough stage by visual scoring (0-9 scale) and the leaf area under greenness (LAUG) measurement. Around 5. 5 % of the lines were staygreen, 10. 5 % were moderately staygreen, and the remaining lines showed little or no expression of the trait. One hundred lines showing diversity for the staygreen trait were sown under three different sowing dates (timely, late and very late) for 3 consecutive years in three replications to determine the association of staygreen with heat tolerance. There was a decline in yield, biomass, grain filling duration (GFD) and 1,000 grain weight (TGW) under late and very late sowing conditions owing to terminal stress at anthesis and later stages. However, the decline was relatively less in staygreen genotypes compared to the non-staygreen (NSG) ones. The correlation study showed that LAUG and canopy temperature depression (CTD) were strongly correlated. LAUG and CTD were also significantly associated with grain yield, GFD and biomass. To further confirm the association of the staygreen trait with terminal heat stress, individual F2-derived F7 progenies from the cross of the 'staygreen' lines with NSG were evaluated for yield and yield traits at the three sowing dates. In each cross, the staygreen progenies showed a significantly smaller decline in yield and TGW under heat stress than the NSG progenies. These results appear to suggest an association between the staygreen trait and terminal heat stress and, thereby, that the staygreen trait could be used as a morphological marker in wheat to screen for heat tolerance. © 2012 Springer Science+Business Media B.V.
From Adversity to Unity: Women, Pandemic, and Social Solidarity
(Springer Science+Business Media, 2025) Maya Kumari; Pushpanjali Kumari; Shubham Kumar Sanu
The global pandemic in 2020 brought unprecedented challenges, particularly affecting vulnerable populations. As countries implemented lockdowns to curb the spread of the virus, the repercussions were profound, forcing many, especially the economically disadvantaged, to return to their homes, grappling with uncertainties surrounding life and livelihood. This period highlighted the struggles of women, often regarded as the backbone of society, through media narratives showcasing the plight of migrant women facing dire conditions. This study delves into the multifaceted challenges confronted by women during the pandemic and explores the role of social solidarity in empowering them to navigate these hardships. Drawing on existing studies and research, the research sheds light on critical issues such as education dropout, economic crises, direct and indirect health concerns (with a significant impact on maternal health), food scarcity, increased workload, and various forms of violence against women. Notably, violence against women emerged as a parallel pandemic, exacerbating the challenges faced by women during this tumultuous time. While media reports and institutional studies amplified the voices of these women, ground-level reports painted a stark reality of worsened conditions, violence, and human rights violations. In a notable contrast, where government interventions often fell short, the resilience and mutual support among women and the community became a source of strength. This study underscores how social solidarity became a protective force, ensuring numerous women’s rights, safety, and fear-free existence within their communities. A comparative analysis between rural and urban societies reveals disparities in social solidarity, with the latter often lacking the cohesive support observed in rural communities. Consequently, women in urban areas found themselves confronting not only the viral pandemic but also a parallel crisis stemming from the dearth of social solidarity. This study is an attempt to highlight the impact of violence faced by women during a pandemic and the role of government. Further, this study also aims to examine the role of social solidarity in adverse situations. © 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
Identification of microsatellite markers associated with staygreen trait in wheat RILs
(2012) Maya Kumari; Ramesh N. Pudake; V.P. Singh
The Recombinant Inbred Lines in F7 generation derived from two crosses (Ning8201 × Sonalika and Yangmai6 × Sonalika) along with parents were evaluated for varying degree of expression for staygreen, for two consecutive years in two replications. LAUG values ranged from 73.87 to 75.62 in staygreen parents, Ning8201 and Yangmai6, as compared to non-staygreen parent Sonalika (10.38). F1 showed intermediate behaviour for staygreen expression indicating additive nature of inheritance for the trait. The distribution of RILs was normal indicating polygenic nature of the trait. Analysis of variance for LAUG displayed significant G×E interaction. Of the 119 SSR markers used in two crosses, 22 were polymorphic on parents. In Ning8201 × Sonalika, SSR markers WMC-10, WMC-74, and WMC-76 were significantly (p<0.001) linked with staygreen trait. In the other cross (Yangmai6 × Sonalika) the markers BARC-109, BARC-1120, BARC-04 and BARC-74 were significantly linked. The polymorphic markers for both crosses grouped under one linkage group (MAPMAKER/ EXP 3.0b).Two QTLs were detected in Ning8201 × Sonalika cross (R2=8.28) and (R2=18.11). A major QTL was detected in Yangmai6 × Sonalika, which alone explained 44.34% of the phenotypic variation. The rest of the loci contributed on an average 12.9% to the total phenotypic variability. The linked markers obtained in the present study for staygreen trait can be used further for fine mapping and cloning of gene that can be used further for molecular assisted breeding in wheat.
Identification of QTLs for stay green trait in wheat (Triticum aestivum L.) in the 'Chirya 3' × 'Sonalika' population
(2010) Uttam Kumar; Arun K. Joshi; Maya Kumari; Rajneesh Paliwal; Sundeep Kumar; Marion S. Röder
Stay green or delayed senescence is considered to play a crucial role in grain development in wheat when assimilates are limited. We identified three QTLs for stay green on the chromosomes 1AS, 3BS and 7DS using a recombinant inbred (RI) population developed by making crosses between the stay green parent 'Chirya 3' and non-stay green 'Sonalika'. The RI lines were evaluated in natural field conditions for 2 years in replicated trial. The QTL on chromosome 1A was identified in both the years, while the QTLs on 3BS and 7DS were identified only in 1st and 2nd year, respectively. The QTLs explained up to 38.7% of phenotypic variation in a final simultaneous fit. The alleles for higher stay green values derived from the stay green parent 'Chirya 3'. The QTLs were named as QSg.bhu-1A, QSg.bhu-3B and QSg.bhu-7D. The QTL QSg.bhu-3B and QSg.bhu-7D were placed in the 3BS9-0.57-0.78 and 7DS5-0.36-0.61 deletion bins, respectively. © 2010 Springer Science+Business Media B.V.
Rainfall-runoff modelling using GIS based SCS-CN method in umiam catchment region, Meghalaya, India
(Elsevier Ltd, 2024) Maya Kumari; Diksha; Pranjit Kalita; Varun Narayan Mishra; Arti Choudhary; Hazem Ghassan Abdo
Rainfall-runoff is a crucial parameter and is often applied in assessing water resources. It is a significant constituent for recharging groundwater and plays an important role in understanding the hydrological characteristics of a basin for watershed management practices. In the current work, the soil conservation service-curve number (SCS–CN), coupled with GIS and remote sensing techniques, has been utilized to estimate the runoff and discharge in the Umiam Catchment area of Meghalaya for the years 2011 and 2020. This area receives heavy rainfall throughout the year, which contributes to the degradation of topsoil and the initiation of landslides. The rainfall-runoff modelling incorporates various thematic layers prepared in the GIS platform to calculate the weighted CN value. The antecedent moisture condition (AMC) was applied for CN correction. Both annual and monthly runoff and discharge were calculated for a total of four sub-watersheds. The result showed that land alteration and an increase in rainfall have been the predominant reasons for an increase in runoff in 2020 compared to 2011. Comparing both years, agricultural land, built-up, and open scrub have increased followed by shrinking open and dense forests. Sub-watersheds 2 and 4 had critical discharge changes of 75.95 and 75.82 cubic meters per second (cumec) from year 2011–2020 respectively. This study emphasizes the efficacy of SCS-CN method for simulating and estimating the runoff of a basin for better watershed planning and sustainable management at the regional level. It may also help in recognizing and examining the characteristics and volume of water resources. © 2024 Elsevier Ltd
Spatially explicit simulation and forecasting of urban growth using weights of evidence based cellular automata model in a millennium city of India
(Elsevier Ltd, 2024) Pankaj Kumar Yadav; Varun Narayan Mishra; Maya Kumari; Akshay Kumar; Pradeep Kumar; Rajeev Bhatla
The present study focuses on quantifying and simulating the future urban growth based on the land use/land cover (LULC) data created from the Landsat images of the year 1999, 2011, and 2022. These LULC maps help in analysing the expansion of urban areas over the years and forecast their potential growth in the future. The spatio-temporal processes of urban growth are quantified, and future patterns are simulated and forecasted using Weights of Evidence based Cellular Automata model built in Dinamica EGO (Environment for Geoprocessing Objects) platform. The process of urban growth was manifested through prominent contributing factors of infill expansion namely, distance to built-up areas, distance to main roads, population density, and public services etc. The model's performance was evaluated using Kappa statistics and the percentage of correct prediction (PCP) based two-way comparison method. For this purpose, the simulated map was first compared with the observed information of year 2022 using Kappa indices followed by the PCP value (90.40%) exhibiting high predictive ability of the model. These findings corroborate that the model can forecast the future urban growth scenarios effectively with reasonable accuracy. Based on the outcomes, the forecasting of future urban growth scenarios for years 2033 and 2044 was accomplished. Analysis of the LULC changes displays that urban land use will experience the highest increase. Growth in the study area is predicted to increase by 23.5% and 26.7% in year 2033 and 2044 respectively where new urban settlements can appear. The results demonstrated that an integrated geospatial model provides essential information about the pattern, simulation, and prediction of urban growth associated with various driving variables. © 2024 Elsevier Ltd