10 research outputs found
Identification and characterization of rhizospheric microbial diversity by 16S ribosomal RNA gene sequencing
In the present study, samples of rhizosphere and root nodules were collected from different areas of Pakistan to isolate plant growth promoting rhizobacteria. Identification of bacterial isolates was made by 16S rRNA gene sequence analysis and taxonomical confirmation on EzTaxon Server. The identified bacterial strains were belonged to 5 genera i.e. Ensifer, Bacillus, Pseudomona, Leclercia and Rhizobium. Phylogenetic analysis inferred from 16S rRNA gene sequences showed the evolutionary relationship of bacterial strains with the respective genera. Based on phylogenetic analysis, some candidate novel species were also identified. The bacterial strains were also characterized for morphological, physiological, biochemical tests and glucose dehydrogenase (gdh) gene that involved in the phosphate solublization using cofactor pyrroloquinolone quinone (PQQ). Seven rhizoshperic and 3 root nodulating stains are positive for gdh gene. Furthermore, this study confirms a novel association between microbes and their hosts like field grown crops, leguminous and non-leguminous plants. It was concluded that a diverse group of bacterial population exist in the rhizosphere and root nodules that might be useful in evaluating the mechanisms behind plant microbial interactions and strains QAU-63 and QAU-68 have sequence similarity of 97 and 95% which might be declared as novel after further taxonomic characterization
Spatiotemporal variation in land use land cover in the response to local climate change using multispectral remote sensing data
Climate change is likely to have serious social, economic, and environmental impacts on farmers whose subsistence depends on nature. Land Use Land Cover (LULC) changes were examined as a significant tool for assessing changes at diverse temporal and spatial scales. Normalized Difference Vegetation Index (NDVI) has the potential ability to signify the vegetation structures of various eco-regions and provide valuable information as a remote sensing tool in studying vegetation phenology cycles. In this study, we used remote sensing and Geographical Information System (GIS) techniques with Maximum Likelihood Classification (MLC) to identify the LULC changes for 40 years in the Sahiwal District. Later, we conducted 120 questionnaires administered to local farmers which were used to correlate climate changes with NDVI. The LULC maps were prepared using MLC and training sites for the years 1981, 2001, and 2021. Regression analysis (R2) was performed to identify the relationship between temperature and vegetation cover (NDVI) in the study area. Results indicate that the build-up area was increased from 7203.76 ha (2.25%) to 31,081.3 ha (9.70%), while the vegetation area decreased by 14,427.1 ha (4.5%) from 1981 to 2021 in Sahiwal District. The mean NDVI values showed that overall NDVI values decreased from 0.24 to 0.20 from 1981 to 2021. Almost 78% of farmers stated that the climate has been changing during the last few years, 72% of farmers stated that climate change had affected agriculture, and 53% of farmers thought that rainfall intensity had also decreased. The R2 tendency showed that temperature and NDVI were negatively connected to each other. This study will integrate and apply the best and most suitable methods, tools, and approaches for equitable local adaptation and governance of agricultural systems in changing climate conditions. Therefore, this research outcome will also meaningfully help policymakers and urban planners for sustainable LULC management and strategies at the local level
Monitoring the dynamic changes in vegetation cover using spatio-temporal remote sensing data from 1984 to 2020
Anthropogenic activities and natural climate changes are the central driving forces of global ecosystems and agriculture changes. Climate changes, such as rainfall and temperature changes, have had the greatest impact on different types of plant production around the world. In the present study, we investigated the spatiotemporal variation of major crops (cotton, rice, wheat, and sugarcane) in the District Vehari, Pakistan, from 1984 to 2020 using remote sensing (RS) technology. The crop identification was pre-processed in ArcGIS software based on Landsat images. After pre-processing, supervised classification was used, which explains the maximum likelihood classification (MLC) to identify the vegetation changes. Our results showed that in the study area cultivated areas under wheat and cotton decreased by almost 5.4% and 9.1% from 1984 to 2020, respectively. Vegetated areas have maximum values of NDVI (>0.4), and built-up areas showed fewer NDVI values (0 to 0.2) in the District Vehari. During the Rabi season, the temperature was increased from 19.93 °C to 21.17 °C. The average temperature was calculated at 34.28 °C to 35.54 °C during the Kharif season in the District Vehari. Our results showed that temperature negatively affects sugarcane, rice, and cotton crops during the Rabi season, and precipitation positively affects sugarcane, rice, and cotton crops during the Kharif season in the study area. Accurate and timely assessment of crop estimation and relation to climate change can give very useful information for decision-makers, governments, and planners in formulating policies regarding crop management and improving agriculture yields
Identification and characterization of rhizospheric microbial diversity by 16S ribosomal RNA gene sequencing
N-Arylation of Protected and Unprotected 5-Bromo-2-aminobenzimidazole as Organic Material: Non-Linear Optical (NLO) Properties and Structural Feature Determination through Computational Approach
The interest in the NLO response of organic compounds is growing rapidly, due to the ease of synthesis, availability, and low loss. Here, in this study, Cu(II)-catalyzed selective N-arylation of 2-aminobenzimidazoles derivatives were achieved in the presence of different bases Et3N/TMEDA, solvents DCM/MeOH/H2O, and various aryl boronic acids under open atmospheric conditions. Two different copper-catalyzed pathways were selected for N-arylation in the presence of active nucleophilic sites, providing a unique tool for the preparation of NLO materials, C-NH (aryl) derivatives of 2-aminobenzimidazoles with protection and without protection of NH2 group. In addition to NMR analysis, all synthesized derivatives (1a–1f and 2a–2f) of 5-bromo-2-aminobenzimidazole (1) were computed for their non-linear optical (NLO) properties and reactivity descriptor parameters. Frontier molecular orbital (FMO) analysis was performed to get information about the electronic properties and reactivity of synthesized compounds
N-Arylation of protected and unprotected 5-Bromo-2-aminobenzimidazole as organic material: Non-Linear Optical (NLO) properties and structural feature determination through computational approach
The interest in the NLO response of organic compounds is growing rapidly, due to the ease
of synthesis, availability, and low loss. Here, in this study, Cu(II)-catalyzed selective N-arylation of
2-aminobenzimidazoles derivatives were achieved in the presence of different bases Et3N/TMEDA,
solvents DCM/MeOH/H2O, and various aryl boronic acids under open atmospheric conditions.
Two different copper-catalyzed pathways were selected for N-arylation in the presence of active
nucleophilic sites, providing a unique tool for the preparation of NLO materials, C-NH (aryl) derivatives of 2-aminobenzimidazoles with protection and without protection of NH2 group. In addition to
NMR analysis, all synthesized derivatives (1a–1f and 2a–2f) of 5-bromo-2-aminobenzimidazole (1)
were computed for their non-linear optical (NLO) properties and reactivity descriptor parameters.
Frontier molecular orbital (FMO) analysis was performed to get information about the electronic
properties and reactivity of synthesized compounds
Arsenic and Cadmium Risk Assessment in a Domestic Wastewater Irrigated Area Using Samples of Water, Soil and Forages as Indicators
The recent research was performed to investigate the toxicity of As and Cd in suburban area of Sargodha, Punjab, Pakistan. Water, soil and forage samples were collected for this purpose in summer and winter season and analysis was done via wet digestion to determine the concentration of selected heavy metals. The mean concentration of As and Cd in water was found above the permissible maximum limit. Mean concentration of Cd in soil and forages was lower than the allowed limit whereas As which was higher than the PML in forages. So, continuous use of domestic wastewater for irrigation purpose should be avoided in order to secure the area from any hazard in near future
Characterizing of heavy metal accumulation, translocation and yield response traits of Chenopodium quinoa
Heavy metal contamination in soil is a major environmental threat that reduces crop productivity. Quinoa as a phytoremediation potential is a viable option to mitigate the effect of heavy metal stress. This study aimed to investigate the phytoremediation characteristics of four quinoa lines when exposed to soil contaminated with heavy metal. Four quinoa lines (A1, A2, A7, and A9) were allowed to grow in three fields (control (UAF), Chakera farm (UAF), and Chakera village) under RCBD split plot arrangement with three replication. Maximum seed yield (4100 kg ha−1) was obtained by A7 which was statistically similar to the A2 line (3648 kg ha−1) obtained from Chakera Farm (UAF) having sewage water application. While low yield was obtained from A9 (1482 kg ha−1) in normal soil (control). Both A7 and A2 lines exhibited higher biomass and seed yield at three fields. Both fields having sewage water application resulted in higher growth and superb seed yield of quinoa lines as compared to the control. Quinoa lines (A2 and A7) attained (51 and 43%) high seed yield at Chakera farm (UAF) having sewage water application in comparison to control having normal irrigation. Seed quality was substantially affected by heavy metal concentration in both contaminated fields. Metals concentration determined in seed samples of A7 was high as compared to A2. Hence A2 may be said a nutritionally superior quinoa line as metal levels were within the permissible level set by FAO/WHO