ABSTRACT
Arsenic (As)-contaminated soil reduces soil quality and leads to soil degradation, and traditional remediation strategies are expensive or typically produce hazardous by-products that have negative impacts on ecosystems. Therefore, this investigation attempts to assess the impact of As-tolerant bacterial isolates via a bacterial Rhizobim nepotum strain (B1), a bacterial Glutamicibacter halophytocola strain (B2), and MgO-NPs (N) and their combinations on the arsenic content, biological activity, and growth characteristics of maize plants cultivated in highly As-contaminated soil (300 mg As Kg-1). The results indicated that the spectroscopic characterization of MgO-NPs contained functional groups (e.g., Mg-O, OH, and Si-O-Si) and possessed a large surface area. Under As stress, its addition boosted the growth of plants, biomass, and chlorophyll levels while decreasing As uptake. Co-inoculation of R. nepotum and G. halophytocola had the highest significant values for chlorophyll content, soil organic matter (SOM), microbial biomass (MBC), dehydrogenase activity (DHA), and total number of bacteria compared to other treatments, which played an essential role in increasing maize growth. The addition of R. nepotum and G. halophytocola alone or in combination with MgO-NPs significantly decreased As uptake and increased the biological activity and growth characteristics of maize plants cultivated in highly arsenic-contaminated soil. Considering the results of this investigation, the combination of G. halophytocola with MgO-NPs can be used as a nanobioremediation strategy for remediating severely arsenic-contaminated soil and also improving the biological activity and growth parameters of maize plants.
ABSTRACT
The impacts of climate change and possible adaptations to food security are a global concern and need greater focus in arid and semi-arid regions. It includes scenario of Coupled Model Intercomparison Phase 5 (CMIP-RCP8.5). For this purpose, two DSSAT maize models (CSM-CERES and CSM-IXIM) were calibrated and tested with two different maize cultivars namely Single Cross 10 (SC10) and Three Way Cross 324 (TW24) using a dataset of three growing seasons in Nile Delta. SC10 is a long-growing cultivar that is resistant to abiotic stresses, whereas TW24 is short and sensitive to such harsh conditions. The calibrated models were then employed to predict maize yield in baseline (1981-2010) and under future time slices (2030s, 2050s, and 2080s) using three Global Climate Models (GCMs) under CMIP5-RCP8.5 scenario. In addition, the use of various adaptation options as shifting planting date, increasing sowing density, and genotypes was included in crop models. Simulation analysis showed that, averaged over three GCMs and two crop models, the yield of late maturity cultivar (SC10) decreased by 4.1, 17.2, and 55.9% for the three time slices of 2030s, 2050s, and 2080s, respectively, compared to baseline yield (1981-2010). Such reduction increased with early maturity cultivar (TW24), recording 12.4, 40.6, and 71.3% for near (2030s), mid (2050s), and late century (2080s) respectively relative to baseline yield. The most suitable adaptation options included choosing a stress-resistant genotype, changing the planting date to plus or minus 30 days from baseline planting date, and raising the sowing density to 9 m-2 plants. These insights could minimize the potential reduction of climate change-induced yields by 39% by late century.
