Drought and salinity are major abiotic stresses that restrict crop productivity by disrupting water relations, ion balance, cellular metabolism, and gene regulation. This study investigated the molecular and genetic responses associated with these stresses using the publicly available rice transcriptomic dataset GSE21651 from the NCBI Gene Expression Omnibus. The analysis included 16 samples representing control, drought, and salinity conditions and 57,381 probe sets. Genome-wide differential-expression analysis revealed that 23,506 probe sets (40.96%) were significant at adjusted P < 0.05, indicating extensive transcriptional reprogramming under stress. Exploratory expression profiling and UMAP analysis further demonstrated distinct global expression patterns among control, drought, and salinity groups. Functional interpretation of significant genes identified major stress-responsive components involved in water transport, osmotic protection, ABA-mediated signalling, antioxidant defence, metabolic adjustment, and transcriptional regulation. Important candidate genes included aquaporins, late embryogenesis abundant proteins, dehydrins, antioxidant enzymes, and WRKY, NAC, MYB, and bZIP transcription factors. The findings demonstrate that drought and salinity tolerance in rice is governed by coordinated molecular networks involving both shared and stress-specific responses. These results highlight the value of transcriptomic analysis for identifying candidate genes that can support marker-assisted breeding, genetic engineering, and genome-editing strategies aimed at developing climate-resilient crops.
Biju Sidharthan, P. V. Pulate, Misha Yadav et al.· Genetics and Molecular Resea...· 0 citations
Irrigated agriculture consumes the largest share of global freshwater withdrawals, and this dependence is intensifying as rainfall variability and evaporative demand increase under a warming climate. Deficit irrigation, defined as the deliberate application of water below full crop evapotranspiration requirements at selected growth stages, has emerged as a central strategy for reconciling food production with water scarcity. This review synthesises evidence on the physiological basis of crop responses to water deficit, the modelling frameworks used to predict yield and water productivity outcomes, and the performance of deficit irrigation across field crops, orchard and vine systems, and vegetables. The economic and behavioural dimensions of farmer adoption, the interaction between deficit irrigation and soil salinisation under changing climatic conditions, and emerging genetic and computational tools that support precision water management are also examined. Findings indicate that regulated and sustained deficit irrigation strategies can improve water productivity substantially, often between eight and thirty per cent relative to full irrigation, although yield penalties vary widely by crop, growth stage sensitivity and environmental context. Climate change is projected to alter the reliability of these gains, particularly where warming erodes the compensatory physiological mechanisms that underpin moderate water stress benefits. Economic viability depends strongly on relative water and commodity prices, and farmer adoption remains constrained by risk aversion and limited technical support. The review concludes that deficit irrigation should be understood not as a fixed prescription but as an adaptive, crop- and context-specific component of climate-risk management, requiring closer integration of physiological monitoring, crop modelling and economic decision support.
Biju Sidharthan, B. Sushmitha, B. Santhosh et al.· Journal of Experimental Agri...· 0 citations
The results showed the utility of the combination of AI with pathway-level bioinformatics analysis to not only provide predictive accuracy but also provide biological interpretability in the field of precision medicine.
Vikram R. Patil, Taru Gupta, Indu Melkani et al.· Genetics and Molecular Resea...· 0 citations
Crop productivity is increasingly threatened by climate change and multiple abiotic stresses that significantly reduce agricultural sustainability and food security worldwide. Molecular breeding and genomics-assisted crop improvement strategies have emerged as effective approaches for developing high-yielding and stress-resilient crop varieties. The present study evaluated quantitative agronomic traits and their significance in molecular breeding applications using a rice genotype–phenotype dataset containing quantitative trait information and SNP-based genomic data. Quantitative trait analysis, correlation analysis, Principal Component Analysis, and machine learning-based predictive modeling were performed to assess phenotypic variability and yield-associated trait relationships. The results demonstrated substantial phenotypic diversity among rice accessions, particularly for grain morphology, plant architecture, and reproductive traits. Correlation analysis revealed significant positive associations among several agronomic traits, while PCA identified plant architecture and grain morphology as major contributors to phenotypic variation. A Random Forest regression model was further developed to predict grain weight using agronomic traits, where grain width and grain length emerged as the most influential predictors of yield-associated performance. The findings highlight the importance of integrating quantitative trait analysis, predictive modeling, and molecular breeding approaches for improving crop yield and adaptive performance. The study also demonstrates the potential application of computational and genomics-assisted breeding frameworks in developing climate-resilient rice cultivars. Overall, the integration of machine learning and molecular breeding strategies may contribute substantially to sustainable crop improvement and future agricultural productivity.
Shikha, Munish Kaundal, D. Upadhyay et al.· Genetics and Molecular Resea...· 0 citations
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