Drought-Resilient Crops for Sustainable Agriculture: Genetic, Physiological, and Agronomic Advances

Reducing wheat yield loss under drought: A review of physiological mechanisms and practical countermeasures

Drought increasingly constrains global wheat production, reducing photosynthetic capacity, accelerating senescence, and shortening grain-filling periods. This review analyses current knowledge on physiological responses (root architecture, reactive oxygen species dynamics, photosystem stability, and abscisic acid signaling) and evaluates agronomic, chemical, nutritional, and genetic countermeasures that aim to protect yield under water deficit. We compare evidence from controlled experiments and field trials to assess which interventions deliver consistent yield benefits, and we highlight trade-offs between water conservation and carbon assimilation. Root traits that access deeper soil moisture, robust antioxidant systems, and balanced hormonal regulation emerge as key biological targets; meanwhile, seed priming, targeted nutrient management, and selected biochemical treatments show promise as near-term, scalable practices. Genetic approaches, including marker-assisted selection and introgression of drought-adaptive alleles, offer longer-term gains but require multi-environment validation. We identify gaps in cross-scale evidence, notably a shortage of multi-location, farmer-level trials that quantify the cost-effectiveness and environmental safety of chemical agents. Finally, we propose an integrated research agenda combining trait-based breeding, optimized nutrient regimes, and pragmatic agronomy to improve wheat resilience to drought. Implementing coordinated strategies across breeding and management is essential to sustain wheat yields as climate variability intensifies.

Bioinformatics in crop research: using genomic data for crop improvement

Sustainable crop development aims to maintain or increase yields while reducing environmental impact and managing the challenges imposed by climate change. As the global population grows and arable land becomes scarcer, the integration of molecular breeding with bioinformatics has emerged as an effective strategy for long-term crop improvement. Bioinformatics enables researchers to analyze and interpret the vast quantities of genetic data generated by high-throughput sequencing, making it possible to identify molecular markers, candidate genes, and regulatory networks linked to specific agronomic traits, which breeders then translate into focused, ecologically sustainable breeding programs. This approach has enabled major progress across several fronts: the identification of genes conferring resistance to biotic stressors (pests, pathogens) and abiotic stressors (drought, salinity, heat); the development of nutrient-efficient, low-input crop varieties; the improvement of agronomic performance and nutritional quality through identification of yield- and quality-related genes; and the conservation and deployment of genetic diversity to safeguard long-term breeding sustainability. By combining genomic data with precision breeding techniques, researchers are developing crops that are better adapted to a growing population and a changing climate, positioning the integration of molecular breeding and bioinformatics as a central pillar of future global food security.

Integrated Drought Resilience in Foxtail Millet: From Molecular Regulation and Multi-Omics to Climate-Resilient Breeding

Climate change and the increasing frequency of extreme temperatures pose severe threats to global agricultural productivity, making the breeding of water-efficient crops a critical imperative. Originating from arid regions, foxtail millet serves as an ideal C4 model crop for elucidating plant adaptations to water deficits. Unlike previous reviews that often isolate genomic features from physiological responses, this review constructs an explicit conceptual framework integrating cross-scale defense mechanisms—mechanistically linking molecular signal transduction and post-transcriptional regulation to cellular homeostasis and field-scale yield stability. We first detail the developmental stage-specific physiological penalties of water stress and dissect proactive water-conservation strategies, including stomatal anatomical optimization, root-carbon reallocation, and dynamic rhizosphere remodeling. At the genetic level, we highlight the application of dynamic quantitative trait loci (QTL) mapping, which transcends the static limitations of conventional QTLs by capturing the spatiotemporal evolution of drought-tolerance traits across distinct developmental nodes. To bridge the gap between intrinsic genetic potential and field application, we spotlight the emerging integration of machine learning-assisted breeding and genomic prediction for the efficient evaluation of superior germplasms. Across this framework, several persistent gaps emerge: most drought-responsive genes identified in foxtail millet remain at the level of expression association without functional validation; dynamic QTL analysis remains underutilized relative to its capacity to resolve reproductive-stage drought tolerance; and ML-based genomic prediction, though demonstrated in this species, has not been integrated into operational breeding. Closing these gaps will require connecting high-throughput field phenotyping to genomic selection and deploying functionally validated editing targets in genetic backgrounds relevant to dryland production.

Phenomics and High-Throughput Phenotyping of Photosynthetic Traits for Improving Abiotic Stress Resilience in Wheat and Rice

Photosynthesis is the fundamental biological process underlying plant growth, crop productivity, and global food security. However, its efficiency is highly vulnerable to abiotic stresses, which disrupt chlorophyll biosynthesis, electron transport, carbon assimilation, stomatal regulation, and photoprotective mechanisms, ultimately reducing crop yield. Improving photosynthetic resilience under adverse environments has therefore become a major objective of modern crop improvement. Recent advances in phenomics and high-throughput phenotyping (HTP) have transformed the evaluation of photosynthesis-related traits by enabling rapid, non-destructive, and large-scale assessment across diverse environments, while facilitating quantitative characterization of structural, physiological, biochemical, and thermal responses to abiotic stress. Technologies including chlorophyll fluorescence, gas-exchange analysis, thermal imaging, hyperspectral imaging, LiDAR, and UAV-based sensing provide comprehensive insights into plant physiological responses and stress adaptation. Integration of these phenomic approaches with genomic information and artificial intelligence (AI)-driven analytical frameworks has strengthened genomic and phenomic prediction, enabling more accurate identification of candidate genes, selection of superior genotypes, and accelerated genetic gain. This review critically synthesizes recent advances in photosynthesis-related traits, phenomics, HTP technologies, and their integration with genomics and AI-assisted breeding, highlighting current challenges, knowledge gaps, and future opportunities for developing climate-resilient wheat and rice cultivars and promoting sustainable crop production.

CRISPR-enabled functional genomics for bolstering plant tolerance to abiotic and biotic stress; a comprehensive review

From Reproduction to Resilience: New Frameworks for Managing Crop Diversity.

Plant breeding has progressed from phenotype-based selection to increasingly precise genetic and agronomic interventions. Advances in molecular breeding, genome engineering, and crop management have improved productivity, but have also promoted the widespread use of genetically uniform cultivars optimized for controlled production systems. While uniformity facilitates predictability and mechanization, it may constrain adaptive capacity under increasingly variable environmental conditions. In parallel, recent developments in digital agriculture, including high-resolution phenotyping, remote-sensing, molecular diagnostics, and AI-assisted decision support, are transforming the ability to monitor and manage biological variation across spatial and temporal scales. In this review, we examine how these technological advances intersect with emerging concepts in crop diversity and reproductive biology. We discuss how digital agriculture enables improved characterization of genotype-environment interactions and consider reproductive mechanisms that expand the accessible breeding space beyond conventional biparental crossing schemes, including haploid induction and multi-parental breeding. These approaches provide opportunities to accelerate trait introgression, generate novel genetic combinations, and overcome reproductive barriers. We argue that digital and diagnostic agriculture provide an informational framework for the deployment and evaluation of genetically heterogeneous plant populations. Together, recent advances suggest that technological precision and biological diversity can be integrated into breeding strategies that improve productivity and resilience.