Editorial: Improving Yield and Quality of Cereal Crops: Exploring and Utilizing Genes for Green and Efficient Traits

Hao Zhou^1*Yuqing He²Ming Luo³

• ¹Sichuan Agricultural University, Ya'an, China
• ²Huazhong Agricultural University, Wuhan, Hubei Province, China
• ³Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, Australian Capital Territory, Australia

In the face of escalating global challenges-population growth, climate change, and limited arable land-enhancing the yield and quality of cereal crops has become a cornerstone of sustainable agriculture. This Research Topic delves into the genetic and molecular mechanisms underpinning stress resilience, nutritional improvement, and breeding innovation in staple crops such as rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays). By integrating cutting-edge genomic technologies, transcriptomic analyses, and artificial intelligence-driven methodologies, the studies presented here illuminate pathways toward precision breeding for green and efficient traits. Below, we contextualize these contributions within broader scientific and agricultural landscapes.Environmental stressors, including soil compaction, pathogen attacks, and herbicide pressure, persistently threaten cereal production. The featured studies leverage multiomics approaches to decode plant responses to these challenges. For instance, genomewide association studies (GWAS) in rice (article link) identified *F-box* genes (Os10g0124700, Os10g0126600, and Os10g0128200) that modulate ethylene signaling and root architecture under compacted soil conditions. These findings offer actionable targets for breeding rice varieties with enhanced root plasticity, a trait critical for nutrient acquisition in degraded soils. Similarly, transcriptomic profiling of Fusarium head blight (FHB)-resistant wheat landrace Wuyangmai revealed miRNA-mediated regulation of glutathione metabolism and phenylpropanoid biosynthesis, pathways pivotal for oxidative stress mitigation and pathogen defense (article link).Such insights not only deepen our understanding of plant immunity but also highlight miRNAs as novel tools for engineering disease-resistant crops.Equally compelling is the GWAS-based dissection of herbicide resistance in rice, which uncovered geographically localized alleles (e.g., RGlu6 and RGly8) inEuropean japonica varieties (article link). These results underscore the importance of preserving genetic diversity in germplasm collections to address region-specific agricultural challenges. Collectively, these studies exemplify how genetic diversity and molecular networks can be harnessed to fortify crops against biotic and abiotic threats.Crop quality is a multidimensional trait shaped by complex metabolic and regulatory networks. The functional characterization of OsG6PGH1 in rice exemplifies this complexity (article link). By interacting with OsAAP6, this gene enhances protein body formation and grain protein content while reducing chalkiness, thereby improving both nutritional and sensory properties. Notably, OsG6PGH1 also contributes to salt stress tolerance, illustrating the pleiotropic roles of genes at the intersection of quality and resilience. Another breakthrough comes from the triple mutant *sbe2b/sbe1/OE-Wxa*, which elevates resistant starch (RS) content to 4.63%-a fivefold increase over wildtype levels-without compromising yield (article link). This achievement demonstrates the power of stacking mutations in starch biosynthesis genes to achieve synergistic improvements in health-promoting traits and agronomic performance. The studies in this Research Topic collectively underscore the importance of interdisciplinary integration-genomics, bioinformatics, and agronomy-to address food security challenges. Future efforts should prioritize:1. Precision editing: Combining CRISPR-based allele replacement with multiomics data to fine-tune trait expression.2. Dynamic network analysis: Elucidating crosstalk between stress-response, metabolic, and developmental pathways.3. Climate-resilient prediction models: Leveraging AI to predict genotype × environment × management (G×E×M) interactions.By bridging fundamental research and translational breeding, we can accelerate the development of "smart" crop varieties that meet the dual demands of productivity and sustainability.