Wheat (Triticum aestivum L.) is a major cereal crop grown worldwide, providing approximately 20% calorie and 25% protein intake. Wheat productivity is significantly affected by high temperatures, particularly during the grain-filling period. Heat stress accelerates leaf senescence, impairs photosynthesis, reduces starch accumulation, and alters protein synthesis, ultimately leading to a decrease in grain yield and quality. To mitigate the adverse effects of heat stress, wheat utilizes adaptation mechanisms, including the expression of heat shock proteins, activation of antioxidant defense systems, osmotic regulation, and transcription factor-mediated gene regulation. Stay-green traits also play a role in maintaining photosynthetic efficiency at high temperatures. Breeding strategies such as traditional breeding, marker-assisted selection , genomic selection , and genome editing are being explored to improve heat tolerance. Recent advances in the CRISPR-Cas9 technology enable precise gene editing, thereby enhancing the resilience of wheat to heat stress. Additionally, quantitative trait locus mapping and genome-wide association studies facilitated the identification of genetic regions associated with heat tolerance, thereby accelerating the development of climate-resilient wheat varieties. Future research should focus on integrating genetic and molecular approaches with sustainable agronomic practices and crop modeling strategies to optimize wheat productivity under rising temperatures. The integration of advanced breeding techniques and improved crop management can facilitate the development of wheat varieties that are more resilient to climate change.

The
objective
of this study is to assess the impacts of additional N fertilization on agricultural traits, flour characteristics, and noodle quality of O-free, a variety known for reduced allergy effects. With increasing fertilization rates, both culm length and spike length showed an increase, accompanied by a rise in grain nitrogen removal due to higher protein content in the grains. The leaf area index (LAI) reached its peak during the booting stage, while the canopy LAI peaked at 21 days after flowering (21-DAF). Although LAI of plant was higher in the treatments with additional nitrogen (N1 and N2) compared to the control (N0), there was no significant difference observed in canopy LAI. Chlorophyll fluorescence values were highest at 21-DAF and lowest at 35-DAF, regardless of the fertilizer rate. The dry weight of leaves and stems was highest at 7-DAF in N0 and N1 treatments, but at 21-DAF in N2 treatment. During grain filling, the nitrogen content decreased in leaves and stems, while it increased in the grains. With higher fertilization rates, there was an increase in the moisture content, flour color value, protein content, and sedimentation value of flour, whereas the particle size of flour decreased. Dough extensibility, mixing time, and protein strength showed an increase as the fertilizer rate increased, but dough stability decreased. The control exhibited the highest starch gel stability and aging, while N1 had the lowest starch swelling. Cooked noodles demonstrated increased brightness, thickness, and hardness with increasing fertilizer rate, but elasticity and viscosity showed no significant changes.

Gluten proteins in wheat grains are generally considered one of the most important factors in determining dough properties and bread quality. In this study, wheat protein quality characteristics were investigated in 607 varieties collected from seven countries grown in a South Korean wheat breeding field for two years. The average protein content was 12.2±1.86%, and the sodium dodecyl sulfate-sediment volume (SDSS) was 46.9±8.39 mL. HI-LINE had the highest protein content (18.3±0.35%) and SDSS (76.7±1.98 mL), while both NE 84557 and Iksan 374 showed small deviations in protein content and SDSS. Protein content and SDSS values were higher in Ax2*+By8 and By9+Dy10 combinations at Glu-A, Glu-B1, and Glu-D1 loci of high molecular weight gluten subunit (HMW-GS) than in other combinations. However, no difference in Glu-A3 and Glu-B3 loci in LMW-GS was observed. Furthermore, in HMW-GS, the composition of Glu-D1 Dy10 and Dy12 had a greater effect on protein quality than Glu-B1 By8 and By9 when the allele of Glu-A1 had Ax2*. Significant differences were found between Dy10 and Dy12 genes of the HMW-GS Glu-D1 and between protein content and SDSS, but not among others. These results suggest that Glu-D1 is extremely important for improving protein quality in HMW-GSs. As a result of this study, HMW-GS allele selection using functional markers, protein content, and SDSS investigation are expected to enable the development of varieties with high protein quality that are stable amid various environmental changes.

Low-temperature damage at the seedling stage is one of the most significant natural obstacles to wheat’s growth. In domestic wheat breeding programs, the selection of cold-tolerant varieties is crucial for the development of superior wheat varieties. Traditionally, the extent of damage caused by freezing wheat is estimated through visual observation. In this study, we compared the RGB image analysis method with conventional visual evaluation and chlorophyll content analysis methods to determine if this method could accurately quantify the cold tolerance discrimination of wheat in the field. First, single-leaf-level RGB image analysis revealed a pattern similar to dead leaf ratio and chlorophyll content in three grades of freezing injury. Next, we compared the significance of plant-level RGB image analysis. The greenness index by RGB image analysis showed a higher correlation with dead leaf ratio by visual evaluation. Finally, 40 wheat varieties were planted in the field and wheat canopy images were collected at the seedling stage after wintering. There was a high correlation between the greenness index and the visual evaluation. However, there was no correlation between dead leaf ratio and visual evaluation or greenness index as determined by RGB image analysis. These findings suggest that using RGB image analysis rather than visual evaluation can be useful in assessing freeze damage in wheat fields.