Institute of Crop Sciences

The ZmYSL2 mutation causes abnormal iron distribution in tissues of maize o213 mutants, disrupting iron transport during the V9/12D developmental stage while simultaneously reducing iron transport efficiency. Iron (Fe) is an essential nutrient for plants. This study demonstrates that the ZmYSL2 mutation alters the distribution of iron within plants. The iron content in the roots of the o213 mutant exhibited a peak at the R1 stage, yet remained significantly lower than that of G213 throughout the developmental process. The mutant exhibited a significantly higher kernel iron content in comparison to G213 at all developmental stages. However, iron content in both the embryo and the endosperm was significantly reduced. In addition, the correlation between iron content in source and sink tissues was reversed in the mutant in comparison with G213. The mutant roots and internodes exhibited a decline in Fe transport efficiency and content. Collectively, these results indicate that the ZmYSL2 gene is crucial for the normal coordination of Fe allocation between nutrient and reproductive tissues during maize growth and development.

Maize seedling count and leaf age are critical indicators of early growth status, essential for effective field management and breeding variety selection. Traditional field monitoring methods are time-consuming, labor-intensive, and prone to subjective errors. Recently, deep learning-based object detection models have gained attention in crop seedling counting. However, many of these models exhibit high computational complexity and implementation costs, making field deployment challenging. Moreover, maize leaf age monitoring in field environments is barely investigated. Therefore, this study proposes two lightweight models, YOLOv8n-Light-Pruned (YOLOv8n-LP) and YOLOv11n-Light-Pruned (YOLOv11n-LP), for monitoring maize seedling count and leaf age in field RGB images. Our proposed models are improved from YOLOv8n and YOLOv11n by incorporating the DAttention mechanism, an improved BiFPN, an EfficientHead, and layer-adaptive magnitude-based pruning. The improvement in model complexity and model efficiency was significant, with the number of parameters reduced by over 73 % and model efficiency upgraded by up to 42.9 % depending on the device computation power. High accuracy was achieved in seedling counting (YOLOv8n-LP/ YOLOv11n-LP: AP = 0.968/0.969, R 2 = 0.91/0.94, rRMSE = 6.73 %/5.59 %), with significantly reduced model size (YOLOv8n-LP/ YOLOv11n-LP: parameters = 0.8 M/0.7 M, trained model size = 1.8 MB/1.7 MB). The robustness was validated across datasets with varying leaf ages (rRMSE = 4.07 % – 7.27 %), resolutions (rRMSE = 3.06 % – 6.28 %), seedling compositions (rRMSE = 1.09 % – 9.29 %), and planting densities (rRMSE = 3.38 % – 10.82 %). Finally, by integrating plant counting and leaf age estimation, the proposed models demonstrated high accuracy in leaf age detection using near-ground images (YOLOv8n-LP/ YOLOv11n-LP: rRMSE = 5.73 %/7.54 %) and UAV images (rRMSE = 9.24 %/14.44 %). The results demonstrate that the proposed models excel in detection accuracy, deployment efficiency, and adaptability to complex field environments, providing robust support for practical applications in precision agriculture. • Compact model size (1.7 MB), with only 0.7 million parameters. • One model counts both maize seedlings and leaves in the complex field environment. • Robust across data collection platforms, growth stages, image resolutions, seedling compositions, and generalizable to a lower planting density. • Model efficiency improved by 40 % on devices with low computation power.

Maize breeding has greatly improved yield through single-cross hybrids, but the underlying gene regulatory changes remain unclear. This study analysed transcriptomes of landmark maize hybrids and their parents across developmental stages and planting densities. Compared with their parents, hybrids showed a trade-off in the expression of photosynthesis-related genes and stress-responsive genes. This expression rebalancing suggested a strategy that prioritises photosynthetic efficiency and growth vigour over stress defence mechanisms. Allele-specific expression (ASE) analysis identified 19.9% of heterozygous loci exhibiting significant allelic imbalance, with notable enrichment in photosynthesis and stress response pathways. Importantly, the suppressed expression of deleterious alleles in hybrids not only correlated with phenotypic performance but also exhibited progressive enhancement through decades of breeding, indicating this regulatory mechanism has been selected during improvement. Consistent with this finding, breeding selection preferentially acted on cis-regulatory regions, with stronger correlation between cis-regulatory complementation of deleterious variants and hybrid release year compared to coding regions. Transcriptomic plasticity across environments was evaluated using the concept of entropy. Results showed that hybrids had lower transcriptomic entropy than their parental lines, and this reduction in entropy was significantly associated with heterosis. These findings highlight the critical role of allelic expression optimization in maize hybrid breeding and provide insights into the transcriptomic dynamics that underlie heterosis.

ABSTRACT Kernel row number (KRN) is a key component of maize ( Zea mays L.) yield. Identifying regulatory genes for KRN and manipulating them genetically represent an important strategy for high‐yield maize breeding. In this study, using inbred lines Y1648 and Y2328, which exhibit great differences in KRN, as recurrent and donor parents, respectively, we developed high‐generation near‐isogenic lines (NILs) and performed fine mapping of KRN. A quantitative trait locus (QTL) was delimited to an interval between 84.15 and 85.74 Mb on chromosome 10 and designated as qKRN10 . Candidate gene association analysis further narrowed down the target region to a 360‐kb segment. Within this region, GRMZM2G005126 encodes a C3H‐type transcription factor and is expressed during maize ear development. Moreover, RNA‐seq analysis revealed significant differences in the expression levels of this gene between the two NILs, suggesting that GRMZM2G005126 is likely the candidate gene underlying the major QTL qKRN10 responsible for KRN regulation. This study provides genetic targets and gene resources with potential application value for maize breeding.

Winter freezing injury is a critical factor limiting wheat(Triticum aestivum L.) productivity in northern China. Since freeze tolerance (FT) correlates with seedling growth traits, this study investigated the relationship between FT and plant architecture (PA) in winter wheat at the tillering stage. We evaluated 550 wheat varieties and advanced lines from the Huang and Huai River Valleys Winter Wheat Zone of China. Seedling PA was classified using the International Union for the Protection of New Varieties of Plants (UPOV) standards, while FT was evaluated through two parameters: severity of leaf necrosis (SLN) and mortality rate of shoots (MRS). The results showed that the PA distribution across germplasms approximated a normal distribution. The relationships between SLN and MRS under freezing stress were highly variable across years with differing winter conditions. SLN and MRS-derived FT levels showed a positive correlation within the same growing season but were inconsistent across different years. PA and MRS showed no correlation whereas correlation between seedling PA and SLN varied substantially across years. Due to inadequate cold acclimation in 2022-2023 and heavy snow cover in 2023-2024, there was no significant correlation between FT levels and seedling PA during these periods. A significant negative correlation was observed between PA and SLN during the 2024-2025 season, indicating that more prostrate growth habits were associated with a reduction in leaf necrosis. These results indicate that architectural traits may contribute to FT only within certain environmental contexts. Thus, enhancing freezing tolerance should focus on direct survival tests in various environments, with secondary traits like SLN and PA considered as context-dependent factors.

The Domain of Unknown Function 506 (DUF506) family, part of the PD-(D/E)XK nuclease superfamily, has been shown to play a vital role in plant development and responses to abiotic stresses. However, the function of the DUF506 family in cultivated peanuts remains unknown. This study identified 23 AhDUF506 genes using bioinformatics approaches; these genes are spread across 15 chromosomes and grouped into 4 subfamilies. Additionally, by analyzing gene structure, upstream cis-acting elements, and transcriptional expression changes of AhDUF506 genes in different tissues and under various stress conditions, their expression levels and response mechanisms to abiotic stresses were examined. In mature tissues, the expression levels of seven AhDUF506 genes in flowers were significantly higher than those in other tissues. Under abiotic stress, their expression levels were all up-regulated in the roots of peanut plant seedlings. These findings provide an important foundation for a deeper understanding of the molecular characteristics of the DUF506 family in Arachis hypogaea (peanut), supporting future research on the functional characterization of its genes.