Introduction: Winter wheat is a crucial crop extensively cultivated in northern China, where its grain yield is influenced by genetic factors (G), environmental conditions (E), and their interactions (GEI). Accurate yield estimation depends on understanding the patterns of GEI in multi-environment trials (METs).Methods: From 2014 to 2018, continuous experiments were conducted in the Heilonggang region of the North China Plain (NCP), evaluating 71 winter wheat genotypes across 16 locations over five years. Leveraging 30 years of environmental data, including 19 meteorological parameters and 6 soil physicochemical properties, the study analyzed GEI and identified four distinct mega-environments (MEs) using advanced environmental classification techniques.Results: Variance analysis of genotype-year combinations at individual locations revealed significant differences among genotypes. Furthermore, the joint analysis showed that GEI variance exceeded the variance attributed to genotypic effects alone. The Additive Main Effects and Multiplicative Interaction (AMMI) model indicates that the first three interaction principal component axes (IPCAs) account for over 70% of the GEI variance, thereby demonstrating the relevance of this model to the current study. Principal Component Analysis (PCA) across the five-year study period revealed positive correlations between grain yield and vapor pressure deficit (VPD), evapotranspiration potential (ETP), temperature range (TRANGE), available soil water (ASKSW), and sunshine duration. Conversely, negative correlations were observed with relative humidity at 2 meters (RH2M), total precipitation (PRECTOT), potential evapotranspiration (PETP), and dew point temperature at 2 meters (T2MDEW). Among the meteorological and soil variables, minimum temperature (TMIN), fruiting rate (FRUE), temperature at 2 meters (T2M), and clay content (CLAY) emerged as the most significant contributors to yield variation during the study period. Based on GGE biplot analysis, superior genotypes were identified for their respective regions: JM196, WN4176, and HN6119 in 2014; ZX4899, H9966, and LM22 in 2015; BM7, KN8162, and KM3 in 2016; HH14-4019, HM15-1, and HH1603 in 2017; and S14-6111 and JM5172 in 2018. Feixiang and Shenzhou were identified as the most discriminative and representative locations.Discussion: These findings provide a scientific basis for optimizing winter wheat cultivation strategies in northern regions. Based on long-term data from the North China Plain, future work can further validate their applicability in other regions.

Breeding and cultivating low-N-efficient maize varieties to obtain high yields with less N fertilizer is important for addressing food demands and environmental pollution. However, few studies have investigated the physiological characteristics of low-N-efficient maize varieties. Therefore, we performed an experiment over four years to test two maize varieties (low-N-efficient variety: JNK728, and high-N-efficient variety: XY335) and five N application rates (N120: 120 kg·ha⁻¹, N180: 180 kg·ha⁻¹, N240: 240 kg·ha⁻¹, N300: 300 kg·ha⁻¹, and N360: 360 kg·ha⁻¹). The optimal N application rates for JNK728 and XY335 were N180 and N300, which obtained the highest yields (11,754 and 12,752 kg·ha⁻¹, respectively), N uptake efficiencies (1.32 and 0.93 kg·kg⁻¹), and N harvest index (67.94% and 61.98%), compared with other N application rates. The key period for plant N accumulation was the R1–R6 stage, which contributed 35.2–49.7% and 40.8–53.8% to plant N accumulation at the maturation stage in JNK728 and XY335, respectively. In addition, N accumulation in the grain accounted for more than half (51.8–63.2%) of the total N accumulation in plants, and the leaf N transport amount after the post-silking stage was the primary source of grain N accumulation in both JNK728 and XY335. We also explored the key enzymes and genes related to the N transport amount and efficiency in leaves in the two maize varieties, and found that GOGAT was the key enzyme and GOGAT2 was the key gene for JNK728, whereas the AS enzyme and AS1 and AS3 genes were most important for XY335. Therefore, we suggest that molecular breeding programs should focus on the GOGAT2 gene in low-N-efficient maize varieties, and cultivation techniques should aim to improve the GOGAT enzyme activity after the post-silking period to achieve high yields and N utilization efficiencies with less N fertilizer.

Introduction: The ear shank, a short branch connecting the stalk and the ear, represented a key agronomic trait influenced both yield and plant architecture in maize, yet the molecular mechanism remained not fully understood. Methods: In this study, BSA-seq was performed using an F ₂ population for two extreme bulks derived from the cross between WL134 and L135. Additionally, transcriptomic analysis and gene annotation were carried out to refine the association interval of ear shank length and identify crucial genes. Results and Discussion: A total of 14 QTLs for ear shank length were detected, which included 334 non-synonymous mutants, synonymous mutants and frameshift mutant genes. Among these loci, five were known to be associated with ear shank length, while nine were newly identified. 3,460 differentially expressed genes (DEGs) were screened through RNA-seq analysis of the ear shank at the silking stage in both parents. Thirteen new candidate genes were identified through the combined analysis of BSA-seq and RNA-seq, as well as gene function annotation and gene expression analysis. Based on functional predictions, the candidate genes Zm00001eb023400 , Zm00001eb023420 and Zm00001eb050490 , which encoded lytic transglycosylases, significantly associated with cell wall remodeling and degradation. The candidate genes Zm00001eb282410 and Zm00001eb282430 enriched the phenylpropanoid biosynthesis pathway and played important roles in the formation of the maize ear shank. These findings provided a foundation for understanding the molecular mechanisms regulating ear shank length in maize.

To clarify the effectiveness of nicosulfuron (NIF) in controlling weeds in cornfields and the physiological response mechanisms of corn, this study synthesized 29 relevant articles for a meta-analysis. A pair of sweet corn sister lines with different resistance to NIF (resistant HK301 and sensitive HK320) was selected as materials, and the responses of non-target metabolic pathways under NIF stress were analyzed using structural equation modeling. The results indicate that NIF is highly effective against weeds such as Eleusine indica and Amaranthus retroflexus, but less effective against weeds such as Cyperus difformis (13.20%) and Abutilon theophrasti (−4.10%). High concentrations (≥60 g hm−2) and prolonged application (more than 30 days) significantly enhance effectiveness (up to 170.60% and 155.50%, respectively). In terms of physiological mechanisms, the antioxidant capacity, photosynthetic performance and levels of non-enzymatic antioxidants in sensitive maize (Maize.G) were significantly lower than those in the resistant Maize.K, with higher accumulation of ROS and MDA. The structural equation model revealed that in HK301, NIF induced oxidative stress effectively activates the antioxidant defense system, inhibits ROS accumulation and membrane lipid peroxidation, reduces photosynthetic damage, and maintains physiological homeostasis. Conversely, in HK320, excessive oxidative stress leads to impaired antioxidant function and a decline in photosynthetic performance. This study demonstrates physiological differences in NIF responses between resistant and sensitive maize lines, providing practical guidance for the rational use of NIF in corn-fields and, theoretical insights into the non-target effects of herbicides and crop resistance mechanisms.