Glutamine synthetase (GS), the key enzyme in plant nitrogen assimilation, is strictly regulated at multiple levels, but the most relevant reports focus on the mRNA level. Using specific antibodies as probes, the effects of nitrogen on the expression and localization of individual wheat GS (TaGS) isoforms were studied. In addition to TaGS2, TaGS1;1 with high affinity to substrate and TaGS1;3 with high catalytic activity were also localized in mesophyll, and may participate in cytoplasmic assimilation of ammonium (NH₄⁺) released from photorespiration or absorbed by roots; TaGS1;2 was localized in xylem of leaves. In roots, although there were hundreds of times more TaGS1;1 than TaGS1;2 transcripts, the amount of TaGS1;1 subunit was not higher than that of TaGS1;2; NH₄⁺ inhibited TaGS1;1 expression but stimulated TaGS1;3 expression. In root tips, nitrate stimulated TaGS1;1, TaGS1;3, and TaGS2 expression in meristem, while NH₄⁺ promoted tissue differentiation and TaGS1;2 expression in endodermis and vascular tissue. Only TaGS1;2 was located in vascular tissue of leaves and roots, and was activated by glutamine, suggesting a role in nitrogen transport. TaGS1;3 was induced by NH₄⁺ in root endodermis and mesophyll, suggesting a function in relieving NH₄⁺ toxicity. Thus, TaGS isoforms play distinct roles in nitrogen assimilation for their different kinetic properties, tissue locations, and response to nitrogen regimes.

The Leaf Area Index (LAI) is a crucial structural parameter linked to the photosynthetic capacity and biomass of crops. While integrating machine learning algorithms with spectral variables has improved LAI estimation over large areas, excessive input parameters can lead to data redundancy and reduced generalizability across different crop species. To address these challenges, we propose a novel framework based on Bayesian-Optimized Random Forest Regression (Bayes-RFR) for enhanced LAI estimation. This framework employs a tree model-based feature selection method to identify critical features, reducing redundancy and improving model interpretability. A Gaussian process serves as a prior model to optimize the hyperparameters of the Random Forest Regression. The field experiments conducted over two years on maize and wheat involved collecting LAI, hyperspectral, multispectral, and RGB data. The results indicate that the tree model-based feature selection outperformed the traditional correlation analysis and Recursive Feature Elimination (RFE). The Bayes-RFR model demonstrated a superior validation accuracy compared to the standard Random Forest Regression and Pso-optimized models, with the R² values increasing by 27% for the maize hyperspectral data, 12% for the maize multispectral data, and 47% for the wheat hyperspectral data. These findings suggest that the proposed Bayes-RFR framework significantly enhances the stability and predictive capability of LAI estimation across various crop types, offering valuable insights for precision agriculture and crop monitoring.

Studies of traits related to nitrogen (N)-use efficiency (NUE) in wheat cultivars are important for breeding N-efficient cultivars. Canopy structure has a major effect on NUE, as it determines the distribution of light and N. However, the mechanism by which canopy structure affects the distribution of light and N within the canopy remains unclear. The N-efficient winter wheat varieties YM49 and ZM27 and N-inefficient winter wheat varieties XN509 and AK58 were grown in the field under two N levels. Light transmittance was enhanced, and the leaf area index and photosynthetically active radiation were lower in the N-efficient cultivar population, which was characterized by moderately sized flag leaves, a low frequency of canopy leaf curling, a low light attenuation coefficient (K_L), and high plant compactness. Reductions in the amount of shade increased the distribution of light and N resources to the middle and lower layers. The photosynthetic rate, transpiration rate, instant water-use efficiency, and canopy photosynthetic NUE were higher, N remobilization of the upper and middle canopy leaves was reduced, and the leaf N content was high in the N-efficient cultivars. A higher ratio of the N extinction coefficient (K_N) to K_L reflects the assimilation ability of the N-efficient winter wheat cultivars, resulting in improved canopy structure and distribution of light and N, higher 1000-grain weight and grain yield, and significantly increased light and NUE. An improved match between gradients of light and N in the leaf canopy promotes balanced C and N metabolism and reduces energy and nutrient losses. This should be a goal when breeding N-efficient wheat cultivars and implementing tillage regimes.

The increasing atmospheric [CO₂] poses great challenges to wheat production. Currently, the response of starch characteristics in different specialized wheat cultivars to elevated [CO₂], as well as the underlying physiological and molecular mechanisms remains unclear. Therefore, an experiment was conducted with open-top chambers to study the effects of ambient [CO₂] [a(CO₂)] and elevated [CO₂] [e(CO₂)] on photosynthetic performance, yield and starch characteristics of bread wheat (Zhengmai 369, ZM369) and biscuit wheat (Yangmai 15, YM15) from 2020 to 2022. The results demonstrated a significant improvement in photosynthetic performance, yield, amylose and amylopectin content, volume ratio of large granules under e[CO₂]. Moreover, e[CO₂] upregulated the gene expression and enzyme activities of GBSS (Granule-bound starch synthase) and SSS (Soluble starch synthase), increased starch pasting viscosity, gelatinization enthalpy and crystallinity. Compared to YM15, ZM369 exhibited a higher upregulation of GBSSI, greater increase in amylose content and volume ratio of large granules, as well as higher gelatinization enthalpy and crystallinity. However, ZM369 showed a lower increase in amylopectin content and a lower upregulation of SSSI and SSSII. Correlation analysis revealed amylose and amylopectin content had a positive correlation with GBSS and SSS, respectively, a significant positively correlation among the amylose and amylopectin content, starch granule volume, and pasting properties. In conclusion, these changes may enhance the utilization value of biscuit wheat but exhibit an opposite effect on bread wheat. The results provide a basis for selecting suitable wheat cultivars and ensuring food security under future climate change conditions.

Optimizing row spacing is an important measure to exploit the full yield potential, achieve a continuous increase in wheat yield and green development without increasing input. The objective of this study was to compare the effects of wide–narrow row spacing pattern (12 cm-12 cm-12 cm-24 cm, R1) and conventional uniform row spacing mode (20 cm, CK; 12 cm. R2) on nitrogen (N) availability in soil, canopy structure, photosynthetic characteristics, radiation use efficiency (RUE) and yield. The results showed that R1 increased the relative abundance of ammonia-oxidizing bacteria and ammonia-oxidizing archaea in the rhizosphere compared to CK. Nitrate in soils treated R1 at post-anthesis and nitrogen use efficiency of the plant were 59.92% and 27.01% higher than those treated with CK, respectively. Above-ground growth of wheat showed that R1 increased leaf area index by 27.42%, specific leaf weight by 22.67% and leaf photosynthetic rate by 8.86%, respectively compared to CK. R2 had similar availability of N and plant growth as CK. Moreover, the ability of the plant to allocate more nitrogen to grains was enhanced at post-anthesis with R1 than it with CK and R2, which greatly improved the RUE by 10.13% and 2.27%. As the result, the yield for R1 increased by 27.54% and 21.46%, respectively, compared to CK and R2. Therefore, using wide–narrow row spacing pattern (R1) is a practically and environmentally feasible approach for wheat production without extra input in the straw-returning wheat-corn rotation system.

Semantic segmentation algorithms leveraging deep convolutional neural networks often encounter challenges due to their extensive parameters, high computational complexity, and slow execution. To address these issues, we introduce a semantic segmentation network model emphasizing the rapid generation of redundant features and multi-level spatial aggregation. This model applies cost-efficient linear transformations instead of standard convolution operations during feature map generation, effectively managing memory usage and reducing computational complexity. To enhance the feature maps’ representation ability post-linear transformation, a specifically designed dual-attention mechanism is implemented, enhancing the model’s capacity for semantic understanding of both local and global image information. Moreover, the model integrates sparse self-attention with multi-scale contextual strategies, effectively combining features across different scales and spatial extents. This approach optimizes computational efficiency and retains crucial information, enabling precise and quick image segmentation. To assess the model’s segmentation performance, we conducted experiments in Changge City, Henan Province, using datasets such as LoveDA, PASCAL VOC, LandCoverNet, and DroneDeploy. These experiments demonstrated the model’s outstanding performance on public remote sensing datasets, significantly reducing the parameter count and computational complexity while maintaining high accuracy in segmentation tasks. This advancement offers substantial technical benefits for applications in agriculture and forestry, including land cover classification and crop health monitoring, thereby underscoring the model’s potential to support these critical sectors effectively.