Balancing plant growth and survival is an important strategy for plants to adapt to different environments. However, the molecular mechanisms of the balance strategy are poorly understood. Our previous study demonstrated that MsNAC73 interacts with the promoter of MsPAE12 , which positively regulates alfalfa shoot branching. In the present study, MsNAC73 was further found to interact with the promoter of MsPG2 and negatively regulate MsPG2 expression through MODMS database analysis and experimental verification (Y1H, EMSA and Dual‐LUC assays). The transgenic alfalfa plants with overexpression or knockdown of MsNAC73 and MsPG2 were obtained, and their responses to abiotic stress were analysed. Overexpressing MsNAC73 negatively and overexpressing MsPG2 positively affected the salt tolerance of alfalfa. MsPG2 increased salt tolerance via hydrolysing pectins, increasing cell wall extensibility and reducing Na ⁺ /K ⁺ ratio, stomatal aperture and vessel diameter. Co‐IP, Y2H, split‐LUC and BiFC assays demonstrated that MsMPK3 interacted with MsNAC73 and phosphorylated MsNAC73 at the Thr‐123 site. Furthermore, low abundances of MsMPK3 and MsNAC73 under normal conditions diminished the MsNAC73 phosphorylation, thereby promoting MsPAE12 expression and increasing alfalfa shoot branching. Under salt stress, however, MsNAC73 and MsMPK3 were upregulated at transcript and protein levels. The increased phosphorylation of MsNAC73 (MsNAC73 ^T123D ) promoted MsPG2 expression. Additionally, overexpression of MsNAC73 ^T123D and MsNAC73 ^T123A (dephosphorylation of MsNAC73) in alfalfa hairy roots increased root elongation under salt conditions and lateral root amounts under normal conditions respectively. In brief, these results revealed that the MsNAC73‐MsMPK3‐ MsPG2/MsPAE12 module plays a key role in the trade‐off between shoot branching and plant survival in response to different environments.

Accurate alfalfa biomass prediction is crucial for pasture management and sustainable livestock production. However, traditional methods often perform poorly under complex field conditions. To address the limited prediction accuracy of traditional methods under complex planting environments, this study proposes an alfalfa biomass prediction method combining multispectral and LiDAR data with ensemble learning model. Based on the multispectral images acquired by unmanned aerial vehicle (UAV) and airborne LiDAR data, the spectral features, three-dimensional structural features, and their interaction features are systematically extracted at the quadrat scale, and a high-quality modeling dataset is constructed by feature selection. Secondly, an ensemble model for alfalfa biomass prediction was constructed, which was composed of random forest, extra trees, and histogram gradient boosting. After model training, the coefficient of determination (R²) of the integrated model on the test set reached 0.813, and the root mean square error (RMSE) and mean absolute error (MAE) were 0.178 kg m⁻² and 0.146 kg m⁻², which were significantly better than those of similar single models. Under feature combinations, the fusion model was better than that of spectral indices only (R² = 0.773) and LiDAR traits only (R² = 0.576), and the model achieved the highest accuracy from bud emergence to early flowering (R² = 0.917). The overall prediction error of the model was approximately normal distribution, and the absolute error of more than 65% of the samples was less than 0.2. However, there was still a trend of underestimation in the high biomass interval. This research showed that the multimodal data fusion and ensemble learning method could achieve high-precision prediction of alfalfa biomass, which provided reliable technical support for pasture resources monitoring and precision agriculture management.

Soil antibiotic resistant genes (ARGs) and mobile genetic elements (MGEs) are associated with agricultural land-use differences. However, assessing the soi

Pollinator health depends critically on gut microbiome composition, yet species-specific responses to agricultural environments remain unclear. We compared gut microbiome dynamics and dietary shifts in Osmia excavata, Megachile rotundata, and Apis cerana during alfalfa pollination using 16S rRNA sequencing and rbcL metabarcoding. Alpha diversity revealed distinct patterns: O. excavata showed reduced richness but increased evenness after foraging, M. rotundata exhibited reduced diversity, while A. cerana remained stable. Beta diversity demonstrated microbiome convergence among species after release, indicating environmental filtering. Dietary analysis revealed polylectic foraging dominated by Citrullus lanatus, Peganum nigellastrum, and Zea mays, with minimal alfalfa consumption. Pseudomonadota and Bacillota predominated, with species-specific signatures: Gilliamella in A. cerana, Lactobacillus in M. rotundata, and Bacillus in O. excavata. These findings demonstrate that microbiome assembly reflects host sociality–environment interactions, with solitary bees showing greater plasticity. This study provides a microbial foundation for pollinator conservation in agricultural systems.