Alfalfa, as a high-quality forage resource, has high nutritional value. Due to the high phenotypic similarity among its varieties and the susceptibility to environmental influences, challenges are encountered in variety identification and breeding. In this study, 23 simple sequence repeat (SSR) markers were screened to distinguish 49 alfalfa varieties, among which 21 SSR markers showed polymorphic fragments. The results indicated that these 21 markers were highly polymorphic, with an average of 5.91 alleles per SSR marker locus and an average polymorphic information content (PIC) of 0.66, suggesting a strong discriminatory efficiency. The results of a population genetic diversity analysis showed that there was a relatively high level of genetic diversity among the tested materials. The analysis of molecular variance (AMOVA) results indicated that the genetic variation within the population of the 49 alfalfa germplasm samples was the main source of the total variation. The results of genetic distance and genetic identity analyses showed that the genetic relationship between population 1 and population 4 was the most distant, while the relationship between population 2 and population 3 was the closest. The cluster analysis results showed that samples S16 and S55 formed a separate branch; that is, there were two main genetic subgroups. These results confirm that SSR markers are effective tools for genetic characterization and precise discrimination of alfalfa varieties and have important application values in breeding, variety registration, and germplasm resource conservation.

Insufficient forage is a significant factor limiting the development of animal husbandry in high-altitude pastoral areas. This study aims to identify suitable high-quality forage Triticale (x Triticosecale Wittmack) varieties for cultivation in the Qaidam Basin and determine their optimal harvest period. Seven triticale varieties were selected as experimental materials, and their production performance and nutritional quality were comprehensively analyzed at four different growth stages [booting stage (BTS), heading stage (HDS), flowering stage (FLS), and milk stage (MKS)] in 2024 and 2025, utilizing Pearson correlation analysis, PCA, and TOPSIS methods. The results indicated that as the reproductive period progressed, plant height, dry matter yield, and dry matter content exhibited a continuous increase. In contrast, indicators such as stem diameter, number of green leaves, and fresh biomass yield initially increased before subsequently declining. The MKS soluble sugar content (SS) and relative feed value (RFV) were the highest, crude protein (CP) and crude ash content (CA) decreased, the neutral detergent fiber content (NDF) and the acid detergent fiber (ADF) content reached their peak in the HDS. The relative forage quality (RFQ) reached its peak during the flowering period. Comprehensive analysis showed that the top five rankings are QSM-8 > JSM-3 > QSM-1 > JSM-2 > QSM-7. In the Qaidam Basin, the optimal harvest period from the FLS to the MKS ensures both high yield and good nutritional quality, making it suitable for promotion in the Qaidam Basin region and similar ecological zones.

Interactions between species give rise to chemical pathways of communication that regulate the interactions of transboundary species. The communication between nematodes and other species primarily occurs through the regulation of chemicals, with key species including plants, insects, bacteria, and nematode-trapping fungi that are closely associated with nematodes. G protein-coupled receptors (GPCRs) play a crucial role in interspecies communication. Certain flp genes, which function as GPCRs, exert varying degrees of influence on how nematodes interact with other species. These receptors facilitate the transmission of corresponding signals, thereby completing the interactions between species. This paper introduces the interactions between nematodes and other species and discusses the role of GPCRs in these organisms, contributing to a deeper understanding of the impact and significance of GPCRs in cross-border regulation between nematodes and other species. Furthermore, it is essential to leverage GPCRs in efforts to control pests.

Soil salinization is a major constraint on sustainable agricultural development in arid regions, yet soil salinity dynamics are commonly assessed using pixel-based remote sensing products that are difficult to relate to agricultural management units. To solve this problem, we developed an agricultural parcel-scale framework for soil salinity monitoring and mitigation assessment in Delingha City, Qinghai Province, China. Cropland parcels were extracted using a Recurrent Residual U-Net (R2U-Net) model, and soil salinity inversion for April during 2021–2025 was conducted by integrating Sentinel-1/2 imagery with a stacking ensemble learning model. The model incorporated Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Multi-Layer Perceptron (MLP), and Support Vector Regression (SVR) to improve prediction accuracy and robustness. The selected features included vegetation indices, salinity indices, and SAR backscatter parameters. Among them, the Soil Adjusted Vegetation Index (SAVI) showed the strongest correlation with soil salinity, while Salinity Index 2 (SI2) exhibited the highest contribution to model performance. The R2U-Net achieved an F1-score of 0.8574 for parcel extraction. The ensemble model produced the best inversion results with an R² of 0.52 and reduced prediction errors compared with individual models. Results indicated an overall decline in soil salinity from 2021 to 2025, suggesting an improvement in soil salinity conditions during the study period. Parcel-scale aggregation reduced spatial noise; improved temporal stability; and revealed heterogeneous field responses, including salinity-declining, fluctuating, and increasing trends. The proposed framework enhances the interpretability and management relevance of soil salinity monitoring and provides practical support for precision agricultural management in arid regions.