Adaptability evaluation of four alfalfa (Medicago sativa L.) varieties in cold regions of northern China: Based on multidimensional indicators, metabolites, and soil microbial community analysis

Abstract

Cold-adapted alfalfa is critical for global forage security, yet limited elite germplasm and inadequate evaluation frameworks constrain its utilization. This study developed a multidimensional evaluation system integrating agronomy, nutrition, root metabolomics, cold resistance, soil enzymes, and rhizosphere microbiomes to assess four alfalfa varieties in northern China’s cold regions. The main findings include: Longmu 801 showed the highest dry-to-fresh ratio (32.09 %) and crude protein content (16.85 %) in the first year. Under 4°C stress, its peroxidase activity increased by 1.8 times, with significant increases in proline content and soluble protein content. The dominant Acidobacteriota in rhizosphere soil was positively correlated with most soil enzyme activities. Reindeer exhibited the maximum plant height (49.45 cm) in the second year but accumulated growth-inhibiting metabolites (such as N-p-Coumaroylspermidine). In Zhongmu No.1, the presence of Desulfobacterota_B reduced soil quality. Fuzzy membership analysis ranked Longmu 801 and Reindeer highest (0.63). This study reveals that local cultivar Longmu 801 adapts via zeatin biosynthesis and ABC transporter pathways, whereas introduced cultivars rely on short-term growth advantages with potential continuous cropping obstacles. The findings provide a scientific basis for precision breeding and cold-region cultivation strategies.