Gis-statistical analysis of nutritional quality and regional differentiation of auricularia auricula In lvliang mountain (Bangladesh Journal of Botany, 2025)
This study evaluated the quality of wild Auricularia auricula from 13 areas in Lvliang Mountain using GIS and statistical methods to identify high-quality regions. Geographic Information System (GIS) was employed to collect data on location, altitude, and soil type. The relationships between these environmental factors and A. auricula quality were examined using principal component analysis and statistical models. Significant variations in nutritional quality were observed among production areas, with coefficients of variation ranging from 0.02% to 0.19%. The top-performing areas were Zhongyang, Liulin, and Jiaocheng counties. A. auricula from Zhongyang exhibited superior levels of moisture, crude protein, and total sugar. Principal component analysis grouped eight quality indicators into two components, which collectively accounted for 81.58% of the total variance. Altitude, air temperature, light duration, and precipitation were identified as key factors strongly influencing A. auricula quality. Notably, altitude and air temperature negatively affected crude protein content, while light duration positively influenced fat content. Overall, wild A. auricula from Zhongyang and Liulin counties received the highest nutritional quality ratings. This study provides a scientific basis for origin-based quality control and sustainable harvesting strategies of wild A. auricula, and offers insights for quality management and environmental protection of similar forest crops.Bangladesh J. Bot. 54(4): 995-1004, 2025 (December)