关键词: 榛种质资源, 比叶面积, 叶干物质含量, 水分利用效率, 相关分析, 主成分分析

Abstract: The variation and correlation of leaf functional traits and photosynthetic characteristic parameters among 38 hazelnut germplasm resources were examined, aiming to provide scientific basis for the selection of excellent germplasm resources and further understanding the mechanisms underlying the influence of leaf functional traits on photosynthetic characteristics. The results showed that the average values of leaf area (LA), leaf shape index (LI), leaf dry weight (LDW), specific leaf area (SLA) and leaf dry matter content (LDMC) of 38 germplasm resources were 78.39 cm², 1.24, 0.73 g, 109.95 cm²·g^-1 and 38.31%, respectively. The largest variation was found for LDW, followed by LA and SLA, and the smallest for LI and DMC. The mean values of net photosynthetic rate (P_n), transpiration rate (T_r), stomatal conductance (g_s), intercellular CO₂ concentration (C_i), stomatal limitation (L_s) and water use efficiency (WUE) were 9.92 μmol·m^-2·s^-1, 3.88 mmol·m^-2·s^-1, 153.04 μmol·m^-2·s^-1, 238.73 μmol·mol^-1, 0.41 and 2.54 μmol·mmol^-1, respectively.g_s had the highest variation coefficients (27.89%), followed by P_n, C_i, L_s and WUE (10.37%-15.14%), and T_r was the smallest (9.99%). Results of Pearson correlation analysis showed that P_n was positively correlated with LA, LDW and LDMC, while negatively correlated with SLA.T_r was positively correlated with LA. g_s and C_i were negatively correlated with LDMC.WUE was positively correlated with LDW and LDMC, but negatively correlated with SLA. Results of the redundancy analysis showed that the first and second order axes together accounted for 88.6% of the total variation of leaf photosynthetic characteristics, withSLA and LDMC being the factors that significantly affected the photosynthetic characteristics. Ping’ou 210, F-03, Ping’ou 11, Ping’ou 88, Ping’ou 119, 85-162, Ping’ou 48, Yuzhui and Ping’ou 110 showed higher water and light use efficiency.

Key words: hazelnut germplasm resource, specific leaf area, leaf dry matter content, water use efficiency, correlation analysis, principal component analysis.