早春树干液流用于白桦营养诊断的可行性

doi:10.11707/j.1001-7488.20221116

摘要/Abstract

摘要：

目的: 探讨树干液流中养分含量用于白桦人工林营养诊断的潜力，探索更经济、方便的树木营养诊断方法。方法: 以12年生白桦人工林为对象，选择地势相对平坦的地块，设立研究样地（30 m×120 m）。随机选择40株生长正常、无病虫害的样木。在完全展叶前采集林木叶片、单株林木所占土体表层土壤、树干液流3种样品，测定养分含量。分析3种样品养分含量与林木长势的相关系数，判定利用树干液流进行营养诊断的可行性。结果: 树干液流元素含量由高到低依次是全碳（TC）（4.03 ±0.16）g·L^-1、全氮（TN）（60.66 ±4.02）mg·L^-1、全钾（TK）（34.41 ±2.14）mg·L^-1、全磷（TP）（5.84 ±0.52）mg·L^-1。树高与土壤、叶片、树干液流的TC含量均呈显著正相关（P < 0.05），逐步回归分析显示树干液流TC含量对树高的贡献度最大（R²=0.145）；3种诊断材料中仅树干液流的TN、TP含量与胸径呈显著正相关（P < 0.05），逐步回归分析中仅树干液流的TP含量对胸径有显著贡献（R²=0.187）。结论: 综合相关分析和逐步回归分析结果判断，树干液流可用于白桦营养诊断，且分析显示长势差的林木受到C、P限制。本研究结果提供了一种在完全展叶前进行树木营养诊断的可行方法，且比土壤和叶片营养诊断更可靠，而且诊断时间可提前。

关键词: 白桦, 树干液流, 营养诊断

Abstract:

Objective: This study aims to explore the potential of nutrient content in sap flow for nutritional diagnosis of Betula platyphylla plantation, so as to explore a more economical and convenient method for tree nutritional diagnosis. Methods: The 12-year-old B. platyphylla plantation was targeted, a relatively flat site was selected, and a sample plot (30 m×120 m) was set up. Forty sample trees with normal growth and free of diseases and insect pests were randomly selected. Three kinds of materials including tree leaves, topsoil occupied by individual trees and sap flow in early spring were collected before leaves were completely spread. Then the correlation coefficient between the nutrient content of the three kinds of samples and tree growth was analyzed to determine the feasibility of sap flow used for nutrition diagnosis. Results: The element contents in sap flow from high to low were total carbon (TC) (4.03±0.16 g·L^-1), total nitrogen (TN) (60.66 mg·L^-1±4.02 mg·L^-1), total potassium (TK) (34.41 mg·L^-1±2.14 mg·L^-1) and total phosphorus (TP) (5.84 mg·L^-1±0.52 mg·L^-1). There was a significant positive correlation between tree height and TC content in soil, leaves and sap flow (P < 0.05). Stepwise regression analysis showed that TC content in sap flow had the greatest contribution to tree height (R²=0.145). Among the three diagnostic materials, only the contents of TN and TP in sap flow were positively correlated with DBH (P < 0.05). Stepwise regression analysis showed that only the TP content of sap flow had a significant contribution to DBH (R²=0.187). Conclusion: The results of correlation analysis and stepwise regression analysis indicate that sap flow can be used for nutrition diagnosis of B. platyphylla, and the analysis shows that the trees with poor growth are limited by C and P. The study provides a feasible method for tree nutrition diagnosis before complete leaf development, which is more reliable than soil and leaf nutrition diagnosis, and the diagnosis time can be advanced.

Key words: Betula platyphylla, sap flow, nutrition diagnosis

中图分类号:

S718.43

胡建文,王庆成. 早春树干液流用于白桦营养诊断的可行性[J]. 林业科学, 2022, 58(11): 174-180.

Jianwen Hu,Qingcheng Wang. Feasibility of Sap Flow in Early Spring Used for Nutrition Diagnosis of Betula platyphylla Plantation[J]. Scientia Silvae Sinicae, 2022, 58(11): 174-180.

图/表 3

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项目 Iten	H	D_1.3	V	STN	STC	STP	STK	SAP	SAK	LTN	LTC	LTP	LTK	SFTC	SFTN	SFTP	SFTK
H	1
D_1.3	0.28	1
V	0.42^**	0.96^**	1
STN	0.37^*	-0.01	0.07	1
STC	0.38^*	-0.02	0.05	0.97^**	1
STP	0.03	-0.25	-0.12	0.34^*	0.24	1
STK	0.16	-0.26	-0.2	0.78^**	0.72^**	0.51^**	1
SAP	0.16	0.03	0.04	0.59^**	0.62^**	0.06	0.49^**	1
SAK	0.26	0.04	0.13	0.70^**	0.63^**	0.58^**	0.65^**	0.36^*	1
LTN	0.11	0.02	0.05	0.03	0.05	-0.06	-0.12	-0.25	0.06	1
LTC	0.39^*	0.27	0.33^*	0.18	0.19	-0.01	0.05	0.21	0.11	-0.01	1
LTP	-0.08	0.15	0.10	-0.10	-0.16	0.10	-0.10	-0.16	0.05	0.17	-0.320^*	1
LTK	-0.03	0.17	0.19	-0.028	-0.04	0.19	-0.07	-0.20	0.27	0.22	-0.198	0.32^*	1
SFTC	0.41^**	0.11	0.16	0.28	0.32^*	0.04	0.17	-0.04	0.11	0.36^*	0.45^**	-0.42^**	-0.02	1
SFTN	-0.08	0.43^**	0.34^*	-0.06	-0.09	-0.10	-0.03	-0.24	0.07	0.09	0.08	0.03	-0.07	0.03	1
SFTP	0.07	0.47^**	0.42^**	0.19	0.16	-0.04	0.09	-0.08	0.21	-0.04	0.15	0.00	-0.0	-0.01	0.81^**	1
SFTK	0.02	0.22	0.12	0.15	0.14	-0.13	0.01	0.19	0.15	0.14	0.02	0.28	-0.10	-0.07	0.37^*	0.29	1

材料Materials	逐步回归方程Stepwise regression equation
土壤 Soil	Y_H = 11.505 + 0.018 X_STC (P = 0.015, R² = 0.125)
叶片 Leaf	Y_H = 0.070 X_LTC - 21.380(P = 0.012, R² = 0.132)
树干液流 Sap flow	Y_H = 0.421X_SFTC + 10.900 (P =0.009, R² = 0.145)
树干液流 Sap flow	Y_D1.3 = 0.220X_SFTP + 9.171 (P = 0.003, R² = 0.187)

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