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    刘晓彤, 李海奎, 曹磊, 张逸如. 广东省森林土壤养分异质性析因[J]. 北京林业大学学报, 2021, 43(2): 90-101. DOI: 10.12171/j.1000-1522.20200164
    引用本文: 刘晓彤, 李海奎, 曹磊, 张逸如. 广东省森林土壤养分异质性析因[J]. 北京林业大学学报, 2021, 43(2): 90-101. DOI: 10.12171/j.1000-1522.20200164
    Liu Xiaotong, Li Haikui, Cao Lei, Zhang Yiru. Analysis on the heterogeneity of forest soil nutrients in Guangdong Province of southern China[J]. Journal of Beijing Forestry University, 2021, 43(2): 90-101. DOI: 10.12171/j.1000-1522.20200164
    Citation: Liu Xiaotong, Li Haikui, Cao Lei, Zhang Yiru. Analysis on the heterogeneity of forest soil nutrients in Guangdong Province of southern China[J]. Journal of Beijing Forestry University, 2021, 43(2): 90-101. DOI: 10.12171/j.1000-1522.20200164

    广东省森林土壤养分异质性析因

    Analysis on the heterogeneity of forest soil nutrients in Guangdong Province of southern China

    • 摘要:
        目的  分析不同环境因子对森林土壤养分的影响,探讨造成森林土壤养分异质性的原因,为森林经营和森林立地质量评价提供依据。
        方法  基于广东省全国森林资源清查土壤调查数据,以有机质(OM)、碱解氮(AN)、有效磷(AP)和速效钾(AK)为因变量,以地理因子、地形因子、土壤因子和林分因子四大类20个变量(10个定性因子,10个定量因子)为自变量,基于一般线性模型,采用向后剔除法筛选主效应因子,引入定性和定量因子交互,分析森林土壤养分的影响因子及影响程度。定量因子进行多元线性回归分析,定性因子通过多重比较分析水平间的差异。
        结果  (1)对土壤养分影响较大的环境因子包括3个地理因子(经度、纬度和流域)、3个地形因子(海拔、坡位和坡度级)、4个林分因子(林下植被盖度、平均胸径、平均年龄、优势树种)。(2)影响因子对不同土壤养分的解释程度不一。AN的解释程度较高,均在0.30以上;OM和AK的解释程度在0.25以上;AP的解释程度较低,在0.20以上。交互作用显著提高了土壤养分的解释程度,平均增加0.141。其中AN提高到接近0.50,OM和AK分别超过0.45和0.40,最低的AP超过0.30。(3)通过多重比较分析,优势树种、流域、坡位不同土壤养分的不同水平间存在显著性差异。
        结论  影响广东省森林土壤异质性的因素包括地理因子、地形因子和林分因子。不同土壤养分的影响因子并不完全相同,纬度、流域、海拔、坡位、成土母岩、平均胸径、平均年龄和优势树种8个因子对大多数土壤养分因子有影响。土壤养分中,OM和AN的解释程度较高,AP和AK的解释程度较低。交互作用显著提高了影响因子对土壤养分的解释程度。本研究提出的含定性因子的主效应筛选方法和引入一阶定性因子和定量因子交互,可以显著提高土壤养分的解释程度,对类似问题的解决有参考价值。

       

      Abstract:
        Objective  The influence of different factors on forest soil nutrients was analyzed to explore the causes of forest soil nutrient diversity and support forest management and site quality evaluation.
        Method  Based on the soil data of forest inventory in Guangdong Province of southern China, organic matter (OM), alkali-hydro-nitrogen (AN), available phosphorus (AP) and available potassium (AK) were taken as dependent variables, and 20 variables (10 qualitative factors and 10 quantitative factors) were taken as independent variables in four categories, namely geographical factor, topographic factor, soil factor and stand factor. The main effector was selected based on the general linear model and backward elimination method. The influencing factors and degree of forest soil nutrient were analyzed by the interaction of qualitative and quantitative factors. The difference was analyzed by multiple linear regression analysis of quantitative factors and multiple comparative analysis of qualitative factors.
        Result  (1) The soil nutrients were mainly affected by three geographical factors (longitude, latitude and watershed), three topographic factors (altitude, slope position and slope grade), and four stand factors (vegetation coverage under forest, mean DBH, mean age and dominant tree species). And the factors on different soil nutrients were various. (2) The influencing factors had different degrees of interpretation for different soil nutrients. The interpretation degree of AN was more than 0.30, the interpretation degree of OM and AK was more than 0.25, and the interpretation degree of AN was more than 0.20. Interaction prominently enhanced the interpretation of soil nutrients, with an average increase of 0.141. AN, OM and AK were up to about 0.50, 0.45 and 0.40, respectively, AP was more than 0.30. (3) Through multiple comparative analysis, there were significant differences in soil nutrients among dominant tree species, watershed and slope position.
        Conclusion  The factors affecting forest soil heterogeneity in Guangdong Province include geographical factors, topographic factors and stand factors. The influencing factors of different soil nutrients are not exactly the same. Latitude, watershed, altitude, slope position, parent rock, mean DBH, mean age and dominant tree species have influences on most soil nutrient factors. For different soil nutrients, OM and AN have a higher degree of interpretation, while AP and AK have a lower degree of interpretation. Interaction significantly improves the degree of interpretation of soil nutrients. In this study, the main effect screening method with qualitative factors is proposed and the interaction between first-order qualitative factors and quantitative factors is introduced, so that it can highly increase the interpretation of soil nutrients.

       

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