Generally, the physical, chemical and biological attributes of a soil combined with abiotic factors (e.g. climate and topography) drive pedogenesis. However, biological indicators of soil quality play no direct role in traditional soil classification and surveys. To support their inclusion in classification schemes, previous studies have shown that soil type is a key factor determining microbial community composition in arable soils. This suggests that soil type could be used as proxy for soil biological function and vice versa. In this study we assessed the relationship between soil biological indicators with either vegetation cover or soil type. A wide range of soil attributes were measured on soils from across the UK to investigate whether; (1) appropriate soil quality factors (SQFs) and indicators (SQIs) can be identified, (2) soil classification can predict SQIs; (3) which soil quality indicators were more effectively predicted by soil types, and (4) to what extent do soil types and/ or aggregate vegetation classes (AVCs) act as major regulators of SQIs. Factor analysis was used to group 20 soil attributes into six SQFs namely; Soil organic matter , Organic matter humification , Soluble nitrogen , Microbial biomass , Reduced nitrogen and Soil humification index . Of these, Soil organic matter was identified as the most important SQF in the discrimination of both soil types and AVCs. Among the measured soil attributes constituting the Soil organic matter factor were, microbial quotient and bulk density were the most important attributes for the discrimination of both individual soil types and AVCs. The Soil organic matter factor discriminated three soil type groupings and four aggregate vegetation class groupings. Only the Peat soil and Heath and bog AVC were distinctly discriminated from other groups. All other groups overlapped with one another, making it practically impossible to define reference values for each soil type or AVC. We conclude that conventionally classified soil types cannot predict the SQIs (or SQFs), but can be used in conjunction with the conventional soil classifications to characterise the soil types. The two-way ANOVA showed that the AVCs were a better regulator of the SQIs than the soil types and that they (AVCs) presented a significant effect on the soil type differences in the measured soil attributes.

中文翻译：

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从常规土壤和植被分类中识别和预测土壤质量因子和指标的可预测性

通常，土壤的物理，化学和生物学属性与非生物因素（例如气候和地形）相结合会驱动成岩作用。但是，土壤质量的生物学指标在传统的土壤分类和调查中没有直接作用。为了支持将它们包括在分类方案中，以前的研究表明，土壤类型是决定可耕土壤中微生物群落组成的关键因素。这表明土壤类型可以用作土壤生物学功能的代理，反之亦然。在这项研究中，我们评估了土壤生物学指标与植被覆盖率或土壤类型之间的关系。在英国各地的土壤上测量了各种各样的土壤属性，以调查是否；（1）可以确定适当的土壤质量因子（SQF）和指标（SQIs），（2）土壤分类可以预测SQIs；（3）通过土壤类型可以更有效地预测哪些土壤质量指标，以及（4）在何种程度上土壤类型和/或聚集植被类别（AVC）可以作为SQI的主要调节者。因子分析被用来将20个土壤属性分为6个SQF。土壤有机质，有机质增湿，可溶性氮，微生物量，还原态氮和土壤增湿指数。其中，土壤有机质被认为是区分土壤类型和AVC的最重要的SQF。在构成土壤有机质因子的测量土壤属性中，微生物商和容重是区分单个土壤类型和AVC的最重要属性。土壤有机质因子区分了三个土壤类型分组和四个聚集植被类别分组。仅将泥炭土和Heath and bog AVC与其他群体区分开来。所有其他组彼此重叠，因此几乎不可能为每种土壤类型或AVC定义参考值。我们得出的结论是，常规分类的土壤类型无法预测SQI（或SQF），但可以与常规土壤分类结合使用来表征土壤类型。双向方差分析表明，AVC比土壤类型对SQI的调节更好，并且它们（AVC）对所测土壤属性中的土壤类型差异具有显着影响。仅将泥炭土和Heath and bog AVC与其他群体区分开来。所有其他组彼此重叠，因此几乎不可能为每种土壤类型或AVC定义参考值。我们得出的结论是，常规分类的土壤类型无法预测SQI（或SQF），但可以与常规土壤分类结合使用来表征土壤类型。双向方差分析表明，AVC比土壤类型对SQI的调节更好，并且它们（AVC）对所测土壤属性中的土壤类型差异具有显着影响。仅将泥炭土和Heath and bog AVC与其他群体区分开来。所有其他组彼此重叠，因此几乎不可能为每种土壤类型或AVC定义参考值。我们得出的结论是，常规分类的土壤类型无法预测SQI（或SQF），但可以与常规土壤分类结合使用来表征土壤类型。双向方差分析表明，AVC比土壤类型对SQI的调节更好，并且它们（AVC）对所测土壤属性中的土壤类型差异具有显着影响。我们得出的结论是，常规分类的土壤类型无法预测SQI（或SQF），但可以与常规土壤分类结合使用来表征土壤类型。双向方差分析表明，AVC比土壤类型对SQI的调节更好，并且它们（AVC）对所测土壤属性中的土壤类型差异具有显着影响。我们得出的结论是，常规分类的土壤类型无法预测SQI（或SQF），但可以与常规土壤分类结合使用来表征土壤类型。双向方差分析表明，AVC比土壤类型对SQI的调节更好，并且它们（AVC）对所测土壤属性中的土壤类型差异具有显着影响。