Abstract Soil organisms play an essential role in ecosystem functioning, including nutrient cycling and climate regulation. Most of soil functions have a microbial origin and recent findings suggest that various microbial community attributes (e.g. diversity, composition, abundance) are linked to the rate of soil functions in natural ecosystems. However, no such evidence is available from the agronomically managed systems. The types of agronomic practice and mechanisms (biotic vs. abiotic) that could impact microbial regulation of soil functions remain poorly understood. We collected soil samples from three long-term field trials across three geographically distinct regions with multiple management treatments (e.g. tillage, stubble management and fertiliser application) and measured the response of a number soil abiotic (e.g. soil aggregate stability, pH, nutrient status) and biotic (e.g. bacterial and fungal community structure, abundance of various microbial taxa) variables, and soil functions linked to nutrient cycling and nutrient availability. We used information theory and multi-model inference to identify key biotic and abiotic predictors of soil functions, and to model their response to land management through a multi-functionality index. Results indicated that no-till treatment generally impacted microbial community (42% decrease in the average gene copy number of all microbes compared to other management practices) and promoted soil structure (27% increase in the small aggregate fraction) but reduced some process rates, while stubble retention increased nutrient availability in the presence of fertilisation (59% on average). But these responses were site specific. The multi-functionality index associated with nutrient cycling declined under no-till system (47% on average) but increased when no-till was combined with stubble retention (49% on average) and fertiliser application (44%). Our modelling suggested that the best models that explained most of the variation in MF always included both abiotic and biotic variables. Amongst the latter, fungal community structure and beta(β)-Proteobacteria abundance were of importance as the model’s R² dropped by 12% when biotic attributes were removed providing evidence that the link between microbial community and soil functions was maintained in agricultural soils. This study provides support for the inclusion of microbial data in decision support systems for sustainable farming practices.

中文翻译：

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土壤多功能性对农业管理实践的响应可以通过关键的土壤非生物和生物特性来预测

摘要 土壤生物在生态系统功能中发挥着重要作用，包括养分循环和气候调节。大多数土壤功能都有微生物起源，最近的研究结果表明，各种微生物群落属性（例如多样性、组成、丰度）与自然生态系统中土壤功能的速度有关。然而，从农艺管理系统中没有这样的证据。可能影响土壤功能微生物调节的农艺实践和机制（生物与非生物）的类型仍然知之甚少。我们从三个不同地理区域的三个长期田间试验中收集了土壤样品，并进行了多种管理处理（例如耕作、茬管理和施肥），并测量了许多土壤非生物（例如 土壤团聚体稳定性、pH 值、养分状况）和生物（例如细菌和真菌群落结构、各种微生物分类群的丰度）变量，以及与养分循环和养分有效性相关的土壤功能。我们使用信息理论和多模型推理来确定土壤功能的关键生物和非生物预测因子，并通过多功能指数模拟它们对土地管理的反应。结果表明，免耕处理通常会影响微生物群落（与其他管理实践相比，所有微生物的平均基因拷贝数减少 42%）并促进土壤结构（小团聚体部分增加 27%），但降低了一些加工速率，而留茬增加了施肥时的养分利用率（平均 59%）。但这些响应是特定于站点的。与养分循环相关的多功能指数在免耕系统下下降（平均 47%），但在免耕与留茬（平均 49%）和施肥（44%）相结合时增加。我们的建模表明，解释大部分 MF 变化的最佳模型始终包括非生物变量和生物变量。在后者中，真菌群落结构和 β(β)-Proteobacteria 丰度很重要，因为当去除生物属性时，模型的 R² 下降了 12%，这提供了证据表明微生物群落与土壤功能之间的联系在农业土壤中得以维持。这项研究为将微生物数据纳入可持续农业实践的决策支持系统提供了支持。与养分循环相关的多功能指数在免耕系统下下降（平均 47%），但在免耕与留茬（平均 49%）和施肥（44%）相结合时增加。我们的建模表明，解释大部分 MF 变化的最佳模型始终包括非生物变量和生物变量。在后者中，真菌群落结构和 β(β)-Proteobacteria 丰度很重要，因为当去除生物属性时，模型的 R² 下降了 12%，这提供了证据表明微生物群落与土壤功能之间的联系在农业土壤中得以维持。这项研究为将微生物数据纳入可持续农业实践的决策支持系统提供了支持。与养分循环相关的多功能性指数在免耕系统下下降（平均 47%），但在免耕与留茬（平均 49%）和施肥（44%）相结合时增加。我们的建模表明，解释大部分 MF 变化的最佳模型始终包括非生物变量和生物变量。在后者中，真菌群落结构和 β(β)-Proteobacteria 丰度很重要，因为当去除生物属性时，模型的 R² 下降了 12%，这提供了证据表明微生物群落与土壤功能之间的联系在农业土壤中得以维持。这项研究为将微生物数据纳入可持续农业实践的决策支持系统提供了支持。