Soil health initiatives have categorized assays for enzyme activities (EAs) that measure p-nitrophenol and ester-linked fatty acid methyl ester (EL-FAME) as Tier 2 indicators for biological activity and community structure analysis, respectively. Quantitative polymerase chain reaction (qPCR) assays of functional and taxonomic communities are emerging Tier 3 indicators. To facilitate comparisons of soil biological health between research groups that may employ different methods, we applied these current and emerging indicators to semiarid soils from the Texas High Plains sampled in the growing season and postharvest from 2014 through 2018. Microbial groups via EL-FAME markers and EAs were strongly correlated (r > .79) with qPCR assays of equivalent taxonomic and functional genes. To further quantify the predictive power of these relationships, we modeled several genes as a function of EA or EL-FAME markers, combined with other related covariates (e.g., soil texture, pH, irrigation, and soil organic C [SOC]) using a generalized linear model. The latter was trained using data from 2014, which was an average year in terms of temperature and precipitation for the region. Subsequently, the model was tested making predictions for 2015–2018, which represented high variability in climatic conditions, ensuring a thorough assessment of its predictive power. In most cases, soil texture, SOC, and Tier 2 indicators were identified as moderate to strong predictors of the biological responses. Our results suggest that the different approaches for assessing either function or community in these semiarid soils were highly comparable and provided similar information on how microbial communities were responding to both management and climate.

中文翻译：

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比较土壤健康评估的生物学方法：EL-FAME、酶活性和 qPCR

土壤健康计划已将酶活性 (EA) 测定法分类为分别作为生物活性和群落结构分析的第 2 级指标的对硝基苯酚和酯连接脂肪酸甲酯 (EL-FAME) 的测定。功能和分类群落的定量聚合酶链反应 (qPCR) 分析是新兴的第 3 层指标。为了促进可能采用不同方法的研究组之间土壤生物健康的比较，我们将这些当前和新兴指标应用于德克萨斯高原半干旱土壤，该土壤在 2014 年至 2018 年的生长季节和收获后采样。 通过 EL-FAME 标记的微生物组和 EA 密切相关（r> .79) 对等效的分类和功能基因进行 qPCR 分析。为了进一步量化这些关系的预测能力，我们将几个基因建模为 EA 或 EL-FAME 标记的函数，并结合其他相关协变量（例如，土壤质地、pH、灌溉和土壤有机碳 [SOC]）使用广义线性模型。后者使用 2014 年的数据进行训练，这是该地区温度和降水的平均年份。随后，对该模型进行了测试，对 2015-2018 年进行了预测，这代表了气候条件的高度可变性，确保对其预测能力进行全面评估。在大多数情况下，土壤质地、SOC 和第 2 层指标被确定为生物反应的中等至强预测因子。