Predicting Gross Nitrogen Mineralization and Potentially Mineralizable Nitrogen using Soil Organic Matter Properties
Predicting Gross Nitrogen Mineralization and Potentially Mineralizable Nitrogen using Soil Organic Matter Properties
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DOI：
10.2136/sssaj2017.02.0055
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发表时间：
2017-09
影响因子：
2.9
通讯作者：
W. Osterholz;Oshri Rinot;A. Shaviv;R. Linker;M. Liebman;G. Sanford;J. Strock;M. Castellano
中科院分区：
文献类型：
--
作者：
W. Osterholz;Oshri Rinot;A. Shaviv;R. Linker;M. Liebman;G. Sanford;J. Strock;M. Castellano
总氮矿化是一个基本的土壤过程，在决定土壤无机氮的供应中起着重要作用，最近的研究表明，植物可以有效地与微生物竞争无机氮。然而，对土壤中植物有效氮供应的预测在很大程度上忽略了总氮矿化。土壤有机质（SOM）是微生物在氮矿化过程中使用的基质，SOM组分的特征相对容易测量，可能作为总氮矿化的预测指标。为了提高对SOM组分特性和总氮矿化之间预测关系的理解，我们评估了32种SOM质量和数量的测量方法，包括物理、化学和生物定义的SOM组分，以预测以色列和美国各种土壤类型（干旱区土壤到Mollisols）和作物管理系统（有机与无机基础肥力）的总氮矿化能力。我们还评估了一个常用的土壤氮有效性指标的预测，即潜在矿化氮（PMN，通过7天厌氧培养确定）。与无机肥力管理系统相比，有机肥力管理系统持续提高总氮矿化和PMN。虽然一些SOM特征与总氮矿化和PMN均显著相关，但其他特征与总氮矿化和PMN的关系存在差异，突出表明这些检测受不同因素控制。利用多元线性回归（MLR）进行N矿化预测：5个（总N矿化）或6个（PMN）预测模型解释了总N矿化和PMN （R²> 0.8）的80%的变化。MLR模型成功地预测了不同土壤类型和管理制度下的总氮矿化和PMN，表明这种关系在广泛的不同农业生态系统中是有效的。开发适用于不同土壤类型的预测模型的能力有助于土壤健康评估和管理工作。
Gross N mineralization is a fundamental soil process that plays an important role in determining the supply of soil inorganic N, highlighted by recent research demonstrating that plants can effectively compete with microbes for inorganic N. However, predictions of the supply of plant available N from soil have largely neglected gross N mineralization. As soil organic matter (SOM) is the substrate that microbes use in the process of N mineralization, characteristics of SOM fractions that are relatively easy to measure may hold value as predictors of gross N mineralization. To improve understanding of predictive relationships between SOM fraction properties and gross N mineralization, we assessed 32 measures of SOM quality and quantity, including physically, chemically, and biologically defined SOM fractions, for their ability to predict gross N mineralization across a wide range of soil types (Aridisols to Mollisols) and crop management systems (organic vs. inorganic based fertility) in Israel and the United States. We also assessed predictions of a commonly employed indicator of soil N availability, potentially mineralizable N (PMN, determined by 7-d anaerobic incubation). Organic fertility management systems consistently enhanced gross N mineralization and PMN compared with inorganic fertility management systems. While several SOM characteristics were significantly correlated with both gross N mineralization and PMN, other characteristics differed in their relationships with gross N mineralization and PMN, highlighting that these assays are controlled by different factors. Multiple linear regressions (MLR) were utilized to generate N mineralization predictions: five (gross N mineralization) or six (PMN) predictor models explained >80% of the variation in both gross N mineralization and PMN (R² > 0.8). The MLR models successfully predicted gross N mineralization and PMN across diverse soil types and management systems, indicating that the relationships were valid across a wide range of diverse agroecosystems. The ability to develop predictive models that apply across diverse soil types can aid soil health assessment and management efforts.