Using spatially explicit soil mapping and modelling to understand and mitigate nitrate leaching in an agricultural catchment

本研究使用数字土壤制图方法、氮模型和农业管理策略来绘制和减轻诺森伯兰郡北部一个脆弱集水区地下水中的氮损失。它将增加我们对驱动土壤氮动态空间变异性的因素的理解，这与制定农场和特定领域的农艺建议有关。该项目的主要目标是：a)通过室内监测和土壤N动态建模，确定fall砂岩含水层的硝酸盐淋滤和污染来源；b)确定并证明这些特定地区的硝酸盐淋滤与农业做法之间的关系；c)评估缓解战略（天然硝化抑制剂、缓释肥料、捕捞作物）对不同土壤类型的N淋滤的影响，同时不影响作物生产；d)建立与Berwick地区相关的土壤中硝酸盐淋溶的校准和验证的n动态模型。为了实现这些目标，该项目分为若干具体任务，包括：审查目前关于北方温度种植系统中控制硝酸盐淋滤和缓解策略的因素的知识（文献综述），监测和模拟硝酸盐淋滤，测试拟议的缓解策略，以及预测Fell砂岩流域在不同管理策略下N淋滤的空间变异。研究人员将使用多孔杯法测量流域氮淋溶的空间变异性，使用过去作物管理的详细信息，电导率和伽马辐射计土壤传感器来绘制土壤特性，以及数字高程模型（DEM），所有这些都将使用最新的数字土壤制图（DSM）技术进行整合。将在高风险地区实施选定的缓解战略，并将全年收集有关硝酸盐淋滤和作物生长的数据。理想情况下，应鼓励土地所有人采用简易办法来执行这些措施，以便与标准做法进行比较。土壤N动态模型（NDICEA）将使用土壤性质和硝酸盐淋失的观测数据进行校准和验证。校准后的模型将用于预测不同农业管理措施对农业集水区地下水中硝酸盐浸出的影响。在这项研究结束时，我们希望能够确定可变的土壤类型和关键的土壤性质，可以支持农业集水区的硝酸盐浸出。我们将确定适当有效的集水区管理技术，以减轻硝酸盐浸出，结果将向农民证明，这些策略可以在不影响生产力的情况下减少硝酸盐浸出。我们将有经过验证的模型和方法来预测硝酸盐浸出基于可测量的属性，可用于识别在不同管理实践下不同土壤类型的硝酸盐浸出风险。

Project summaryThis study uses digital soil mapping approaches, N models and agricultural management strategies to map and mitigate N losses to groundwater in a vulnerable catchment in north Northumberland. It will increase our understanding of the factors driving the spatial variability of soil-N dynamics at a scale that is relevant for producing farm and field-specific agronomic recommendations. The main objectives of this project are: a) To identify the source of Nitrate leaching and contamination of the Fell Sandstone aquifer through infield monitoring and soil N dynamic modelling, b) To identify and demonstrate the relation between nitrate leaching and agricultural practices in those specific areas, c) To evaluate the impact of mitigation strategies (natural nitrification inhibitors, slow release fertilizers, catch crops) on N leaching for different soil types without compromising crop production, and d) To produce a calibrated and validated N-dynamic model of nitrate leaching within soils relevant to the Berwick area. To achieve these objectives the project is divided into specific tasks including: review of current knowledge on factors controlling nitrate leaching and mitigation strategies in northern temperature cropping systems (literature review), monitoring and modelling of nitrate leaching, testing of proposed mitigation strategies, and predicting spatial variability of N leaching under different management strategies in the Fell Sandstone catchment. The factors driving spatial variability of N leaching in the catchment will be studied using porous cup methods for N leaching measurement, detailed information on past crop management, conductivity and Gamma-Radiometer soil sensors to map soil properties, and Digital Elevation Models (DEM), all integrated using the latest Digital Soil Mapping (DSM) techniques. Selected mitigation strategies will be implemented in high risk fields and data regarding nitrate leaching and crop growth will be collected throughout the year. Ideally, landowners will be encouraged to use a strip approach to implementing the measures so that some comparisons with standard practice are possible. The soil N dynamic model (NDICEA) will be calibrated and validated using observed data of soil properties and nitrate leaching. The calibrated model will be used to predict impact of different agricultural management practices on nitrate leaching to ground water from agricultural catchments. At the end of this research we expect to be able to identify variable soil types and key soil properties that can support nitrate leaching from an agriculture catchment. We will identify appropriately effective catchment management techniques to mitigate nitrate leaching and the result will be a proof for farmers that these strategies work to reduce nitrate leaching without compromising productivity. We will have validated model and approach to predict nitrate leaching based on measurable properties that can be used to identify risk of nitrate leaching from different soil types under different management practices.