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

项目概述本研究使用数字土壤制图方法、N模型和农业管理策略来绘制和减少诺森伯兰北部一个脆弱的集水区地下水中的N损失。它将增加我们对驱动土壤氮素动态空间变异性的因素的理解，其规模与产生农场和田间具体农艺建议有关。该项目的主要目标是：a)通过田间监测和土壤氮素动态模拟，确定砂岩含水层硝酸盐淋失和污染的来源；b)确定和论证硝酸盐淋失与这些特定地区农业实践之间的关系；c)在不影响作物产量的情况下，评估缓解策略(天然硝化抑制剂、缓释化肥、捕获作物)对不同土壤类型氮素淋失的影响；以及d)建立与Berwick地区相关的土壤中硝酸盐淋失的校准和验证的N动态模型。为实现这些目标，该项目分为具体任务，包括：审查关于北方温作系统硝酸盐淋失控制因素和缓解策略的现有知识(文献综述)，监测和模拟硝酸盐淋失，测试拟议的缓解策略，以及预测在不同管理策略下瀑布砂岩集水区氮素淋失的空间变异性。将使用多孔杯氮素淋溶测量方法、过去作物管理的详细信息、绘制土壤属性图的电导率和伽马辐射计土壤传感器以及数字高程模型(DEM)来研究驱动流域氮素淋失空间变异性的因素，所有这些都使用最新的数字土壤制图(DSM)技术进行集成。将在高风险领域实施选定的缓解战略，全年将收集有关硝酸盐淋失和作物生长的数据。理想情况下，将鼓励土地所有者使用条带式方法来实施这些措施，以便与标准做法进行一些比较。土壤氮素动态模型(NDICEA)将利用土壤性质和硝态氮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硝酸盐淋溶的影响。在这项研究结束时，我们希望能够确定支持从农业集水区淋失硝酸盐的各种土壤类型和关键土壤属性。我们将确定适当有效的集水区管理技术来减少硝酸盐淋失，结果将向农民证明这些策略在不影响生产力的情况下有效减少硝酸盐淋失。我们将根据可测量的特性来验证预测硝酸盐淋失的模型和方法，这些模型和方法可以用于识别不同管理措施下不同土壤类型的硝酸盐淋失风险。

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.