Throughout recent decades, the excessive use of animal manure and fertiliser put a threat on the quality of ground and surface waters in main agricultural production areas in Europe and other parts of the world. Finding a balance between agricultural production and environmental protection is a prerequisite for sustainable development of ground and surface waters and soil quality. To protect groundwater quality, the European Commission has stipulated a limit value for NO₃ ⁻ of 50 mg l⁻¹. Member states are obliged to monitor and regulate nitrate concentrations in groundwater. In the Netherlands, this monitoring is carried out by sampling nitrate concentrations in water leaching from the root zone at farm level within the national Minerals Policy Monitoring Program. However, due to the costly procedure, only a limited number of about 450 farms can be sampled each year. While this is sufficient for providing a national overview of nitrate leaching, as a result of current and future challenges regarding the sustainable development of the agricultural system, Dutch policymakers need to gain insight into the spatial distribution of nitrate at smaller spatial scales. This study aimed to develop a predictive modelling framework to create annual maps with full national coverage of nitrate concentrations leaching from the root zone of Dutch agricultural soils, and to test this model for the year 2017. We used nitrate data from a national monitoring program and combined them with a large set of auxiliary spatial data, such as soil types, groundwater levels and crop types. We used the Random Forest (RF) algorithm as a prediction and interpolation method. Using the model, we could explain 58% of variance, and statistical errors indicate that the interpolation and map visualisation is suitable for interpretation of the spatial variability of nitrate concentrations in the Netherlands. We used the variable importance from the RF and the partial dependency of the most important variables to get more insight into the major factors explaining the spatial variability. Our study also shows the caveats of data-driven algorithms such as RF. For some areas where no training data was available, the model’s predictions are unexpected and might indicate a model bias. The combination of visualisation of the spatial variability and the interpretation of variable importance and partial dependence results in understanding which areas are more vulnerable to NO₃ ⁻ leaching, in terms of land use and geomorphology. Our modelling framework can be used to target specific areas and to take more targeted regional policy measurements for the balance between agricultural production and protecting the environment.

近几十年来，过度使用动物粪便和肥料对欧洲和世界其他地区主要农业生产区的地下水和地表水质量构成威胁。在农业生产和环境保护之间找到平衡是地下水和地表水和土壤质量可持续发展的前提。为了保护地下水质量，欧盟委员会规定了 NO ₃ ^-的限值为50 mg l ^-1. 成员国有义务监测和调节地下水中的硝酸盐浓度。在荷兰，这种监测是通过在国家矿产政策监测计划内对农场层面根区浸出的水中硝酸盐浓度进行采样来进行的。但是，由于程序成本高昂，每年只能对数量有限的大约 450 个农场进行抽样。虽然这足以提供关于硝酸盐浸出的全国概览，但由于当前和未来农业系统可持续发展面临的挑战，荷兰政策制定者需要深入了解硝酸盐在较小空间尺度上的空间分布。本研究旨在开发一个预测建模框架，以创建涵盖从荷兰农业土壤根区浸出的硝酸盐浓度的年度地图，并在 2017 年测试该模型。我们使用了来自国家监测计划的硝酸盐数据和将它们与大量辅助空间数据相结合，例如土壤类型、地下水位和作物类型。我们使用随机森林 (RF) 算法作为预测和插值方法。使用该模型，我们可以解释 58% 的方差，统计误差表明插值和地图可视化适用于解释荷兰硝酸盐浓度的空间变异性。我们使用来自 RF 的变量重要性和最重要变量的部分依赖性来更深入地了解解释空间变异性的主要因素。我们的研究还显示了数据驱动算法（如 RF）的注意事项。对于某些没有可用训练数据的区域，模型的预测是出乎意料的，可能表明模型存在偏差。空间可变性的可视化与变量重要性和部分依赖性的解释相结合，可以了解哪些区域更容易受到 NO 的影响₃ ^-浸出，在土地利用和地貌方面。我们的建模框架可用于针对特定领域，并针对农业生产和环境保护之间的平衡采取更有针对性的区域政策措施。