In this study, the spatial variability of nitrogen (N) balances and potential nitrate leaching were determined in heterogeneous arable fields in southern Germany using digital methods (tractor-mounted multispectral sensor, satellite data, vegetation indices and models) and measurements of nitrate stocks in deeper zones (1−2.5 m soil depth) with deep drilling. The aim of this study was to achieve precise localisation of N losses at high spatial resolution (sub field level; 10 × 10 m grid). The spatial variability of plant parameters within uniformly fertilised fields (crop yield, N uptake) was determined for the calculation of the N balances. The spatial variability of soil properties (e.g. soil organic carbon content, soil total nitrogen content) were determined to identify the causes of high or low N surpluses and nitrate leaching in sub-fields. N surplus determined using different digital methods and measured nitrate stocks showed similar spatial patterns. Site-specific N balancing identified zones with high N loss potential (N surplus up to 86 kg ha⁻¹). Deep drilling showed zones with high nitrate loss (nitrate N up to 94 kg ha⁻¹). N surplus and nitrate loss correlated with r = 0.49. This relationship could be impacted by many other soil and management factors. Soil properties showed considerable spatial variation within the fields. Soil organic carbon (SOC) and soil total nitrogen (TN) content were closely correlated in all fields (up to r = 0.96) and were most closely positively correlated with crop yield and N uptake (up to r = 0.74) and negatively correlated with N surplus (up to r = − 0.73). The sensor data and satellite data (crop yield, N uptake, N surplus) showed similar distribution patterns. Based on these results, digital technologies are suitable for the calculation of site-specific N surplus and estimation of nitrate leaching risk. Satellite or sensor based site-specific and yield-oriented fertilization is one approach to reduce N surplus on sub fields with low yield potential and high nitrate leaching risk.

在这项研究中，使用数字方法（拖拉机安装的多光谱传感器、卫星数据、植被指数和模型）和对德国南部硝酸盐储量的测量，确定了德国南部异质耕地中氮 (N) 平衡和潜在硝酸盐浸出的空间变异性。更深的区域（1-2.5 m 土壤深度）进行深钻孔。本研究的目的是在高空间分辨率（子场级；10 × 10 m 网格）下实现 N 损失的精确定位。确定均匀施肥田地内植物参数的空间变异性（作物产量、N 吸收）以计算 N 平衡。确定了土壤特性（例如土壤有机碳含量、土壤总氮含量）的空间变异性，以确定子田中氮过剩和硝酸盐淋失高或低的原因。使用不同数字方法确定的氮过剩和测量的硝酸盐储量显示出相似的空间模式。特定地点的氮平衡确定了具有高氮损失潜力的区域（氮过剩高达 86 公斤公顷^-1 )。深钻显示出硝酸盐损失高的区域（硝酸盐 N 高达 94 kg ha ^-1）。N 过剩和硝酸盐损失与 r = 0.49 相关。这种关系可能受到许多其他土壤和管理因素的影响。土壤特性在田间表现出相当大的空间变化。土壤有机碳 (SOC) 和土壤总氮 (TN) 含量在所有领域都密切相关（最高 r = 0.96），与作物产量和氮吸收最密切相关（最高 r = 0.74），与负相关N 剩余（最多r = - 0.73）。传感器数据和卫星数据（作物产量、氮吸收、氮过剩）显示出相似的分布模式。基于这些结果，数字技术适用于计算特定地点的氮过剩和硝酸盐浸出风险的估计。基于卫星或传感器的特定地点和以产量为导向的施肥是减少产量潜力低和硝酸盐淋失风险高的子田的氮过剩的一种方法。