Site-specific nitrogen (N) management in precision agriculture is used to improve nitrogen use efficiency (NUE) at the field scale. The objective of this study has been (i) to better understand the relationship between data derived from an unmanned aerial vehicle (UAV) platform and the crop temporal and spatial variability in small fields of about 2 ha, and (ii) to increase knowledge on how such data can support variable application of N fertilizer in winter wheat ( Triticum aestivum ). Multi-spectral images acquired with a commercially available UAV platform and soil available mineral N content (Nmin) sampled in the field were used to evaluate the in-field variability of the N-status of the crop. A plot-based field experiment was designed to compare uniform standard rate (ST) to variable rate (VR) N application. Non-fertilized (NF) and N-rich (NR) plots were placed as positive and negative N-status references and were used to calculate various indicators related to NUE. The crop was monitored throughout the season to support three split fertilizations. The data of two growing seasons (2017/2018 and 2018/2019) were used to validate the sensitivity of spectral vegetation indices (SVI) suitable for the sensor used in relation to biomass and N-status traits. Grain yield was mostly in the expected range and inconsistently higher in VR compared to ST. In contrast, N fertilizer application was reduced in the VR treatments between 5 and 40% depending on the field heterogeneity. The study showed that the methods used provided a good base to implement variable rate fertilizer application in small to medium scale agricultural systems. In the majority of the case studies, NUE was improved around 10% by redistributing and reducing the amount of N fertilizer applied. However, the prediction of the N-mineralisation in the soil and related N-uptake by the plants remains to be better understood to further optimize in-season N-fertilization.

中文翻译：

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低空遥感和土壤数据支持的冬小麦定点氮管理

精准农业中特定地点的氮 (N) 管理用于提高田间规模的氮使用效率 (NUE)。本研究的目的是 (i) 更好地了解来自无人机 (UAV) 平台的数据与约 2 公顷小田地作物时空变异之间的关系，以及 (ii) 增加有关这些数据如何支持冬小麦 (Triticum aestivum) 氮肥的可变施用。使用商用无人机平台获得的多光谱图像和在田间采样的土壤有效矿物氮含量 (Nmin) 用于评估作物氮状态的田间变异性。一项基于地块的田间试验旨在比较统一标准施肥量 (ST) 与可变施肥量 (VR) 施氮量。未施肥 (NF) 和富氮 (NR) 图被放置为正负 N 状态参考，并用于计算与 NUE 相关的各种指标。整个季节对作物进行监测，以支持三种分开施肥。两个生长季节（2017/2018 和 2018/2019）的数据用于验证光谱植被指数 (SVI) 的灵敏度，该指数适用于与生物量和 N 状态性状相关的传感器。与 ST 相比，VR 中的谷物产量大多在预期范围内，并且不一致地更高。相比之下，根据田间异质性，在 VR 处理中氮肥施用量减少了 5% 到 40%。研究表明，所使用的方法为在中小型农业系统中实施可变比例施肥提供了良好的基础。在大多数案例研究中，通过重新分配和减少施氮量，NUE 提高了 10% 左右。然而，土壤中氮矿化和植物相关氮吸收的预测仍有待更好地理解，以进一步优化季节性施氮。