Spatio-temporal fluctuations in soil N₂O emissions are well documented, but it is difficult to predict the location of ‘hot spots’ and the timing of ‘hot moments’ of N₂O emissions from agroecosystems with dynamic, heterogeneous soil environments. Although the soil nitrite (NO₂⁻) concentration is a potential indicator of soil N₂O production, it is seldom measured. This perspective paper explains the emerging methods for NO₂⁻ quantification with ion-selective electrodes and infrared spectroscopy, and describes how these tools may be used to adjust nitrogen fertilization through site-specific management. We envision a new system for soil N₂O mitigation that integrates knowledge from portable NO₂⁻ sensors, machine learning, and the internet of things to predict the ‘hot spots’ and ‘hot moments’ of N₂O in agroecosystems. This integrated system will produce a real-time soil NO₂⁻ map that is validated by in situ soil NO₂⁻ sensors. The integrated system requires further development of ion-selective electrodes and spectrometric methods for real-time NO₂⁻ quantification under field conditions, as well as appropriate machine learning models and communication technologies. Our concept supports the adoption of site-specific nitrogen fertilizer management as a strategy to reduce soil N₂O emissions from agroecosystems.

中文翻译：

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土壤亚硝酸盐测量值有可能估算一氧化二氮排放量

土壤N ₂ O排放的时空波动有充分的文献记载，但是很难预测动态和非均质土壤环境下农业生态系统N ₂ O排放的“热点”位置和“热点时刻” 。尽管土壤亚硝酸盐（NO ₂ ^- ）浓度为土壤氮的电位指示器₂ ö生产，它很少测量。这个角度文件解释为NO新兴方法₂ ^-定量用离子选择性电极和红外光谱，并介绍了这些工具可如何使用通过位点特异性管理调整氮施肥。我们设想了一种新的土壤N ₂系统Ø缓解集成从便携NO知识₂ ^-传感器，机器学习，以及对事物预测n的“热点”和“热时刻”互联网₂？在农业生态系统。此集成系统将产生一个实时土壤NO ₂ ^- ，其通过原位土壤NO验证地图₂ ^-传感器。集成系统需要实时NO离子选择性电极和光谱测定法的进一步发展₂ ^-在野外条件下的量化，以及适当的机器学习模型和通信技术。我们的理念支持采用特定地点的氮肥管理作为减少土壤N ₂的策略农业生态系统的O排放。