The potent greenhouse gas nitrous oxide (N₂O) is accumulating in the atmosphere at unprecedented rates largely due to agricultural intensification, and cultivated soils contribute ∼60% of the agricultural flux. Empirical models of N₂O fluxes for intensively managed cropping systems are confounded by highly variable fluxes and limited geographic coverage; process-based biogeochemical models are rarely able to predict daily to monthly emissions with >20% accuracy even with site-specific calibration. Here we show the promise for machine learning (ML) to significantly improve field-level flux predictions, especially when coupled with a cropping systems model to simulate unmeasured soil parameters. We used sub-daily N₂O flux data from six years of automated flux chambers installed in a continuous corn rotation at a site in the upper US Midwest (∼3000 sub-daily flux observations), supplemented with weekly to biweekly manual chamber measurements (∼1100 daily fluxes), to train an ML model that explained 65%–89% of daily flux variance with very few input variables—soil moisture, days after fertilization, soil texture, air temperature, soil carbon, precipitation, and nitrogen (N) fertilizer rate. When applied to a long-term test site not used to train the model, the model explained 38% of the variation observed in weekly to biweekly manual chamber measurements from corn, and 51% upon coupling the ML model with a cropping systems model that predicted daily soil N availability. This represents a two to three times improvement over conventional process-based models and with substantially fewer input requirements. This coupled approach offers promise for better predictions of agricultural N₂O emissions and thus more precise global models and more effective agricultural mitigation interventions.

强大的温室气体一氧化二氮（N ₂ O）以空前的速率在大气中积累，这主要是由于农业集约化造成的，而耕种土壤贡献了农业通量的60％。集约经营的耕作系统中N ₂ O通量的经验模型由于通量高度可变和地理覆盖范围有限而混淆。基于过程的生物地球化学模型即使经过特定地点的校准，也很少能够以> 20％的准确性预测每日到每月的排放量。在这里，我们展示了机器学习（ML）有望显着改善田间水平通量预测的希望，特别是与种植系统模型结合以模拟未测得的土壤参数时尤其如此。我们使用了次日N ₂O流量数据来自在美国中西部上层某地点连续玉米旋转安装的六年自动流量传感器腔室（约3000次子日流量观测），并辅以每周至每两周的手动腔室测量（每日约1100个流量），训练一个ML模型，用很少的输入变量（土壤湿度，施肥后的天数，土壤质地，空气温度，土壤碳，降水和氮（N）肥料比率）来解释每日通量变化的65％– 89％。当应用于不用于训练模型的长期测试场所时，该模型解释了每周至每两周从玉米进行的人工房测量中观察到的变化的38％，以及将ML模型与预测每日土壤氮素有效性。与传统的基于过程的模型相比，这代表了两到三倍的改进，并且输入需求大大减少。这种耦合方法为更好地预测农业氮素提供了希望。₂ O排放量，从而建立更精确的全球模型和更有效的农业缓解措施。