Diurnal Variation in Soil Nitrous oxide Emissions (DIVINE): drivers and mechanisms

一氧化二氮（N2O）是一种强效温室气体（GHG），其全球变暖潜能值是二氧化碳的298倍。农业贡献了大约三分之二的人为一氧化二氮排放，是减排方面最具挑战性的部门。农业也是国家温室气体报告的最大不确定性来源，因为一氧化二氮排放量在空间和时间上变化很大，因此难以准确测量。为了实现迫切需要的巨大减排，我们必须能够准确测量和预测土壤一氧化二氮的排放。我们的工作表明，在农业土壤中，N2O排放量的日变化可达400%。目前对N2O排放的测量和预测没有捕捉到这种日变化。在DIVINE中，我们将首次研究N2O排放日变化的机制基础，并使用建模方法来量化这种日变化如何导致用于温室气体清单报告的N2O排放因子的不确定性。我们的目标是实现更准确的温室气体清单报告和更有效地测试农业一氧化二氮排放减缓战略。我们的工作表明，在肥沃的土壤中，N2O的排放量每天都在变化，排放在下午中午达到峰值。土壤N2O排放量通常在一天中的单一时间测量，当测量按比例放大到年排放估计值时，会导致显着误差。虽然人们普遍接受N2O日变化的存在，但人们对其潜在机制知之甚少。白天N2O排放量的增加通常归因于随着温度升高而增加的微生物活动。然而，我们最近的研究表明，光线强度也很重要。我们假设这不是光强的直接影响，而是昼夜植物代谢（光合作用和根渗出）的差异改变了土壤特性（例如碳有效性和氧），从而促进了N2O的日变化。直到最近，我们才能够得出这些结论，因为重大的技术进步使我们能够在昼夜循环中几乎连续地测量多个地点的一氧化二氮排放。在该技术的支持下，我们将通过四个工作包来研究N2O日变化的重要性、驱动因素和机制。我们将：1；在整个作物生命周期进行田间试验，量化日N2O排放量随管理做法和土壤物理性质的变化（WP1）；2. 在控制土壤温度和遮阳处理的条件下构建室外中生态系统，以确定温度和光照强度对N2O排放（WP2）日变化的影响程度。3. 开展室内实验，确定植物代谢影响土壤氮循环和增加白天N2O排放（WP3）的机制4。量化N2O排放的日变化如何影响国家农业温室气体清单报告的准确性，并使用贝叶斯模型通过考虑日变率来提高N2O排放因子的准确性（WP4）。该项目的成果将使一氧化二氮的日变化能够纳入报告和预测农业温室气体排放的工具和模型，并将使对农业一氧化二氮减缓战略的测试更加准确。

Nitrous oxide (N2O) is a potent greenhouse gas (GHG) with a global warming potential 298 times that of carbon dioxide. Agriculture contributes approximately two thirds of anthropogenic N2O emissions and is the most challenging sector for emissions reductions. Agriculture is also the largest source of uncertainty for national GHG reporting because N2O emissions are highly variable in space and time and consequently difficult to measure accurately.To achieve the enormous reductions urgently needed, we must be able to accurately measure and predict soil N2O emissions. Our work has demonstrated that N2O emissions can vary diurnally by up to 400% in agricultural soils. This diurnal variation is not captured in current measurements and predictions of N2O emissions. In DIVINE we will investigate for the first time, the mechanistic basis of diurnal variation in N2O emissions and use modelling approaches to quantify how this diurnal variation contributes to uncertainty in N2O emission factors used for GHG inventory reporting. Our aim is to enable more accurate GHG inventory reporting and more effective testing of mitigation strategies for agricultural N2O emissions. Our work has demonstrated that N2O emissions can vary diurnally in fertilised soils with emissions peaking in mid-afternoon. Soil N2O emissions are conventionally measured at a single time of day, leading to significant errors when measurements are scaled up to annual emission estimates. Whilst the existence of diurnal N2O variation is generally accepted, the underlying mechanisms are poorly understood. Greater daytime N2O emissions are typically attributed to increased microbial activity with increasing temperature. However, our recent research suggests light intensity is also important. We hypothesise that this is not a direct effect of light intensity, but rather differences in plant metabolism (photosynthesis and root exudation) between day and night altering soil properties (e.g. carbon availability and oxygen) and so promoting diurnal variation in N2O. It is only recently that we have been able to make these conclusions due to major technological advances that allow us to measure N2O emissions near-continuously from multiple locations over day-night cycles. Supported by this technology we will investigate the importance, drivers and mechanisms of diurnal N2O variation through four work packages. We will: 1. Conduct field experiments over whole crop life-cycles to quantify how diurnal N2O emissions vary with management practices and soil physical properties (WP1); 2. Construct outdoor mesocosms with soil temperature control and shading treatments to determine to what extent temperature and light intensity drive diurnal variations in N2O emissions (WP2). 3. Conduct laboratory experiments to identify the mechanisms by which plant metabolism affects soil nitrogen cycling and amplifies daytime N2O emissions (WP3)4. Quantify how diurnal variation in N2O emissions affects the accuracy of national GHG inventory reporting for agriculture and use Bayesian modelling to improve the accuracy of N2O emission factors by accounting for diurnal variability (WP4). The outcomes of the project will enable diurnal N2O variation to be factored into tools and models for reporting and predicting agricultural GHG emissions and will enable more accurate testing of agricultural N2O mitigation strategies.