Deng Jia, Guo Lei, Salas William, Ingraham Pete, Charrier-Klobas Jessica G, Frolking Steve, Li Changsheng
Earth Systems Research Center Institute for the Study of Earth, Oceans and Space University of New Hampshire Durham NH USA.
California Air Resources Board Sacramento CA USA.
Earths Future. 2022 Apr;10(4):e2021EF002526. doi: 10.1029/2021EF002526. Epub 2022 Apr 19.
Mitigation of greenhouse gas emissions from agriculture requires an understanding of spatial-temporal dynamics of nitrous oxide (NO) emissions. Process-based models can quantify NO emissions from agricultural soils but have rarely been applied to regions with highly diverse agriculture. In this study, a process-based biogeochemical model, DeNitrification-DeComposition (DNDC), was applied to quantify spatial-temporal dynamics of direct NO emissions from California cropland employing a wide range of cropping systems. DNDC simulated direct NO emissions from nitrogen (N) inputs through applications of synthetic fertilizers and crop residues during 2000-2015 by linking the model with a spatial-temporal differentiated database containing data on weather, crop areas, soil properties, and management. Simulated direct NO emissions ranged from 3,830 to 7,875 tonnes NO-N yr, representing 0.73%-1.21% of the N inputs. NO emission rates were higher for hay and field crops and lower for orchard and vineyard. State cropland total NO emissions showed a decreasing trend primarily driven by reductions of cropland area and N inputs, the trend toward growing more orchard, and changes in irrigation. Annual direct NO emissions declined by 47% from 2000 to 2015. Simulations showed NO emission variations could be explained not only by cropland area and N fertilizer inputs but also climate, soil properties, and management besides N fertilization. The detailed spatial-temporal emission dynamics and driving factors provide knowledge toward effective NO mitigation and highlight the importance of coupling process-based models with high-resolution data for characterizing the spatial-temporal variability of NO emissions in regions with diverse croplands.
减轻农业温室气体排放需要了解一氧化二氮（N₂O）排放的时空动态。基于过程的模型可以量化农业土壤中的N₂O排放，但很少应用于农业高度多样化的地区。在本研究中，一个基于过程的生物地球化学模型，即反硝化-分解模型（DNDC），被用于通过将该模型与一个包含天气、作物面积、土壤特性和管理数据的时空差异化数据库相链接，来量化加利福尼亚州采用多种种植系统的农田直接N₂O排放的时空动态。DNDC通过合成肥料和作物残茬的施用，模拟了2000 - 2015年期间氮（N）输入产生的直接N₂O排放。模拟的直接N₂O排放量在3830至7875吨N₂O-N/年之间，占N输入量的0.73% - 1.21%。干草和大田作物的N₂O排放率较高，果园和葡萄园的排放率较低。该州农田的总N₂O排放量呈下降趋势，主要是由于农田面积和N输入的减少、果园种植面积增加的趋势以及灌溉方式的变化。2000年至2015年期间，年度直接N₂O排放量下降了47%。模拟结果表明，N₂O排放变化不仅可以由农田面积和氮肥输入来解释，还可以由气候、土壤特性以及除施肥外的管理措施来解释。详细的时空排放动态和驱动因素为有效减轻N₂O排放提供了知识，并突出了将基于过程的模型与高分辨率数据相结合以表征不同农田地区N₂O排放时空变异性的重要性。
