Dhaliwal Jashanjeet Kaur, Panday Dinesh, Robertson G Philip, Saha Debasish
Biosystems Engineering and Soil Science, University of Tennessee, Knoxville, Tennessee, USA.
Rodale Institute, Kutztown, Pennsylvania, USA.
J Environ Qual. 2025 Jan-Feb;54(1):132-146. doi: 10.1002/jeq2.20637. Epub 2024 Oct 9.
Soil nitrous oxide (NO) emissions exhibit high variability in intensively managed cropping systems, which challenges our ability to understand their complex interactions with controlling factors. We leveraged 17 years (2003-2019) of measurements at the Kellogg Biological Station Long-Term Ecological Research (LTER)/Long-Term Agroecosystem Research (LTAR) site to better understand the controls of NO emissions in four corn-soybean-winter wheat rotations employing conventional, no-till, reduced input, and biologically based/organic inputs. We used a random forest machine learning model to predict daily NO fluxes, trained separately for each system with 70% of observations, using variables such as crop species, daily air temperature, cumulative 2-day precipitation, water-filled pore space, and soil nitrate and ammonium concentrations. The model explained 29%-42% of daily NO flux variability in the test data, with greater predictability for the corn phase in each system. The long-term rotations showed different controlling factors and threshold conditions influencing NO emissions. In the conventional system, the model identified ammonium (>15 kg N ha) and daily air temperature (>23°C) as the most influential variables; in the no-till system, climate variables such as precipitation and air temperature were important variables. In low-input and organic systems, where red clover (Trifolium repens L.; before corn) and cereal rye (Secale cereale L.; before soybean) cover crops were integrated, nitrate was the predominant predictor of NO emissions, followed by precipitation and air temperature. In low-input and biologically based systems, red clover residues increased soil nitrogen availability to influence NO emissions. Long-term data facilitated machine learning for predicting NO emissions in response to differential controls and threshold responses to management, environmental, and biogeochemical drivers.
在集约化管理的种植系统中，土壤一氧化二氮（N₂O）排放具有高度变异性，这对我们理解其与控制因素之间复杂相互作用的能力构成了挑战。我们利用了凯洛格生物站长期生态研究（LTER）/长期农业生态系统研究（LTAR）站点17年（2003 - 2019年）的测量数据，以更好地了解采用常规、免耕、减少投入以及基于生物/有机投入的四种玉米 - 大豆 - 冬小麦轮作体系中N₂O排放的控制因素。我们使用随机森林机器学习模型来预测每日N₂O通量，针对每个系统分别使用70%的观测数据进行训练，使用的变量包括作物种类、每日气温、累积两日降水量、土壤水分孔隙率以及土壤硝酸盐和铵浓度。该模型解释了测试数据中每日N₂O通量变异性的29% - 42%，对每个系统中玉米阶段的预测性更强。长期轮作显示出影响N₂O排放的不同控制因素和阈值条件。在常规系统中，模型确定铵（>15 kg N ha⁻¹）和每日气温（>23°C）为最具影响力的变量；在免耕系统中，降水和气温等气候变量是重要变量。在低投入和有机系统中，由于种植了红三叶草（白车轴草；玉米前）和谷物黑麦（黑麦；大豆前）覆盖作物，硝酸盐是N₂O排放的主要预测因子，其次是降水和气温。在低投入和基于生物的系统中，红三叶草残茬增加了土壤氮素有效性，从而影响N₂O排放。长期数据有助于机器学习预测N₂O排放，以应对管理、环境和生物地球化学驱动因素的差异控制和阈值响应。
