基于分子生物学的农田土壤氧化亚氮排放过程的贝叶斯模型研究

Nitrous oxide (N2O) is a potent greenhouse gas that contributes to global warming, and fertilized soils are considered as an important source of N2O. Understanding the N2O formation pathways in fertilized soils is thus of great significance. With the development of molecular biology and stable isotope techniques, our comprehension of nitrogen cycle and N2O formation pathways is advancing. Therefore, it is necessary to update the model structure of nitrogen cycle based on the new findings in the context of our current understanding with molecular biological information. The current models combining with molecular biological information are still limited to statistical models, which consider each process of nitrogen cycle separately and unable to systematically analyze the whole nitrogen cycle. Besides, the prior knowledge of kinetic parameters of nitrogen cycle model in fertilized soils is poor owing to the lack of statistical analysis in previous studies about soil nitrogen dynamics. This project will focus on the previous literatures about nitrogen dynamics in fertilized soils. A process-based nitrogen cycle model will be built, which can adjust the model structure with different combinations of nitrogen processes flexibly based on molecular biological information, to simulate the diverse cases from literatures. An efficient Bayesian inversion approach will be applied to estimate unknown kinetic parameters of nitrogen cycle model accurately. Moreover, a new reversible-jump Markov chain Monte Carlo (RJMCMC) algorithm will be developed to fully explore the combinations of nitrogen processes and infer the model structure automatically, in order to reduce the uncertainty from model structure. According to the inversion results of different cases, the contributions of separate nitrogen processes for N2O emissions in fertilized soils under various environmental conditions will be analyzed, while a comprehensive database for kinetic parameters of nitrogen cycle in fertilized soils will be built. This project will develop a three-in-one system with methods of molecular biology, stable isotope and numerical modeling to study soil nitrogen cycle. Experimental studies will be further carried out to validate the effectiveness and practicability of this sytem. This project will provide a new perspective and powerful tool to reveal more insight information for the complex biogeochemical cycles.

农田土壤是温室气体氧化亚氮（N2O）的重要排放源，深入理解农田土壤N2O的产生途径对全球变暖研究具有重要意义。随着分子生物学和稳定同位素技术的发展，我们正不断更新对N2O产生途径的认识，因而有必要开展结合分子生物学信息的氮循环模型研究。目前结合分子生物学信息的模型研究仍局限于统计模型，同时缺少农田土壤氮循环动力学参数的资料统计。本项目将整理氮循环文献资料，构建基于过程的可灵活调整路径组成的氮循环模型，结合分子生物学等信息调整模型结构，采用高效的贝叶斯反演方法估计未知参数；同时开发可自动探索氮循环路径组合的可逆跳转蒙特卡洛算法推断模型结构。根据文献中各案例的反演结果，解析各环境条件下农田土壤中各氮循环途径对N2O排放的贡献，并建立农田土壤氮循环参数库。本项目将发展分子生物学、稳定同位素和数值模拟“三位一体”的研究方法，并通过验证实验说明方法的有效性，为生物地球化学研究提供新的视角和有利工具。

本研究在整理分子生物学信息的基础上，发展了基于过程的氮循环模型，构建了新式速率方程进行氮循环路径组合推断模型结构，采用贝叶斯反演方法估计未知参数，并利用贝叶斯模型平均方法进行模型选择。在试验方面，进行了室内培养实验，解析了不同温度与湿度条件下水稻土壤各氮循环途径对氧化亚氮的贡献，验证模型的有效性。本项目研究首次提出了基于贝叶斯方法的土壤氮素循环路径选择方法，该方法可以帮助研究者在试验之初利用预实验的浓度数据判断实验条件下土壤中可能进行的各个氮素循环路径可能性大小，并初步给出氮循环路径图，帮助研究者进行下一步更深入的分子生物学分析。