应用DNDC模型分析管理措施对稻麦轮作系统CH4和N2O综合温室效应的影响

以南京地区稻麦轮作体系为对象,研究了不同农业管理措施对CH4和N2O排放通量及年度动态变化的影响,用田间观测数据验证生物地球化学循环模型(DNDC)在该耕作制度下的适用性;利用DNDC模型模拟不同环境因子和管理措施对CH4和N2O综合温室效应(GWP)的影响.结果表明:除了对照和麦季对CH4排放的模拟偏差较大外,DNDC对其余各处理模拟的CH4和N2O累积排放量与田间观测结果基本吻合,相对偏差变幅7.1%～26.3%,可以直接应用DNDC模型模拟环境因子和主要管理措施对CH4和N2O累积排放量引起GWP的影响.模型灵敏度检验结果表明,年均温度、土壤容重、土壤有机碳、土壤质地、土壤pH等环境因子对GWP的影响显著;施用氮肥、秸秆还田量和烤田期长短等管理因子对GWP的影响明显.在估算我国稻麦轮作制度下温室气体的点或区域排放规律时,应考虑上述这些影响因子.     

滨海盐渍土地区水稻田C、N 及生物量动态变化的模拟与应用

滨海盐渍土地区耕地的有机碳(SOC)、氮(N)及生物量演变是反映该区域土壤质量的重要内容。借助DNDC(Denitrification-Decomposition)模型模拟江苏滨海滩涂地区盐渍土水稻田土壤有机碳(SOC)、铵态氮(NH4⁺-N)、硝态氮(NO^–3 -N)和生物量的变化过程, 分析DNDC 模型在江苏滨海盐渍土垦区土壤的适用性, 并对DNDC 模型模拟结果进行验证。结果表明：DNDC 模型在滨海地区水稻田对生物量、土壤有机碳和氮的模拟结果良好; 土壤有机碳均方根误差(RMSE)为0.069, 土壤铵态氮RMSE 为11.1, 硝态氮RMSE 为0.28, 总生物量RMSE 为397.92; 滨海盐渍土土壤有机碳和作物产量模拟精度较高(RMSE<10%), 铵态氮和硝态氮模拟精度较好(RMSE<20%); 模型能正确反映土壤有机碳、硝态氮、铵态氮在水稻生长过程中的变化规律, 同时能很好地揭示施肥与氮素含量、硝态氮与土壤水之间以及铵态氮和硝态氮含量上的相互关系。     

中国农田土壤N2O排放通量分布格局研究

中国作为世界上一个重要农业大国,对全球大气中N2O浓度的影响正在引起人们的普遍关注.该研究采用针对农业土壤痕量气体排放估算建立的、基于N2O的产生、传输和消耗机理的反硝化分解(DNDC)模型,在建立了有关中国气候、农业土壤和农业生产的分县数据库基础上,估计了我国各县农业土壤N2O的排放通量,发现我国农田土壤N2O排放通量有较明显的地区差异,西北地区较低,东南地区较高.还发现无论温度升高,还是施肥量变化,对我国农田土壤N2O排放通量的影响,都存在区域差异,表现为东南地区的变化幅度较西北地区大,这可能与我国气候的干湿变化有较密切的关系.     

应用DNDC模型分析施肥和翻耕方式变化对旱田土壤N_2O释放的潜在影响

以贵州省玉米 油菜轮作田和大豆 冬小麦轮作田为N2 O释放通量测量对象 ,根据DNDC模型能较好地拟合田间N2 O释放通量季节变化及施肥和翻耕对释放的影响 ,采用DNDC模型定量探讨了上述作物生长季节有机肥施用量、N肥施用量及施肥日期、N肥类型和施肥深度、翻耕深度和翻耕日期等变化对亚热带旱田生态系统N2 O释放的潜在影响 .为今后通过改变施肥和耕作方式来控制农业土壤N2 O释放量提供研究基础和参考 .     

应用DNDC模型分析施肥和翻耕方式变化对旱田土壤N2O释放的潜在影响

以贵州省玉米-油菜轮作田和大豆-冬小麦轮作田为N₂O释放通量测量对象,根据DNDC模型能较好地拟合田间N₂O释放通量季节变化及施肥和翻耕对释放的影响,采用DNDC模型定量探讨了上述作物生长季节有机肥施用量、N肥施用量及施肥日期、N肥类型和施肥深度、翻耕深度和翻耕日期等变化对亚热带旱田生态系统N₂O释放的潜在影响.为今后通过改变施肥和耕作方式来控制农业土壤N₂O释放量提供研究基础和参考.     

基于DNDC模型模拟气候变化影响下的中国水稻田温室气体排放

当前国内外开展了很多农业温室气体对气候变化的响应研究,但气候变化对农业温室排放的影响机制研究还很少见.本文利用国际上广泛应用的生物地球化学过程模型——DNDC模型,模拟了中国过去40年(1971—2010年)气候变化对水稻田温室气体排放的影响.结果表明:与1971—1990年相比,1991—2010年的全国稻季平均气温上升了0.49℃,稻季平均降雨上升了11 mm;由于稻季气候变化导致全国水稻田1991—2010年的平均CH4排放量上升了0.25 kg C·hm-2,平均N2O排放量上升了0.25 kg N·hm-2.表明温度升高会促进CH4排放,降雨量增加会促进N2O排放,气候变化影响了水稻温室气体排放机制.     

辽宁省农田土壤碳库分布及变化的模拟分析

中国作为世界上一个重要的农业大国,农业土壤对全球大气中CO2浓度的影响正在引起人们的普遗关注。研究以辽宁省为对象,采用针对农业土壤碳库和痕量气体排放估算建立的反硝化分解(DNDC)模型,在建立有关辽宁省气候、农业土壤和农业生产的分县数据库基础上,估算了辽宁省农田土壤碳库储量,为118．55TgC,并发现辽宁省农田土壤碳量有较明显的地区差异,东北地区较高,西南地区较低。经过一年的耕种后,农田土壤碳的变化量也有较明显的地区差异,东北地区减少幅度较大,西南地区相对变化不大或有所增加。还发现无论何种施肥方式,辽宁省农田土壤碳的变化都存在地区差异。且施用有机肥有利于农田土壤碳的积累。在输入的土壤性质数据中,有机质数据的详细程度对DNDC模型模拟结果的精确度影响较大。     

应用DNDC模型分析施肥和翻耕方式变化对旱田土壤N2O释放的潜在影响

以贵州省玉米－油菜轮作田和大豆－冬小麦轮作田为N2O释放通量测量对象,根据DNDC模型能较好地拟合田间N2O释放通量季节变化及施肥和翻耕对释放的影响,采用DNDC模型定量探讨了上述作物生长季节有机肥施用量、N肥施用量及施肥日期、N肥类型和施肥深度、翻耕深度和翻耕日期等变化对亚热带旱田生态系统N2O释放的潜在影响,为今后通过改变施肥和耕作方式来控制农业土壤N2O释放量提供研究基础和参考。     

基于DNDC模型的稻田温室气体排放通量模拟

理解土地利用方式转变过程影响生态系统生物地球化学循环及温室气体排放的机理,并利用模型模拟土地利用方式转变过程对温室气体通量的影响是一项长期、艰巨的科学任务。本研究基于国际上广泛应用的生物地球化学过程模型(DNDC模型),结合气象、土壤和管理措施等数据,模拟了旱田转水田土地利用方式转变后稻田CH_4、CO_2和N_2O三种温室气体的通量和常年种植水稻的稻田温室气体通量,并将模拟值与观测值进行比较。结果表明:DNDC模型能够较好地模拟新、老稻田温室气体通量的季节变化,但对老稻田温室气体的排放通量模拟效果(R~20. 89,n=40,P0. 01)优于新转稻田(R~20.79,n=265,P0.01),且对CH_4和CO_2的模拟效果优于对N_2O的模拟效果;根据田间观测数据,改变模型模拟土地利用方式转换前后土壤SOC浓度和p H值,并不能完全模拟土地利用变化对温室气体的影响,微生物群落在土地利用方式转变过程中可能发生较大变化,需要在模型中进一步体现。通过模型模拟土地利用方式改变对温室气体排放的影响,可为国家温室气体、碳排放清单的编制及管理政策的制定提供参考依据。     

西双版纳热带雨林凋落叶分解的生态过程I.凋落叶分解动态

 通过野外试验和室内模拟相结合,系统研究了西双版纳热带雨林生态系统混合凋落叶分解的生态过程。野 外试验采用网袋法,即1 mm和100μm网眼网袋,分别限制大型土壤动物和螨类的进入,从而分别观测小型 土壤动物（螨类）、线虫的分解作用;室内试验则通过控制温、湿度条件,采用灭菌_接种法分别观测微 生物和线虫对凋落叶的分解。研究结果表明,凋落叶的分解是一个先快后慢的过程,在这个过程中存在分 解“滞留”阶段,分解速率变化发生波动,且波动的程度与食物链的复杂程度有关,食物链越复杂,波动 程度越强烈。利用单指数衰减模型xt/xo=exp(-kt)和双指数模型xt/xo=a×exp(-k1t)+b×exp(-k2t) 对凋 落叶分解过程进行模拟,后者将凋落叶前欺的快速分解和后期的慢速分解两个过程分别拟合,不但弥补了 分解前期单指数衰减模型与观测值之间不能吻合的缺陷,而且消除了单指数模型对长期分解进程的过高预 测,因此能更好地反映实际分解进程。利用双指数生物模型研究生物和非生物因子对凋落叶分解速率的贡 献表明,土壤动物是影响分解进程的最重要因子,占影响因子总量的78.1%,非生物因素的作用为14.1%, 微生物对分解速率的贡献只有7.8%。在热带森林生态系统中,土壤动物是最重要的分解者。     

To model or not to model

Summary Self-improving control systems may belong to either of two categories, according to whether or not they embody an explicit model of the part of their environment with which they interact. The two forms of operation are discussed and compared, and it is shown that the two may be mathematically equivalent. The treatment also gives theoretical justification for a particular mode of operation for nonmodel-forming controllers.     

A future of the model organism model

Changes in technology are fundamentally reframing our concept of what constitutes a model organism. Nevertheless, research advances in the more traditional model organisms have enabled fresh and exciting opportunities for young scientists to establish new careers and offer the hope of comprehensive understanding of fundamental processes in life. New advances in translational research can be expected to heighten the importance of basic research in model organisms and expand opportunities. However, researchers must take special care and implement new resources to enable the newest members of the community to engage fully with the remarkable legacy of information in these fields.     

Infinite-allele model and infinite-site model in population genetics

Both the infinite-allele model and infinite-site model have contributed to development of population genetics. Although the former is a model mainly for protein polymorphism and the latter is mainly for DNA polymorphism, these two models are related: the expected heterozygosity and homozygosity can be obtained from the infinite-site model, and the expectation of the amount of DNA polymorphism can be obtained from the infinite-allele model.     

The influence of model parameters on model validation

The study examined the sensitivity of two musculoskeletal models to the parameters describing each model. Two different models were examined: a phenomenological model of human jumping with parameters based on live subject data, and the second a model of the First Dorsal Interosseous with parameters based on cadaveric measurements. Both models were sensitive to the model parameters, with the use of mean group data not producing model outputs reflective of either the performance of any group member or the mean group performance. These results highlight the value of subject specific model parameters, and the problems associated with model validation.     

Timeline: Neurospora: a model of model microbes

In the 1940s, studies with Neurospora pioneered the use of microorganisms in genetic analysis and provided the foundations for biochemical genetics and molecular biology. What has happened since this orange mould was used to show that genes control metabolic reactions? How did it come to be the fungal counterpart of Drosophila? We describe its continued use during the heyday of research with Escherichia coli and yeast, and its emergence as a biological model for higher fungi.     

Antibodies as a model system for comparative model refinement

Predicting the conformations of loops is a critical aspect of protein comparative (homology) modeling. Despite considerable advances in developing loop prediction algorithms, refining loops in homology models remains challenging. In this work, we use antibodies as a model system to investigate strategies for more robustly predicting loop conformations when the protein model contains errors in the conformations of side chains and protein backbone surrounding the loop in question. Specifically, our test system consists of partial models of antibodies in which the “scaffold” (i.e., the portion other than the complementarity determining region, CDR, loops) retains native backbone conformation, whereas the CDR loops are predicted using a combination of knowledge‐based modeling (H1, H2, L1, L2, and L3) and ab initio loop prediction (H3). H3 is the most variable of the CDRs. Using a previously published method, a test set of 10 shorter H3 loops (5–7 residues) are predicted to an average backbone (N? Cα? C? O) RMSD of 2.7 Å while 11 longer loops (8–9 residues) are predicted to 5.1 Å, thus recapitulating the difficulties in refining loops in models. By contrast, in control calculations predicting the same loops in crystal structures, the same method reconstructs the loops to an average of 0.5 and 1.4 Å for the shorter and longer loops, respectively. We modify the loop prediction method to improve the ability to sample near‐native loop conformations in the models, primarily by reducing the sensitivity of the sampling to the loop surroundings, and allowing the other CDR loops to optimize with the H3 loop. The new method improves the average accuracy significantly to 1.3 Å RMSD and 3.1 Å RMSD for the shorter and longer loops, respectively. Finally, we present results predicting 8–10 residue loops within complete comparative models of five nonantibody proteins. While anecdotal, these mixed, full‐model results suggest our approach is a promising step toward more accurately predicting loops in homology models. Furthermore, while significant challenges remain, our method is a potentially useful tool for predicting antibody structures based on a known Fv scaffold. Proteins 2010. © 2010 Wiley‐Liss, Inc.