利用作物模型研究气候变化对农业影响的发展过程

模型模拟是研究气候变化对农业生产影响的有效途径,得到了广泛关注和应用。本文着重介绍了利用作物模型研究气候变化对农业生产影响的发展过程,即从最初通过人为改变气候参数模拟气候变化对农业的可能影响,到与气候情景结合模拟未来气候变化对农业的可能影响及近年来与其它模型结合综合模拟未来气候变化对农业的影响,并通过对气候变化农业影响模型模拟研究中经验模型与机理模型、站点尺度与区域尺度、确定性气候情景与概率气候情景几个关键问题的评述,指出了存在的问题及未来发展趋势。     

作物生长模型的应用研究进展

作物生长模型不仅能够进行单点尺度上作物生长发育的动态模拟,而且能够从系统角度评价作物生长状态与环境要素的关系。本文通过梳理当前作物生长模型应用的诸多研究成果,剖析模型在气候变化对农业生产影响研究、作物生长模型区域应用中的关键问题,总结了当前以作物生长模型为核心的农业决策支持系统开发的研究情况,意在促进作物生长模型在生态、农业、区域气候资源和气候变化等研究中更广泛地应用。结果表明,作物生长模型在国内外的研究与应用广泛而深入,在气候变化背景下,应用作物生长模型进行历史时期气候条件和农业气象灾害对作物生产状况和产量的影响研究已相当广泛且相对成熟。利用全球气候模式（GCM）或区域气候模式（RCM）构建未来气候变化情景,再与作物生长模型耦合已发展成为评估未来气候变化对农业生产影响的重要手段。通过集成与整合多作物生长模型、多气候模式集合模拟、优化气候模拟数据订正方法可有效降低气候变化对农业生产影响评估的不确定性。遥感数据同化技术能够将站点模型运用到区域尺度上评价不同环境因子对农业生产的影响,拓宽了作物生长模型的应用尺度范围并有效提高作物产量估算的精度。以作物生长模型为核心的农业决策支持系统的研究与应用越来越多元化,是辅助农业生产管理和决策的重要工具。然而,由于作物生态系统的复杂性,作物生长模型模拟结果仍存在很大的不确定性,今后对作物生长机理及过程间耦合机制的探索还需加强,以便进一步完善和改进模型,促进作物生长模型更广泛地应用。     

作物模型应用与遥感信息集成技术研究进展

作物模型和遥感技术以各自独有的优势在作物生产监测、评估及未来预测等方面发挥着关键作用。作物模型与遥感信息集成技术在大尺度、高精准的农业生产监测、评估与预测上具有明显的应用优势和广阔的发展前景。为了促进这些技术在区域尺度上的作物产量预测、农业气象灾害影响评估及农业应对气候变化决策等方面更加广泛地应用,本文采用文献综述的方法,系统归纳了欧洲、美国、澳大利亚及中国作物模型的发展与应用,总结了当前主流的数据集成方法的原理、特点和不足,概述了作物模型与遥感信息集成技术的实际应用,探讨了提升数据集成精度存在的问题,并对未来研究方向进行展望。结果表明,国内外对于作物模型及其与遥感数据集成的研究与应用广泛而深入,利用同化方法能够有效提高作物模型模拟精度,为作物模型实现区域尺度作物生长及产量评估、气候变化对产量影响、农田管理决策等提供技术支撑。作物模型模拟结果及遥感反演数据的不确定性、数据同化策略的多样性以及尺度效应是进一步提高集成系统精度与效率的限制因素。因此,遥感数据多源融合、同化过程多变量协同、作物模型多类型耦合以及数据高性能并行计算是未来作物模型与遥感数据集成研究的发展趋势。     

作物模型研究与应用中存在的问题

该文以CERES-Wheat、Maize模型为例,系统阐述了作物模型的研究与应用发展情况,分析了模型当中仍然存在的问题,指出在冠层结构与作物光能截获计算方法和作物水分、养分对生长过程的胁迫机理与定量方法方面需要深入的实验研究,以探索其机理,提高模型模拟预测性能.该文强调指出目前绝大多数的作物模型是空间一维的田间尺度模型,借鉴区域生物地球化学循环模拟方法或与分布式水文模型耦合进行尺度扩展开发区域尺度作物模型,既是区域水、土资源评价与管理的应用需求,也将能促进作物模型发展并发挥更大作用.     

区域尺度作物生产力对全球变化响应的研究进展及展望

作物生产力对气候变化高度敏感,因而成为全球气候变化科学研究中的焦点问题之一。开发作物生产力的区域模型,与区域气候模式相耦合,有利于对作物生产力形成过程中的时间和空间变量进行分析,为针对各地不同的气候、土壤、作物与耕作管理条件制定适应性对策奠定基础。本文综述了作物生产力对CO2 增加和气候变化响应的研究方法,指出应用遥感、地理信息技术与作物模拟技术、高分辨率的区域气候变化模式相结合,研究区域尺度上的作物生产力及其气候变化响应是未来研究的热点和发展方向     

遥感与作物生长模型数据同化应用综述

遥感是获取大面积地表信息最有效的手段,在农业资源监测、作物产量预测中发挥着不可替代的重要作用;作物生长模型能够实现单点尺度上作物生长发育的动态模拟,可对作物长势以及产量变化提供内在机理解释。遥感信息和作物生长模型的数据同化有效结合二者优势,在大尺度农业监测与预报上具有巨大的应用潜力。该文系统综述了遥感与作物生长模型的同化研究,概述了遥感与作物生长模型数据同化系统的构建,在归纳国内外研究进展的基础上,总结了当前主流同化方法的特点以及在不同条件下的同化效果。进而具体分析影响同化精度的关键环节,明确了相关科学概念,并相应指出改善精度的策略或者方向。最后从多参数协同、多数据融合、动态预测、多模型耦合以及并行计算环境5个方面展望了遥感与作物生长模型数据同化的未来研究重点和发展趋势,同时结合农业应用现实需求,介绍一种数据同化与集合数值预报结合的应用框架,为大区域、高精度同化研究提供新的思路与借鉴。     

作物产量农业气象统计预报基本模型研究

通过对作物产量农业气象统计预报基本模型的研究,综合考虑农业技术进步、气候变化对作物产量形成的直接效应和农业技术-气候相互作用对作物产量形成的间接效应,提出了一个包括农业技术趋势、气候变化和农业技术-气候相互作用三项的作物产量农业气象统计预报模型,并以江苏省苏南地区单季晚稻预报实例对模式作了检验.     

作物生长模型与定量遥感参数结合研究进展与展望

作物生长模型与定量遥感参数的结合,不仅满足前者实现区域应用的需求,也有助于提高后者的反演精度,在生态、农业、资源调查与全球气候变化等研究上意义重大。该文从作物生长模型空间应用拓展的角度,对国内外主流作物生长模型、定量遥感参数以及两者结合的参数与方法进行了概述,分析了典型作物生长模型的主要模拟过程及其驱动、初始化、输出等参数,总结了当前定量遥感正反演结果可为作物生长模型区域应用提供的参数数据;建立了作物生长模型模拟过程与定量遥感参数的对应关系,对比分析了作物生长模型与定量遥感参数的不同结合方式。基于以上内容,对作物生长模型面应用的限制因素及其与定量遥感参数的关系、作物生长模型面应用时参数尺度效应的影响、作物生长模型与定量遥感参数耦合方法的发展3个方面展开了讨论,以期为作物生长模型与定量遥感参数开展更好的结合研究提供参考。     

多尺度蒸散发估测与时空尺度拓展方法研究进展

蒸散发时空尺度信息是作物高效用水调控与节水灌溉管理的基础,某一尺度下获得的蒸散发理论或参数具有高度的尺度依赖性,蒸散发多时空尺度耦合关系的缺乏常导致人们对农业用水效率与效益评价的片面性,因而多尺度蒸散发估测与时空尺度拓展方法研究对优化区域灌溉制度、实现区域农业水资源可持续利用有着非常重要的理论和实际意义。本文系统评述了多尺度蒸散发监测方法和估算模型,指出了其适用的尺度、适宜的应用条件及其优缺点,评述了现有蒸散发多时空尺度拓展方法,表明量化作物对多因子的协同耦合响应机制,研究多方法、多时空尺度下多源蒸散发监测数据融合技术,构建不同时空尺度间的蒸散发拓展模式,建立将时空尺度二维耦合、水-热-碳耦合纳入统一系统的蒸散发转换体系将成为未来的研究热点。     

国外作物生长模型区域应用中升尺度问题的研究

系统研究总结了小区或田间尺度上开发的作物生长动力模拟模型应用于更大区域尺度和更高级系统水平时 ,易发生环境变量的时空变异、响应变量的空间归并以及由于空间平均、时间变异和现有模型未考虑的新特性及其新过程时所产生的偏差等升尺度问题 ,归纳并提出有效控制和减小误差的方法     

作物模型研究进展

作物模型的研究和应用有利于科研成果的综合集成、作物种植管理决策的现代化和辅助国家决策,是农业研究中的重要工具。一个完整的作物模型一般包括作物生长模块、水分运动模块与氮素迁移转化模块。这三者相互联系,相互影响。本文对上述3个模块的发展历程以及应用比较广泛、综合性比较强的几个模型进行综述,对比各自的优缺点,指出了作物模型今后的发展方向。     

Multi metric evaluation of leaf wetness models for large-area application of plant disease models

Leaf wetness (LW) is one of the most important input variables of disease simulation models because of its fundamental role in the development of the infection process of many fungal pathogens. The low reliability of LW sensors and/or their rare use in standard weather stations has led to an increasing demand for reliable models that are able to estimate LW from other meteorological variables. When working on large databases in which data are interpolated in grids starting from weather stations, LW estimation is often penalized by the lack of hourly inputs (e.g., air relative humidity and air temperature), leading researchers to generate such variables from the daily values of the available weather data.Although it is possible to find several papers about models for the estimation of LW, the behavior and reliability of these models were never assessed by running them with inputs at different time resolutions aiming at large-area applications. Furthermore, only a limited number of papers have assessed the suitability of different LW models when used to provide inputs to simulate the development of the infection process of fungal pathogens. In this paper, six LW models were compared using data collected at 12 sites across the U.S. and Italy between 2002 and 2008 using an integrated, multi metric and fuzzy-based expert system developed ad hoc. The models were evaluated for their capability to estimate LW and for their impact on the simulation of the infection process for three pathogens through the use of a potential infection model. This study indicated that some empirical LW models performed better than physically based LW models. The classification and regression tree (CART) model performed better than the other models in most of the conditions tested. Finally, the estimate of LW using hourly inputs from daily data led to a decline of the LW models performances, which should still be considered acceptable. However, this estimate may require further work in data collection and model evaluation for applications at finer spatial resolutions aimed at decision support systems.     

论生态农业模式的基本类型

生态农业模式的基本类型可按照生物组织层次分为:在景观层次,以农业土地利用布局为核心的景观模式; 在生态系统层次,以农业生态系统组分能物流连结为核心的循环模式; 在群落层次,以生物种群结构安排为核心的立体模式; 在种群层次,以食物链关系设计为核心的食物链模式; 在个体与基因层次上,以动植物品种选择为核心的物种与品种搭配模式.处于上一层次的生态农业模式基本类型可以与向下各个层次的模式套叠,形成复合模式.生态农业基本类型属于基础分类,不排斥其他根据方便利用、容易理解的其他分类方式.这种基本类型的区分有利于认定生态农业建设的重点,有利于模式改进、模式筛选和推广、模式标准制定及模式的深入研究.     

Fractional polynomial response surface models

Second-order polynomial models have been used extensively to approximate the relationship between a response variable and several continuous factors. However, sometimes polynomial models do not adequately describe the important features of the response surface. This article describes the use of fractional polynomial models. It is shown how the models can be fitted, an appropriate model selected, and inference conducted. Polynomial and fractional polynomial models are fitted to two published datasets, illustrating that sometimes the fractional polynomial can give as good a fit to the data and much more plausible behavior between the design points than the polynomial model.     

家庭牧场模型模拟研究进展

家庭牧场是草地资源利用的基本单元,其在生态恢复、多样性保护、农牧民经济收入提高等方面具有重要意义。家庭牧场中包含着环境、资源、经济、社会、管理等多层面的内容,是一个复杂系统,其研究正在朝着定量化、精细化、模式化、市场化的方向发展。模拟模型对于家庭牧场尺度上的复杂系统进行生产、经营、管理和研究是一个重要工具,其目的在于将限制牧场经营和发展的因素最小化,实现牧场效益的最优化。近年来,国内外出现了大量与家庭牧场相关的模型、软件,不仅用于模拟牧草生长、动物生长、温室气体排放等内容,同时还用于模拟相应的管理措施,其模拟结果用来研究家庭牧场复杂生态经济系统运行,指导该尺度上的生产经营决策。本文通过对国内外24个相关模拟模型的整理总结,从家庭牧场管理、牧草生长和温室气体排放等方面进行比较分析,提出了家庭牧场模拟模型发展的主要特点为:1)模拟模型在畜牧业发达国家发展迅速;2)农场模型的发展更为全面,尤其是奶牛和肉牛饲养农场模型更为系统;3)饲草平衡、能量平衡、效益最大化是模拟的主要目标;4)模型参数数量大且部分数据的获取较难;5)普适性和广域性模型缺乏。结合我国家庭牧场生产方式状况的区别,提出了ACIAR、Grass Gro和SEPATOU适用于以天然放牧为主的家庭牧场,而IFSM等模型较适合于集约化管理的牧场。综合考虑我国草地和家畜利用方式的现状,提出了我国家庭牧场模拟模型的建立和应用需着眼于已有研究和实践成果,跨学科、跨地域协作,最终实现我国家庭牧场模拟模型的快速发展。     

Autologistic models with interpretable parameters

Ecologists are interested in characterizing succession processes, in particular monitoring the spread of invasive species and their effect on resident species. In situations for which binary response variables representing presence or absence of plants are observed over a spatial lattice, it may be desirable to use a model that accounts for the statistical dependence in the data, as well as the effect of potential covariates. One such model is the autologistic regression model. We show that the typical parameterization of the autologistic model presents difficulties in interpreting model parameters across varying levels of statistical dependence, and propose an alternative (centered) parameterization that overcomes this difficulty.We use the centered autologistic model to study the dynamics over time of two species, Rumex acetosella and Lonicera japonica, in an abandoned agricultural field in New Jersey, and compare the results to those obtained from using the traditional autologistic parameterization.     

Assessing the fit of site-occupancy models

Few species are likely to be so evident that they will always be detected at a site when present. Recently a model has been developed that enables estimation of the proportion of area occupied, when the target species is not detected with certainty. Here we apply this modeling approach to data collected on terrestrial salamanders in the Plethodon glutinosus complex in the Great Smoky Mountains National Park, USA, and wish to address the question “how accurately does the fitted model represent the data?” The goodness-of-fit of the model needs to be assessed in order to make accurate inferences. This article presents a method where a simple Pearson chi-square statistic is calculated and a parametric bootstrap procedure is used to determine whether the observed statistic is unusually large. We found evidence that the most global model considered provides a poor fit to the data, hence estimated an overdispersion factor to adjust model selection procedures and inflate standard errors. Two hypothetical datasets with known assumption violations are also analyzed, illustrating that the method may be used to guide researchers to making appropriate inferences. The results of a simulation study are presented to provide a broader view of the methods properties.     

Bayesian analysis of semiparametric linear-circular models

In many environmental and agricultural studies, data are collected on both linear and circular random variables, with possible dependence between the variables. Classically, the analysis of such data has been carried out in a classical regression framework. We propose a Bayesian hierarchical framework to handle all forms of uncertainty arising in a linear-circular data set. One novelty of our multivariate linear-circular model is that, marginally, the circular component is assumed to be a mixture model with an unknown number of von Mises (or circular normal) distributions. We use the Dirichlet process to introduce variability in the model dimensionality, and develop a simple Gibbs sampling algorithm for simulating the mixture components. Although we illustrate our methodology on von Mises mixtures, it is widely applicable. We thus avoid complicated reversible-jump Markov chain Monte Carlo methods, which are considered ideal for analyzing mixtures of unknown number of distributions. We illustrate our methodologies with simulated and real data sets. Using pseudo-Bayes factors, we also compare different models associated with both fixed and variable numbers of von Mises distributions. Our findings suggest that models associated with varying numbers of mixture components perform at least as well as those with known numbers of mixture components. We tentatively argue that model averaging associated with variable number of mixture components improves the model’s predictive power, which compensates for the lack of knowledge of the actual number of mixture components.     

Uncertainty in spatially explicit population models

Spatially explicit population models (SEPMs) are often used in conservation planning. However, confidence intervals around predictions of spatially explicit population models can greatly underestimate model uncertainty. This is partly because some sources of uncertainty are not amenable to the classic methods of uncertainty analysis. Here, we present a method that can be used to include multiple sources of uncertainty into more realistic confidence intervals. To illustrate our approach, we use a case study of the wood thrush (Hylocichla mustelina) in the fragmented forest of the North Carolina Piedmont. We examine 6 important sources of uncertainty in our spatially explicit population model: (1) the habitat map, (2) the dispersal algorithm, (3) clutch size, (4) edge effects, (5) dispersal distance, and (6) the intrinsic variability in our model. We found that uncertainty in the habitat map had the largest effect on model output, but each of the six factors had a significant effect and most had significant interactions with the other factors as well. We also found that our method of incorporating multiple sources of uncertainty created much larger confidence intervals than the projections that incorporated only sources of uncertainty included in most spatially explicit population model predictions.     

GIS与氮淋溶模型的结合

Geographical information systems(GIS) are increasingly being applied to surface and subsurface flow and transport modling issues,In this paper,more attentions are focused on the methodology and strategies of coupling GIS with non-point pollution models.Suggestions are made on how to best itegrate current available or selected nitrogen leaching models ,especially in the aspect of programming development so as to effectively and fiexibly address the specific tasks.The new possibilities for dealing with non-point pollution problems at a regional scale are provided in the resulting integrated approach,including embedding grid-based GIS components in models.