农业土地规模经营的理论探讨

实现农业土地规模经营,有利于生产力有效运转并取得最佳的规模效益。其适度性受各种因素的影响,具有区域性和动态性。规模经营与劳动生产率有着密切的关系,应该在确保提高土地生产率的基础上来提高劳动生产和土地规模经营的效益。土地适当集中、土地经营主体的总人数尽量精简和劳动组合的优化是优化土地经营规模的三个相互制约的因素。要采取积极的措施逐步实现土地的规模经营。对现有土地经营形式要做具体分析,其调整应从有利于生产力发展出发,在尊重群众意愿的基础上稳步进行。     

Modelling Plant Disease Epidemics

An epidemic is the progress of disease in time and space. Each epidemic has a structure whose temporal dynamics and spatial patterns are jointly determined by the pathosystem characteristics and environmental conditions. One of the important objectives in epidemiology is to understand such spatio-temporal dynamics via mathematical and statistical modelling. In this paper, we outline common methodologies that are used to quantify and model spatio-temporal dynamics of plant diseases, with emphasis on developing temporal forecast models and on quantifying spatial patterns. Several examples of epidemiological models in cereal crops are described, including one for Fusarium head blight.     

An aggregation index (AI) to quantify spatial patterns of landscapes

There is often need to measure aggregation levels of spatial patterns within a single map class in landscape ecological studies. The contagion index (CI), shape index (SI), and probability of adjacency of the same class (Qi), all have certain limits when measuring aggregation of spatial patterns. We have developed an aggregation index (AI) that is class specific and independent of landscape composition. AI assumes that a class with the highest level of aggregation (AI =1) is comprised of pixels sharing the most possible edges. A class whose pixels share no edges (completely disaggregated) has the lowest level of aggregation (AI =0). AI is similar to SI and Qi, but it calculates aggregation more precisely than the latter two. We have evaluated the performance of AI under varied levels of (1) aggregation, (2) number of patches, (3) spatial resolutions, and (4) real species distribution maps at various spatial scales. AI was able to produce reasonable results under all these circumstances. Since it is class specific, it is more precise than CI, which measures overall landscape aggregation. Thus, AI provides a quantitative basis to correlate the spatial pattern of a class with a specific process. Since AI is a ratio variable, map units do not affect the calculation. It can be compared between classes from the same or different landscapes, or even the same classes from the same landscape under different resolutions.     

Soil saturation effects on forest dynamics: scaling across a southern boreal/northern hardwood landscape

Patch modeling can be used to scale-up processes to portray landscape-level dynamics. Via direct extrapolation, a heterogeneous landscape is divided into its constituent patches; dynamics are simulated on each representative patch and are weighted and aggregated to formulate the higher level response. Further extrapolation may be attained by coarsening the resolution of or lumping environmental data (e.g., climatic, edaphic, hydrologic, topographic) used to delimit a patch.Forest patterns at the southern boreal/northern hardwood transition zone are often defined by soil heterogeneity, determined primarily by the extent and duration of soil saturation. To determine how landscape-level dynamics predicted from direct extrapolation compare when coarsening soil parameters, we simulated forest dynamics for soil series representing a range of drainage classes from east- central Maine. Responses were aggregated according to the distribution of soil associations comprising a 600 ha area based on local- (1:12,000), county- (1:120,000) and state- (1:250,000) scale soil maps. At the patch level, simulated aboveground biomass accumulated more slowly in poorer draining soils. Different soil series yielded different communities comprised of species with various tolerances for soil saturation. When aggregated, removal of waterlogging caused a 20–60% increase in biomass accumulation during the first 50 years of simulation. However, this early successional increase and the maximum level of biomass accumulation over a 200 year period varied by as much as 40% depending on the geospatial data. This marked discrepancy suggests caution when extrapolating with forest patch models by coarsening parameters and demonstrates how rules used to rescale environmental data need to be evaluated for consistency.     

叶顶间隙对多相混输泵内流动特性的影响

为了揭示叶顶间隙对多相混输泵内流动特性的影响规律,在进口含气率为10%时,利用ANSYS CFX软件对不同叶顶间隙下泵内气液两相流态进行模拟,同时分析和总结了叶顶间隙对流道内流动特性的影响.研究表明,当叶顶间隙增加时,叶轮叶片进口附近低压区占比明显减少,并且叶轮进出口的压差也在逐渐减小.另外,在靠近叶片顶端时,压力等值线存在偏折现象.随着叶顶间隙逐渐增加,偏折加剧.存在叶顶间隙时,叶轮轮毂附近气相聚集得到了明显的改善,并且随着叶顶间隙的增加,气相在叶顶间隙内靠近叶顶处的聚集现象更加明显,导叶轮毂气相聚集位置沿着流动方向向下游移动.研究结果可为多相混输泵水力优化、性能改善和结构设计等提供依据.同时,研究结果进一步揭示了多相混输泵叶顶间隙流对混输泵性能的影响规律.     

开垦对土壤团聚体含量及微生物群落代谢多样性的影响

以新疆玛纳斯河流域盐渍化弃耕地为研究对象,基于Biolog生态平板法,分析开垦前后土壤团聚体组成分布及团聚体微生物代谢多样性的变化,从而评价人为开垦对弃耕地土壤质量的影响。结果表明:弃耕地开垦增加了各土层0.25 mm团聚体的含量,增加范围在3%～18%。弃耕地开垦前后土壤均以0.25～0.053 mm团聚体比例最高,与其它各粒径团聚体差异显著(P0.05)。开垦后土壤团聚体微生物平均颜色变化率显著高于弃耕地。弃耕地0.053 mm团聚体微生物群落AWCD显著高于0.25 mm各粒径团聚体,开垦后各粒径团聚体微生物群落AWCD关系为:2～1 mm0.25～0.053 mm1～0.25 mm(0.053 mm)(5 mm)5～2 mm。开垦增加了0.25 mm团聚体微生物物种丰富度指数,而0.25 mm团聚体却相反;开垦后土壤各粒径团聚体微生物均匀度指数均显著高于弃耕地,而土壤微生物优势度指数均低于弃耕地,其中在5～2、2～1、1～0.25 mm和0.25～0.053 mm四个粒径中更为显著。开垦前后土壤团聚体微生物对6大碳源利用强度存在着差异,弃耕地土壤团聚体微生物对多聚物的利用率最高,开垦后其优势碳源转变为碳水化合物。主成分分析表明,开垦前后土壤团聚体微生物群落碳源利用在PC1上出现差异。碳水化合物类、羧酸类和氨基酸类碳源是影响土壤团聚体微生物代谢多样性的主要碳源。总体来说,开垦对土壤团聚体微生物群落代谢多样性具有重要影响。     

蝗虫的聚集行为机制研究进展

蝗虫具有聚集为害的特征。群居型蝗虫形态、生理、生化、行为等与散居型明显不同。蝗虫的密度、资源的空间分布方式、化学信息素和特定行为均能够引起蝗虫聚集行为反应。蝗群聚集后,通过遵守简单的行为规则,形成了一个高度有序的复杂系统,在这个有序的复杂系统里群体蝗虫能够取得散居个体不能获取的生态效益。     

Geostatistical interpolation and aggregation of crop growth model outputs

Many crop growth models require daily meteorological data. Consequently, model simulations can be obtained only at a limited number of locations, i.e. at weather stations with long-term records of daily data. To estimate the potential crop production at country level, we present in this study a geostatistical approach for spatial interpolation and aggregation of crop growth model outputs. As case study, we interpolated, simulated and aggregated crop growth model outputs of sorghum and millet in West-Africa. We used crop growth model outputs to calibrate a linear regression model using environmental covariates as predictors. The spatial regression residuals were investigated for spatial correlation. The linear regression model and the spatial correlation of residuals together were used to predict theoretical crop yield at all locations using kriging with external drift. A spatial standard deviation comes along with this prediction, indicating the uncertainty of the prediction. In combination with land use data and country borders, we summed the crop yield predictions to determine an area total. With spatial stochastic simulation, we estimated the uncertainty of that total production potential as well as the spatial cumulative distribution function. We compared our results with the prevailing agro-ecological Climate Zones approach used for spatial aggregation. Linear regression could explain up to 70% of the spatial variation of the yield. In three out of four cases the regression residuals showed spatial correlation. The potential crop production per country according to the Climate Zones approach was in all countries and cases except one within the 95% prediction interval as obtained after yield aggregation. We concluded that the geostatistical approach can estimate a country’s crop production, including a quantification of uncertainty. In addition, we stress the importance of the use of geostatistics to create tools for crop modelling scientists to explore relationships between yields and spatial environmental variables and to assist policy makers with tangible results on yield gaps at multiple levels of spatial aggregation.     

基于PID神经网络的滑翔增程制导炮弹解耦控制系统设计

采用Wiener空间的两个算子以及相关的恒等式，提出了新的方法证明了关于高斯过程函数的中心极限定理，并给出了该中心极限定理的应用实例.