结合遥感技术与水稻生长模型来预算水稻产量

Since remote sensing can provide information on the actual status of an agricultural crop, the integration betwee nremote sensing data and crop growth simulation models has become an important trend for yield estimation and prediction. The main objective of this research was to combine a rice growth simulation model with remote sensing data to estimate rice grain yield for different growing seasons leading to an assessment of rice yield at regional levels. Integration between NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) data and the rice growth simulation model ORYZA1 to develop a new software, which was named as Rice-SRS Model, resulted in accurate estimates for rice yield in Shaoxing, China, with an estimation error reduced to 1.03% and 0.79% over-estimation and 0.79% under-estimation for early, single and late season rice, respectively. Selecting suitable dates for remote sensing images was an important factor which could influence estimation accuracy. Thus, given the different growing periods for each rice season, four images were needed for early and late rice, while five images were preferable for single season rice. Estimating rice yield using two or three images was possible, however, if images were obtained during the panicle initiation and heading stages.     

应用NOAA/AVHRR遥感资料对阿拉善草地进行动态监测的研究

应用ＮＯＡＡ／ＡＶＨＲＲ遥感资料对阿拉善地区的６个荒漠类草地进行了生长季内的动态监测。结果表明，应用ＮＯＡＡ／ＡＶＨＲＲ遥感资料对阿拉善荒漠进行动态监测是可行的而且是必要的；提出了以“面”取样，“面”与“面”样本区配的建模方式，克服了以往建模方法中样本间“点”对“点”不能很好匹配的缺点。     

人才培养模式探析

人才培养模式是对人才培养过程的总体表述，属于过程范畴，它和办学模式、教学模式、培养途径、培养方法等其它教育范畴存在着差异，人才培养模式由培养目标、培养制度、培养过程和培养评价四个子系统的构成，是系统性与独立性的统一，是中介性与操作性的统一，是稳定性与灵活性的统一，是个性与共性的统一。     

毛竹叶斑枯病的损失估测及防治指标

研究表明,当毛竹(Phyllostachys heterocyclacv.Pubescens)对叶斑枯病(Coccostroma arundi-nariae)中度或重度感病时,新竹的直径生长、重量均受到严重影响,成竹的竹壁物质积累下降,感病林分的出笋量大大减少,严重阻碍了毛竹生产的发展.用材林的损失估测模型以逻辑斯蒂模型为最优:L=24.236 1/[(1+20.908 1exp(-0.107 1X)];笋用林的损失估测模型以简单线性回归模型为最优:L=5.0209+0.702 9X.     

Measurement of the Motion of Fertilizer Particles Leaving a Centrifugal Spreader Using a Fast Imaging System

Although mechanically simple, centrifugal spreaders used for mineral fertilization involve complex physics that cannot be fully characterized at the present time. We are developing sensors to evaluate the spatial distribution of the fertilizer on the ground based on the measurement of initial flight conditions of fertilizer granules after their ejection by the spreading disk. The techniques developed are based on the analysis of images of the area around the disk showing the granule ejection. A high resolution – low cost imaging system for the analysis of high speed particle projection developed for this specific purpose is presented in this paper. The system, based on a camera and a sequence of flashes, is used to characterize the centrifugal spreading of fertilizer particles ejected at speeds of approximately 30ms^–1. It automatically computes the direction of ejection and velocity of each granule observed in the image. Multi-exposure images collected with the camera installed perpendicular to the output flow of granules are analyzed to estimate the trajectories of the fertilizer granules, using different motion estimation methods.     

基于夭折试验样本寿命分布的参数估计

研究了在夭折试验样本下指数分布与威布尔分布的参数估计问题。     

应用仿真技术估计模型参数的方法

将有多个参数的模型(微分的、代数的、超越的、线性的或非线性的、动态的或静态的)纳入仿真运行,通过反复寻求相邻三次仿真运行的误差曲线的极小值,使模型参数得到修正,并逐步达到最佳拟合。对三个不同类型问题的应用,说明了本方法的有效性和通用性。     

湖羊蛋白酶遗传型结构及系统地位的研究

用“中心产区典型群随机抽样”的方法，对湖羊品种进行遗传检测，在所检测的12个位点中的11个（Alb,Tf,Alp,Lap,Ary-Es,Hb-β，X-p,CA,Cat,Ly和Le)具有多态性，仅Po位点为单态，多态座位比例为91．7％，在27个等位基因中，只有Po^R,Tf^A,Hb-β^A,CA^F和Fe^L的估计可靠程度分别为0.5222,0.7458,0.5222,0.6212,0.8990和0.9070外余21个等位基因估计可靠程度均在0．9545以上。说明“中心产区典型群随机抽样”在特定遗传背景下是一种有效的抽样方法。湖羊的平均杂合度比国内其他绵羊种群偏低，而与国外其他群体相比，平均纯合度偏低。引用国内外14个绵羊群体的相同资料，根据血液酶和其他蛋白质变异的10个基因座位共计33个等位基因的频率，进行系统聚类分析，表明湖羊与蒙古羊亲缘关系较近。     

桉树焦枯病对桉树生长量的损失估计研究

按照桉树焦枯病不同发病等级调查桉树各因子生长量,分析不同病级对桉树胸径、树高和材积生长的影响。结果表明,当发病等级达到Ⅳ级时对胸径、Ⅲ级时对树高、Ⅱ级时对材积的生长已经造成极显著影响。根据不同发病等级与损失率的关系进行回归分析,建立了胸径、树高和材积的损失率估测模型,该模型估测结果与调查实测数据相对误差1.32%(精度为98.68%)。以此模型估算福建省桉树人工林焦枯病危害,材积年损失率23.53%,年经济损失0.518亿元。     

A semi-empirical model to assess uncertainty of spatial patterns of erosion

Distributed erosion models are potentially good tools for locating soil sediment sources and guiding efficient Soil and Water Conservation (SWC) planning, but the uncertainty of model predictions may be high. In this study, the distribution of erosion within a catchment was predicted with a semi-empirical erosion model that combined a semi-distributed hydrological model with the Morgan, Morgan and Finney (MMF) empirical erosion model. The model was tested in a small catchment of the West Usambara Mountains (Kwalei catchment, Tanzania). Soil detachability rates measured in splash cups (0.48–1.16 g J^− 1) were close to model simulations (0.30–0.35 g J^− 1). Net erosion rates measured in Gerlach troughs (0.01–1.05 kg m^− 2 per event) were used to calibrate the sediment transport capacity of overland flow. Uncertainties of model simulations due to parameterisation of overland flow sediment transport capacity were assessed with the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The quality of the spatial predictions was assessed by comparing the simulated erosion pattern with the field-observed erosion pattern, measuring the agreement with the weighted Kappa coefficient of the contingency table. Behavioural parameter sets (weighted Kappa > 0.50) were those with short reinfiltration length (< 1.5 m) and ratio of overland flow power α to local topography power γ close to 0.5. In the dynamic Hortonian hydrologic regime and the dissected terrain of Kwalei catchment, topography controlled the distribution of erosion more than overland flow. Simulated erosion rates varied from − 4 to + 2 kg m^− 2 per season. The model simulated correctly around 75% of erosion pattern. The uncertainty of model predictions due to sediment transport capacity was high; around 10% of the fields were attributed to either slight or severe erosion. The difficult characterisation of catchment-scale effective sediment transport capacity parameters poses a major limit to distributed erosion modelling predicting capabilities.