基于最小数据集的黄土高原矿区复垦土壤质量评价

矿产开发影响了土壤质量,特别是在黄土高原生态脆弱地区。通过植被恢复能够改善矿区复垦土壤质量。为了揭示矿区复垦土壤质量在植被恢复过程中的变化,该文以黑岱沟矿区排土场不同恢复年限不同植被类型、未复垦地和周边自然植被恢复区为研究对象,选取21项理化生指标作为总数据集(totaldataset,TDS),运用主成分分析(principal component analysis,PCA)结合Norm值构建评价指标最小数据集(minimum data set,MDS),通过非线性(non-Linear,NL)和线性(linear,L)两种评价方法对研究区土壤质量进行了评价。研究表明:黄土高原北部典型矿区复垦土壤质量评价指标MDS包括粉粒百分比、有机质、速效磷、钠吸附比和微生物碳;2种评价方法下,植被恢复均对复垦土壤质量指数(soil quality index,SQI)有了显著提升(P 0.05),复垦20 a灌木SQI高于复垦10 a灌木SQI,复垦12 a草本SQI高于复垦20 a草本SQI,然而所有复垦土壤SQI均未达到自然植被恢复土壤SQI;由于非线性土壤质量评价方法(SQI-NL)具有更大的土壤质量指数变化区间和变异系数,此外,在SQI-NL和线性土壤质量(SQI-L)评价两种方法下,MDS和TDS之间决定系数分别为0.911和0.866,因此,非线性土壤质量评价方法在该区域具有更好的适用性,并且最小数据集能够较准确地进行土壤质量评价。     

基于最小数据集的吉林省黑土耕层土壤质量评价

黑土作为吉林中部平原粮食主产区主要土壤类型,其质量的优劣直接影响区域生态安全和农业可持续发展.该研究以吉林省典型黑土耕层土壤作为研究对象,采集1401个土壤样本,测定8项土壤理化指标及玉米产量.采用最小数据集法筛选评价指标,对于黑土耕层土壤进行质量评价,并综合土壤质量指数和产量提出评价指标的适宜范围.结果表明:吉林省黑...     

基于时间相似数据的支持向量机水质溶解氧在线预测

为及时辨识集约化水产养殖水质变化趋势、动态调控水质,确保无应激环境下健康养殖,该文提出了基于时序列相似数据的最小二乘支持向量回归机(least squares support vector regression,LSSVR)水质溶解氧在线预测模型。采用特征点分段时间弯曲距离(feature points segmented time warping distance,FPSTWD)算法对在线采集的时间序列数据进行分段与相似度计算,以缩减规模的子序列数据集对LSSVR模型进行快速训练优化,实现了多个LSSVR子模型在线建模,将预测数据序列与LSSVR子模型的相似度匹配,自适应地选取最佳的子模型作为在线预测模型。应用该模型对集约化河蟹福利养殖水质参数溶解氧浓度进行在线预测,模型评价指标中最大相对误差、平均绝对百分比误差、相对均方根误差和运行时间分别为4.76%、8.18%、5.23%、8.32 s。研究结果表明,与其他预测方法相比,该模型具有较好的综合预测性能,能够满足河蟹福利养殖水质在线预测的实际需求,并为集约化水产养殖水质精准调控提供研究基础。     

Graphical interpretation of water quality data

Management of our nation's water resources through planning and control of water pollution hinges on the availability and interpretation of water quality data on which to base management decisions. This paper is aimed at exploring graphical methods which allow rapid and informative analysis of water quality data. The graphical methods presented in this paper fall into two main categories. The first category relates to graphical procedures which are developed as part of a statistical test, such as discriminant analysis. The second category involves approximation of multivariate water quality data into two dimensions. The methods for accomplishing the latter are canonical decomposition and high-dimensional plotting. Each of these methods is developed and used on an example set of data to demonstrate their utility. These methods seek to not only present a graphical representation of the data, but also to explain variations and interrelationships within the data itself.     

Analysis of toxicology data with covariates specific to each observation

The standard approach for analysis of teratology studiesis to use a population-averaged model with primary interest on evaluating dose-response effects. For example, generalized estimating equations (GEEs) have become popular for analysis of both developmental toxicity and reproducti vestudies (Ryan 1992). Such an approach is typically appropriate since the covariate of interest—exposure level—is constant for each litter of animals. Recently, however, there has been growing interest in evaluating effects of covariates that can vary between individuals within a cluster. This article explores several opportunities generated by teratology studies with individual-level covariates both in terms of modeling approaches and in decomposition of litter effects into genetic and environmental components.     

基于最小数据集的集约化葡萄园土壤健康评价

健康土壤是生产高产优质葡萄的基础,目前葡萄园不合理管理导致果园土壤生产力下降和生态失衡。为摸清集约化葡萄园土壤健康状况,该研究以河北省曲周县典型葡萄园为研究对象,通过测定20项土壤物理、化学和生物学指标,利用主成分分析法构建最小数据集,开展土壤健康评价并揭示葡萄园存在的主要障碍因子。结果表明,集约化葡萄园土壤健康评价最小数据集由有机碳、亚表层土壤硬度、交换性钠、容重、含水率和水稳性团聚体6个指标构成。利用线性和非线性评分函数,基于全数据集和最小数据集计算的土壤健康指数间呈显著正相关（P < 0.01）,这说明最小数据集可以代替全数据集用于葡萄园土壤健康评价。基于最小数据集,利用线性和非线性评分函数获得的葡萄园土壤健康指数范围分别为0.39～0.59和0.36～0.66,平均值分别为0.52和0.51,处于中等水平。不同树龄葡萄土壤健康指数差异不显著。集约化葡萄园土壤障碍因子主要有土壤压实、养分不平衡和有机碳含量低等问题。通过适当减少田间管理频率,结合增施（生物）有机肥、种植覆盖作物和养分综合管理能有效消减土壤障碍因子,提升葡萄园土壤健康水平,促进当地葡萄产业可持续发展。     

基于有效含水量的土壤水分监测点布设的空间分层采样方法

为了优化灌溉实践,构建准确估计平均土壤水分的监测点布设准则,该研究引入有效含水量(Available WaterCapacity,AWC)作为辅助变量,结合经典统计学和地统计学构建了一种基于辅助变量空间自相关的分层采样方法(Stratified Sampling method based on spatial auto...     

Analysis of water quality data using spectral analysis

Considerable effort has been expended by many State, Federal and private agencies in collecting data by installing automatic water quality monitoring stations on many streams and estuaries. This work discusses techniques for analyzing the data and presents some results based on the analysis of monitoring stations along the Ohio, Detroit, Missouri, and Coosa Rivers. Autocorrelation, spectral estimates, cross correlation, amplitude of transfer function and coherence square have been evaluated to correlate a water quality variable measured at different positions or different variables measured at the same location. Results from the different rivers have been compared and some conclusions have been drawn.     

中国东北农田土壤质量评价的最小数据集选择

Soil quality assessment provides a tool for agriculture managers and policy makers to gain a better understanding of how various agricultural systems afect soil resources.Soil quality of Hailun County,a typical soybean (Glycine max L.Merill) growing area located in Northeast China,was evaluated using soil quality index(SQI)methods.Each SQI was computed using a minimum data set(MDS) selected using principal components analysis(PCA)as a data reduction technique.Eight MDS indicators were selected from 20 physical and chemical soil measurements.The MDS accounted for 74.9% of the total variance in the total data set(TDS).The SQI values for 88 soil samples were evaluated with linear scoring techniques and various weight methods.The results showed that SQI values correlated well with soybean yield (r=0.658**) when indicators in MDS were weighted by the regression coefcient computed for each yield and index.Stepwise regression between yield and principal components (PCs) indicated that available boron(AvB),available phosphorus (AvP),available potassium (AvK),available iron (AvFe) and texture were the main factors limiting soybean yield.The method used to select an MDS could not only appropriately assess soil quality but also be used as a powerful tool for soil nutrient diagnosis at the regional level.     

An application of ranked set sampling for mean and median estimation using USDA crop production data

Ranked set sampling (RSS) is a sampling approach that leads to improved statistical inference in situations where the units to be sampled can be ranked (either through some subjective judgment or via the use of an auxiliary variable) relative to each other prior to formal measurement. It has the most promise for leading to improved methodology in situations where ranking of the items to be sampled can be carried out relatively easily and cheaply compared to the effort and expense required for actual quantification of the characteristic of interest. Although the theoretical benefits of RSS in estimation and statistical inference have been extensively demonstrated in the literature, the methodology has not yet been widely adopted by practitioners. The aim of this study is to use a crop production dataset from the United States Department of Agriculture to demonstrate the practical benefits of RSS relative to the more commonly used simple random sampling in estimation of the mean and median of a population. The results of our study provide clear evidence that the use of RSS can lead to substantial gains in precision of estimation for both of these situations.     

基于田间数据的土壤肥力质量综合评价

摘要：采集了小清河济南样区土壤表层的104个样点数据,进行了土壤速效钾、土壤速效磷、土壤有机质、土壤碱解氮和土壤酸碱度含量等区域化变量的实验室测定,基于内梅罗指数进行了土壤肥力质量评价,综合运用统计方法,序贯高斯模拟和空间滞后模型进行了土壤肥力要素变量的空间建模和综合评判。内梅罗评判结果表明研究区田块具有中等的土壤肥力质量,土壤有机质含量与土壤肥力质量具有较强的相关性,是影响该区肥力质量的关键因子。土壤有机质、土壤速效磷和土壤酸碱度变量的空间诊断建模结果表明,变量之间基于最大似然法的空间滞后模型的估计精度优于基于最小二乘法的经典统计模型估计,序贯高斯2 000次土壤内梅罗指数模拟结果表明土壤肥力质量具有一定的梯度变化,并呈现出高值和低值明显的条带状的分布,总体上表现为由内向外肥力质量减小的趋势特点,该研究对小清河济南样区肥料的施用具有参考意义。     

基于GF-1卫星数据的农作物种植面积遥感抽样调查方法

GF-1号卫星是中国2013年4月26日发射的一颗高分辨率遥感卫星,为解决该新型卫星数据在农作物对地抽样遥感调查中的应用技术方法问题,该文针对GF-1号卫星数据的特点,研究了基于GF-1号卫星16m WFV传感器和2m/8m PMS传感器卫星数据的农作物种植面积遥感抽样调查方法。根据研究区物候历,选择农作物识别关键期的16m WFV传感器数据进行多时相农作物种植面积的中分辨率遥感提取;在中分辨率农作物面积遥感分类图基础上,计算研究区域的MORAN I指数,确定格网抽样单元的大小,进行多目标农作物的MPPS(multivariate probability proportional to size)抽样;对抽样单元采用2m/8 m PMS传感器卫星数据进行高分辨率农作物面积制图;最后根据MPPS抽样方法进行总体农作物种植面积的推断,并计算CV值,评价抽样精度。以江苏省东台市为研究区对GF-1号卫星数据进行了应用研究。研究结果表明,GF-1号卫星数据完全可以应用于县级农作物种植面积的提取,农作物种植面积提取精度优于90%。     

农作物种植面积遥感抽样调查的误差影响因素分析

空间抽样技术在农作物种植面积调查中具有不可替代的作用,各抽样要素(抽样率、抽样调查单元尺寸及布局)对于抽样精度的影响至关重要。该文以湖南省晚稻为研究对象,设计了9种抽样调查单元和31种抽样率水平,以晚稻面积百分比为分层标志进行空间分层抽样,分析抽样格网大小、抽样率及样本空间分布格局对面积估算精度的敏感性及控制途径,并建立3种影响因素对面积估算的综合评估模型。结果表明:1)作物面积估计的平均抽样误差随抽样格网尺寸的增加而增加(R2=0.92),当抽样格网控制在5 km以内时,平均误差基本限制在5%以下,标准差变幅稳定在0.12以内;2)作物面积估计的平均抽样误差随抽样率的增加而逐渐降低(R2=0.82),当抽样率达到0.4%时,平均误差基本限制在5%以内,标准差变幅稳定在0.12以内;3)在抽样率确定的情况下,样本的空间分布是影响抽样精度的重要因素,随着样本空间分布由近似均匀分布向随机分布再向集群分布变化,作物面积估计量的平均抽样误差逐渐增大,当样本空间分布的方差均值比指标0.7时,平均误差控制在5%以内,标准差变幅稳定在0.1以内;4)得到3种影响因素对面积估算精度的定量评估模型。该成果揭示了农作物种植面积抽样过程中样方尺寸、抽样率和样本空间分布对精度影响的敏感性,为农作物种植面积监测空间抽样方案的选取以及确定特定的抽样方案可以达到的面积估算水平提供了理论基础。     

一种基于样点代表性等级的土壤采样设计方法

采样设计是获取土壤空间分布信息的关键环节,直接影响到土壤制图的精度。目前常用的采样设计方法大多存在着设计样本量大、采样效率不高的问题。当可投入资源难以完成一次性大量采样时,采样往往需要多次、分批进行。然而现有分批采样方法多考虑各批采样点在地理空间的互补性,可能造成样本点在属性空间的重叠,影响采样资源的高效利用。鉴于此,本研究通过对与土壤在空间分布具有协同变化的环境因子进行聚类分析,寻找可代表土壤性状空间分布的不同等级类型的代表性样点,建立一套基于代表性等级的采样设计方法。将该采样方法应用于位于黑龙江省嫩江县鹤山农场的研究区,利用所采集的不同代表性等级的样点进行数字土壤制图并进行验证,探讨采样方案与数字土壤制图精度的关系,以评价本文所提出的采样方法。结果表明,通过代表性等级最高的少量样点可获取研究区的大部分主要土壤类型(中国土壤系统分类的亚类级别),且制图精度较高;随着代表性等级较低样点的加入,土壤图精度提高;但当样点增加到一定数量时,土壤图的精度变化不大。因此,与样点数相比,样点的代表性高低对制图精度的影响更大。该方法所提出的代表性等级可以为样点采集顺序提供参考,有助于设计高效的逐步采样方案。     

Identification of parameters in transient water quality models from stochastic data

An approach is presented for the identification of parameters in time-varying water quality models from stochastic data measured at two points along a stream (input-output type data). The models are plug flow models described by linear-first order partial differential equations. The method employed is to reduce the partial differential equations to a set of ordinary differential equations with the method of characteristics from which an analytical solution is obtained. The time-lag phenomenon presented in plug flow models is used to correlate the input-output data. A gradient method (Bard's method) is employed to identify the parameters in the correlation equation.     

基于GPS的自学习导航游弋式水质监测系统设计

针对水质监测系统单点定位测量范围有限,多点定位测量成本高的问题,设计了一种自动导航游弋式水质监测系统。首先,采用CC2530芯片作为游弋船的运动主控制器和小船的遥控控制器,其中船上的CC2530模块作为Zigbee网络的汇聚节点,遥控器中的CC2530模块作为终端节点,通过遥控器实现测量船的现场手动路线示范遥控;其次,将小船运动控制芯片,数字传感器和GPS定位模块通过485总线连接到GPRS模块,再通过GPRS网络将信息上传到服务器,服务器对水质参数信息进行解码还原存入数据库,对各测量点GPS地理信息进行存储,自学习出合理的自动导航测量路径;最后,自动方式下,根据自动导航测量路径,测量船自动运行,服务器与Android客户端进行数据交互,实现对水质信息的多点移动监测。该系统不仅增加了测量范围,也降低了测量成本,可以广泛用于水产养殖、江河管理和城市供水的水源取水口的水质安全监控。     

基于水质监测技术的水产养殖安全保障系统及应用

为解决水产养殖中的风险问题,设计了基于水质监测技术的水产养殖安全保障系统。系统由水质监测与信息处理系统、电路控制系统、增氧和投饲设备组成,系统根据养殖水体的溶氧变化调控增氧、水层交换和投饲。常规淡水鱼池塘养殖情况下,安全增氧时间不低于6.2 h/W·d·kg,机械增氧下限为3 mg/L,上限为5 mg/L,上限运行时滞为0.5～1 h,水层交换时滞为1～2 h。应用表明,系统比传统增氧方式节约运行时间33.4%,平均降低饲料系数21.6%,系统具有节能、节饲和保障养殖安全的效果。     

基于粗糙集和突变级数法的区域水资源可持续利用评价

为减少评价过程中指标信息筛选和权重赋予的人为主观影响,提出粗糙集和突变级数相耦合的区域水资源可持续利用评价方法.以福建省为例,以9个设区市为研究单元,从水资源条件、开发利用、生态环境和经济社会等方面,构建由4大准则20项指标组成的指标体系,进行水资源可持续利用评价.结果表明：1）应用粗糙集约简冗余指标,依据属性重要度对指标排序,并耦合突变级数法进行水资源可持续利用评价是可行的,不仅可筛除冗余指标信息,而且无需过多人工干预,可很大程度地减少人为主观影响;2）福建省各设区市水资源可持续利用水平排序为龙岩＞三明＞宁德＞南平＞漳州＞福州＞泉州＞莆田＞厦门,呈现自西向东、西北向东南可持续利用水平递减的空间格局,且根据评价结果划分的4个可持续利用分区,可为水资源分区开发利用和管理保护提供理论依据.     

基于最小数据集的塔里木河上游绿洲土壤质量评价

以塔里木河上游绿洲阿拉尔垦区为靶区,综合考虑表征土壤物理、化学和生物学性质的14个土壤指标,利用数理统计方法确定最小数据集并评价土壤质量,同时与不含土壤酶指标的土壤质量评价结果进行对比分析。结果表明:(1)研究区土壤质量评价适用的最小数据集(MDS)包括:土壤水分、全盐、全氮和过氧化氢酶活性;(2)研究区棉田、新开垦农田、果园和林地等大部分耕地土壤质量属于中等及以上水平(SQI≧0.5的占78.12%),大部分分布于河岸及绿洲区域;(3)不包含土壤酶活性指标的土壤质量评价使土壤全盐含量的权重降低,且导致土壤质量评价结果偏低。