Evaluation of sampling methods and assessment of the sample size to estimate the weed seedbank in soil, taking into account spatial variability

As field sampling is time consuming, it is necessary to develop efficient sampling techniques to obtain accurate estimates of the weed seedbank in soil. The relative efficiency between sampling schemes depends on the spatial variability in weed seed density across agricultural fields. Spatial variability of the weed seed density was characterized by theoretical correlograms. A systematic sampling (square grill) scheme was considered and it was found that, taking into account spatial variability, this sampling scheme was more efficient than simple random sampling. As a result, the sample size can be reduced in comparison with that given in previous studies, where spatial correlation was ignored. The reduction depends on the correlation structure defined as a function of the ratio, τ, between the nugget effect and the sill of the variogram. The maximum reduction of the sample size, without loss of either precision or confidence level corresponds to the case where there is no nugget effect, τ = 0. The opposite extreme case, where the reduction is nil, corresponds to the case of a pure nugget effect τ = 1. The abaci based on given expressions are provided to determine the sample size in species whose spatial pattern can be fitted either to a Poisson or to a negative binomial distribution.     

The nearest-plant method is robust and powerful enough for different survey teams to monitor change in mesic grassland species composition

A long-term rangeland monitoring programme needs to employ a field survey technique that is practicable, precise, powerful enough to distinguish change, not prone to worker bias, and able to distinguish real change from operator error arising from staff turnover. These criteria were used to evaluate a widely used grassland sampling technique in South Africa – the nearest-plant (with 200 points) method (NP) – against common alternatives, namely NP excluding forbs (NP-nf), the plant number scale (PNS; a cover-abundance method) and quadrat frequency (QF), using multivariate ordination and permutation tests. Four trained teams surveyed four grasslands using each method. PNS took more than twice as long as the other techniques, which were similarly rapid. Estimates of composition using NP methods were the most precise and PNS was least repeatable, with QF intermediate. Compositional differences between sites were most finely distinguished using NP-nf, followed by NP and QF. PNS was able to detect only marked differences and had the greatest potential for surveyor bias. The NP method, with or without including forbs, is therefore recommended for monitoring the species composition of mesic, dense grassland. Some suggestions for monitoring agencies on how best to use multivariate methods are presented.     

Application of Multivariate Statistics in Early-warning of Corporation Financial Crisis

This paper is focused on early warning of corporation financial crisis for the China's household electric appliance manufacturers in stock market. Firstly, using company's open-disclosed annual statements as data resource, the financial indexes system is constructed. Secondary, adopting the multivariable statistical analysis method, such as cluster, discriminate and principle component analysis, the multivariate models for the empirical study on early warning of financial crisis by making use of SPSS statistical software are set up. The corporation might discover the financial affairs crisis that shall probably arise beforehand by using the financial affairs crisis early warning system. This study and analysis may serve as reference for domestic industries and enterprises.     

桑树资源概况及其多元化利用

桑树是具有广泛用途的木本植物。就我国桑树种质资源分类以及不同生态类型的分布情况,从养蚕业、生态建设、畜牧饲料、果业开发、庭园观赏、医药保健、食品生产、化工应用等方面对桑树植物资源的多元化利用价值进行了概述,并对桑树植物多元化利用研究作了展望。     

基于多元统计分析的茄衣烟叶生产气候资源配置

为探究茄衣烟叶生产中的气候资源配置情况,以“云雪1号”品种为试验材料,通过调整移栽期以改变烟株生育期内的气候指标,采用多元统计分析法研究茄衣烟叶的气候资源配置及其对烟株农艺性状及发酵后烟叶化学成分的影响。相关性分析结果显示,烟株大田期的积温、≥10 ℃有效积温、总降水量与总温差对烟株农艺性状各指标的影响较大,其相关系数分布范围为0.478~0.939,而积温、≥10 ℃有效积温、平均气温对烟叶总糖、烟碱、总氮、蛋白质含量影响较大,其相关系数分布范围为-0.955~-1.000。采用因子分析的方法在8项气候指标中提取出气候积累因子与湿度因子2个主因子,进一步通过灰色关联分析得出上述二者分别是影响烟株农艺性状和烟叶常规化学成分的主因子。以上结果表明,确定茄衣烟株移栽期的主要气候因子依据为大田生育期的气温、降水量和相对湿度的综合配置。     

中国圆田螺与中华圆田螺形态比较研究

为研究中国圆田螺(Cipangopaludina chinensis)和中华圆田螺(Cipangopalndina cathayensis)两者之间的形态差异.通过单因子方差分析、主成分分析、聚类分析和判别分析对185个中国圆田螺和中华圆田螺个体的11个可量性状及23项性状参数进行了比较研究.单因子方差分析的结果显示,...     

基于GC-MS对水稻种子耐镉差异代谢物的筛选与分析

为了筛选耐镉性较好的水稻品种,本研究利用气相色谱质谱联用仪,对24份不同耐镉程度的水稻品种进行了代谢物的定性与定量分析,通过主成分分析(PCA)和偏最小二乘判别分析(PLS-DA)等多元统计分析方法筛选出水稻耐镉差异性代谢物。结果表明:24份水稻种子共检测出59种主要代谢产物,有机酸类物质可将不同耐镉水平的水稻区分开;同时结合PLS-DA与T检验筛选后得出4种差异代谢物(VIP>1,p<0.05),其中9,12-亚油酸主要存在于低耐品种,而丙二酸、扁桃酸和D-甘露醇主要存在于高耐品种中。说明有机酸类物质与水稻耐镉性相关,可作为区分水稻种子耐镉差异的标志代谢物。     

Fitting growth with the von Bertalanffy growth function: a comparison of three approaches of multivariate analysis of fish growth in aquaculture experiments

Three approaches for multivariate analysis of fish growth in aquaculture experiments with Nile tilapia (Oreochromis niloticus niloticus L.) based on the von Bertalanffy growth curve are presented and compared. The approaches are: an extended Gulland‐and‐Holt (GH) plot, a forced extended GH plot and a multilinear regression analysis for the growth parameter K. All three models provide valuable insight into the major environmental factors influencing the daily growth rate and explain 28–46% of the variance of the observed daily growth rate of the used data set. For all three methods, the modelled parameter is significantly related to the net yield of Nile tilapia and can, therefore, be used for the predictive modelling of management scenarios. The extended GH plot loads the influence of environmental parameters upon L_∞, while the forced extended GH plot and Direct fitting of K load the influence on the growth parameter K. The latter is more in the tradition of aquaculture research. But the forced extended GH plot and Direct fitting of K can only be applied if L_∞ of the cultured species is known, as the selected L_∞ influences the variance in the regression variables.