Some Comments on Gerechte Designs

In a companion paper we derived the correct analysis for gerechte designs with uncorrected errors. Here we show that this correct analysis cannot be justified by the usual randomization argument. However, when the regions are rectangular there is a randomization procedure which validates an analysis with three separate error terms. We also outline other developments in design and analysis that may be more satisfactory.     

MapX在空间数据绑定与林业专题图制作中的应用探讨

从空间地图数据与属性数据的关联、专题图的生成各方法,就MapX在林业地理信息系统开发中空间数据绑定与专题图制作中的技术等作了简要探讨。     

茶梢蛾幼虫空间分布及其抽样技术

对茶梢蛾幼虫的空间分布型及抽样技术的研究结果表明,幼虫为普通聚集分布,其基本成分是面积为一株油茶大小的个体群,个体间相互吸引。调查时以棋盘式取样为佳。根据空间分布型分析,计算出序贯抽样模型为:最适理论抽样数模型为:     

Spatial distribution of weeds in arable crops: are current sampling and analytical methods appropriate?

This paper reviews the literature concerning the spatial distribution of weeds; highlighting the limitations of our current sampling and analytical methodologies, and suggesting how these inadequacies can be addressed. Most research studies have used discrete sampling, i.e. weeds are counted within a quadrat, on a grid basis. Few have mapped weeds at a whole-field scale, either with a resolution appropriate to spraying operations or key ecological processes. Statistical analyses used to describe the data can be divided into two main types, spatially implicit (also at the scale of the sampling unit) or spatially explicit, in which the location of individuals is included in the analyses. Spatially implicit methods can be strongly affected by quadrat size and mean density and are of doubtful benefit. More attention is required to address sampling resolution issues for spatially explicit methods. Our understanding of the formation and dynamics of spatial pattern, as well as predicting the consequences of site-specific management, can be improved with models. Unfortunately, most models consider only newly expanding patches and appear incapable of predicting spatial distributions when an area has been fully invaded. More detailed biological information is required if models are to become more realistic and informative. We also need to ensure that we understand the spatial processes in the context of the whole field environment, to optimize the success of site-specific weed management in the longer term.