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Full-Text Articles in Physical Sciences and Mathematics
Application Of Fractal On Ecosystem In Grassland, Yaxin Chen, Bing Xu, Kezhen Guo, Quanming Liu
Application Of Fractal On Ecosystem In Grassland, Yaxin Chen, Bing Xu, Kezhen Guo, Quanming Liu
IGC Proceedings (1997-2023)
No abstract provided.
Rangeland Sustainability Modeling Using Soil Exposure And Soil Moisture Parameters, J. Tibbitts, Keith T. Weber, J. Théau, Temuulen Tsagaan Sankey
Rangeland Sustainability Modeling Using Soil Exposure And Soil Moisture Parameters, J. Tibbitts, Keith T. Weber, J. Théau, Temuulen Tsagaan Sankey
IGC Proceedings (1997-2023)
No abstract provided.
Using Gis And Geostatistics To Study Spatial Variation Of Soil Test Phosphorus In Grassland, Weijun Fu, H. Tunney, C. Zhang
Using Gis And Geostatistics To Study Spatial Variation Of Soil Test Phosphorus In Grassland, Weijun Fu, H. Tunney, C. Zhang
IGC Proceedings (1997-2023)
No abstract provided.
Spatial Heterogeneity Of Soil And Vegetation Characteristics And Soil-Vegetation Relationships Along An Ecotone In Southern Mu Us Sandy Land, China, Yingzhong Xie, Kaiyang Qiu, Dongmei Xu, Richard Pott
Spatial Heterogeneity Of Soil And Vegetation Characteristics And Soil-Vegetation Relationships Along An Ecotone In Southern Mu Us Sandy Land, China, Yingzhong Xie, Kaiyang Qiu, Dongmei Xu, Richard Pott
IGC Proceedings (1997-2023)
Spatial pattern analysis is an essential component of spatial heterogeneity studies on soil properties and vegetation characteristics. It was conducted in several studies for both soil and vegetation characteristics (Strand et al., 2007; Dick and Gilliam, 2007; Zuo et al., 2010). This study aims to examine the changes in the spatial heterogeneity of soil properties at different soil layers, the spatial heterogeneity of soil and vegetation characteristics along an ecotone, and soil-vegetation relationships along the ecotone in a critical area of desertification.
Spatial Clustering Using The Likelihood Function, April Kerby, David Marx, Ashok Samal, Viacheslav Adamchuk
Spatial Clustering Using The Likelihood Function, April Kerby, David Marx, Ashok Samal, Viacheslav Adamchuk
Conference on Applied Statistics in Agriculture
Clustering has been widely used as a tool to group multivariate observations that have similar characteristics. However, most attempts at formulating a method to group similar multivariate observations while taking into account their spatial location are relatively ad hoc and do not account for the underlying spatial structure of the variables measured [12, 13, 14]. This paper proposes a method to spatially cluster similar observations based on the likelihood function. The geographic or spatial location of the observations can be incorporated into the likelihood of the multivariate normal distribution through the variance-covariance matrix. The variance-covariance matrix can be computed using …
Modelling Within-Plant Spatial Dependencies Of Cotton Yield, E. B. Moser, R. E. Macchiavelli, D. J. Boquet
Modelling Within-Plant Spatial Dependencies Of Cotton Yield, E. B. Moser, R. E. Macchiavelli, D. J. Boquet
Conference on Applied Statistics in Agriculture
In field experiments during 1987-1990, cotton plants were grown under 8 different levels of nitrogen application to assess the impact of nitrogen fertilization on the fruiting and yield distribution of cotton within the plant (Boquet et al. 1993).lr.dividual boll weights and average seedcotton yield were determined at each fruiting site fur each main-stem node along the plant. Various models of dependence and independence are possible to explain and account for the dependencies of the yields among the sites and nodes of the plant. Here we investigate models of total yield per node and yield per node adjusted for the number …
Designed Experiments In The Presence Of Spatial Correlation, David B. Marx
Designed Experiments In The Presence Of Spatial Correlation, David B. Marx
Conference on Applied Statistics in Agriculture
Soil heterogeneity is generally the major cause of variation in plot yield data and the difficulty of its interpretation. If a large degree of variability is present at a test site, some method of controlling it must be found. Controlling experimental variability can be achieved either by good experimental design or by analysis procedures which account for the spatial correlation. Classical designs are only moderately equipped to adjust for spatially correlated data. More complex designs including nearest neighbor designs, Williams designs, and certain restricted Latin square designs are developed for field experimentation when spatial correlation causes classical designs to be …