Abstract

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 of sites using several models for the spatial dependence among the nodes. Typical univariate models would either assume a simple homogeneous error structure or a compound symmetry error structure among the nodes, leading to the split-plot-type models. A multivariate unstructured approach ignores obvious spatial dependencies among the nodes. Spatial models and ante-dependence models permit a parsimonious summary of the error structure and are compared with the compound symmetry and multivariate models.

Keywords

Geostatistics, Spatial Models, Variogram, Repeated Measures

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Apr 24th, 6:45 PM

MODELLING WITHIN-PLANT SPATIAL DEPENDENCIES OF COTTON YIELD

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 of sites using several models for the spatial dependence among the nodes. Typical univariate models would either assume a simple homogeneous error structure or a compound symmetry error structure among the nodes, leading to the split-plot-type models. A multivariate unstructured approach ignores obvious spatial dependencies among the nodes. Spatial models and ante-dependence models permit a parsimonious summary of the error structure and are compared with the compound symmetry and multivariate models.