A score test for the agronomical overlap effect in a two-way classification model

Abstract

In some agricultural research, a treatment applied to an experimental unit may affect the response in the neighboring experimental units. This phenomenon is known as overlap. In this article, a test to evaluate this effect in the Draper and Guttman model was developed by imposing side conditions on the parameters of a two-way classification model to obtain a re-parameterized model which can be used in different neighboring patterns of experimental units, usually plants within a crop, whenever the nearest neighbor is considered a directly affected experimental unit and the two-way model is used. Three methods, namely maximum likelihood, least squares with side conditions and generalized inverse, were used to estimate the parameters of the original model in order to calculate the value of the test statistics for the null hypothesis associated with the absence of the overlapping effect. The three alternatives were invariant with respect to the use of test. The proposed test is simple to adopt and can be implemented in agronomy since its asymptotic nature is in agreement with the large number of experimental units which generally exist in this type of research, where each plant represents the experimental unit being assessed.

The effect of spatial lag on modeling geomatic covariates using analysis of variance

“…In each simulation, the ANOVA F statistic was extracted, normality and homoscedasticity of residues were tested for treatments. The same O T lag statistic was calculated based on the Chi-square distribution (with a degree of freedom) that appears in Darghan et al (2012) and that is annexed in this document in a script of R for use, as well as the coe cient associated with the spatial lag ( ) which was obtained from:…”