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Nearest Neighbour Adjustment and Linear Variance Models in Plant Breeding Trials
Author(s) -
Piepho HansPeter,
Richter Christel,
Williams Emlyn
Publication year - 2008
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200710414
Subject(s) - covariance , dimension (graph theory) , variance (accounting) , nearest neighbour , linear model , mixed model , mathematics , variety (cybernetics) , statistics , focus (optics) , computer science , analysis of covariance , econometrics , artificial intelligence , combinatorics , accounting , business , physics , optics
This paper reviews methods for nearest neighbour analysis that adjust for local trend in one dimension. Such methods are commonly used in plant breeding and variety testing. The focus is on simple differencing methods, including first differences and the Papadakis method. We discuss mixed model representations of these methods on the scale of the observed data. Modelling observed data has a number of practical advantages compared to differencing, for example the facility to conveniently compute adjusted cultivar means. Most models considered involve a linear variance‐covariance structure and can be represented as state‐space models. The reviewed methods and models are exemplified using three datasets. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)