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International Journal of Agronomy
Volume 2013 (2013), Article ID 494026, 8 pages
http://dx.doi.org/10.1155/2013/494026
Research Article

Covariance Structures in Conventional and Organic Cropping Systems

USDA-ARS and Department of Agronomy & Plant Genetics, University of Minnesota, 803 Iowa Avenue, Morris, MN 56267, USA

Received 30 August 2013; Revised 2 November 2013; Accepted 4 November 2013

Academic Editor: Silvia Imhoff

Copyright © 2013 Abdullah A. Jaradat. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Guidelines are needed to develop proper statistical analyses procedures and select appropriate models of covariance structures in response to expected temporal variation in long-term experiments. Cumulative yield, its temporal variance, and coefficient of variation were used in estimating and describing covariance structures in conventional and organic cropping systems of a long-term field experiment in a randomized complete block design. An 8-year database on 16 treatments (conventional and organic cropping systems, crop rotations, and tillage) was subjected to geostatistical, covariance structure, variance components, and repeated measures multivariate analyses using six covariance models under restricted maximum likelihood. Differential buildup of the cumulative effects due to crop rotations being repeated over time was demonstrated by decreasing structured and unstructured variances and increasing range estimates in the geostatistical analyses. The magnitude and direction of relationships between cumulative yield and its temporal variance, and coefficient of variation shaped the covariance structures of both cropping systems, crop rotations, and phases within crop rotations and resulted in significant deviations of organic management practices from their conventional counterparts. The unstructured covariance model was the best to fit most factor-variable combinations; it was the most flexible, but most costly in terms of computation time and number of estimated parameters.