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Covariance structures in conventional and organic cropping systems

Abdullah A. Jaradat
International journal of agronomy 2013 v. no. pp. 1-8
conventional tillage, covariance, crop rotation, cropping sequence, cumulative distribution, fertilizer rates, long term experiments, multivariate analysis, organic production, probability, strip tillage, temporal variation, variance
A long-term split-plot experiment with four replicates in a randomized complete block design, was comprised of 16 treatment combinations of cropping systems (conventional and organic, crop rotations (2-Yr and 4-Yr; all phases of each crop rotation were present in each of 8 years), tillage (conventional and strip), and fertility (with and without recommended nitrogen fertilizer rate and source) was used to estimate covariance structures in conventional and organic cropping systems. Cumulative yield and its temporal variance and coefficient of variation were subjected to geostatistical, variance components, and repeated measures multivariate analyses using six covariance models under restricted maximum likelihood. Total variation in each of cumulative yield and its temporal variance and coefficient of variation were partitioned into their components; and appropriate covariance models were selected to describe the correlation between effects of random factors. Crop rotations, treatment combinations, and phases of crop rotations, in this order, had decreasing, and smaller temporal variances than cropping systems; thus emphasizing the importance of crop sequence within a crop rotation in modifying temporal variation. The covariance matrices of conventional and organic cropping systems, quantified by their respective sums of squares, were independent of each other (r = -0.12, p > 0.05) and the difference between their covariances was associated positively (r = 0.89, p < 0.05) and negatively (r = -0.56, p < 0.05) with the covariance matrices of conventional and organic cropping systems, respectively. Results of the study can be used to formulate guidelines to develop proper statistical analyses procedures and select appropriate models of covariance structures in response to expected temporal variation in long-term experiments.