Location: Soil Management ResearchTitle: Covariance structures in conventional and organic cropping systems) Author
Submitted to: International Journal of Agronomy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/4/2013
Publication Date: 12/30/2013
Publication URL: http://handle.nal.usda.gov/10113/58254
Citation: Jaradat, A.A. 2013. Covariance structures in conventional and organic cropping systems. International Journal of Agronomy. DOI: 10.1155/2013/494026. Interpretive Summary: Long-term experiments that include several factors and treatments and conducted over an extended period of time are valuable tools in understanding the effect of land variability and variation over time on crop yield. A long-term experiment over eight years investigated the effect of 16 treatments on yield of four crops. We measured annual crop yields and calculated cumulative yield and its variability over time. We employed several methods to partition variation due to its sources and identified the effects of treatment factors on crop yield and its variation over time. It is concluded that farmers may be able to produce reasonably stable crop yields, especially with low inputs under organic cropping systems.
Technical Abstract: 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.