Abstract
Corn and soybean production dominates the agricultural systems of the mid-western United States. Studies have found that when a single crop species is grown continually, without the rotation of other crops, yield decline occurs. At present, this phenomenon, remains poorly understood, but there are possible links to microbial community dynamics in the associated rhizosphere soil. In this study, corn plants were grown in disturbed and undisturbed soils with a 24 year history of growth as a mono culture crop or two crops grown in annual rotation. Characteristic profiles of the microbial communities were obtained by denaturing gradient gel electrophoresis of polymerase chain reaction amplified 16S rDNA from soil extracted DNA. This problem is approached as the statistical analysis of high-dimensional multivariate binary data with an emphasis on modeling and variable selection.
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Recommended Citation
Wilbur, J. D.; Nakatsu, C. H.; Brouder, S. M.; Ghosh, J. K.; and Doerge, R. W.
(2001).
"STATISTICAL ISSUES IN THE ANALYSIS OF MICROBIAL COMMUNITIES IN SOIL,"
Conference on Applied Statistics in Agriculture.
https://doi.org/10.4148/2475-7772.1221
STATISTICAL ISSUES IN THE ANALYSIS OF MICROBIAL COMMUNITIES IN SOIL
Corn and soybean production dominates the agricultural systems of the mid-western United States. Studies have found that when a single crop species is grown continually, without the rotation of other crops, yield decline occurs. At present, this phenomenon, remains poorly understood, but there are possible links to microbial community dynamics in the associated rhizosphere soil. In this study, corn plants were grown in disturbed and undisturbed soils with a 24 year history of growth as a mono culture crop or two crops grown in annual rotation. Characteristic profiles of the microbial communities were obtained by denaturing gradient gel electrophoresis of polymerase chain reaction amplified 16S rDNA from soil extracted DNA. This problem is approached as the statistical analysis of high-dimensional multivariate binary data with an emphasis on modeling and variable selection.
