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most common ways to describe the relationship between two or more variables. Correlation coefficients are a mea-Intercorrelation among soil properties can result in multicollinearsure of the degree of linear association between any two ity problems regarding relationships between soil properties and crop yield. The objective of this study was to compare statistical methods variables when other variables are fixed. The regression of examining relationships between cotton (Gossypium hirsutum L.) model can indicate the extent by which the dependent yield, quality, and soil properties. Soil and plant samples were collected variable can be predicted by the independent variables; from 1-ha grids on an irrigated production cotton field in Texas from the strength of prediction is expressed as r 2 , known as 1998 through 2000. Ordinary least square regression (OLS), partial variation in dependent variable explained by indepenleast square regression (PLS), and principal component regression dent variables or coefficient of determination. The inter-(PCR) were compared as methods for quantifying relationships becorrelation between soil properties does not violate the tween cotton yield or quality and soil properties. The PLS method assumptions of the classical regression modeling, but can eliminated multicollinearity problems and resulted in the coefficient cause multicollinearity problems that may result in large estimations with meaningful signs compared with their associations variances of parameter estimates, unexplainable signs to cotton yield and fiber quality. Furthermore, loadings from linear combinations of variables in PLS allowed identifying soil properties of explanatory variables, and rejection of regression cothat had the greatest influence on yield. While PCR identified the prin-efficient estimates that are significant (Morzuch and cipal components that maximized the variance of independent vari- Ruark, 1991; Neter et al., 1996; Fekedulegn et al., 2002). ables, it did not improve the modeling of crop-soil relationships. AmongThe general methods to solve multicollinearity probthe selected soil and landscape properties, sand and clay content, exlems between independent variables include removing changeable Ca 2؉ and Mg 2؉ , NO 3 Ϫ , Olsen-P, pH, relative elevation, and less important variables; combining variables via princislope were important factors affecting lint yield and fiber quality.pal component analysis (PCA) or factor analysis; andHigher lint yields were usually accompanied by higher fiber quality.using ridge regression, partial least squares (PLS), or Magnitudes of influence of different soil properties on yield and qual-Bayesian methods (Neter et al., 1996). Each method has ity, however, varied among the 3 yr, suggesting that long-term studies its own limitations. For example, variable removal can are needed to establish robust relationships for site-specific management.focus on the influential variables, but could lose some information due to reduced number of independent variables (Morzuch and Rua...

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Aims:  To (i) compare the limits of detection of Bacillus anthracis spores in three soils (one Florida, one Texas, and one a commercial Garden product) by PCR using DNA extracted with five commercial extraction kits and (ii) examine if removing organic acids or adding an enrichment step utilizing a growth medium will improve the detection limits. Methods and Results:  Bacillus anthracis spores were added to soil aliquots and used immediately with a DNA extraction kit or pretreated to remove organics or incubated overnight in a selective growth medium before the DNA extraction was performed. Using hybridization and PCR assays for capC, pag and lef genes, 105–106B. anthracis spores were detected in untreated Florida soil, 104–107 spores in untreated Texas soil and 106–107 in Garden soil. Pretreatment did not reliably improve detection. DNA from untreated and pretreated soils was suitable for hybridization but not always for PCR. When 101–102 spores were added to the soils and allowed to amplify in a growth medium selective for B. anthracis, DNA extracted using four methods reliably produced PCR acceptable DNA positive for the B. anthracis genes. Conclusions:  The quality of DNA extracted with commercial kits appears to be influenced by the soil type and pretreatment. Yet, with an enrichment step added, four of five extraction methods produced PCR suitable DNA and detected ≤102 spores. Significance and Impact of the Study:  The enrichment step could enhance the detection of B. anthracis spores in soils and small samples contaminated with soil.