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Multivariate analysis of turfgrass cell wall components and relationship with black cutworm larval develpment

Multivariate analysis of turfgrass cell wall components and relationship with black cutworm larval develpment. S.C. Hong and R.C. Williamson The Dept. Entomology Univ. Wisconsin-Madison. Objectives.

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Multivariate analysis of turfgrass cell wall components and relationship with black cutworm larval develpment

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  1. Multivariate analysis of turfgrass cell wall components and relationship with black cutworm larval develpment S.C. Hong and R.C. Williamson The Dept. Entomology Univ. Wisconsin-Madison

  2. Objectives • To examine multi-dimensional relationship between cell wall components of turfgrasses and black cutworm larval development • To compare multivariate analyses

  3. Turfgrass species and cultivars • Creeping bentgrass (cbe) • Reveillie (tbr): The hybrid of Kentucky bluegrass and Texas bluegrass • Kentucky bluegrass • Challenger (chl) • Julia (jla) • Midnight (mid) • Monopoly (mono) • South Dakota (sd) • Young vs. Old based on planting dates • Young: less than 60 d • Old: greater than 365 d

  4. Cell wall components • Dry matter • Acid detergent fiber (adf) • Neutral detergent fiber (ndf) • Lignin • Ash • Total nitrogen • Leaf toughness • Larval weight (lw) from non-choice feeding assay

  5. Multivariate analysis • Fisher’s discriminant analysis • Cluster analysis • Hierarchical clustering method • Kruskal’s non-metric multidimensional scaling • Factor analysis

  6. Fisher’s discriminant analysis • Objective: • To describe graphically the different features of observations from populations • To classify turfgrasses into groups based on collected variables.

  7. Cluster analysis • Exploratory data analysis tool • Sorting different objects into groups in a way that the degree of association between two objects is maximal. • Kruskal’s non-metric multidimensional scaling (MDS) • Hierarchical clustering method (HCM)

  8. Kruskal’s non-metric multidimensional scaling

  9. Hierarchical clustering method

  10. Factor analysis • Objective: • To discover if the observed variables can be explained in terms of a much smaller number of variables called ‘factors’ • To fix collinearity • Regression analysis using the result of factor analysis

  11. Factor analysis

  12. Factor analysis • ntmlw: log-transformed larval weight • ntmfa1: new data calculated from the first factor loadings and cell wall data • ntmfa2: new data calculated from the second factor loadings and cell wall data

  13. Factor analysis

  14. Summary • Discriminant and Cluster analysis • Useful to describe graphically the features of cell wall data • Discriminant analysis and two cluster analyses show similar grouping pattern. • Group one: intratypes • Group two: Young vs. old

  15. Summary • Factor analysis • Remedy for collinearity among some variables (ADF, NDF, lignin, and leaf toughness) • Fitted model with larval weight has a negative coefficient for factor 1 (fa1).

  16. Acknowledgements • United States Golf Association • Amber Klawitter • Dow AgroSciences

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