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Potentielle forklarende variabler for udbytte i forskellige miljøer Hans Pinnschmidt

Potentielle forklarende variabler for udbytte i forskellige miljøer Hans Pinnschmidt Danmarks JorgbrugsForskning Afdeling for Plantebeskyttelse Forskning Center Flakkebjerg 4200 Slagelse. hans.pinnschmidt@agrsci.dk. Background

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Potentielle forklarende variabler for udbytte i forskellige miljøer Hans Pinnschmidt

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  1. Potentielle forklarende variabler for udbytte i forskellige miljøer Hans Pinnschmidt Danmarks JorgbrugsForskning Afdeling for Plantebeskyttelse Forskning Center Flakkebjerg 4200 Slagelse hans.pinnschmidt@agrsci.dk

  2. Background BAROF WP1 data: multivariate measurements on 86 spring barley genotypes in 10 environments (2 years: 2002 & 2003, 3 sites: Flakkebjerg, Foulum, Jyndevad, 2 production systems: ecological & conventional). hans.pinnschmidt@agrsci.dk

  3. factors: genotypeenvironment G1 E1 . . . . . . . Ej . . Gi variables: X1(i,j) ... Xm(i,j) parameters Xm(i)1 ... Xm(i)p Xm(j)1 ... Xm(j)p • variables: • yield • 1000 grain weight • grain protein contents • culm length • date of emergence • growth duration • mildew severity • rust severity • scald severity • net blotch severity • disease diversity • weed cover • broken panicles & culms • lodging • parameters: • raw data • mean/median/max./min. • rank/relative values • main effects • interaction slopes • raw data adjusted for E/G main effects/slopes (residuals) • IPCA scores • SD/variance } derive information on general properties, specificity, stability/variability hans.pinnschmidt@agrsci.dk

  4. Objectives Multivariate characterisation of genotypes with emphasis on yield-related properties. hans.pinnschmidt@agrsci.dk

  5. Statistical methods • Non-linear Canonical Correlation Analysis (NCCA): an optimal scaling procedure suited for handling multivariate data of any kind of scaling (numerical/quantitative, ordinal, nominal). • Multiple Regression Analysis (MRA) hans.pinnschmidt@agrsci.dk

  6. Non-linear Canonical Correlation Analysis (NCCA) data treatment: quantitative variables (vm) were converted into ordinal variables with n categories (v11 ... v1n, ..., vm1 ... vmn). hans.pinnschmidt@agrsci.dk

  7. Characterisation of environments • based on data adjusted for • G main effects (= residuals) hans.pinnschmidt@agrsci.dk

  8. Flakkebjerg 2002: high rust & 1000 grain weight; late sowing Foulum 2002 conventional & Jyndevad 2003 ecological: high mildew & lodging; low yield % net blotch Jyndevad 2002 ecological: low yield, 1000 grain weight, weed infestation, protein content Flakkebjerg 2003: high yield, net blotch & panicle breakage; low mildew & lodging hans.pinnschmidt@agrsci.dk

  9. Characterisation of genotypes • based on data adjusted for • E main effects (= residuals) hans.pinnschmidt@agrsci.dk

  10. high yield & 1000 grain weight; low protein content & lodging high mildew; low net blotch & disease diversity low yield & 1000 grain weight dimension 5 (sq. root) low mildew dimension 1 (sq. root) hans.pinnschmidt@agrsci.dk

  11. Characterisation of genotypes in • individual environments based on: • actual yield data • disease main effects (ME) of G’s • environmental disease variability (SD) of G’s (= standard deviation of E adjusted data) hans.pinnschmidt@agrsci.dk

  12. Flakkebjerg 2003: high yield, net blotch & panicle breakage; low mildew & lodging hans.pinnschmidt@agrsci.dk

  13. Flakkebjerg 2003: high yield, net blotch & panicle breakage; low mildew & lodging high yield; low net blotch ME & SD short straw high rust ME & SD dimension 6 (sq. root) low yield; high net blotch ME & SD long straw dimension 4 (sq. root) hans.pinnschmidt@agrsci.dk

  14. Jyndevad 2002 ecological: low yield, 1000 grain weight, weed infestation, protein content Flakkebjerg 2003: high yield, net blotch & panicle breakage; low mildew & lodging Foulum 2002 conventional & Jyndevad 2003 ecological: high mildew & lodging; low yield & net blotch hans.pinnschmidt@agrsci.dk

  15. Jyndevad 2003 ecological: high mildew & lodging; low yield & net blotch high yield low mildew ME & SD dimension 5 (sq. root) low yield; high mildew & net blotch ME & SD dimension 1 (sq. root) hans.pinnschmidt@agrsci.dk

  16. Multiple Regression Analysis (MRA) • dependent variables: yield (actual, E-adj. G mean & SD) • independent variables: E-adj. G mean & SD of disease severity, weed infestation, growth duration, culm length • criteria: Pin/out = 0.05/0.10; Fin/out = 3,84/2.71; tolerance = 0.0001 hans.pinnschmidt@agrsci.dk

  17. Variables must pass both tolerance and minimum tolerance tests in order to enter and remain in a regression equation. Tolerance is the proportion of the variance of a variable in the equation that is not accounted for by other independent variables in the equation. The minimum tolerance of a variable not in the equation is the smallest tolerance any variable already in the equation would have if the variable being considered were included in the analysis. If a variable passes the tolerance criteria, it is eligible for inclusion based on the method in effect.

  18. hans.pinnschmidt@agrsci.dk Mean versus standard deviation of environment-adjusted yield of spring barley genotypes; BAROF 2002-2003

  19. hans.pinnschmidt@agrsci.dk

  20. hans.pinnschmidt@agrsci.dk Observed versus estimated mean environment-adjusted yield of spring barley genotypes; BAROF 2002-2003

  21. hans.pinnschmidt@agrsci.dk

  22. hans.pinnschmidt@agrsci.dk Observed versus estimated standard deviation of environment-adjusted yield of spring barley genotypes; BAROF 2002-2003

  23. Yield of spring barley genotypes versus main effect yield of the environment; BAROF 2002-2003 hans.pinnschmidt@agrsci.dk

  24. hans.pinnschmidt@agrsci.dk

  25. Yield of spring barley genotypes estimated based on yield main effect of environment and E-adjusted mean & standard deviation of genotype property variables (disease severity, weed infestation, culm length, growth duration); analysis across environments; BAROF 2002-2003 hans.pinnschmidt@agrsci.dk

  26. hans.pinnschmidt@agrsci.dk

  27. Yield of spring barley genotypes estimated based on E-adjusted mean & standard deviation of genotype property variables (disease severity, weed infestation, culm length, growth duration); analysis by environment; BAROF 2002-2003 hans.pinnschmidt@agrsci.dk

  28. Conclusions & outlook • NCCA: • “intuitive” method good for “visualising” the main features in multivariate data of various scales • useful for obtaining an overall synoptic orientation of G properties and E characteristics •  “soft systems approach” • MRA: •  “hard systems approach” • synoptic view neglected • Mildew & net blotch had highest yield-related effect, although not always functional (especially in MRA!) hans.pinnschmidt@agrsci.dk

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