Potentielle forklarende variabler for udbytte i forskellige miljøer Hans Pinnschmidt Danmarks JorgbrugsForskning Afdeling for Plantebeskyttelse Forskning Center Flakkebjerg 4200 Slagelse

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).

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 factors: genotypeenvironment G 1 E 1....E j. G i variables: X 1(i,j)... X m(i,j) parameters X m(i)1... X m(i)p X m(j)1... X m(j)p } derive information on general properties, specificity, stability/variability

Objectives Multivariate characterisation of genotypes with emphasis on yield-related properties.

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)

Non-linear Canonical Correlation Analysis (NCCA) data treatment: quantitative variables (v m ) were converted into ordinal variables with n categories (v v 1n,..., v m1... v mn ).

Characterisation of environments based on data adjusted for G main effects (= residuals)

Flakkebjerg 2003: high yield, net blotch & panicle breakage; low mildew & lodging 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

Characterisation of genotypes based on data adjusted for E main effects (= residuals)

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

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)

Flakkebjerg 2003: high yield, net blotch & panicle breakage; low mildew & lodging

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

Flakkebjerg 2003: high yield, net blotch & panicle breakage; low mildew & lodging 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

dimension 1 (sq. root) dimension 5 (sq. root) low yield; high mildew & net blotch ME & SD low mildew ME & SD high yield Jyndevad 2003 ecological: high mildew & lodging; low yield & net blotch

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 =

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.

Mean versus standard deviation of environment-adjusted yield of spring barley genotypes; BAROF

Observed versus estimated mean environment-adjusted yield of spring barley genotypes; BAROF

Observed versus estimated standard deviation of environment- adjusted yield of spring barley genotypes; BAROF

Yield of spring barley genotypes versus main effect yield of the environment; BAROF

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

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

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!)