Brian Cullis

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Salinity tolerance in bread wheat ST E-BLUPS Scatter plot of the ST E-BLUPS for each of the 151 DH lines and indicative “observed” vs fitted values for a subset of lines −0.5 0.0 0.5 1.0 0.3 HW−893*B010 HW−893*C029 HW−893*C020 HW−893*C029 HW−893*C070 1.0 0.5 0.2 0.0 −0.5 slope 0.1 0.0 HW−893*C070 yield (site:centred) 1.0 HW−893*A029 HW−893*A040 HW−893*B010 −1.0 0.5 −0.1 HW−893*C020 0.0 −0.5 −0.2 HW−893*A040 HW−893*A029 −1.0 −0.6 −0.4 −0.2 0.0 0.2 intercept −0.5 0.0 0.5 1.0 EM (centred and scaled) −0.5 0.0 0.5 1.0 VSN Applied Statistics Conference 2011 Modelling variance structures of multi-term effects
Competition modelling in sorghum plant breeding trials Background • Stringer et al. (2011) considered joint modeling of spatial variability and within-row interplot competition in field trials grown by the BSES sugar breeding programme • Earlier empirical evidence suggested that AR1×AR1 model provided an inferior fit to these trials (Stringer and <strong>Cullis</strong>, 2002) • They showed that a random effects treatment interference model (R-TIM) provided an improved fit to data from sugar cane breeding trials which exhibited substantial inter-plot competition VSN Applied Statistics Conference 2011 Modelling variance structures of multi-term effects
Page 1 and 2: Modelling variance structures of mu
Page 3 and 4: Linear mixed model The model Let y
Page 5 and 6: R-structures General Framework •
Page 7 and 8: Separability Direct products in R-s
Page 9 and 10: Separability Direct products in G-s
Page 11 and 12: Syntax for multi-term variance mode
Page 13 and 14: Multi-site toy example Recall: mult
Page 15 and 16: Salinity tolerance in bread wheat A
Page 17 and 18: Salinity tolerance in bread wheat P
Page 19 and 20: Salinity tolerance in bread wheat M
Page 21: Salinity tolerance in bread wheat M
Page 25 and 26: Competition modelling in sorghum pl
Page 37 and 38: Partial compositing example from Sm

Brian Cullis

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