Varietal selection for yield from a series
of multi-environment trials can be regarded as a
multi-trait selection problem in which the yields in
different environments are synonymous with traits.
As such an analysis of the data combined across
environments should be conducted in order to form
an index for selection. Analytical methods that
include appropriate models for both the genetic
variance structure (that is, the variances and covariances
of genotype effects from different environments)
and the residual variance structure (which
typically comprises spatial covariance models for
each trial) have been published previously. In the
case of perennial crops, yields are often obtained
from multiple harvests which implies that the data
comprise short sequences of repeated measurements.
Varietal performance in individual harvests is important
for selection so that a combined analysis across
both trials and harvests is required. The repeated
measures nature of the data provides additional
modelling challenges. In this paper we propose an
approach for the analysis of multi-environment,
multi-harvest data that accommodates the major
sources of variation and correlation (including temporal).
The approach is illustrated using two examples
from sugarcane breeding programmes. The
proposed models were found to provide a superior
fit to the data and thence more accurate selection
decisions than the common practice of conducting
separate analyses of individual trials and harvests.