  Assessing the performance and the characteristics (e.g. yield, quality,
disease resistance, abiotic stress tolerance) of new varieties is a key
component of crop performance improvement. However, the variety testing process
is presently exclusively based on experimental field approaches which
inherently reduces the number and the diversity of experienced combinations of
varieties x environmental conditions in regard of the multiplicity of growing
conditions within the cultivation area. Our aim is to make a greater and faster
use of the information issuing from these trials using crop modeling and
simulation to amplify the environmental and agronomic conditions in which the
new varieties are tested.
  In this study, we present a model-based approach to assist variety testing
and implement this approach on sunflower crop, using the SUNFLO simulation
model and a subset of 80 trials from a large multi-environment trial (MET)
conducted each year by agricultural extension services to compare newly
released sunflower hybrids. After estimating parameter values (using plant
phenotyping) to account for new genetic material, we independently evaluated
the model prediction capacity on the MET (model accuracy was 54.4 %) and its
capacity to rank commercial hybrids for performance level (Kendall's $\tau$ =
0.41, P < 0.01). We then designed a numerical experiment by combining the
previously tested genetic and new cropping conditions (2100 virtual trials) to
determine the best varieties and related management in representative French
production regions.
  We suggest that this approach could find operational outcomes to recommend
varieties according to environment types. Such spatial management of genetic
resources could potentially improve crop performance by reducing the
genotype-phenotype mismatch in farming environments.
