Molecular Breeding for Stay-Green: Progress and Challenges in Sorghum - Translational Genomics for Crop Breeding: Abiotic Stress, Yield and Quality - page 137

associated with one or more stay-green QTLs
in sorghum.

line for “precision breeding,” that is, analysis
of the environments from the crops' perspective
and development of genotypes possessing spe-
cific features that permit maximum utilization of
environment potential. In other words, modeling
allows reasonable diagnostics of environment-
restricting factors, such as type of drought stress
and probability that a crop will face a particular
stress type at a specific location (Chenu et al.
2011, Hammer and Jordan 2007, Chapman et al.
2008).
Such knowledge can be used further for (1)
in vivo selection and screening for crop traits
providing putative adaptation in the well-defined
target environmental conditions and manage-
ment practices (2) in silico designing of vir-
tual genotypes possessing hypothetical/existing
traits and estimation of their benefits across time
in the location of interest with a given suite
of management practices. This approach has
already been used for the characterization of
environments for wheat and sorghum in Aus-
tralia (Chenu et al. 2011, Hammer and Jordan
2007, Chapman et al. 2008) and there is an
on-going effort to diagnose the sorghum pro-
duction constraints using this methodology for
winter cropping seasons (post-rainy season) of
the semi-arid tropics in peninsular India. Here
the modeling tool allowed, for the first time,
the differentiation between various water-stress
types, quantification of stress types' frequen-
cies, and their effects on sorghum production
across heterogeneous parts of major production
regions (Kholova et al. in preparation). At the
same time, substantial progress has been made
in understanding the mechanisms contributing
to drought adaptation (e.g., water utilization
dynamics and efficiency, plant developmental
dynamics, and N utilization - many of which
may result in so called “stay-green” phenotypes;
see section above on “Mechanisms Explain-
ing Stay-Green”). The well-defined physiolog-
ical basis of any genotypes' specific machin-
ery can be simulated using such models and
tested across a range of specific environments.
In this way, modeling can help approximate the

Use of Modeling to Manipulate
Mechanisms Associated with
Stay-Green

Among research areas attempting to address the
food and feed demand of growing human and
livestock populations living under conditions of
harsh climate and erratic rainfalls across the
semi-arid tropics, crop improvement efforts are
not only particularly challenging but are also par-
ticularly promising. Despite the progress made
in the field of crop breeding strategies, for exam-
ple quantitative genetics, marker-assisted selec-
tion processes, improvement of trait-screening
techniques, investigation of stress tolerance dif-
ferences, and so forth, the progress made in
the development of improved cultivars has been
slow because of complex interactions of genoype
and environmental factors, including manage-
ment practices (i.e., the G

M problem).
This slowed progress is partially because investi-
gating these interactions in vivo requires years of
precisely managed multi-locational field trials,
which are extremely time- and cost-intensive,
and often simply impossible to do properly cov-
ering all possible relevant environments.
This as yet unresolved G

× E ×

M problem
suggests that the existence of crop genotypes that
can adapt to a broad range of stress environments
is very unlikely and that breeding strategies for
stressful environments should probably instead
focus on the development of crop genotypes
suited to particular environments. In recent years
a pragmatic way appeared for at least begin-
ning to decipher the complexity of G

× E ×

M
interactions by crop simulation modeling. This
approach interlinks mechanistic knowledge of
crop growth characteristics and allows estimates
of crop productivity across the region(s) of inter-
est. The crop model sensibility is highly depen-
dent on knowledge of a given production envi-
ronment (weather, soil) as well as knowledge of
the crop, making it extremely useful as a guide-

× E ×