Cultivar Selection and
The success of agronomic crop production
in various locations in the United States
depends on the availability of plant varieties
and hybrids specifically adapted to local
production conditions. Cultivar selection
and development represent a major use of
NIFA federal Hatch and multistate formula
grants by land-grant experiment stations.
Most of them have active breeding programs
to develop the more important crops grown
in their state.
This research work focuses on developing
cultivars that are adapted to local climatic
and soil conditions. These cultivars provide
resistance to major pests and diseases, produce
acceptable yield and quality, and have appropriate
agronomic characteristics that are compatible
with production systems.
Research activities in this area span a
wide range, including:
- Fundamental genetic studies.
- Identifying genes of agronomic importance.
- Screening genetic stocks for breeding
material that carry the desired traits.
- Breeding and selection of lines as potential
new cultivars for release to growers.
Novel characteristics are added to existing
cultivars or breeding lines to meet the ever-changing
requirements for profitable crop production.
These include, but are not restricted to:
- Genes for resistance to new diseases
or insect pests.
- Alterations in quality to meet changing
- Modifications in plant architecture or
growth habit to fit changing production
Many experiment stations also conduct research
on potential new or alternative crops that
may be grown profitably in the area.
In addition to the cultivar selection and
development work, most experiment stations
carry out variety testing programs. This
work provides growers with unbiased side-by-side
comparisons of both public and proprietary
cultivars to help them select appropriate
ones to plant. Because of the rapid turnover
in commercial hybrids of crop species, such
as corn and soybeans, experiment stations
conduct tests annually in representative
locations. They replicate these tests sufficiently
to provide statistically sound comparisons.
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