Ames, Iowa
March 26, 2007
Source:
Integrated Crop
Management IC 498 (3), Iowa
State University
by Jim Rouse, Department of
Agronomy
The following article is an abridged version.
Complete article:
http://www.croptesting.iastate.edu/downloads/SoybeanVarietySelection.pdf
Integrating soybean variety selection with soybean pest
management strategies seems like a simple task. Many pest
management strategies tout variety selection as a key component
of pest management. Variety selection is not, however, quite as
simple as "choosing the best one." Difficulty arises in
identifying which data reports should be used. Using the proper
information will allow you to make better variety selection
decisions and improve your profitability.
No pest scouting and management techniques will increase the
genetic yield potential of your soybean varieties. Instead,
those practices allow varieties to perform more closely to their
yield potential by reducing losses due to pests, pathogens, or
various other environmental factors. The consequence is that
selection of varieties with high yield potential is crucial to
maximizing your return.
Variety selection is obviously about more than just yield, yet
the decisions revolve around yield potential. Growers will not
choose low-yielding varieties, no matter what level of pest
resistance they may have.
But there is a catch, and this is where many researchers and ag
professionals get tripped up:
Variety selection is not about identifying which lines did best
in the past year--it is about predicting which lines will do
best in the future. So how do you evaluate which sources of data
can provide predictive information and which cannot?
The answer is simple: Predictive information should come only
from multi-environment trial averages.
Without this, you have a lower probability of success because
you are not incorporating all available data into your
decisions.
The data collected from any single location is a measure of the
yields produced by the interactions of the varieties (genetics)
with the environment (everything else). In these experiments,
the environment is comprised of soil type(s), soil conditions,
weather, nutrients, pests, pathogens, and any other factor that
can impact the expression of genetic yield potential during that
season. But the only factors in this equation that you can know
for next season will be the soil type(s) where you plant and the
genetics you choose. Because of this, you cannot expect the
results from a single-location trial in one season to be
duplicated in another season.
If data are not averaged across locations, how then does one
evaluate the results? If you want to select the best variety,
from which location do you select? Many criteria could be used
to choose the location upon which to base your soybean variety
selection. These include, but are not limited to, the location
that:
- is nearest to you;
- had the same rainfall or
heat units you had;
- had the soil type most
similar to yours; and
- had initial SCN counts
closest to yours.
Remember that all of these
criteria will interact in various unknown and unpredictable ways
to impact the final data measurements in each field. Thus, for
these results to be predictive, your field next year must
experience conditions essentially identical to the yield trial
field where the data were collected.
Since it is highly unlikely that next season's conditions will
be the same as those in any single-location report, you will
increase your probability of success by selecting a variety that
can perform well in many environments. These varieties can be
found in reports that display averages over locations and years.
Understanding the data
To thoroughly review reports, you
must first understand the data that are provided. The least
significant difference (LSD) will help you evaluate entries. Any
entries that differ by less than the reported LSD for a trait
must be considered equal for that trait. Measurements within an
LSD for any trait could be due to a number of different factors,
including site selection, seed quality, measurement error, or
random chance. These differences are not considered to be
statistically significant and are not likely repeatable in your
field under any circumstances.
The LSD is widely considered to be a measurement of the quality
of an experiment. Lower values for an LSD give more
statistically significant results and indicate higher quality
experiments. An added benefit of multi-location reports is that
they will almost always have lower LSD values than
single-location data. When evaluating various sources of variety
information, reports with lower LSD values should be given a
higher priority than others.
Do not rely on incomplete summary tables or diagrams to
determine if one variety is better or worse than another--look
for all of the supporting information. All data provided without
LSD values should be considered unreliable and should not be
used. The risk is that viewing data without the accompanying
statistics may lead to conclusions that are not supported by the
experimental results.
Using the data
Variety selection is composed of
two distinct but related components. The first is selecting
high-yielding varieties for your operation. The second is risk
management, as defined by the number of varieties you select,
their mix of maturities, defensive traits, seed treatments, and
their acreage allocation. If variety selection was just about
finding the highest yield, it would be a simple task. It is the
risk management element that makes variety selection difficult.
Even though the risk management aspect of variety selection can
instill some variability in methodology, there are certain
characteristics that should remain consistent among all users of
yield trial data:
1) Only
multiple-location data should be used to make predictive
selection decisions.
2) Sort the data by yield. Make initial selections based
on yield and appropriate maturity.
3) Once you have a pool of candidates, sort among these
to identify lines that have the desired mix of defensive
traits.
4) More information is better information, so use all
reliable sources of data.
Because variety selection is a
multi-step process, the most effective approach will incorporate
several sources of information. At Iowa State University (ISU),
the most comprehensive source of information for soybean yields
and defensive traits can be found at Iowa Crop Performance
Tests. Supplemental data for SCN tolerance is found on ISU's
Nematology Lab site.
Additional SCN resistance ratings and assorted disease screening
results can be found at the Illinois Varietal Information
Program for Soybeans.
Jim Rouse is a program manager with research and extension
responsibilities in corn hybrid and soybean variety testing.
This article originally appeared on page 77 of
Integrated Crop
Management -498 (3) -- March 26, 2007 issue.
Complete article:
http://www.croptesting.iastate.edu/downloads/SoybeanVarietySelection.pdf
|
|
