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STANDARD VIEW MARC VIEW
MODIFIED STABILITY ANALYSIS
AND ON-FARM RESEARCH
TO BREED SPECIFIC ADAPTABILITY FOR ECOLOGICAL DIVERSITY
Peter E. Hildebrand
In this paper, I argue that the cumulative effect of four factors has led to at least a
quarter century of rejecting genetic material which would have demonstrated superior
yield capability in the best or the poorest farm environments,
and has not necessarily led to varieties or hybrids with superior yield capability in
These four factors are: one, statistical dependence on ANOVA that leads to the concern
with reducing genotype-by-environment interaction.
This in turn leads to the nearly universal practice of evaluating material on experiment
stations and farms with real or artificially created superior environments.
Adding to the effect has been the capability of many farmers in the developed world to
use their resources to modify unfavorable environments and thus adjust their conditions
to those required by the varieties and hybrids developed under superior conditions.
The final factor has been the widespread use of a regression coefficient of one as a
measure of stability when evaluating environmental effect on genotype.
8 (Wheat trial)
For more than a half century, dating, to my knowledge, from the Yates and Cochran
article in 1938, agricultural scientists, and in particular plant breeders, have been using
some form of stability analysis to evaluate germplasm over different locations and
environments. Recently, persons in developing countries and working with farming
systems research and extension methodologies have been using a modified form of the
procedure to develop practices and germplasm for the widely varying environments
found in many of these countries. While the activities cited above use a common
methodology to find superior technology, the reasons for its use, the philosophy behind it
and the results differ.
9 Irrigated Tobacco
Over the last few decades, and in the developed world where resources were relatively
abundant, widely adaptable technology, achieved by manipulation of the crop growing
environment on farms, has been feasible. Mechanization, irrigation and heavy use of
chemicals allowed farmers to provide the right environment to achieve maximum
production with widely adapted crop varieties. However, in the developing world where
resources are not sufficient to allow farmers to modify adverse environments, farmers
and crop scientists have had to tailor technology to specific environments. The 'Green
Revolution' had a large and critical impact on food production in this part of the world,
but it affected mostly those farms blessed with the better environments.
It has been only recently that organized development efforts have been able to reach the
majority of farmers -- those with few resources and living in relatively hostile
environments -- by adapting technology to their environments.
11 Perennial peanut
Now in the developed world, with increasing concern for an agriculture that is
environmentally and economically more sustainable, crop adaptation to environment,
rather than adapting environment to the crop, is also becoming more important.
Breeding for specific adaptability requires a different interpretation and approach to the
stability analysis procedure than breeding for broad adaptability.
This paper discusses the use of a modified approach to stability analysis as an important
research tool which can provide the basis for an effective and efficient plant breeding
program for which adaptability to specific environments is desired as a critical factor in
enhancing the sustainability of agriculture throughout the world.
First, in this paper, the philosophical history of stability analysis is discussed.
Then the effect of this history on plant technology development is argued.
Finally, the farming systems philosophy is discussed and the use of stability analysis in
this approach is presented.
Yates and Cochran, apparently the first to conceptualize stability analysis, understood the
potential to differentially recommend varieties based on their performance in different
environments: "The fact that the experimental sites are a random sample of this nature
does not preclude different recommendations being made for different categories
included in this random sample."
In fact, they argue that... the deliberate inclusion of sites representing extreme
conditions may be of value.
However, their primary concern was the statistical problem of how to evaluate mean
differences among or between treatments which were tested over a wide range of
environments and where genotype-by-environment interaction was present.
Finlay and Wilkinson refined and enhanced the Yates and Cochran procedure. Perhaps
their most important contribution was to describe the characteristics of varieties with
high (> 1.0) and low (< 1.0) stability (regression) parameters and relate these to variety
mean yield over environments. They defined varieties with general adaptability as those
with average stability (b = 1.0) when associated with high mean yield over all tested
However, they also recognize that above average stability (b approaching zero) is
associated with increasing adaptability to low-yielding environments,
and below average stability with increasing adaptability to high-yielding environment.
They argue in favor of testing over a wide range of environments and not discarding low
yielding environments because this "... will bias the selection towards types specifically
adapted to high-yielding environments, and will pass over those with general
As this statement emphasizes, however, their interest ultimately was on generally adapted
varieties. In speaking of the varieties included in the tests they were reporting, they said:
"The varieties with the high phenotypic stability all have low mean yields.
They are so stable, in fact, that they are unable to exploit high-yielding
environments. On the other hand, varieties can be too sensitive to
environmental change, as is shown by the low mean yields of the varieties
with high regression coefficients. The generally adapted varieties balance
between these extremes, the actual point of balance depending on the
particular genotype and range of environments" (p. 752).
At the same time, Finlay and Wilkinson were cognizant of the hazard resulting from
discarding potentially valuable materials when searching only for varieties with high
mean yields over all environments.
Eberhart and Russell, authors of probably the most cited article regarding the use of
stability parameters, were concerned that large genotype-by-environment interactions
reduced 'progress' from selection.
One means of reducing this interaction, they reported, is to stratify environments to
make them more similar. However, they thought the resulting interaction still
"frequently remains too large".
Their solution was a method to select stable genotypes that interact less with the
environments in which they are grown and then use only the more stable genotypes for
the final stages of testing. They then set their task in the article as finding the criteria
necessary to rank varieties by stability. Ultimately they define a stable variety as one
with an average response to environment, or what Finlay and Wilkinson called average
stability, and a minimum of deviations from regression.
Based on this interpretation of the philosophical history of stability analysis, the
hypothesis to be explored here is that the cumulative effect of four factors has led to at
least a quarter century of rejecting genetic material which would have demonstrated
superior yield capability in the best or the poorest farm environments and has not
necessarily led to varieties or hybrids with superior yield capability in average
The four factors are, again, statistical dependence on ANOVA that leads to the concern
with reducing genotype by environment interaction, which in turn leads to
the nearly universal practice of evaluating material on experiment stations and farms
with real or artificially created superior environments, in order to control this interaction
or to permit the material to manifest its 'yield potential'.
Third, the capability of many farmers in the developed world, over the last few decades,
to use their resources to modify unfavorable environments; and
fourth, the widespread use of a regression coefficient of one as a measure of stability.
If progress from selection has been slow, it was the result of these factors, not the
presence of genotype-by-environment interaction which Eberhart and Russell considered
to have a negative impact.
If the present argument is convincing, then it could be concluded that genotype-by-
environment interaction should be viewed as positive.
Furthermore, the philosophy toward the use of stability analysis, and the reasons to use
on-farm testing, should change if we are to effectively and efficiently move toward a
more sustainable agriculture.
Analysis of variance is an indispensable tool to evaluate replicated research conducted at
only one location. It can be used by a skilled statistician to evaluate multilocation
research, but it is easy to make errors (in part depending upon whether the locations are
considered fixed or random) if the analyst is not skilled. Furthermore, in the field, but
seldom published, it is not uncommon to see an analysis showing significant location-by-
treatment interaction which masks mean treatment differences (the concern of Yates and
Cochran). The researcher concludes it is not possible to detect treatment differences
and recommends either repeating the experiment or controlling the sources of
interaction in a new experiment.
This latter recommendation leads many researchers to try to minimize differences among
locations by stratification; by environmental control techniques such as irrigation,
fertilization and rigorous pest control; by excluding low-yielding environments, and/or by
rejecting on-farm testing in favor of the experiment station. As a consequence, the
results apply only to farms and farmers with similar, usually superior environments. The
only reason hybrids (or other material) bred and tested under these conditions have had
any successful adoption is that many farmers in the developed world, and a select few in
developing countries, have had the resources and capability to duplicate favorable
environments similar to those created for purposes of the testing program.
Because testing is carried out only under a very narrow range of environments,
extrapolation to poorer environments (frequently those found on the majority of farms)
may well convey erroneous conclusions. This is not to argue that experiment stations
and farms with superior environments should not be used in a testing program.
To the contrary, when used in conjunction with average and poor environments, data
from superior environments can improve the regression estimates.
Informal observation indicates that if the range of environments in one year
approximates the range to be expected in the same region over a period of years, and/or
the magnitude of the range is at least as large as the overall mean yield, then the
regression estimates stabilize with only one year of testing.
Adherence to the concept that a regression coefficient of unity is favorable has a
separate set of implications. This figure shows hypothetical results of three varieties that
have been tested over an appropriately wide range of environments. In this hypothetical
case, all three have the same overall mean yield and s2di = 0. The regression coefficients
are 1.5, 1.0 and 0.5 for varieties A, B and C, respectively. In the absence of other
disqualifying characteristics, variety B (the most generally adaptable according to Finlay
and Wilkinson, or the most stable according to Eberhart and Russell) would be selected
based on the value of the regression coefficient. The argument against variety A is that
because it has a coefficient much higher than unity, it is too sensitive to environmental
change and does poorly in poor environments. Variety C, because it has a coefficient
much lower than unity, is unable to exploit high-yielding environments. Therefore,
variety B, which is not superior in any environment, is chosen as the best of the three.
Notice that the argument against variety A with a high coefficient, moves from right to
left or toward low environments (it does poorly in poor environments). The opposite is
true of the argument against variety C with a low coefficient, which moves from left to
right or toward high environments (it is unable to exploit good environments). These are
negative interpretations which lead to the rejection of these varieties for all
environments and the selection of variety B for all environments even though it excels in
If the emphasis regarding varieties with a high regression coefficient were toward, rather
than away from the best environments (which variety can exploit the best
environments?), variety A would be selected. Likewise, if for varieties with a low
coefficient, emphasis were toward (rather than away from) the poorer environments
(which variety can maintain yield even in poor environments?), variety C would be
selected, as in this figure.
The difference is not one of analytical procedure, but of philosophy, goal and/or attitude
toward selection. The impact on a breeding program could be dramatic.
The farming systems research-extension approach to technology development
was a response in the early 1970s to a worldwide concern with the plight of small-scale,
resource poor farmers, particularly in the third world, who were not benefitting from the
international public and private investment in agricultural research, extension and
technology development efforts.
The methods developed to reach these clients revolved around a multidisciplinary
approach based on the philosophy not that these farmers resisted new technology and
change, rather that the technology being offered was not appropriate to their conditions.
Early thoughts that social scientists (anthropologists, sociologists and economists) were
necessary to help "sell" modem technology to these farmers (that is, "convince" them to
use it) were soon changed.
It became apparent that the production environments on their farms were as much a
product of the socioeconomic factors facing these families as of the biophysical
conditions associated with the often unfavorable locations of their farms. This meant
that technology had to be tailored to the socioeconomic (date of planting, pest control
practices, fertilizer use, soil preparation) as well as the biophysical (soils, climate,
altitude) factors affecting the environments of these farmers. It also became apparent
that technology testing had to be carried out under the wide range of environments
represented and that recommendations varied depending on the nature of the
That is, "scale neutral" does not apply just to field size. It also applies to the whole
package of resources available to the farmer. An important characteristic of farming
systems methodology is extensive use of on-farm evaluation under the biophysical and
socioeconomic conditions faced by the farmers who are expected to adopt the varieties
or hybrids and practices being developed. As early in the technology development
process as feasible, the new material or practices are evaluated under the "good" and the
"bad" that farmers are able to provide. This reduces the possibility of eliminating
technology that would be superior in poor as well as in good environments.
How early in the development process new genetic material is placed in on-farm
evaluation depends on the breeders who must supply it to the persons who conduct the
on-farm testing. In the most successful programs the field testing teams are considered
as essential elements of the breeding program and the breeders participate actively in the
on-farm activities. Without these field teams, it would be difficult for the breeders to
have the opportunity for widespread evaluation of their material under the conditions of
the small farmers who are the principal clients of the institute. Responses in the field
help orient the direction of the breeding program. In the least successflrograms, field
testing teams have no official connection with the breeding program..-.hey have access
only to material that has already been released by the breeding program. There is little
or no feedback of the results of the field testing program to the breeding program, and
there is a low rate of adoption of the tested materials which seldom excel in the
conditions of the farmers. Even worse, there is a continual loss of genetic material
which might have excelled under the farmers' conditions.
The number and range of environments under which on-farm tests are conducted depend
on the testing budget (personnel, transportation and materials); the philosophy of the
breeding and/or testing program leaders; and crop production conditions. The range and
distribution of environments is probably more important than number so long as some
minimum number (probably around 10) is achieved.
As mentioned above, the magnitude of the range of environments divided by the overall
mean yield is a useful measure as a basis for evaluating adequacy. A quick search
resulted in locating the following ratios:
A potato study on agronomic practices from 20 farms by the International Potato Center
in the Mantaro Valley of Peru: 1.42/1.
A maize variety study conducted by the farming systems program in Paraguay on 24
A maize variety and fertilizer use study conducted on 14 farms by the farming systems
team in Malawi: 1.57/1.
These compare with a ratio of 0.55/1 in the Yates and Cochran study when years are
combined (as the authors did) or 0.83/1 when years and locations are separate,
and ratios of 0.39/1 up to 0.78/1 in a study on peanut production in stress and non-stress
environments over four years reported by Knauft and Gorbet. Other cited articles do
not show data. However, Eberhart and Russell's recommendation of minimizing
genotype-by-environment interaction and then selecting the highest yielding materials
would have the tendency to reduce this ratio by either reducing the environmental range
used for testing or increasing the overall mean of the materials tested, or both.
The conclusions of this paper are based on the premises that:
1) in the developing world, there is an urgent need for a diversity of genetic
materials, as well as other agricultural technologies, that excel in the poorest as
well as the best farm environments; and
2) in the developed as well as the developing world, the concern for achieving a
more sustainable agriculture, also creates a need for some agricultural
technologies that excel in inferior environments and not just those which function
well only in favorable environments.
These needs can be satisfied most efficiently by
1) enhancing and taking advantage of genotype-by-environment interaction, rather
than discouraging, depressing or rejecting it;
2) subjecting new materials as early in the development process as possible to
poor as well as good farm conditions; and
3) searching for those materials which have the capability to maintain productivity
in poor environments or which excel in superior environments (low and high
regression coefficients, respectively) rather than suppressing them in favor of
materials with a regression coefficient of unity.