Title: Modified stabilty analysis and on-farm research to breed specific adaptability for ecological diversity
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Title: Modified stabilty analysis and on-farm research to breed specific adaptability for ecological diversity
Physical Description: Book
Creator: Hildebrand, Peter E.
Publisher: Food and Resource Economics Dept., Institute of Food and Agricultural Sciences, University of Florida,
Publication Date: 1990
Copyright Date: 1990
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DRAFT 01/03
MODIFIED STABILITY ANALYSIS
AND ON-FARM RESEARCH
TO BREED SPECIFIC ADAPTABILITY FOR ECOLOGICAL DIVERSITY1

Peter E. Hildebrand2

ABSTRACT

In this article it is argued 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 environments. These four factors are: 1) statistical
dependence on ANOVA that leads to the concern with reducing genotype-by-environment
interaction. This in turn leads to 2) 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 3) the capability of many farmers in the developed world to use their
resources to modify unfavorable environments. The final factor has been 4) the widespread use
of a regression coefficient of unity as a measure of stability when evaluating environmental
effect on genotype. To enhance genetic diversity and to provide materials that excel in poor as
well as favorable environments in order to help achieve a more sustainable agriculture worldwide
it will be necessary to 1) capitalize on genotype-by-environment interaction rather than depress
it, 2) subject new materials as early in the development process as possible to poor as well as good
farm conditions, and 3) search for materials with low and high regression coefficients rather than
suppressing them.























1 An invited paper presented at the symposium on Genotype-by-Environment Interaction
and Plant Breeding, February 12 and 13, 1990, Louisiana State University, Baton Rouge.

2 Professor, Food and Resource Economics Department, University of Florida, Gainesville,
FL 32611.









DRAFT 01/03


MODIFIED STABILITY ANALYSIS
AND ON-FARM RESEARCH
TO BREED SPECIFIC ADAPTABILITY FOR ECOLOGICAL DIVERSITY1

Peter E. Hildebrand2


For more than a half century (Yates and Cochran, 1938), agricultural scientists, and in
particular plant breeders, have been using some form of stability analysis (Eberhart and Russell,
1966) to evaluate germplasm over different locations and environments. Seed companies have
also used stability analysis in their breeding and evaluation work for a number of years (Bradley
et al., 1988). Recently, persons in developing countries and working with farming systems
research and extension methodologies (Hildebrand and Poey, 1985) 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 works cited above use a common methodology to
find superior technology, the reasons for its use, the philosophy behind it and the results differ.

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. 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.

"The traditions of farming have usually called for deciding on a crop variety and
pushing and pulling on the environment until it grows that variety. But in the
past few years, an increasing number of scientists have been approaching the
problem of growing crops at the extremes of climate and soil type from the other
side of the problem. Rather than figuring out what has to be added to the soil or
which way to shift the planting date, they have been adapting the crops --
breeding them to naturally suit the circumstances instead of manipulating their
environment." (J. K. Kaplan, 1989).

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 (Hildebrand, 1984) 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 the philosophical history of stability analysis is
discussed. Then the effect of this history on plant technology development is argued. Finally,


1 An invited paper presented at the symposium on Genotype-by-Environment Interaction
and Plant Breeding, February 12 and 13, 1990, Louisiana State University, Baton Rouge.
2 Professor, Food and Resource Economics Department, University of Florida, Gainesville,
FL 32611.










the farming systems philosophy is discussed and the use of stability analysis in this approach is
presented.

PHILOSOPHICAL HISTORY OF STABILITY ANALYSIS


Yates and Cochran (1938), 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." (p. 557). In fact, they argue that ". . the deliberate inclusion of sites
representing extreme conditions may be of value." (p. 558). However, the primary concern in
their seminal article 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, and did so
apparently without knowledge of the seminal article (1963, p. 745). 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, Fig. 1.
They define varieties with general adaptability as those with average stability (b = 1.0) when
associated with high mean yield over all tested environments. 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 (p. 749). 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
adaptability" (p. 752). 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 (1966), 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" (p. 36). 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 (b 1), or what Finlay and Wilkinson called average
stability, and a minimum of deviations from regression where S2 d = 0, or nearly so (P. 38).










EFFECT ON PLANT TECHNOLOGY DEVELOPMENT


Hvyothesis
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 environments. The four factors are:

1) statistical dependence on ANOVA that leads to the concern with reducing genotype by
environment interaction, which in turn leads to

2) 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';

3) the capability of many farmers in the developed world, over the last few decades, to
use their resources to modify unfavorable environments; and

4) widespread use of a regression coefficient of unity 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.

Discussion

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 (McIntosh, 1983). 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 (thereby making the results
non-publishable) 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.

The result of the above tendencies is that much testing of germplasm is carried out under
favorable conditions and superior environments. This reduces the range of environments in
which the materials are evaluated and limits the validity of the results to similar favorable
environments. In discussing pre-1980 corn (Zea mays L.) selection procedures by seed
companies, Bradley et al. (1988) state:

"High-yield environments were chosen for testing locations because yield was the
trait of most interest and because high yields gave low coefficients of variation.
Outliers (values exceeding the researcher's expected data set) were often excluded
from the data summaries. Stress environments were avoided when choosing
locations or, if they occurred due to seasonal stress, they were often excluded
from data summaries. . As a result of past performance testing systems, hybrids










introduced to the product line generally had high yields in high-yielding
environments. They also had many defensive problems such as barrenness, broken
stalks, dropped ears, and root-lodging because these traits are most likely to occur
in stress environments, which were least likely to have been sampled or reported...
Customer acceptance of most hybrids was poor because the marketplace itself was
often used to evaluate the hybrids" (pp. 34-35).

There are two consequences of this type of approach. First, 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. Second, the 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. Figure 2 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 none, Figure 3. 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, Figure 4. 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.


FARMING SYSTEMS RESEARCH-EXTENSION

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
(Gilbert et al., 1980; Simmonds, 1985; Shaner et al., 1982; Merrill-Sands, 1988; U.S. Congress,










1988; Byrnes, 1988; Chapman and Brown, 1988). The methods developed to reach these clients
revolved around a multidisciplinary approach based on the philosophy DnO 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" modern 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 environment. The term "recommendation domain" (Perrin et al.,
1976), commonly used by farming systems practitioners, reflects this pairing of technologies with
environments.

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 (for example, ICTA in Guatemala), the field testing
teams are considered as essential elements of the breeding program and the breeders participate
actively in the on-farm activities (McDermott and Bathrick, 1982; Ruano and Fumagalli, 1988).
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 successful programs, field testing teams have no official connection with
the breeding program. They 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 I) the testing budget (personnel, transportation and materials); 2) the philosophy of the
breeding and/or testing program leaders; and 3) annual 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 divided by the overall mean yield is a useful measure to use as a basis for evaluating this
result. A quick search resulted in locating the following ratios:

1) A potato study (reported in Caldwell and Taylor, 1987, p. 353) on agronomic practices from
20 farms by the International Potato Center in the Mantaro Valley of Peru: 1.42/1.

2) A maize variety study (Poey, n.d., p. 58) conducted by the farming systems program in
Paraguay on 24 farms: 1.21/1.

3) A maize variety and fertilizer use study (Hansen et al., 1982) 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. 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.

CONCLUSIONS AND RECOMMENDATIONS


Basic premises of this paper are 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, with the concern for achieving a
more sustainable agriculture, there is a need for agricultural technologies that respond to a
highly variable set of environmental niches created by socioeconomic as well as
biophysical factors.

Conclusions

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.




REFERENCES

Bradley, J. P., K. H. Knittle, and A. F. Troyer. 1988. Statistical methods in seed corn product
selection. J. Prod. Agric. 1:34-38.

Byrnes, K. J. 1988. A review of AID experience: Farming systems research and extension
(FSR/E) projects 1975-1987. Proceedings of Farming Systems Research/Extension Symposium.
University of Arkansas. pp. 363-368.

Caldwell, J. and D. Taylor. 1987. Analysis and interpretation of on-farm experimentation.
FSR/E Training Units: Volume III. Farming Systems Support Project. University of Florida.
Gainesville.











Chapman, J. A., A. L. Brown and R. J. Castro. 1988. Possible future directions for AID activity
in farming systems research: A concept paper. Proceedings of Farming Systems
Research/Extension Symposium. Univeristy of Arkansas. pp. 369-385.

Eagles, H. A., P. N. Hinz, and K. J. Frey. 1977. Selection of superior cultivars of oats by using
regression coefficients. Crop. Sci. 17:101-105.

Eberhart, S. A. and W. A. Russell. 1966. Stability parameters for comparing varieties. Crop Sci.
6:36-40.

Finlay, K. W. and G. N. Wilkinson. 1963. The analysis of adaptation in a plant breeding
programme. Aust. J. Agric. Res. 14:742-754.

Gilbert, W. H., D. W. Norman and F. E. Winch. Farming systems research: A critical appraisal.
1980. MSU Rural Development Paper No. 6. Michigan State Universtiy. East Lansing.

Hansen, A., E. N. Mwango and B. S. C. Phiri. 1982. Farming systems research in Phalombe
project, Malawi: Another approach to small holder research and development. Center for
Tropical Agriculture, University of Florida. In cooperation with Farming Systems Analysis
Section, Department of Agricultural Researchl, Ministry of Agriculture, Government of Malawi.

Hildebrand, P. E. 1984. Modified stability analysis of farmer managed, on-farm trials. Agron.
J. 76:271-274.

Hildebrand, P. E. and F. Poey. 1985. On-farm agronomic trials in farming systems research and
extension. Lynne Rienner Publishers, Inc. Boulder.

Kaplan, J. K. 1989. Adapt-a-plant: Breeding plants to suit the environment. Agricultural
Research/August, 1989. USDA. Washington, D.C. pp. 16-18.

Knauft, D.A. and D.W. Gorbet. 1989. Analysis of peanut production in stress and non-stress
environments. Trop. Agric. 66:243-248.

McDermott, J. K. and D. Bathrick. 1982. Guatemala: Development of the Institute of
Agricultural Science and Technology (ICTA) and its impact on agricultural research and farm
productivity. Project Impact Evaluation No. 30. USAID. Washington, D.C.

McIntosh, M. S. 1983. Analysis of combined experiments. Agron. J. 75:153-155.

Merrill-Sands, D. and J. McAllister. 1988. Strengthening the integration of on-farm client-
oriented research and experiment station research in national agricultural research
systems(NARS): Management Lessons from nine country case studies. ISNAR. The Hague,
Netherlands.

Perrin, R. K., Winkelmann, D. L., Moscardi, E. R. and Anderson, J. R. 1976. From agronomic
data to farmer recommendations: An economics training manual. Mexico: CIMMYT.

Poey, F. n.d. Anatomy of on-farm trials: A case study from Paraguay. Farming Systems
Support Project Training Materials series #603. University of Florida. Gainesville.

Ruano, S. and A. Fumagalli. 1988. Guatemala: Organizaci6n y manejo de la investigaci6n en
finca en el Institute de Ciencia y Tecnologia Agricolas (ICTA). OFCOR Case Study No. 2.
ISNAR. The Hague, Netherlands.

Shaner, W. W., P. F. Philipp and W. R. Schmehl. 1982. Farming systems research and
development: Guidelines for developing countries. Westview Press. Boulder.









8

Simmonds, N. W. 1985. Farming systems research: A review. World Bank Technical Paper
Number 43. Washington, D.C.

U.S. Congress, Office of Technology Assessment. 1988. Enhancing agriculture in Africa: A role
for U.S. development assistance. OTA-F-356. U.S. Gov. Print. Office. Washington, D.C.

Yates, F. and W. G. Cochran. 1938. The analysis of groups of experiments. J. Agric. Sci.,
Camb. 28:556-580.

24/dec 1.02B



















>1 ENVIRONMENTS


BELOW
AVERAGE STABIUTY

.0 POORLY ADAPTED TO VEA TAI WELL ADAPTED TO
SALL ENVIRONMENTS AVRT ALL ENVIRONMENT

| ABOVE
AVERAGE STABILITY


<1 SPECIFICALLY ADAPTED
TO UNFAVOURABLE
ENVIRONMENTS



Variety mean yield



FIGURE 1
RELATIONSHIP OF VARIETY REGRESSION COEFFICIENT AND
MEAN YIELD TO STABILITY ACCORDING TO FINLAY AND WILKINSON (1963)


DATA/YATES4






































1 2 3 4 5


Environmental index, e



FIGURE 2
HYPOTHETICAL RESPONSE OF THREE VARIETIES
TO ENVIRONMENT


DATA/YATES5.DRW


Yield


t ha -1














6
A

5 B

C
4

Yield
3
t ha -1

2


1


0
0 1 2 3 4 5 6
Environmental index, e


FIGURE 3
Negative interpretation of the response of
varieties to environment resulting in choice of variety B
for "broad adaptation".


DATA/YATES6














A

5 B


4

Yield
tha-1

2


1


0
0 1 2 3 4 5 6
Environmental index, e


FIGURE 4
Positive interpretation of the response of varieties
to environment resulting in a choice of variety A for the
better environments and of variety C for the poorer environments.


DATA/YATES7




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