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Recommendation domains in the CIMMYT on-farm research strategy
Guidelines for domain formation
Issues and complications
Recomre~ndat-ion Dom~atins: A F~ramewdork for
* Economics Program, CI'iYT The opinions exoressed are not
necessarily th~Iose of CIIP1ffr
We wish to acknowledge the contribution of numerous colleagues in
the developmet~dnt of this paper. The phrase recommendationn domain" was
originally used in the first CIlUffl' Economnics btinual (Perrin et al,
1976) P. Anandajayasekeram, Derek Byerlee, Mike Collinson, Greg
Edme~ades, Dan Galt, Edith Hesse, Alberic Hibon, Ron Knapp, Allan Low,
A.F.E. Palmer, David Rohrbach, Gustavo Sain, Don Winkelmann and Michael
Yates provided useful cosments on earlier versions but, of course, are
not responsible for remaining errors or deficiencies. Maria Luisa
Rodriguez cheerfully typed numerous drafts and this final version.
1.0) IntrYoduction.............................................. 1
2.0) Recsmnendation Domains in the CIM1YT On-Farm Research Strategy. 2
3.0) Definitions......................................... 4
4.0) Guidelines for Damain Formation...................... .......... 6
5.0) Issues and Complications...............,.............,......... 20
6.0) SummarLTt-y................................................. 23
Bibliography. ............................................. 26
Many national agricultural research programs are moving toward the
adoption of on-farm research techniques. This irp~lies a
location-specific research program for representative farmers. Anmng
the challenges that scientists face in this type of research are the
identification of priority themes for investigation, t~he selection of
representative sites for on-farm experimentation, and, m~ost important,
the definition of the clientele for whom the recomnenda~t-ions are to be
developed. The concept of the "recommendation domain" is a powerful tool
for resolving these problems and for organizing an efficient on-farm
The term recommendationn domain" was first introduced in the CIml.1YT
Economics manual on the use of partial budgets for economic analysis of
agronomic data (Perrin, Winkelmann, Moscardi and Anderson, 1976) In
this manual, the recommendation domain was described as follows:
"It is impossible to conduct experiments on each farm to make
recommendations tailored to each farmr. Instead, you mulst define a
target group of farmers, conduct experiments under conditions
representative of their farmns, and make recommendations which are
applicable to the entire group. We shall call such a group of
farmers a recommendation domain. Generally, a recommendation domain
will consist of fa!rmerswithi an agroclimatic zone whose farms are
similar and who use similar practices..." (p.1) .
Further discussion of the recommendation domain concept was
presented in the second CIM1YT Economnics manual, on assessing farmers'
circumstances (Byerlee, Collinson, et al, 1980) In this manual, the
recommendation domain was defined as "a group of roughly homodgeneous
farmers with similar circumstances for whom we can make mocre or less the
same recom~endat~ion" (p.71) .
The aim of this paper is to discuss in more detail the concepts and
procedures associated with forming recourne~ndation domains. First, the
The term "on-farm research" will be taken to me~an "research with a
farming systems perspective, using on-farm research techniques".
For a discussion of concepts and vocabulary associated with on-farm
research, see Byerlee, Harrington and Winkelmann, 1982.
need for domains will be discussed with emphasis on their operational
role in OFR. Then the recournendation domain concept itself will be
examined and techniques for forming domains will be presented, followed
by a discussion of issues and complications involved in the practical use
of domains in on-farm research.
2.0) Recomnendation Dma~iins in the CIl41YT On-Farm Research Strategy
Over the past several years, CIMMYT agronomists and economists have
developed a set of procedures for m~rultidisciplinary, on-farm research
with a farming systems perspective. -These procedures are designed to
be used by biological scientists, social scientists and farmers, in order
to derive appropriate recournndations. They include the following series
of steps: the diagnosis of fanrmers' circumstances, the design and
management of on-farm experiments, the analysis of experimental results,
and the presentation of recommendations to farmers. The concept of the
recommendation domain is vital to every one of these steps of on-farm
2.1) Diagnosis .
The early stages of on-farm research are concerned with diagnosing
farmers' practices and problems and identifying opportunities for on-farm
experimentation. The diagnosis begins with a review of secondary data
and talks with local officials, extension agents, etc. Then researchers
carry out an exploratory info-rmal survey of farmers. This may be
followed by a formal survey with a short questionnaire. It is at this
time~ that researchers muRst propose at least tentative recommendation
domains. The delineation of the domains helps answer the following
questions: What are the principal research opportunities in this area?
What are the target crops that deserve first attention? Are target crops
and opportunities the same throughout the area, or are there significant
differences? And most importantly, on what themes should research
concentrate in order to derive useful reccxmelndations for farmers in the
shortest time possible?
-See Byerlee, Collinson et al (1980); Palner, Violic and Kocher
(1982); Perrin et al (1976); and Violic, Kocher and Palmer (1981) .
2.2) Design of Experiments
Once an experimental program has been defined there are a number of
issues with regard to actually setting up on-farm experinrents that must
be addressed through reference to recommendation domains.
What is a representative site and a representative farmer cooperator
for an on-farm trial? No fa~rme~r or site is ever completely
representative but many mistakes in selecting cooperators may~ be avoided
by careful attention to the current characteristics and- practices of
farmers. Clearly, a site should be representative of the conditions of
the recomnendation domain that is being studied.
At what levels should non-experimental variables (fixed factors) be
set? In any experiment, experimental variables are distinct from fixed
factors; the former vary over trea-tments within an experiment but the
latter do not. Nonetheless the (unvarying) level of each fixed factor
must be chosen. CIMMYrT OFR procedures (e.g. Palmeur, Violic and Kocher,
1982, p. 12; Moscardi et al, 1982) advocate setting fixed factors at
" rep~re.sentative": levels, so that on~-fiarmi experiments may measure the
yields and profits that farmers can expect when they superi~p~ose one or
more of the treatments on top of their own current practice. `Once again,
"representativeness" can be defined with reference to a given
recommendation domain: Fixed factors should be set at levels
representative of those for the domain being studied.
Researchers must analyze the experimental data in order to formulate
farmer recamlendations. Three kinds of analysis are usualy needed: (1)
agronomic analysis (how may observed responses be explained in terms of
biological and physical processes?) (2) statistical analysis (arse
observed responses real or due to random chance?) (3) economic analysis
For a more comprehensive discussion of issues related to site
selection and the level of non-e~xperim~ental variables, see Tripp,
(which alternative technologies will be preferred by farmers?)
In doing these kind of analyses pooling the data is generally
recamended. Data from trials within the same domain should be pooled
but data from different domains ~should be! analyzed separately.
The ultimate purpose of on-farm research is of course to derive
recommendations for farm~ers. If the concept of the recamendation domain
has been followed faithfully, then by the time recommendations are ready
for farmers, extension agents know exactly who their targets are. Using
recomendation domains helps avoid two equally unpalatable alternatives:
(1) offering a different recamesndation for each farmer (too expensive)
(2) offering a single recommendation for the whole fairer population,
despite differences among farmers (inappropriate for many farmers) .
Instead, recamendation~s are derived and offered with well-defined groups;
of farmer clientele in mind.
2.5) The, Policy Cont:ext
At any point in the on-farm research process the use of
recommendation domains allows researchers to be able to spell out which
groups of farmers they are working for, approximately how many farmers
are in each group, what their principal practices and problems are, and
what types of recamendat~ions are likely to be produced. This is a great
help in developing good relations between researchers on the one hand,
and institutional or national policy makers on the other. Not only does
.this kind of information help researchers in the allocation of their olwn
research resources but it also makes them much more articullate .in their
interactions with those who set research policy.
Befor-e proceeding, some definitions are in order.
For a review of partial budgeting techniques for economic analysis
of agronomnic data, see Perrin et al, 1976, and Harrington, 1982.
Recommendation domain has already been defined as a group of farmers
whose circumstances are similar enough so that they are all eligible for
the same recomrmendation. It should be emphasized that the domain is a
group of farmers, not necessarily a geographical area or land type.
Damains are composed of farmers because farmers, not land types, take
decisions on new elements of technology. Defining domaiins in terms of
groups of farmers underlines the possible importance of socioeconomnic
criteria in domrain identification. It also alloJws the possibility of
domain distinctions that are not a~e~nable to mapping (neighboring farmers
can belong to different domains or, as well, a given farmer can belong to
mo~re than one domain) .
It usually happens that there are a number of research opportunities
for a particular commo~dity, or even for several commodities, that a group
of farmers have in commorun. These opportunities should of course be
considered together, taking account of their interactions and relative
importance as plans for a research program evolve. It is natural to
think of the group of farmers that share these opportunities as a single
recommendation domain. BuLt because ~two groups of farmers miay~ share samecu
opportunities, but not others, it is well to remember that a
recormiendation domain is really specific to a particular enterprise and a
particular research problem. That is, our interest is in defining the
group of farmers for whom a specific recommendation is applicable.
Research area in this paper will simply mean the area in which
investigation is to take place. This is usually defined by the research
institution and may have administrative or agroclimatic boundaries.
Although the concept of recommendation domain is often quite helpful in
refining these boundaries, we will assume~ here that the research area is
given. Our job is .to take the mandated research area and decide how it
should be divided into recommendation domains.
Farmetrs' circumstances are used in~ order to identify recommendation
domains. They are defined as "all those factors which affect farmers'
decisions with respect to use of a crop technology. They include natural
factors such as rainfall and soils, and socioeconomnic factors such as
markets, the farmers' goals andi resource constraints" (Byerlee,
Collinson, et al 1980: 70) Figure 1 shows how circumstances may affect
farmers' practices and their abilities to adopt new recommendations.
FIGURE 1 FARMERS
Byerlee, Collinson, et al 1980.
!n:corne, fo:od plref-erences,
Land, labor, cap~ital
Ov.Erall Farmfing:: System
Croppingir Pa;tterni, Crotvtion, Foodu
Supply, Labor H~iring3, etc.~
---FCircumstances which are often major sources of uncertainty for decision-making.
A recermnendation is a description of a new element in a production
technology (an improved variety, a new chemical, a different practice, a
change in the timing of an operation, etc.) which researchers believe
fanners will use. In the case of the on-farm research paradigm described
here it is derived from an understanding of farmers' problems and a
thorough testing under fanrers' conditions. Recoirnendations are
somewtimres made in groups or "packages", as when a new variety is
recommended along with a certain planting density, insect control and
fertilizer level. The emphasis here, however, will be on recommendations
that farmers can adopt in a step-wise fashion. There is norw considerable
evidence that farmers are more likely to adopt sirp~le recamnendations and
make changes gradually, rather than mraki abrup~t, large-scale changes
which require them to alter their practices drastically (e.g. Byerlee and
Hesse de Polanco, 1982) Thus on-farm research identifies and tests
technologies with a limited number of new elemetnts under farmers
conditions, to assure that recormmendations can be accommodated by
The process of domain formation is usually a gradual one, as
researchers gain m~ore experience in their area. Although there is no
unique formula for determining domains there are a set of guidelines that
can be used. These are discussed in the following sections.
4.1) Principles of Recommaendation Domain Formation.
Recnmnendation doarins are formed based on the researcher' s
understanding of farmers' circumstances and practices. Som~et-imes the
identification of domains can be achieved in the early stages of
diagnosis, after examination of secondary data and conversations with
extension agents, for instance. At other times they are not really well
defined until after a formal survey, and occasionally the final
delineation of recommendation domains must await the results of a year or
more of experimentation. But from the very beginning of the process
researchers should at least begin forming impressions about possible
domains. These impressions are tested and refined as the on-farm
research progresses, until a final definition of the domains in the
research area is established.
The concept of farne~rs' circumstances is used both for identifying
opportunities for investigation and for forming recommendation domains.
An understanding of farmers' circumstances allowEs the researcher to
explain current farmer practices, identify key problems and propose
improvements that can be tested on farmers' fields. It also provides the
researcher with an idea of whether or not a particular improvement is
appropriate for all farmers in the research area or only for sone~.
There is a sense in which the formation of recommendation domains is
related to the statistical concept of stratification. The statistician
stratifies a sample in order to eliminate certain types of variability
and better concentrate on the particular factors under study. In forming
recommendation domains we are grouping farmers who have roughly
homogeneou-s circumstances and whose needs for technology are thought to
be similar. Through that grouping we are eliminating from consideration
elements which we believe would imply the need for modifications in
Recommendation domain formation can be thought of as a -process of
considering all the various circumstances that might affect farmer
practices and deciding, for each one, if it is the basis of significant
differences in practices and possibilities within the research area. One
way of making this operational is to think of a checklist, such as that
in Table 1, which lists major categories of circumstances that may be
used to define recommendation domains. The list is by no me~ans complete,
and researchers working in different areas will surely add other factors
to this list. It will also be appreciated that many of these factors are
interrelated: altitude affects temperature and frost incidence, for
instance, and rainfall affects weed population.
Several examples may make clear horw the variables on this checklist
can be used to define recommendation dam?-ains. Consider the case of soil
differences, which are of ten important in determining farmer practices.
In one research area in southern Veracruz, Mexico, there were two basic
soil types. Farmers in the river floo plain had alluvial soils and grew
wet season vegetables and dry season maize. Neighboring farmers had
sandy, acid soils and grew pineapple and maize, in the wet season only.
The difference in soils is responsible for very different maize practices
and problems with respect to such factors as moisture stress, disease and
insect i~ncidence, and fertility requirements. Recommaendations about
maize appropriate for one group would not likely be appropriate for the
other. Thus we have two separate recommendation domains for maize, in
this case determined by soil type.
TABLE 1 VARIABLES OFTEN CONSIDERED IN FORhIlNG RECXOMMIENDATION DOMINS.
Riiskv of Drough~ct
Risk of Flooding
Access to Markets and Inputs
Access to Other Labor
Access to Credit
Access to Cash
Access to Mlarkets for
Access to Irrigation
Off-Fiant Labor Opportuniti~es
Food Preferences and Diet
Community Customns and
There are often differences in soils within a research area. Does
this me~an they will always correspond to different recournendation
dolmains? No, not at all. In another part of Mexico, in a highland
barley area, soil type varied fromn clay loam to sandy loam, and
researchers hypothesized that these might cause different domains. But a
closer study of the area revealed no significant differences in farmers'
practices or problems by soil type, and researchers realized that they
were either dealing with a single domain, or that another circumstance on
their checklist besides soil type might be used for distinguishing
Another natural circumstance that may lead to significant
differences in practices and research opportunities is altitude. In part
of the Callejon de Huaylas in Peru, maize researchers identified two
recommendation domains, based on altitude. In the lower domain, from
2,600 to 3,000 meters, farmers could plant two crops a year and had
serious problems with leaf fungus diseases. In the higher doaMin, above
3,000 meters, only: one crop a year was possible and one of the principal
parole tat arersfacd as rot dmag t thirmaie.Altitude
here serves to distinguish two domains, with different maize practices,
problems, and opportunities for research. Again, altitude will not always
serve to distinguish rece~mnendation domains. If variation in altitude is
not associated with significant differences in farmer practices then it
can be crossed off the checklist.
The sam~e holds true for other natural circumstances. In their study
of farmers' practices and problems researchers will want to ask whether
such things as rainfall pattern, slop~e, or pest incidence can be used to,
define different recournndation domains. Important factors are of course
not limited to natural circumstances, and Table 1 presents a number of
socioeconomic circumstances that may also be useful in identifying
dam~ains. An example or two may be helpful.
It is often the case that farmers who share the same natural
circumstances nevertheless have different access to resources which
affects their practices and their ability to adopt innovations. In one
area in Zimbabwe maize farmers prepared their plots with ox plow~s before
planting. As only about: half of the farmers owned oxen the3 rest had to
rent them. The renters were delayed in their planting, which affected
their production through drought risk, disease, and' other factors
specific to late planted maize. There are obviously a series of research
opportunities for animal renters which are not applicable to owners, and
thus it is worth considering two recommendation domains, distinguished by
Another case will provide a counter-examp~le. In a research area on
the north coast of Honduras most farmoers controlled weeds in the maize
crop with herbicides, but only one-third of the farmers owned backpack
sprayers. Researchers believed there might be a difference in weed
control practices between sprayer renters and owners. But a survey
showed no differences in weed control practices or timing between the two
groups and revealed that the rental market for backpack sprayers was
quite adequate. Thus access to a sprayer did not affect farmer practices
an~d did not serve to distinguish reconvend3nation domains.
Farmers can also of ten be distinguished by access to land.
Differences in farm size may not only directly affect the type of
practice that a farmer follows, but may also be correlated with many
other differences, such as access to equipment, credit, or marketing
facilities. At times these distinctions are quite clear and are
responsible for the formation of different recommendation domains. In
parts of the highlands of Ecuador small and large wheat farmers occupied
the same natural environment, but their soc~ioecnornoicr circumstancs were
quite different. The former relied on animal traction and had no access
to credit, while the latter used tractors and credit facilities (which
lowered their costs for obtaining fertilizer. ) This led to quite
different practices (e.g. different rotations and fertilizer treatments)
and these in turn indicated different research opportunities. The result
is ~two recommendation domains in a biologically homogeneous area one of
small wheat farmers (under 5 ha) and the other of large wheat farmers.
It is of course not always the case that farm size is a determining
factor for domain formations. Researchers want to ask if two farmers in a
given region with different size farms use essentially the sam
technology for a particular enterprise and if they have access to the
same type of resources and m~arkets. Do they use the samea variety, the
same seeding techniques, the same seeding dates, the same fertilizers,
etc. ? If there are differences, then there may be two domains. If there
are no significant differences, then farm size will not be used in
defining dom~ains. In this case researchers will go on and ask the same
questions about other natural and socioeconomuic circumstances on their
checklist (Table 1) If farm size is not important, does altitude or
soil type or land tenure serve to distinguish farmers' practices and
problems? If not one or more of these factors, what else on the
checklist might define differentL domains? As researchers gain mosrre
experience in domain formation they will probably rely less on a formal
checklist. But the process is always the same, considering howr a series
of circumstances affect how a fa~rmer undertakes a particular enterprise.
In the examles considered so far a single factor (e.g. altitude or
farm size) has been used to divide a research area into recommendation
damaEins. But it is not always the case that only one factor influences
farme~r practices and research opportunities. Researchers must exhaust
the- possibilities on their checklist in the search for relevant
circumstances for defining domains. An example of maize research in Peru
was discussed earlier, in which researchers identified two domains, based
on altitude. In fact, the actual situation was mrre complicated, as
there were other important differences in farmers' circumstances in the
research area. In the lower zone there were two principal farm types -
small farms averaging less than 2 hectares and large farms averaging 40
has. These two farm types had quite different, patterns of rotation,
input use, varietal requirements and maize sales. In the higher zone
there were not such marked differences in farm size, but sam~e farmers had
access to irrigation while others did not (all farmers in the loJe~r zone
had irrigation) Th~is was responsible for significant differences in
rotations and input use. Thus there were actually four different
recommendation domains in the research area, based on altitude, fann size
and access to irrigation.
In order to form reconn~endation domains researchers nust study the
circumstances and practices of farmers in their area. Using ,a checklist
of circumstances they can consider in turn various possibilities for
defining reconnmendation domains. It may be that the area is hamageneous
enough to constitute a single recaarnendation domain. If not, there are
usually one or at mrost a few key circumstances that can be used to define
domains. This is not to say that the differences between the domains are
necessarily simple, but only that there should be a relatively
straightforward way of identifying and describing them.
In the case of the two damains fornned by differences in altitude,
researchers are not so rmuch interested in altitude per se but rather in
the way altitude is responsible for detennining two quite different,
comrplex patterns of disease and pest incidence, cropping cycle and
varietal preferences. It is these factors that determine the practices
It is these factors that dictate two separate sets of on-farm experiments
for researchers. Delimiting the two domains in terms of altitude is
simply- a convenient way of identifying the domains and helping
researchers to plan their work. It may be that the distinction between
altitude zones is even correlated with other factors such as population
density, with lower densities at the higher elevations. This would lead
to differences in rotation patterns and soil fertility between the two
domains, even though there is no a p~riori relationship between altitude
and rotation. Again, the denomination of the high elevation and the larw
elevation domains is a convenient way of describing a whole series of
different circumstances arrong two groups of farmers.
It is often asked if this process of domain formation is adequate
for covering all fanrmers in a research area. Will there not be a few
fanrmers in the high elevation domain, for instance, whose practices are
different from the rest? Or might there not be someu farmers whose
circumstances are in between the two altitude zones? There may well be,
but are there enough such farmers to make it worthwhile to form separate
recommendation domains? Recall that dHmains are formed so that
researchers can effectively deal with the majority of farmers in a
particular area. The selection of good criteria for dcamain formation
will result in a few large domains, each roughly homogeneous with respect
to major research opportunities and current production practices, with
distinct differences between domains. There may be some farmers who are
not covered by the definitions, but formning special domains for them
might not be a wise use of research resources.
4.2) Policy Variables in Recomrmendiation Domain Formation.
The question of which farmers should be addressed by an experimental
program is not only related to research efficiency, but also to policy.
If several domains have been identified it is often necessary to decide
which ones will receive attention. Veryr often national policy will
contribute to making these decisions, as priority may be given to certain
types of farmers (small farmers, comnercially-oriented~ farmers, etc.) or
to certain types of crops (basic grains, cash crops, etc.) As research
policyr is usually cnc:erned with obta~cining high benfits from a given~i
research investment, this also often implies concentration on domains
that contain the largest numbers of farmoers and present the most
promising opportunities for improving productivity.
The relationship between policy and on-farm research is not one-way,
however. There are substantial opportunities for feedback from on-farm
research to policy makers. In the case of recolnmendation domains there
is the opportunity for providing policy makers a mruch clearer idea of the
nature of the farming population. Very often policy mandates are stated
in terms of "target groups" whose definition (e.g. "the small farmers of
region X") masks considerable variation in circumstances and potential.
Dividing the research area into recommendation domains can contribute to
murch more precise targeting.
4.3) Acquiring Data for Donrain Formation.
There is an apparent paradox in the definition of a recarme~ndation
domain. If it is defined as a group of farmevrs whose circumstances are
similar enough to make them eligible for the same~ recommendation, how can~
we be sure of the constitution of the domain before the recormmendation
has been made? The answer is that we of ten cannot be completely certain,
but as the process of on-farm research passes from diagnosis through
experimentation to recormmendations researchers become6 morue and more
confident of the boundaries of their domains. From the 'beginning of the
research process hypotheses are fonred about possible domains. These
hypotheses are tested during surveys and the conclusions are used in the
design of an on-farm experimental program. At timets it i~s only after a
year or more of experiments that researchers are able to make the final
adjustments in their domains.
In order to acquire information useful for domain formation adequate
data collection methods are required. The initial diagnosis must be done
rapidly and efficiently, so that on-farm experiments can be planted as
quickly as possible. Thus elaborate studies which collect great amouunts
of detailed information are not appropirate. The idea is to identify
research opportunities and likely recommendation domains and use this
,nfsir.ormt~ion to~ - ~~ begin xriments.s Procedures for assessing~ fancer~:S'
circumstances are described in Byerlee, Collinson et al, 1980. These
procedures include a review of secondary data, an exploratory 'survey and,
often, a short, well-focused formal survey.
Initial hypotheses on variables for dividing fanrers into domains
may be developed during a review of secondary data for the research area.
Keeping in mind the checklist (Table 1) of circumstances which may a-ffec~t
domain formation, the researchers can examine the secondary data with an
eye towards identifying possible key factors. Soils maps, census reports
or other data may suggest possible sources of variation in farerus'
practices. Conversations with local extension staff can also be quite
valuable. With the initiation of the exploratory survey the evaluation
of these hypotheses may commlence. For exuarp~le, if census data indicated
three major land tenure classes in the research area the exploratory
survey could be used to ascertain whether these tenure classes had any
important effect on farmlers' practices or problems. The exploratory
survey is the time when the checklist is mrost fully utilized. By talking
to fanrers and observing their fields researchers have the opportunity to
decide which circumstances on the list are likely determinants of
differences in farmers' practices.
During the exploratory survey, development of hypotheses on
recommendation domains and hypotheses on research opportunities proceed
together. Researchers strive to understand how different circumstances
lead to different practices and problems, and whether or not these
differences are relevant to the research opportunities that have been
identified. For example, if the important research opportunities in a
maize area appear to be insect control and disease-resistant varieties,
then soil differences may not define recommendation domains. If, in the
same area, the principal research opportunities turn out to be
fertilization and moisture conservation, using the residues of the
previous crop as mulch, then the difference between maize farms on sandy
soils and those on heavier soils is probably enough to determine two
separate recommendation domains.
There nee not be any~ difference in culr~rent fa~rmer practice in order
for a particular research opportunity to divide an area into different
domains. In one area in Honduras both land owners and renters had
similar maize practices, using a maize-fallow rotation which allowed
several years between crops of maize on one piece of land. Research
opportunities for weed control and variety were the sam~e for both groups.
But in thinking about the possibility of intensifying the system by
introducing a cover crop of velvet beans which would allow several years
of continuous maize plantings, the difference between owners (who had
assured access to their plots over time) and renters (who did not) became~
important, and defined two different domains with respect to th2is
opportunity. The interaction between research opportunities and domain
boundaries is therefore quite important.
At the end of the exploratory survey the checklist has been
significantly reduced so that researchers generally have only a few
possible candidates for defining recommendation domains. The exploratory
survey is often followed by a formal survey in which random sampling and
a short, well-focused questionnaire are employed. Samples for the survey
should be drawn so that each tentative recommendation domain is
represented by at least 25-30 farmers. During the survey, information
should be collected on the "short list" of variables that are proposed
for defining recommendation domains as well as on variables that measure
key aspects of farmer practice (i.e., practices related to important
research opportunities) Cross-tabulation of "short list" variables by
farmer practice variables will indicate which criteria no~st strongly and
consistently influence the farner practice.
The survey analysis should seek to identify a small number of
domains, each as hanaogeneous as possible, which allow efficient research
on the highest priority themes. The survey may, for instance, define two
recmndatioati n domain~s with very distinct research opportunities, as in
the example of domains defined by altitude in Peru. In that case,
research on maize varieties (one of several opportunities) was oriented
by farmer responses to a question on principal problems. Those at the
lowerT altitude indicated a problem with leaf fungus disease, while those
at the higher altitude expressed interest in maize of a shorter cycle
because of frost damage.
In other cases, two domains may share at least some3 research
opportunities, but require experimentation under different conditions.
Domains that are defined by access to irrigation, for instance, may share
chemical weed contro;L in maize as a research opportunity, but different
products, levels and application methods may be indicated for each
domain. The survey is used in this case to define the circumstances that
are representative of irrigated and non-i~rrigated domansins, in order to
choose the levels of non-experim~ental variables for each domain.
Particular care mtust be taken when proposed criteria for domain
formation are proxies for actual practices and conditions. Analysis of
the survey should lead to distinct, as opposed to merely statistically
significant, differences. For example, in -one survey in a barley
producing area tractor ownership was proposed as a criterion for
distinguishing recommendation doma~ins. Analysis of the survey showed
several differences in land preparation between tractor owners and
renters. In the case of early harrowing before ploughing, for instance,
54% -of the owners, but only 41% of the renters, did a pre-plough
harrowing. Thne difference was statistically significant (at 5%) and
showed, not surprisingly, a tendency for tractor owners to do a no~re
thorough job of preparing their fields than tractor renters. Differences
of this magnitude were observed for several other land preparation
methods. They were not, however, sufficient to define recommendation
domains. Whether or not the fanrmer did an early harrowing was affected
by competing labor demands, previous crops, soil conditions, and several
other factors besides machinery ownership. Thus more effort should be
made to *specify the complex -of circumstances that conditions land
preparation methods. The single factor of tractor ownership identified
in 'the survey, although responsible for statistically significant
differences in practices, is not sufficient to divide the research area
into two clearly distinguishable domains. In the meantime, if research
opportunities are identified which interact with land preparation
(seeding methods .and timing, for instance) then research should be
carrTied ouit. for- theI major categories of land preparation, using land
preparation itself as a defining characteristic of the domains, rather
than tractor ownership, which is only a weak proxy.
Once recarm~endation domains are identified they are used as the
basis for th~e on-farm experimental program. Experimeunts are designed for
specific recommendation domains; the exact number of a certain experiment
to be planted in one domain depends largely on the type of experiment.
If it is an experiment of an exploratory nature then it may be repeated
only a few times, while if it is a verification experimetnt (the stage
just before demonstration) then it will be very widely distributed within
the reccarrmendation domain.-
This example assumes that land preparation itself is not an
opportunity of investigation, which of course may not be the case.
For more on the stages of on-farm experimentation, see Violic,
Kocher and Palmeur, 1981.
It should be rioted that the number of experiments required for a
given domain does not depend on the size of the domain. It was pointed
out .earlier that one can think of domains as statistical strata, and
on-farmi experimentation can be considered an exercise in sampling. Each
experiment measures the effect of new elements of technology on crop
yields, income and risk for the respective cooperu,~ing farmer. The
benefits of a particular elem-ent may be estimated for the target farmer
population by averaging the results of several trials. When strata are
internally homoageneous (as recommendation domains should be) a small
sample from each is sufficient to obtain a precise estimate of ~the
stratum medan. This is because the sampqle size needed to achieve a
desired level of precision at a certain level of probability does not
depend on the population size, but rather on its variability.
The experiments are of course planted under conditions
representative of the recommre~ndatioo n dom~ain. If the domain is defined as
all farmers who have less than 10 has, have fields between 2,600 and
3,000 meters and do not have access to irrigation, then the experimnts
for this domnain muJst to plan~ted underT these cisrJ;-c~umcstacs eyn h
the survey will have specified what the representative farmer practices
are for the particular domain. Non-experim~ental variables are usually
set at the farme~r's level, unless there is the expectation that farmlers
will soon adopt a new practice which warrants being included as a
Although recommendation domains can usually be identified before
planting the first year's experiments it occasionally happens that the
results of the experiments themselves are useful in refining domain
definitions. If a domain is a group of: farmers who face similar
circumstances, follows similar practices and share similar opportunities,
then one would expect .similar results from experiments planted with
different members of the same domain. In terms of analysis of variance,
"site by treatment interaction" should not be consistently significant.
When this interaction term is significant (at an appropriate level)
researchers should see if this is merely random variation (e.g. because
of rainfall differences) or if there is a constant factor (e.g. a
previously unidentified difference in soil types) which is responsible.
In the latter case, this m~ay lead to a division of what was formally one
domain into two or rmore. Similarly, experimental results are
occasionally uniform. across two domains, which may then be combined into
Once domain boundaries are firmly identified, the agronomnic,
economic and statistical analysis of experiments proceeds by pooling the
data within each domain. The results are then used to form
recommendations for the domain.
4.4) Preliminary Zoning. -
At times research programs wish to use a set of tentative domains to
organize OFR in a large area. Senior research planners may feel, for
example, that one domain may occur in numerous small defined areas (each-
handled by a different OFR field team) To reduce duplication of effort
in on-farm trials, these senior researchers may wish to make a first
"rough cut" at dcnairai formation, assigning each OFR field team to work
In these cases, zoning" procedures can be used. Specifically,
formation of numerous tentative domains in a large area can be initiated
by means of a very brief formal survey with local extension personnel
that provides data for grouping togetLher farmers with similar farming
systems. As OFR teams are assigned to initiate fieldwork, they can
accept or adjust the tentative domains identified in the zoning process.
5.0) Issues and Comolications.
As researchers deal with drain formation in their study areas
several issues and complications tend to arise. These include questions
of domain size, domain permanence and others. The purpose of this
section is to discuss these questions and show how they may be addressed.
This section draws heavily on the experience of M. Collinson and S.
Franzel in Eaist Africa. See Collinson (1979) and Franzel (1981) .
5.1)Domrain SiZe: What is the appropriate size of a recormmendation
domrain? There is no set answer to this question, but obviously the
larger the domains the more cost-effective will be the research program.
Domain size is influenced by the heterogeneity of the area. In
places where there are many different microclimates and 'great variations
in the socioeconomic circumstances of farmers a relatively large number
of dom~ains are likely to be identified. In other places, vast areas may
be subject to similar circumstances and farmer practices, and a few
domains will suffice.
Domain size is also determined by the availability of research
resources. More resources a~llow the exploration of more research
opportunities and thus the delineation and management of more and smaller
domains. At times these factors may have -contradictory influences on
domain size, as when work is carried out in a very complex, heterogeneous
target area with very few resources available for implementing OFR. In
these cases a decision is often taken to carry out research in only a few
high-priorityl domains, selected according to researcht opportuniiities
farmer characteristics or national policy.
Domain size is thus bounded on the small side by expected returns to
research expenditures. Domains should not be so small that benefits fromn
new technology for that domain are less than corresponding research costs
(or better, less than the expected returns fromn alternative uses of
research resources) Domain size need not be bounded on the large side.
In fact, domains should be as large as possible, with the condition that
farmoers in the domain can still be expected to adopt recommendations
arising from wo~rk on major research opportunities. Large domnains allocw
the fixed costs of. on-farm trials to be spread over a wider number of
In practice, domain sizes demonstrate considerable variation. They
have ranged from a few thousand farmers to several tens of thousands, or
more. There is clearly no "best" size for a recommendations domain.
5._2) The Permtanence of Domains: W\e have already seen that the
definition of domains may be refined during the process of on-farm
research. As workers become better acquainted, with the area, their
perspective of research opportunities and agronomic responses will
change, leading at times to redefinition of domnains.
Similarly, we have seen that domain definitions are linked to
research opportunities, and as research themes tend to shift over time3
these shifts often require adjustmednts in domnain boundaries. In one
research area in Ec~uador, for example, where maize was the principal
crop, farmers with and without comrplemeantary irrigation constituted a
single domain for maize research, as no significant differences in
practices could be detected betwreen these two groups of farmers. But: as
research progressed, and especially as an early-maturing maize was
released for farmers which allowed new rotation patterns, the difference
in access to irrigation became important. Rotation possibilities that
included crops grown~ in the d~ry season were much different between these
two groups of farmers, and where previously there had been a single
domain two w73ere formed as reSeaLrc advanced.
It must be' kept in mind that the simplified, shorthand definitions
of domrains really serve to summarize researchers' perceptions of how a
complex of farmer practices and circumstances influence the
identification and development of research opportunities. As these
opportunities change and evolve so do domain boundaries. Dom~ains may be
joined, split or otherwise redefined, and researchers should do so when
research efficiency may be iprrqoved.
5.3) Farmrxs Belonging to Mlore than One Domai~n: Occasionally it is
found that a farme~r belongs to more than one domain (because
circumstances differ in different parts of his farm) This may occur
when farmlers operate several fields on varying land types, for example,
when domain formation i~s based on these land types. In an earlier
example researchers distinguished ~between farmers on a flood plain and
farmers on nearby acid soils. A farmer with a field in each land type
would clearly belong to both domnains.
5.4) Cor~respondence BetwceenDoan and On-Farm Experimrents:
Recommendation domains are formed in order to help researchers define
different experimental programs. At times the difference between domrains
in a given research area may be extreme, including different target crops
completely different research opportunities. In this case the
on-farm experiments planted in the two domains would bear no relationship
to each other. Even when the target crops are the same, research
opportunities some~times differ so greatly that the maize experiments (for
example) in one domain are totally different from those in another.
Because recommendation domains are determined ?by research
opportunities, it sometimes happens that two domains (with respect to one
opportunity) are included in, and share the experiments of, another
larger domain. As an example, in one wheat area ~two domains were basedr
on soil type. The soil type determined land preparation and crop
rotation possibilities and hence strongly influenced the nature of the
weed population. Thus separate sets of weed control experiments were
planted in the two domains. Soil type had no influence on varietal
reqiremenTI~ ~tshwve2r, so the are(a conYs Litutedi only one domain with
respect to variety, and the number of variety trials planted (across the
two soil types) was appropriate for a single domain.
In certain cases, the experimental program may be exactly the same
between dam~ains. In the example of the two domains distinguished by soil
type it may be that similar fertilizer trials should be planted in both
domarins, although the agronomnic responses and exact recommendations will
likely be different. Or in the case of domains distinguished not by soil
type but rather by access to a road, it may be that the sa~e~ fertilizer
trials give the same agronomic response but the higher costs of bringing
fertilizer into the isolated area (and getting the crop out) mean that
the economic analysis will indicate somewhat different fertilizer
recommendations for the two domains.
As national agricultural research programs move toward on-farm
research, the need grows for a way to specify the clientele for that
research. The xrecorurnendation domain co~ncep~t can fill this need.
Conceptually, a domrain is a group of farneurs with similar
circumstances who will adopt the san~e recommendation, given equal access
to information. Operationally, domains are formed around farmers with
similar practices for a given enterprise and for wrhom8 researchers see
similar opportunities for thne impgroveme~nt of these practices. Such
farmers can be grouped together in terms of biological and/or
Recommendat~ion domains are useful as a framework for on-farm
research. As researchers strive to select the few most important
exper~imental variables and then study them under representative.
conditions, domains provide the necessary context for defining
"irpJortant"l and "representative". Recournndation domains also provide a
criterion for pooling the data obtained from on-farm trials, thus
resolving the classic problem of extrapolating research results beyond
the farms on which trials are conducted.
Dormarins are formed by considering farmesrs' circumstances. As
researchers begin their work, they are interested in howJ these
circumstances affect farmers' practices and how they condition research
opportunities. As ideas for research opportunities eme~rge so do clear
definitions of recommendation domains. Beginning with a comprehensive
list of farmers' circumstances researchers conduct an informal survey
which helps to elim~inate many of these as potential criteria for defining
domains. A reduced nrmber of -possibilities may be tested through a
formal survey to see if they are in fact, useful for dividing farmers into
rough-ly homogeneous groups who could benefit fromn the same
recenrnendation. TIhis information is utilized in the design and planting
of on-farm. experiments. It may be that the final domain boundaries are
not decided upon until the experirunental results are analyzed. In any
case, by the time recommendations are ready, they are already targeted to
well-defined groups of farmers.
In providing a framework fo~r on-farm research, recommendation
domrains are a useful tool. Like all. tools, however, they are most
helpful when used with imagination and care.
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